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\begin{center}
\textbf{Implementation of multi-electrode arrays for\\
functional, pre-clinical modeling of Alzheimer's disease}\\
\bigskip
\bigskip
\bigskip
A dissertation presented\\
by\\
Joseph Negri\\
to\\
The Division of Medical Sciences\\
\bigskip
\bigskip
\bigskip
in partial fulfillment of the requirements\\
for the degree of\\
Doctor of Philosophy\\
in the subject of\\
Biological and Biomedical Sciences\\
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\bigskip
\bigskip
Harvard University\\
Cambridge, Massachusetts\\
\bigskip
November 2018
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\textcopyright\ 2018 Joseph Negri\\
All rights reserved.\\
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Dissertation Advisor: Professor Tracy Young-Pearse \hfill
Joseph Negri

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\begin{center}
\textbf{Implementation of multi-electrode arrays for functional, pre-clinical modeling of \\ Alzheimer's disease}

Abstract
\end{center}

Alzheimer's disease (AD) is the most common form of age-related
dementia, affecting millions of patients globally. Unfortunately, the
development of therapies for AD is lagging with no newly approved
treatments in the last 15 years. This dearth of new therapies requires a
reconsideration of the pre-clinical models of AD, in order to facilitate
the discovery of efficacious treatments. Ideally such models would allow
for monitoring the functional behavior of neurons in conditions
approximating the AD brain. One potential instrument platform for such
models are multi-electrode arrays (MEA), as these systems allow for
prolonged, non-destructive recordings of spontaneously firing neurons in
vitro. Towards this goal of developing more representative, pre-clinical
models of AD; methods of experimental design and analysis for assessing
neuronal firing using MEAs were established. This system was used to
examine changes in neuronal activity following treatment with soluble
human brain extract from a panel of 19 individuals comprised of: AD
patients, unaffected persons not exhibiting AD pathology (low pathology
not cognitively impaired, LP-NCI), and individuals not cognitively
impaired who exhibited AD-related pathology post mortem (high pathology
not cognitively impaired, HP-NCI). While the `amyloid hypothesis' of AD
posits that hydrophobic oligomers of the peptide amyloid-\(\beta\)
(A\(\beta\)) lead to synaptotoxicity and neurodegeneration, experiments
were unable to demonstrate that changes in spontaneous firing elicited
by these brain extracts were directly mediated by A\(\beta\). To
determine the molecular etiology of these functional changes in an
unbiased manner, label-free proteomic analysis was performed on a larger
panel of 43 brain extracts of AD, LP-NCI, and HP-NCI individuals. The
proteomic profiles of those brain extracts tested in the MEA-based
neuronal firing assay were compared with a similar cohort of brain
extracts tested in a morphological neurite integrity assay. This
analysis revealed several candidates related to protein homeostasis and
microtubule remodeling whose expression correlated with changes in
neuronal structure and activity. The techniques and data herein provide
a system for modeling the function of neurons and insights into proteins
that may be mediating changes in cellular behavior within the AD brain.

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\section{Acknowledgements}\label{acknowledgements}

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I would first like to thank Dr.~Young-Pearse for being a generous and
patient advisor throughout my dissertation research. I am grateful for
the encouragement that was provided to me to tackle the challenges that
arose during the pursuit of this project and the latitude to devise
solutions that enabled me to grow as both an individual and scientist.

I wish to extend my gratitude to the scores of scientists with whom I
have worked along side from my time at Trinity College Dublin,
Dana-Farber Cancer Institute, the Broad Institute, and here at Harvard.
Only due to the knowledge, skills, and wisdom imparted to me by these
individuals was I sufficiently capable and confident to complete this
thesis. In particular, thank you to past mentors: Drs. Veronica
Campbell, Thomas Connor, Constantine Mitsiades, Jose Perez, Nicola
Tolliday, and Michelle Palmer.

A special thank you to two dear and long time friends, Drs. Thomas Lau
and Megan Rokop who have provided continuous support and advice both
personal and scientific over these many years.

I also thank the members of my thesis advisory committee, Drs. Paola
Arlotta, Jesse Gray, and John Flanagan. As with all good scientific
discourse, our conversations were illuminating and thought-provoking, at
times contentious yet always amicable. Your perspectives, insights, and
ideas were invaluable to furthering the course of this project.

Thank you to the faculty, students, and administrators of the Biological
\& Biomedical Sciences program for creating a welcoming and supportive
environment for pursuing graduate studies here at Harvard.

I am thankful for the love and support of my family. I am indebted to my
parents for the home they created, the work ethic they instilled by
example, and the value they placed on education. To my five siblings for
being wonderful, uniquely talented, and good fun; the allies that I can
always call upon and I know will be there to respond. To my in-laws, who
are endlessly caring and encouraging, a surfeit to someone blessed with
great family. To my wife Tina for her steadfast support through my
graduate studies. She is my best friend and companion, who provides me
with both the strength to carry on when things are difficult and the
reason for doing so.

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\section{Chapter 1: Introduction}\label{chapter-1-introduction}

\doublespacing

\subsubsection{Alzheimer's Disease and the Amyloid
Hypothesis}\label{alzheimers-disease-and-the-amyloid-hypothesis}

Alzheimer's disease (AD) is the most common cause of age-related
dementia and its prevalence is expected to grow in the coming years,
with estimates as high as 100M sufferers worldwide by 2050 (Cummings,
Morstorf, and Zhong 2014). While numerous hypotheses explaining the
pathology of AD have been put forth, the `amyloid hypothesis' of
Alzheimer's disease is most widely supported by the evidence provided by
the genetic, biochemical, cellular, and clinical data available. The
most recent iteration of the amyloid hypothesis states that an increase
in the total production of amyloid-\(\beta\) peptide (A\(\beta\)) and a
shift in the ratio A\(\beta_{42}\):A\(\beta_{40}\) toward the more
hydrophobic A\(\beta_{42}\), results in the accumulation of soluble
A\(\beta\) oligomers which impede synaptic function leading to eventual
cell death (Hardy and Selkoe 2002; Haass and Selkoe 2007; Sheng,
Sabatini, and Südhof 2012; Selkoe and Hardy 2016). A\(\beta\) is a
fragment of the single-pass type-I transmembrane protein, Amyloid
Precursor Protein (APP). APP undergoes sequential proteolytic processing
via one of two mutually exclusive pathways: the amyloidogenic pathway in
which APP is cleaved by \(\beta\)-secretase followed by
\(\gamma\)-secretase resulting in the liberation of soluble
extracellular A\(\beta\), or the non-amyloidegenic pathway in which APP
is first cleaved by \(\alpha\)-secretase followed by
\(\gamma\)-secretase resulting in the liberation of another peptide
fragment, p3, which is not associated with AD (O'Brien and Wong 2011).
A\(\beta\) exists within the brain in a dynamic equilibrium between low
molecular weight monomers, intermediate oligomers, and high molecular
weight fibrils. While there is ambiguity around what specific species of
A\(\beta\) is most relevant to the pathology of AD, a growing body of
evidence is leading to a consensus in the field that oligomers are
primarily responsible for pathogenic effects of A\(\beta\) (Walsh et al.
2002; Walsh et al. 2005; Deshpande et al. 2006; Shankar et al. 2007;
Mucke and Selkoe 2012).

\subsubsection{A Cascade of Events}\label{a-cascade-of-events}

From the original description of AD by Alois Alzheimer in 1906, the
pathology was described as the co-occurrence of extracellular plaques
and intracellular neurofibrillary tangles, subsequently shown to be
composed of A\(\beta\) and a hyper-phosphorylated form of the
microtubule associated protein tau, respectively (O'Brien and Wong
2011). How these (and other) aspects of AD pathology are related remains
an area of on-going research. However, the `amyloid cascade' hypothesis
proposes a connection, postulating that A\(\beta\) production and
aggregation is the initiating event in the cascade, ultimately
contributing to tau phosphorylation and the formation of neurofibrillary
tangles, and eventually neuronal toxicity and cognitive and memory
deficits (Hardy and Higgins 1992; Karran, Mercken, and De Strooper
2011).

In the nearly three decades since it was put forth, the amyloid
hypothesis has been questioned as being insufficient to explain AD. The
most damming criticism leveled is that clinical trials designed to
target A\(\beta\), either through blocking its production through the
use of \(\gamma\) and \(\beta\)-secretase inhibitors or promoting its
clearance with therapeutic antibodies have been unsuccessful in the past
at slowing the progression of symptoms (O'Brien and Wong 2011; Karran,
Mercken, and De Strooper 2011; Schaper 2018). Additionally, findings
that amyloid deposition within the brain initiates years to decades
prior to the onset of symptoms, has led to questions of the cause of the
delay, if A\(\beta\) indeed principally underlies the etiology of AD (De
Strooper and Karran 2016). Finally, the definitive mechanism of action
of A\(\beta\) is unclear as it is purported to interact with a variety
of receptors, disrupt cellular membranes, and even be pore forming
(Benilova, Karran, and De Strooper 2012).

Despite these critiques, an overwhelming amount of data indicates a
fundamental role for A\(\beta\) in AD. Mutations in APP, PSEN1, and
PSEN2, the majority of which result in increased levels of
A\(\beta_{42}\) relative to A\(\beta_{40}\), cause a familial form of
the disease (Scheuner et al. 1996). Individuals with Down's syndrome,
trisomy of chromosome 21 (the chromosome on which APP is located) show
high incidence of AD, with pathology arising as early as the teens
(Lemere et al. 1996). This is presumably due to increased production of
A\(\beta\) over their lifetimes. Moreover, individuals who inherit
partial duplications of chromosome 21 containing the APP locus also
develop AD, while those who inherit partial duplications of chromosome
21 without the APP locus do not (Prasher et al. 1998, Rovelet-Lecrux et
al. 2006).

A\(\beta\) also has been shown to directly influence neurons as well.
A\(\beta\) treatment of primary neurons results in a decrease in synapse
number (Coleman and Yao 2003; Shankar et al. 2007; Lacor et al. 2007).
Synaptic loss is highly correlated with cognitive decline and is
detected post-mortem in the brains of individuals who exhibited symptoms
of early AD (Masliah et al. 1994, Scheff et al. (2006)). Additionally,
aqueous soluble brain extract has been shown to inhibit LTP in
organotypic slices and \emph{in vivo} in an A\(\beta\) dependent fashion
(Walsh et al. 2002; Shankar et al. 2008; Barry et al. 2011; Yang et al.
2017). A\(\beta\) also has been shown experimentally to induce the type
of changes in tau phosphorylation associated with the formation of
neurofibrillary tangles. Treatment of neuronal cultures with A\(\beta\)
oligomers leads to tau hyper-phosphorylation and atrophy of neurites,
but neurite loss is prevented if tau is knocked-down prior to A\(\beta\)
treatment (Shankar et al. 2008, Jin et al. 2011). Crossing a human APP
transgenic mouse to a tau transgenic mouse leads to exacerbation of the
tau pathology with no additional effect on the amyloid pathology (Lewis
et al. 2001). Further, crossing a human APP transgenic mouse to a tau
knockout mouse results in less behavioral defects in the tau-null
offspring (Roberson et al. 2011). Therefore, a fairer assessment may be
that our understanding of the events occurring down stream of A\(\beta\)
is incomplete.

Recently, interest in the AD field has turned in part to the role of the
innate immune response serving as the intermediary between the
A\(\beta\) and tau pathologies, casting microglia and astrocytes as
important actors in the cascade. A\(\beta\) oligomers bind receptors on
microglia and astrocytes, provoking an immune response and leading to
the release of pro-inflammatory cytokines (Heneka et al. 2015; McGeer,
Rogers, and McGeer 2016). Some have hypothesized that the release of
pro-inflammatory cytokines in turn promotes aberrant truncation and
phosphorylation of tau within neurons via cdk-5, p38/MAPK, JNK-1, and
ERK mediated pathways (Ballatore, Lee, and Trojanowski 2007; Garwood et
al. 2011). Further, loss of function mutations in TREM2, a protein
involved in phagocytosis by microglia have been found to be a risk
factor for AD, indicating that decreased efficiency in A\(\beta\)
clearance by microglia may contribute to the disease (Colonna and Wang
2016). These finding indicate the need for models that account for the
dynamics between cells of the nervous system in assessing the functional
changes associated with AD.

Finally, reports have shown the capacity for neurons to restore synapses
following termination of exposure to A\(\beta\) (Shankar et al. 2007).
This indicates that there may be a potential for regeneration of
neuronal structures following the initiation of amyloid pathology.
Therefore, it is important to determine if there is a critical period
where cognitive decline could be mitigated through impeding the amyloid
cascade and/or promoting synapse restoration. Establishing an
experimental system that models this process would be of value for
screening for agents that promote synapse restoration.

\subsubsection{Generalized challenges of disease
modeling}\label{generalized-challenges-of-disease-modeling}

During a seminar in 2017, the renowned Alzheimer's researcher Dr.~Dennis
Selkoe noted, ``What constitutes a disease state is often a subtle
deviation from normal physiology''. While this observation is applicable
to many diseases, it is especially apt in the context Alzheimer's. The
clinical manifestation of AD has multiple symptoms but the most
prominent and widely recognized is a decline in memory. Memory is a
complex behavior, perhaps the most sophisticated of behaviors, and one
in which the underlying biological process is far from being completely
understood. Further, with the growing appreciation that AD pathology
initiates years to decades before symptoms emerge, the biochemical and
cellular changes that are associated with this deviation from `normal'
physiology are inevitably occurring on similarly long time scale
(Bateman et al. 2012; Villemagne et al. 2013). Finally, as a disease of
the human central nervous system, an organ that is essentially
inaccessible to direct experimentation, the study of AD must largely be
carried out using representative experimental models.

Attempting to study any disease or biological process in model systems
always comes with inherent compromises. The system must be sufficiently
complex to mimic the multiple facets influencing the \emph{in vivo}
physiology of the disease state. Yet the system must be sufficiently
reductionist to: \emph{i} have clearly defined end-points, \emph{ii} be
executable within reasonable time frames, and \emph{iii} allow for
parallel testing of multiple perturbations. Therefore, in devising a
strategy for modeling a disease one must contend with these confounding
challenges: a trade off between complexity and reductionism, and have
the ability to robustly detect subtle change.

The military historian Thomas Ricks observed that the terms ``strategy''
and ``tactics'' are often used synonymously when in fact one is a
dependency of the other. Mr.~Ricks posited that a strategy can largely
be established by answering a limited number of questions: ``\emph{What
are we trying to do here?}''; ``\emph{How will we do it?}'';
``\emph{What will be needed to do so?}''. Answering these questions is
often not easy, but having done so one's \emph{tactics} will derive from
those answers. What is true of military campaigns is also true of
disease modeling. Here, the answer to the first question is, we are
trying to understand how the behavior of the brain changes at a cellular
level in the context of AD pathology. How this can be done is to create
an environment that is a reasonable approximation of AD pathology in the
brain and to monitor how cellular behavior changes as a result. What is
needed is both a cellular system that replicates essential aspects of an
actively behaving brain, and a means to detect changes in that behavior.
This in a sense is the foundation of phenotypic assays, one seeks to
recreate the biological system of interest and examine how perturbations
affect the behavior of that system.

\subsubsection{Tactics (assays) for modeling neuronal
behavior}\label{tactics-assays-for-modeling-neuronal-behavior}

Having attempted to answer the questions posed by Mr.~Ricks, one must
chose a tactic, an assay for monitoring the changing behavior of
neurons. Within the field of neuroscience there are a number of
electrophysiological and fluorescence imaging-based techniques to
monitor the behavior of neurons under experimental conditions. As
depicted in Table \ref{ePhysAssayTable}, the most widely used techniques
differ with respect to several parameters influencing experimental
design and analysis. Ultimately the method chosen is influenced by the
question of interest and the compromises between complexity and
reductionism one is willing to make.

\rowcolors{2}{gray!6}{white}

\begin{table}

\caption{\label{tab:ePhysAssay_table}\label{ePhysAssayTable} Comparison of common electrophysiology and imaging-based assays for neuronal activity}
\centering
\resizebox{\linewidth}{!}{
\begin{tabular}[t]{>{\raggedright\arraybackslash}p{8em}>{\raggedright\arraybackslash}p{7em}>{\raggedright\arraybackslash}p{7em}>{\raggedright\arraybackslash}p{8em}l>{\raggedright\arraybackslash}p{7em}>{\raggedright\arraybackslash}p{6em}>{\raggedright\arraybackslash}p{6em}>{\raggedright\arraybackslash}p{6em}l}
\hiderowcolors
\toprule
Assay & Throughput 
 (cells-per-day) & End Point 
 / Longitudinal & Native cells 
 (non-engineered) & Label-Free & Data Acquistion 
 Efficiency & Parallel 
 Acquistion & Spatial 
 Resolution & Temporal 
 Resolution & Tunable\\
\midrule
\showrowcolors
Patch Clamping & 10s & End Point: Minutes/Hours & Yes & Yes & Low & No & High & High & Yes\\
Sharp Electrode Recording & 10s & End Point: Minutes/Hours & Yes & Yes & Low & No & High & High & Yes\\
Calcium Imaging & 1000s & Longitudinal & Yes & No & High & No & Medium & Low & No\\
Optogenetic Actuators/Sensors & 10s-100s & Longitudinal & No & No & Medium & No & High & Low & Yes\\
Multi-Electrode Arrays & 100s-1000s & Longitudinal & Yes & Yes & High & Yes & Low & High & No\\
\bottomrule
\end{tabular}}
\end{table}

\rowcolors{2}{white}{white}

Establishing criteria for the model can help facilitate the decision
between potential assay platforms. While the evidence supports
A\(\beta\) as principal in AD pathology, the physiological events that
culminate in the symptoms of the disease are all but certain to occur in
a non-cell-autonomous manner (De Strooper and Karran 2016). In order to
capture these dynamics, it is necessary to assay the behavior of
neuronal activity within a dense, mixed culture of neurons and glial
cells. Given the latency with which the symptoms of AD emerge relative
to the initiation of AD pathology, it is desirable to have a system in
which the changes in activity can be measured over time. Many studies
attempting to replicate the effects of A\(\beta\) have been critiqued
for resorting to the use of unrealistic levels of intrinsically
expressed or exogenously added protein to elicit a phenotype (De
Strooper and Karran 2016). Thus, a model perturbed at a physiologically
relevant magnitude may only show a small, yet meaningful response.
Therefore, the method should generate a rich dataset in order to allow
for the detection of modest changes. The method also should be amenable
to modeling human biology. While the experiments performed in this study
primarily employed primary rodent cultures, the source of the
perturbation was post mortem human brain. Further, with recent
advancement in differentiation efficiency and reliability, human induced
pluripotent stem cell (iPS) derived neurons and glia can be readily
substituted for rodent primary cells. Of the assay platforms available,
one which seems capable of fulfilling all of these criteria are
multi-electrode arrays.

\subsubsection{Multi-electrode arrays as an assay platform for
monitoring neuronal
behavior}\label{multi-electrode-arrays-as-an-assay-platform-for-monitoring-neuronal-behavior}

\emph{In vitro} multi-electrode arrays (MEA) instruments consist of a
large number (dozens to hundreds) of planar electrodes embedded in the
base of a tissue culture chamber (Figure \ref{meaExampleImages}) that
allow for the parallel detection of local field potentials generated by
the spontaneous or evoked firing of neurons in cultures (Spira and Hai
2013; Obien et al. 2014). Using MEA platforms it is possible to record
neurons over time frames lasting hours to days, or even longer. These
features make MEA technology an attractive method to study the magnitude
and kinetics of changes in spontaneous firing activity that A\(\beta\)
is able to elicit in cultured neurons.

\begin{figure}
\includegraphics[width=111.94in]{~/Documents/YoungPearseLab/Analysis/introductionGraphics/meaImageLayout} \caption{\label{meaExampleImages}{\small \textbf{Examples of the \textit{in vitro} MEAs typical of those used in this study.} \textbf{A} 12-well MEA plate \textit{Axion Biosystems}. \textbf{B} 64 electrode MEA, position of recording electrodes indicated in yellow and reference ground electrode in red. \textbf{C} Primary rodent cortical cultures on MEA at \textit{day in vitro} 21, imaged at 20X magnification.}}\label{fig:meaExampleImages}
\end{figure}

The ability to detect action potentials from neurons \emph{in vitro}
using extracellular planar electrodes was first reported by Thomas and
colleagues in 1972. The justification for the development of the
technique provide at the time still holds true today (Thomas et al.
1972).

\singlespacing

\begin{quote}
``Currently available and rapidly improving techniques permit the
\emph{in vitro} culture of an increasing variety of bio-electrically
active tissues and single cells. Perhaps the most interesting questions
to be asked of such cultures are those dealing with the development and
plasticity of electrical interactions among the cultured elements
(tissues or single cells). Exploration of these questions would be
greatly facilitated by a convenient non-destructive method for
maintaining electrical contact with an individual culture, at a large
number of points, over periods of days or weeks. This report describes
one approach to the development of such a method.''
\end{quote}

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The physical basis for signal detection with these instruments lies with
the principle that upon initiation of an action potential, the influx of
sodium and calcium ions from the extracellular space into a neuron
through Na\(^+\) and Ca\(^{2+}\) voltage gated channels respectively,
will create a potential gradient between an electrode in close proximity
to the neuron and a reference electrode within the solution set an
appreciable distance away. Demonstrated by Fejtl et al. in Figure
\ref{meaCircuitDiagram}, as the neuron itself can be modeled as a
resistor-capacitor (RC) circuit, with capacitance maintained by the
lipid bi-layer of the membrane and the voltage channel serving as
veritable resistors, within the context of MEA recording the neuron
forms the part of a larger RC circuit between the recording and
reference electrodes (2006).

\begin{figure}
\includegraphics[width=139.68in]{~/Documents/YoungPearseLab/Analysis/introductionGraphics/meaCircuitDiagramSized} \caption{\label{meaCircuitDiagram}{\small \textbf{Idealized circuit diagram of MEA system} Adapted from Fejtl et al. p26. R - resistance, C - capacitance; \textit{Subscripts}: M - membrane, FM - free membrane, JM - junction membrane, J - junction, E - electrode, b - buffer, sh - shielding.}}\label{fig:meaCircuitDiagram}
\end{figure}

The voltage potentials detected from an extracellular electrode during
the occurrence of an action potential will parallel those recorded by an
intracellular electrode, but will differ in a number of key ways. First,
by virtue of being on the opposite side of the membrane, the polarity of
the potential is reversed. Second, due to additional resistance from the
extracellular medium, the magnitude of the potential detected
extracellularly will be two to three orders lower than that which would
be recorded from an intracellular electrode (10s - 100s of \(\mu\)V
verse 10s mV). Third, due to the RC nature of the cellular membrane, the
extracellular potential is approximately equal to the derivative of the
intracellular potential. Finally, due to the neuron being more proximal
to a cable rather than a point source, the shape and magnitude of the
potential will vary depending on the spatial orientation of the neuron
with respect to the recording electrode (Holt and Koch 1999; Gold et al.
2006; Gold, Henze, and Koch 2007; Gibson 2012; Buzsáki et al. 2015).
Features of these differences are shown by Buzsaki et al in Figure
\ref{meaVoltageTrace}.

\begin{figure}
\includegraphics[width=140.94in]{~/Documents/YoungPearseLab/Analysis/introductionGraphics/extraCellularVoltageTrace} \caption{\label{meaVoltageTrace}{\small \textbf{Voltage waveforms recording from MEA} Adapted from Buzsaki et al. 2015, Figure 2. \textbf{A} Voltage traces from simultaneous recording of a CA1 hippocampal neuron during an action potential from an intracellular electrode (\textit{upper}) and extracellular electrode (\textit{lower}). \textbf{B} Shapes and amplitudes of voltage waveforms recorded from extracellular electrode at different orientations with respect to a hippocampal neuron. \textit{Lower left}, Overlay of traces recorded a increasing radial distance to emphasize magnitude of amplitude. \textit{Lower right}, Overlay of traces recorded a increasing radial distance normalized to maximum amplitude to emphasize peak width.}}\label{fig:meaVoltageTrace}
\end{figure}

Action potentials are detected by MEA instruments by sampling the
potential difference across recording and reference electrodes at rates
typically between 10-60kHz. These sample values are used to calculate a
background potential and action potentials, `spikes', are detected when
the sampled values deviate substantially from the background potential.
Activity within cultures is quantified by the frequency of spikes
events, and in some applications the occurrence of synchronized spiking
events, or `bursts' (Obien et al. 2014).

While MEAs are broadly used in the field of toxicology as well as the
study of other neurological diseases, to date there have been
comparatively few studies that have employed MEAs for modeling the
effects of AD pathology \emph{in vitro} (Kuperstein et al. 2010;
Varghese et al. 2010; Charkhkar et al. 2015). Therefore, this requires
the development of new experimental design and data analysis workflows
to better enable the use of MEAs in studying AD experimental models. By
validating the ability of MEAs to monitor changes in neural activity in
AD experimental models, we will determine if this technology could serve
as a platform for answering basic questions such as: \emph{i} the extent
to which A\(\beta\) exposure can influence the spontaneous firing of
neurons, and \emph{ii} how these functional changes fall within the
chronology (proceeding, coincide with, or lagging behind) other AD
associated pathologies such as the increase in tau phosphorylation, or
loss of synaptic structures. In addition, defining the potential and
limitations for studying AD with MEA may promote their use in
translational applications such as the testing of investigational
therapeutic agents for the treatment of AD as well as other
neurodegenerative conditions.

The following chapters will discuss: \emph{i} assay development efforts
to refine the use of MEAs as an \emph{in vitro} model of AD, \emph{ii}
the use of MEAs and human brain extract for assessing the effect of
A\(\beta\) on the spontaneous firing of neurons, \emph{iii} the use of
unbiased proteomic profiling coupled with results from MEA and
imaging-based phenotypic assays to identify candidate proteins
functionally associated with AD pathology, and \emph{iv} development and
implementation of an unsupervised spike-sorting method to allow for
resolving electrophysiological activity detected by MEAs to the single
cell level.

\FloatBarrier
\newpage

\singlespacing

\section{Chapter 2: Assessment of multi-electrode array experimental
methodology}\label{chapter-2-assessment-of-multi-electrode-array-experimental-methodology}

\doublespacing

\subsection{Introduction}\label{introduction}

Multi-electrode arrays (MEAs) are instruments that provide a means of
monitoring spontaneous electrophysiological activity within \emph{in
vitro} neuronal cultures. By monitoring the voltage potentials generated
endogenously by firing neurons, these instruments do not require the
fluorescent dyes or exogenously expressed fluorescent proteins and
microscopy apparatus required of calcium-imaging techniques.
Additionally, MEAs are able to record simultaneously across dozens to
hundreds of channels in a much less labor intensive manner than the
single channel recordings possible with conventional
electrophysiological techniques such as patch-clamping or
sharp-electrode recording, albeit with a lower degree of spatial
resolution. Moreover, MEA recordings are non-destructive to the cultures
allowing for repeated recordings to be performed over time for as long
as the integrity of the cultures can be maintained. For these reasons
the use of MEAs is becoming more common in neuroscience and biomedical
research.

With the increased use of MEAs, methodologies for MEA experiments are
still being developed across the field. At this time there are no
broadly accepted, standardized protocols for acquisition and analysis
for the data collected with these instruments. To date, the most
comprehensive methodological review of \emph{in vitro} MEA techniques in
the literature was performed by Novellino and colleagues (2011).
Novellino et al. surveyed six toxicology laboratories employing MEAs;
comparing methods of tissue culture, recording, signal processing and
assay metrics across the different research groups. Additionally, the
study had each laboratory test clinically used neuro-pharmacological
agents in their assays to assess the reproducibility of the results.
Based on the parallel testing of pharmacological agents, Novellino et
al. found that Mean Firing Rate (MFR), defined as the ratio of total
spike events to recording duration in seconds, was the most consistent
metric of activity, and has been used by several other studies for
reporting MEA activity (see Table \ref{meaStudyTable}).

Published in 2011, the Novellino et al. study assessed the use of
single-well MEAs, single chamber tissue culture vessels typically with
approximately 60 recording electrodes. Since that time, several
\emph{multi-well} MEAs systems have become commercially available such
as the Axion Biosystems \emph{Maestro} (released mid-2011), Alpha Med
Scientific \emph{MED64 Allegro} (released 2015) and \emph{MED64 Presto}
(released 2018), and Multichannel Systems \emph{Multiwell MEA} (released
late-2000s). These multi-well MEAs take the same form as 12, 48, or 96
well tissue culture plates, with a separate array occupying a single
well, and as such the term \emph{array} or \emph{well} may be used
interchangeably to refer to an isolated culture chamber being assayed
amongst others in a multi-well plate. The availability of multi-well
MEAs has allowed for changes in experimental design and analysis, by
increasing the through-put of data acquisition. This allowed for more
conditions to be tested in parallel, and provides more experimental
replicates for utilizing array-level activity metrics. This is a
distinction from some studies using the traditional single-well MEAs
that reported on electrode-level metrics (Kuperstein et al. 2010;
Bateup, Johnson, et al. 2013), a questionable method since it is unclear
whether signal from detectors within the same culture should be
considered independent experimental replicates.

\begin{table}

\caption{\label{tab:unnamed-chunk-11}\label{meaAssayTable} Considerations for MEA experimental design}
\centering
\begin{tabular}[t]{ll}
\toprule
experimental conditions & analysis parameters\\
\midrule
array surface preparation & spike detection/event filtering\\
culture media & assay metric\\
age of cultures & array exclusion criteria\\
recording duration & treatment group assignment\\
environmental control & statistical analysis\\
\bottomrule
\end{tabular}
\end{table}

Given the increased use of MEAs since the time of the Novellino et al.
review, and since our study relies solely on the use of
\emph{multi-well} multi-electrode arrays, a review of MEA methodologies
in the reported literature was performed to gauge best practices for MEA
based experiments. As with all bio-assays, there are several facets to
experimental design, and MEA-specific considerations are detailed in
Table \ref{meaAssayTable}.

While all the elements listed in Table \ref{meaAssayTable} were
addressed during this study, not all are discussed here in detail. For
others, such as recording duration and age of cultures, the reported
metrics ranged so substantially that these were thoroughly evaluated to
determine appropriate conditions for this study.

Through a review of reported methodology, empirical evaluation, and
simulation analysis, assay parameters for MEA experiments were
established that were then applied to a pre-clinical model of
Alzheimer's disease, discussed in subsequent chapters.

\newpage

\blandscape

\begin{table}

\caption{\label{tab:mea_ref_table}\label{meaStudyTable} Comparison of MEA methodology}
\centering
\resizebox{\linewidth}{!}{
\begin{tabular}[t]{llllllllll}
\toprule
reference & instrument format & culture model & surface preparation & culture media & age (DIV) & duration (min) & detection threshold & assay metric(s) & statistical analysis\\
\midrule
Varghese et al. (2010) & single-well & primary rat (E18) & DETA & NB & 12-16 & - & - & MFR & t-test\\
Kuperstein et al. (2010) & single-well & primary mouse & - & - & 8-10 & 45 & 5 SD & MFR & t-test\\
McConnell et al. (2012) & multi-well & primary rat (P2) & laminin & NB & 12-22 & 33 & 8 SD & MFR, active channels & -\\
Frega et al. (2012) & single-well & primary rat (E18) & PDL+laminin & NB & 21-28 & 20 & - & MFR, burst rate & Kruskal-Wallis\\
Bateup et al. (2013) $\textbf{a}$ & single-well & primary mouse & PDL+laminin & NB & - & 2 & 9 $\mu$V & MFR & t-test, ANOVA\\
\addlinespace
Biffi et al. (2013) & single-well & primary mouse & PLL & NB & 4-35 & 10 & 5X baseline & MFR, active channels & Kruskal-Wallis\\
Vincent et al. (2013) & single-well & primary rat (E18) & PEI & - & - & 20 & 3 SD & MFR & Wilcoxon\\
Bateup et al. (2013) $\textbf{b}$ & single-well & primary mouse & PDL+laminin & - & 12-19 & 2 & 9 $\mu$V & MFR & t-test, ANOVA\\
Wainger et al. (2014) $\textbf{a}$ & single-well & reprogram-fibroblast neurons & PDL+laminin & DMEM/F12 & >28 & - & 10 $\mu$V & MFR & Mann-Whitney\\
Wainger et al. (2014) $\textbf{b}$ & both & hIPS-derived neurons & PDL+laminin & - & >24 & - & 10 $\mu$V, 5.5 SD & MFR & Kruskal-Wallis, linear regression\\
\addlinespace
Illes et al. (2014) & single-well & primary mouse & PDL+laminin & DMEM/F12 & >21 & - & 6.2 SD of baseline & MFR & t-test, ANOVA\\
Weir et al. (2014) & single-well & primary mouse (P1) & - & aCSF & 7-21 & - & 7SD & MFR, burst rate, etc. & t-test, ANOVA\\
Charkhkar et al. (2015) & single-well & primary mouse (E17) & PDL+laminin & DMEM & 21 & 20 & 5 SD & MFR & t-test, ANOVA\\
Vertkin et al. (2015) & single-well & primary mouse (P2) & - & - & 14-21 & - & - & MFR & t-test, ANOVA\\
Bardy et al. (2015) & multi-well & hIPS-derived neurons & laminin & BP & 2-21 & 10 & 6 SD & MFR & -\\
\addlinespace
Slomowitz et al. (2015) & single-well & primary mouse (P2) & - & - & 15-22 & 60 & 4-5 SD & MFR & -\\
Liu et al. (2017) & single-well & primary mouse & - & - & - & 15 & 6 SD & MFR & t-test\\
Alshawaf et al. (2018) & single-well & hESC-derived neurons & - & NB & 12-56 & 5 & 5 SD & MFR, active channels & Kruskal-Wallis\\
Feng et al. (2018) & multi-well & primary mouse (P1) & PEI+laminin & NB & 7-28 & 30 & - & MFR, burst rate, etc. & t-test\\
Garcia-Leon et al. (2018) & multi-well & hIPS-derived neurons & PEI+laminin & DMEM/F12 & 70 & 5 & - & MFR, burst rate, etc. & t-test\\
\addlinespace
Black et al. (2018) & multi-well & primary adult mouse DRG & PEI+PDL+laminin & DMEM/F12 & 3-21 & 30 & 5.5 SD & MFR, active channels & Mann-Whitney\\
Nehme et al. (2018) & multi-well & hIPS-derived neurons & - & NB & 7-42 & - & 5 SD & MFR, burst rate, etc. & Kruskal-Wallis\\
DeRosa et al. (2018) & multi-well & hIPS-derived neurons & PEI+laminin & NB & >30 & 10 & 5.25 SD & MFR & ANOVA\\
Sarkar et al. (2018) & multi-well & hIPS-derived neurons & PO+laminin & DMEM/F12 & >21 & 10 & 5.5-6 SD & MFR & t-test, ANOVA\\
Russo et al. (2018) & multi-well & hIPS-derived neurons & PO+laminin & NB & >30 & 3 & 5.5 SD & MFR & Mann-Whitney\\
Blake et al. (2018) & single-well & primary adult mouse DRG & PEI+laminin & NB & 7 & - & 5.5 SD & MFR & t-test, ANOVA\\
\bottomrule
\multicolumn{10}{l}{\textbf{Abbreviations}}\\
\multicolumn{10}{l}{\textit{surface preparation}: \textbf{DETA} - diethylenetriamine, \textbf{PDL} - poly-D-lysine, \textbf{PLL} - poly-L-lysine, \textbf{PEI} - polyethylenimine, \textbf{PO} - polyornithine}\\
\multicolumn{10}{l}{\textit{culture media}: \textbf{NB} - neurobasal media, \textbf{DMEM/F12} - Dulbecco's modified eagle medium: nutrient mixture F-12, \textbf{aCSF} - artifical cerebral-spinal fluid, \textbf{BP} - BrainPhys media}\\
\multicolumn{10}{l}{}\\
\multicolumn{10}{l}{\textsc{Note} - Vacancies in the table indicate instances were the reagent or technique used was not reported or unclear in the study methods}\\
\end{tabular}}
\end{table}

\elandscape

\subsection{Results}\label{results}

\subsubsection{Calculation of mean firing
rate}\label{calculation-of-mean-firing-rate}

Following the recommended findings of Novellino and colleagues (2011),
Mean Firing Rate (MFR) was used as the primary metric of firing activity
for this study. Spontaneously generated action potentials (``spikes'')
were detected whenever a voltage was recorded that exceeded a threshold
set at 5.5 standard deviations (SD) above the root mean square of the
background voltage calculated over a 10ms moving window. This level of a
crossing threshold is similar to many reports detailing MEA recordings
(see Table \ref{meaStudyTable}), although others have used crossing
thresholds as low as 3SD (Vincent et al. 2013) or as high as
8SD(McConnell et al. 2012). The use of a dynamically calculated
threshold value as employed here while common, is not universal, as
others have used static crossing thresholds either offset from values of
background potentials obtained at the initiation of recording(Biffi et
al. 2013; Illes et al. 2014), or simply set at an absolute value
(Bateup, Denefrio, et al. 2013; Wainger, Kiskinis, et al. 2014).

While any of these approaches are valid, here the dynamically calculated
crossing threshold was extended for use in a post-hoc analysis to
eliminate spike calls arising from spurious electrical noise. During
recording, sporadic electrical noise can cause the background potential
to increase several fold, resulting in the erroneous determination of
these events as spiking events. This can occur while using a fixed or
dynamically calculated threshold, since while the dynamic threshold will
eventually adjust there is latency in doing so. Further, due to the
hundreds of channels being recorded for tens of minutes or more, the
probability of this occurring is increased, and manual supervision
impractical. To address this, the values of the crossing threshold at
the time of the spike detection for all instances within a recording
were examined. Since the background potential is expected to be random
normal, all spikes for which the value of the \emph{crossing threshold}
at time of detection exceeded 3SD of distribution across all
\emph{crossing thresholds} were excluded from analysis.

Following the removal of spike events detected at these aberrantly high
crossing threshold, the mean firing rate was calculated as described
above integrating all spike events recorded across all electrodes on an
array during a recording session.

\subsubsection{Firing frequency from neuronal arrays exhibits log-normal
distribution}\label{firing-frequency-from-neuronal-arrays-exhibits-log-normal-distribution}

Examining a large number of arrays revealed a distinct pattern in the
distribution of firing frequencies across the population. Figure
\ref{firingFreqDist} shows the firing frequencies of untreated rat
primary cortical cultures from 1272 unique arrays across 35 plates and
22 separate experiments. Arrays exhibiting low firing frequencies, MFR
\textless{} 0.02Hz have been removed from this dataset. The histogram in
the upper left showing firing frequencies displayed on a linear scale of
Hz, reveals that the distribution is highly skewed. Most studies
reporting on MEA experiments make no mention of this highly skewed
distribution in firing rates, with the noted exception of the report by
Biffi and colleagues that discusses it specifically (2013). This
observation may have gone unnoticed due to studies being performed in
single-well MEAs having insufficient data for this distribution to be
apparent, however, a similar highly skewed distribution has been
reported for firing frequencies at individual recording electrodes
within MEAs (Vincent et al. 2013). This distribution shape is not
surprising, given that any frequency based metric is lower bound at zero
and the only upper limit is the sampling rate at which the observations
are being made.

A practical consequence of this pattern of behavior is that the observed
distribution array firing frequencies and a theoretical random normal
distribution deviate substantially from each other, as demonstrated in
the quantile-quantile (QQ) plot in the upper right of Figure
\ref{firingFreqDist}. Despite this, many studies reporting on
spontaneous firing activity of neuronal cultures recorded by MEAs use
common parametric statistical tests such as Student's t-test and ANOVA,
which assume a normally distributed dependent variable. Other
researchers have presumably observed the asymmetry in the data sets of
MEA recordings, and instead have used non-parametric tests such as
Krustal-Willis and Mann-Whitney U tests for reporting their results (see
Table \ref{meaStudyTable}).

The distribution of array firing frequencies does form a symmetrical,
approximately normal distribution following transformation of the values
by \(log_{10}\), as shown in the lower panels of Figure
\ref{firingFreqDist}. Other studies have used \(log_{10}\)
transformation in the reporting of MEA data, including Wainger et al.
and Black et al. who did so to compare firing frequencies of individual
electrodes (2014; 2018), as well as Slomowitz et al. to compare the
activity of individual neurons following spike sorting (2015). Given the
log-normal behavior of the observed frequencies, \(log_{10}\) Hz rather
than Hz is used through this report to describe the spontaneous firing
activity of neuronal cultures.

\begin{figure}

{\centering \includegraphics{figures/firing_frequency_distribution_plot-1} 

}

\caption{\label{firingFreqDist}{\small\textbf{Distribution of mean firing rates observed in primary cortical cultures.} \textit{Left}, distribution of firing frequencies (Hz) from } 1272 {arrays from multi-well MEAs on a linear (red) or log10-transformed (blue) scale. \textit{Right}, quartile-quartile plots comparing firing frequencies on a linear, log scale to a random normal distribution (line).}}\label{fig:firing_frequency_distribution_plot}
\end{figure}

\subsubsection{Duration of recording for achieving intra-array
reproducibility}\label{duration-of-recording-for-achieving-intra-array-reproducibility}

Given the broad range of firing activity observed across arrays,
extending more than two orders of magnitude, an important consideration
for MEA based experiments is the duration of recording to perform. While
the inter-array variance is the accumulation of the implicit variability
of the biological system, and any variability introduced by the
construction of the experimental system, the goal is to minimize
sampling error by identifying a duration of recording sufficient to
accurately capture the activity across the cultures. Most studies
employing MEAs report using recordings on the order of 10's of minutes
(see Table \ref{meaStudyTable}), though the duration can vary
dramatically with some studies performing recordings as short as 2
minutes (Bateup, Denefrio, et al. 2013) and others up to an hour
(Slomowitz et al. 2015).

In order to formally address this question of what is a sufficient
duration of recording, the correlation of firing frequencies was
examined as a function of recording length in repeated recording
sessions. Extended 3-hour recordings were taken of mature cortical
cultures performed on consecutive days (DIV20, 21). From these data, the
Pearson's correlation (\(\rho\)) was calculated between the distribution
of mean firing rates of arrays on the first day verse the second day,
for accumulating 1-minute intervals starting from the initiation of
recording out to the entirety of the recording. Figure
\ref{recDuraCorrelation} shows the change in correlation with increasing
duration for the first 2-hours of recordings, calculated from the
repeated measurement of 96 MEAs across 4 culture preparations. Comparing
the first 30-minutes of recording across both days within each of the 4
experiments show a correlation of \(\rho\) \textgreater{} 0.8, yet there
are only modest increases in \(\rho\) seen comparing longer intervals.
Fitting a linear regression to the data shows that the the correlation
between recordings is approximated by the natural log of recording
duration \(\rho\ \propto\ ln(time)\). This suggests that substantially
increasing the length of recording beyond 30-minutes will only
marginally improve the correlation, since
\(\frac {\delta}{\delta x}ln(x) = \frac {1}{x}\). Based on these
observations, 30-minute recordings were used as the standard recording
duration for this study to measure firing activity within MEAs .

\begin{figure}

{\centering \includegraphics{figures/recording_duration_corr_plot-1} 

}

\caption{\label{recDuraCorrelation}{\small\textbf{Correlation of firing frequencies in repeated recording as a function of time.} Pearson’s Correlation $\rho$ between firing frequencies of arrays on consecutive days (DIV 20, 21) as function of recording time for 4 culture preparations. Line indicates fit of $\rho$ $\propto$ ln(time). Insert: Distribution of array firing frequencies $log_{10}$Hz for 30min  during each recording.}}\label{fig:recording_duration_corr_plot}
\end{figure}

\subsubsection{Changes in spontaneous firing across arrays with culture
maturity}\label{changes-in-spontaneous-firing-across-arrays-with-culture-maturity}

From the published literature, studies performing MEA experiments using
primary cultures of rodent cortical neurons have a range of reported age
of cultures: day \emph{in vitro} (DIV), at the time of analysis ranges
from DIV7-35, although DIV14-28 is typical (see Table
\ref{meaStudyTable}). Conversely, studies that instead used neuronal
cultures derived from stem cells have a range of culture ages differing
to a greater degree, over 30 days (DeRosa et al. 2018; Russo et al.
2018), including up to DIV70 (García-León et al. 2018), however this
heterogeneity is expected given the variety of differentiation protocols
used and increased interval until these cells become electrically
active.

For our own studies, in order to assess how firing activity changed with
maturation, 4 culture preparations were recorded every 2-3 days between
ages of DIV3-22. As shown in Figure \ref{cultureMature}, the cultures
are electrically active by the end of the first week, and that activity
remains generally consistent over the next two weeks. Of course, as
described above, the range of activity observed across the population of
arrays is broad. Additionally, this depiction shows all arrays,
including the portion from which little electrical activity is observed.
These are apparent in the lower portion of the figure where the data
appears striated, where differences in a single spike event are seen as
step-wise changes.

Given the level of consistency in activity beyond DIV14, for the
purposes of this study the cultures used for experiments were between
DIV19-35, unless otherwise noted.

\begin{figure}

{\centering \includegraphics{figures/culture_maturation_plot-1} 

}

\caption{\label{cultureMature}{\small\textbf{Changes in firing frequency with culture maturation.} Firing frequencies of arrays observed during repeated 30min recordings over a 3 week period (DIV 03 - DIV 22) across 4 culture preparations. Each point represents an individual array per day. 384 replicate arrays, cultures: 1,3,4; 284 culture: 2. Line indicates mean logHz per recording for each culture.}}\label{fig:culture_maturation_plot}
\end{figure}

\subsubsection{Assignment of treatment groups for MEA
experiments}\label{assignment-of-treatment-groups-for-mea-experiments}

The variability observed across MEAs presents a challenge for the
assignment of treatment groups for experiments. The common practice for
biological assays within multi-well plates is to use the orientation of
the wells in rows and columns to spatially assign treatment groups.
However, this practice can be problematic for multi-well MEAs where
variability is large and non-uniform. This is demonstrated for a typical
multi-well MEA plate shown in Figure \ref{treatGroupAssignment}A.
Relying on plate rows for the designation of treatment groups can result
in significant differences in the firing activity between cohorts even
before the initiation of treatment, as shown in Figure
\ref{treatGroupAssignment}B, where 8 treatment groups (\emph{n} = 12)
were designated using the 8 rows of the 96well plate.

While plate-based variability does occur in conventional, colorimetric,
fluorescence, or luminescence-based plate assays; in these instances the
cause is usually an external factor, such as liquid handling or excess
evaporation along exterior wells, and are typically uniform
plate-to-plate. Therefore, these can be addressed by changes to the
assay protocol, or post-hoc application of statistical regression
techniques (Clemons, Tolliday, and Wagner 2009). However, unlike in many
bioassays data acquisition of MEAs is non-destructive to the cultures,
so the varying levels of activity between arrays can be assessed before
initiation of experimental treatments.

The first step in remediation of this issue posed by variability across
MEAs is to exclude those arrays that exhibit substantially lower levels
of firing activity than the population as a whole. These wells are
clearly distinguishable in the QQplot in the left panel of Figure
\ref{treatGroupAssignment}C, as the tail that deviates from the
otherwise normally distributed population. In practice these
\emph{low-activity} wells are excluded by setting a threshold at 2SD
below the median value of firing frequency across all arrays in the
experiment. In this case, 7 arrays of 96 are excluded. Having excluded
these arrays, the remainder closely follow a normal distribution, shown
in the QQplot on the right hand panel. Of course, having eliminated
these arrays, this now invalidates the approach of using plate rows or
columns for the assignment of treatment groups, since the number of
available replicates in each group would differ.

While aligning treatment groups to the rows and columns is convenient,
it imposes an unnecessary constraint on the positioning of replicates
within cohorts. For the original configuration of assigning 96 arrays to
8 treatment groups with 12 members each, there are
\(6.25\times 10^{14}\) potential combinations, of which the option that
aligns with the plate rows is only one. When the assay signal is uniform
across the plate, one combination of wells is effectively as good as
another, so opting for the most convenient makes the most sense.
However, with the convenient option removed due to the exclusion of
inactive arrays and the non-uniform variability across the plate, there
is reason to explore the other options in order to achieve a comparable
level of pre-treatment activity between groups.

With the inactive arrays removed, the goal is to find an alternative
assignment using the 88 most active of the remaining 89 arrays to
generate 8 treatment groups with 11 members each. To do so, we have
applied a bootstrapping simulation approach is applied where \(10^{4}\)
possible treatment assignments are generated, then a one-way ANOVA is
applied to each iteration assessing firing frequency (\(log_{10}\)Hz) as
a function of group assignment. From these ANOVA results, the iteration
that results in the lowest F-statistic, which represents the smallest
ratio of \emph{between group} and \emph{within group} variance, is
selected for the assignment of the treatment groups. The results of this
approach are shown in the Figure \ref{treatGroupAssignment}D \& E, where
the alternative assignment results have near equal levels of activity
across all 8 cohorts. Using the described method of removing inactive
arrays and determining the optimal assignment through simulation results
in a 78.7\% decrease in the variance in firing frequency associated with
treatment group assignment (\(TotalSS_{sim}\) = 17.64 verse
\(TotalSS_{row}\) = 82.6).

Additionally, this method can be extended to generate treatment groups
with comparable activity levels across multiple plates for large
experiments, which exceed the capacity of a single plate. It also can be
used to generate treatment groups with comparable activity but uneven
numbers of replicates for instances where the experiment incorporates a
finite resource, like primary patient material, necessitating an
\emph{unbalanced} design, as will be discussed further in Chapter 3.

\begin{figure}

{\centering \includegraphics{figures/group_assigment_plot-1} 

}

\caption{\label{treatGroupAssignment}{\small\textbf{Assignment of treatment groups across multi-well MEAs.} \textbf{A} Firing frequencies observed across a single recording of a 96-well MEA. \textbf{B} Firing frequencies across 8 cohorts with 12 members (96 total), designated by plate row. \textbf{C} QQ plots comparing firing frequencies to normal distribution, for all wells (\textit{left}), or following exclusion of wells > 2 S.D. below median (\textit{right}). \textbf{D} Assignment for 8 cohorts with 11 members (88 total), with minimal between group variance determined by bootstrapping simulation. \textbf{E} Firing frequencies within groups from alternative group assignment. Data presented as mean +/- SEM}}\label{fig:group_assigment_plot}
\end{figure}

\subsubsection{Adding it all up: Power to detect changes in neuronal
firing using
MEAs}\label{adding-it-all-up-power-to-detect-changes-in-neuronal-firing-using-meas}

Ultimately for a researcher seeking to model changes in spontaneous
firing activity within neuronal cultures using MEAs, one of the most
practical considerations of experimental design comes down to the
decision of the number of replicates per condition. Having characterized
the distribution of firing frequencies observed across a large number of
MEAs (Figure \ref{firingFreqDist}) and having established the
correlation of activity between repeated recordings (Figure
\ref{recDuraCorrelation}), it is possible to use these factors to
estimate the statistical power of MEA experiments.

For demonstration purposes, a two condition (\emph{control},
\emph{treatment}), repeated measures (\emph{pre}, \emph{post}),
experiment is modeled. Again, a bootstrapping approach is applied to
simulate experiments utilizing sample sizes ranging from 3-16 replicates
per condition, and scenarios in which the treatment resulted in effect
sizes of a difference in \(log_{10}\)Hz ranging from 0.1-2.0. The
dataset used for the simulation is generated from the firing frequencies
of 1272 unique MEAs. The \emph{pre} treatment time point
\(log_{10}\)Hz\(_{t0}\), is taken from the existing data, while the
\emph{post} treatment time point \(log_{10}\)Hz\(_{t1}\) is randomly
generated as to be related to \(log_{10}\)Hz\(_{t0}\) by a correlation
coefficient \(\rho\)=0.8, estimated from repeated recordings of MEAs
(Figure \ref{recDuraCorrelation}). The firing frequencies of
experimental replicates are pairs of \(log_{10}\)Hz\(_{t0}\) and
\(log_{10}\)Hz\(_{t01}\) with values drawn from random arrays in the
population. The \(log_{10}\)Hz\(_{t01}\) values within the
\emph{treatment} condition are offset by the specified effect size. In
order to estimate the difference between \emph{control} and
\emph{treatment} at the \emph{post} time point, and an analysis of
covariance (ANCOVA) is performed assigning time as a co-variate and
determining the effect of treatment. This method has been shown to
provide higher power than other approaches, such as percent of baseline
or absolute change (Vickers 2001).

Applying the conventional standard of statistical power \(\geq\) 80\%
for a robust assay, the results of this simulation show that it is
difficult to reliably detect changes below 0.7 \(\Delta log_{10}\)Hz,
about 5 \(\frac{spikes}{sec}\), even with large numbers of replicates.
Based on the results of addition simulations, detecting changes on the
order of 0.5 \(\Delta log_{10}\)Hz would require treatment groups with
\textgreater{}40 replicates, and a change of 0.1 would require
\textgreater{}600 replicates. In order to confidently detect changes of
1.0 \(\Delta log_{10}\)Hz (i.e.~a 10-fold difference in
\(\frac{spikes}{sec}\)), requires treatment groups with 8-9 replicates.
Only with large changes in in firing frequency of \(\Delta log_{10}\)Hz
2.0 or greater (i.e.~a 100-fold or more difference in
\(\frac{spikes}{sec}\)) would it be reasonable to use treatment groups
as small as an n=3. To illustrate the magnitude of these proposed
effects, repeated experiments with tetrodotoxin (TTX), showed that
treating cultures with 10nM induced decreases of -1.14 \(log_{10}\)Hz
and with 100nM induced decreases of -2.24 \(log_{10}\)Hz. The reported
IC\(_{50}\) of TTX-sensitive Na\(_v\) channels in rats is 10nM or less
(Catterall, Goldin, and Waxman 2005). Therefore, this analysis suggests
that in order to have sufficient power to detect changes in spontaneous
firing activity comparable to those elicited by treatment with TTX at
its IC\(_{50}\) concentration requires treatment groups with 8-9 arrays
per condition. Of course, it is important to emphasize that these power
estimates are based on a simple two condition experiment, so any
experiment seeking to examine more conditions would need to allow for
more replicates per condition in order to provide sufficient power for
multiple comparisons.

\begin{figure}

{\centering \includegraphics{figures/power_calc_plot-1} 

}

\caption{\label{powerCalc}{\small\textbf{Calculation of statistical power for MEA experiments.} Estimates of percent power$_{(1-\beta)}$ based on simulation for a 2 condition (\textit{control,treatment}) repeated measures (\textit{pre,post}) experiment across sample sizes 3-16 arrays and effect sizes of 0.1-2.0 $\Delta log_{10}$Hz. Simulated experiments analysed by ANCOVA, p-value extracted from comparison between \textit{control} and \textit{treatment} at \textit{post} measure, 5000 iterations per sample size:effect size pairing. \textit{Upper}, distributions of observed p-values from simulated treatments per sample size for effect size of 0.5, 1.0, and 2.0 $\Delta log_{10}$Hz, green line indicates p=0.05. }}\label{fig:power_calc_plot}
\end{figure}

\pagebreak

\subsection{Discussion}\label{discussion}

A methodological review was performed of published studies reporting on
experiments utilizing MEAs in order to assess standard practices within
the field. Particular attention was paid to whether the study employed
single or multi-well recording systems, how spike detection was
performed, the duration of recordings, the age of cultures at time of
experiments, and methods used for assessing differences in firing
frequencies between experiment conditions. Due to the plurality of
methods reported, experimental conditions such as recording duration and
culture age were systematically evaluated to verify their applicability
to the primary rat cortical cultures being used in this study. Having
generated recording data from a large population of over a thousand
MEAs, firing frequencies at the array level were examined, revealing a
highly skewed and highly variable distribution. It was shown that
application of log-transformation should be used to obtain approximately
normal distributions of firing frequencies prior to statistical
analysis, and that implementation of a bootstrapping simulation can be
used to accommodate for the broad distribution of firing frequencies to
generate treatment groups with comparable levels of activity. Finally a
series of simulated experiments were performed to determine the expected
statistical power for a two condition, repeated measure experiment
across a range of sample and effect sizes, based on the observed
distribution of firing frequencies of arrays and the empirically
determined correlation of firing in repeated recordings.

Having assessed several methodological aspects of MEA experiments, many
of the commonly reported methods appear adequate for estimating firing
activity within \emph{in vitro} neuronal cultures, while other elements
of MEA experimental design and analysis have either been under-reported
in the literature or investigators have employed techniques that may be
inappropriate for the task. Specifically, recording duration of
20-minutes or longer as reported by several studies (Kuperstein et al.
2010; McConnell et al. 2012; Vincent et al. 2013; Slomowitz et al. 2015;
Feng et al. 2018; Black et al. 2018), appear sufficiently long to
capture the activity in individual arrays with a high degree of
reproducibility (Figure \ref{recDuraCorrelation}). Similarly, the common
practice of performing experiments with primary cultures that have been
aged 2-3 weeks is reasonable (see Table \ref{meaStudyTable}), since the
primary rat cortical cultures examined here showed a high degree of
spontaneous firing by the end of the first week that plateaued through
these time frames (Figure \ref{cultureMature}). Conversely, the highly
skewed distribution of firing frequencies across both individual
electrodes and entire arrays has been largely omitted from reports or
accounted for in presentation of results, with a limited number of noted
exceptions (Biffi et al. 2013; Vincent et al. 2013; Wainger, Buttermore,
et al. 2014; Slomowitz et al. 2015; Black et al. 2018). Despite this
observation, several of the reviewed publications utilized parametric
statistical tests such as Student's t-test and ANOVA for assessing
differences in firing frequency between MEAs (see Table
\ref{meaStudyTable}). This is concerning given that these tests assume a
normally distributed dependent variable, a condition that these data
fail to meet when examined on a linear scale. Further, some studies
utilized these methods for analyzing longitudinal experiments (Feng et
al. 2018; Sarkar et al. 2018), which similarly violate the underlying
assumptions of the common forms of these tests which expect measurements
to be independent and do not account for the anticipated correlation of
values between repeated measurements of the same subject. Rather, ANCOVA
and other linear regression based methods that handle time as covariate
within the data would be more appropriate (Singer and Willett 2003;
Diggle et al. 2002), as was employed here for the simulation of
treatment or as was reported by Wainger et. al. (Wainger, Buttermore, et
al. 2014).

Despite the increasing number of studies utilizing multi-well MEAs, none
of the reports examined described methods for accounting for the wide
variability of firing frequencies when assigning treatment groups. Given
the ability to easily record MEAs repeatedly, implementation of a
simulation based assignment technique using baseline recording data such
as described here seems adequate for the purpose (Figure
\ref{treatGroupAssignment}). While the use of complex treatment maps
generated by the process may seem impractical for the addition of
experimental perturbations, it is certainly feasible for experiments
being performed manually, and is trivial for MEA recording systems that
are integrated with automated liquid-handlers as is the case with
instruments such as the Maestro Apex (Axion Biosystems, Hamilton
Robotics).

Finally, using simulation methods incorporating data on the variability
and reproducibility of MEA recordings to calculate statistical power of
experiments showed the magnitude of effects that can be detected with
with confidence this system. Comparing these simulated results with
empirical data from treatment of cultures with the neurotoxin TTX shows
that it is possible to detect changes in firing frequency similar to
those induced by treatment with TTX at its IC\(_{50}\) using treatment
groups containing 8-10 replicates. Conversely, detecting large changes
in firing, such as those induced by high doses of TTX (10X IC\(_{50}\)),
only requires a few replicates, while detecting more modest changes will
require many more replicates. This is an important consideration for
experimental design in the the different research areas in which MEAs
are being used. For toxicology studies in which the effect sizes of
pharmacological agents maybe larger, a few replicates per condition may
suffice, compared to disease modeling studies assessing the functional
effects of genetic variants where the effect sizes are expected to be
smaller. For these studies, much larger numbers of arrays would be
required to discern statistically significant differences.

\newpage

\subsection{Methods}\label{methods}

\paragraph{Tissue Culture}\label{tissue-culture}

\textit{Preparation of MEA plates} 96-well multi-electrode plates (Axion
Biosystems, Atlanta GA) were prepared prior to the addition of cells by
coating with poly-Ornithine (poly-O), laminin (\textit{both}, Sigma
Aldrich, St.~Louis MO), and Matrigel (Corning Life Sciences, Corning
NY). Briefly, 1-day prior to establishment of cultures, a solution of
20\(\mu\)g/mL poly-O : 5\(\mu\)g/mL laminin in 1X PBS was added to the
MEA plates in a volume of 60 \(\mu\)L per well, and incubated over-night
at 37C. The day of culture plating, the poly-O:laminin solution was
aspirated and the plates were washed once with 1X PBS. Matrigel was
reconstituted 1:20 in ice-cold DMEM culture media (Gibco; ThermoFisher,
Waltham MA) without added serum or antibiotics and passed through a
40\(\mu\)m strainer (BD Falcon, Franklin Lakes NJ). The Matrigel
solution was added to the MEA plates in a volume of 60 \(\mu\)L per
well, and incubated for a minimum of 1hr at 37C.

\textit{Preparation of cortical cultures} Primary cortical cultures were
established from E18 Sprague Dawley rats (Charles River Laboratories,
Wilmington MA). Dams were sacrificed by CO\(_2\) euthanasia under a
protocol approved by IACUC of Brigham and Women's Hospital (Boston, MA).
Dissection of complete cortex from pups was performed in ice-cold HBSS
(Gibco) under a dissecting microscope (Zeiss, Jena Germany). The
dissected cortices were suspended in 0.25\% trypsin-EDTA (Gibco) for
10min at 37C, the excess trypsin-EDTA solution was then aspirated. The
tissue was then triturated in \textit{Complete DMEM}: DMEM culture media
(Gibco) supplement with 5\% fetal calf serum (Lonza, Portsmouth NH) and
1X penicillin/streptomycin/L-glutamine (Gibco); with a 10mL serological
pipette before being passed through a 100\(\mu\)m strainer (BD Falcon).
Counts of the cell suspension were taken in triplicate, and the cell
suspension was back diluted to \(1.5 \times 10^6\) cells/mL in Complete
DMEM. The cell suspension was added to the MEA plates in a volume of
50\(\mu\)L for a plating concentration of \(7.5 \times 10^4\) cells/well
or \(2.4 \times 10^5\) cells/cm\(^2\). Plates were then place in a
tissue culture incubator (37C, 95\% humidity, 5\% CO\(_2\)) for 4hrs to
allow for cells to attach to the culture surface. After the 4hr
incubation, 150\(\mu\)L of \textit{BrainPhys Media}: 1X BrainPhys
culture media containing SM-1 neuronal supplement (StemCell
Technologies, Vancouver BC); was added to each well. The cultures were
maintained in a tissue culture incubator in BrainPhys Media, with
semi-weekly half-volume media changes.

\paragraph{MEA recording}\label{mea-recording}

\textit{MEA recordings} All MEA recordings were performed using a
Maestro multi-well MEA recorder (Axion Biosystems). During recordings,
plates were kept on a heated stage maintained at 37C and ventilated with
a mixture of 5\% CO\(_2\):95\% air (AirGas, Randor PA) at a rate of 1
cubic-foot per hr. In order to prevent evaporation of liquid within
wells by convection and condensation on the underside of the plate lid,
the MEA plate was covered with an air-activated oxidizing iron heater
(HotHands, Kobayashi, Dalton GA) placed on top of an aluminum plate cut
to size, for even dispersal of heat. Voltage potentials within wells
were simultaneously recorded across 768 channels (8 electrodes per well,
96-well plate) at a sampling frequency of 12kHz using AxIS acquisition
software version 2.4 (Axion Biosystems). The raw voltage recordings were
subjected to a Butterworth filter of 200Hz - 2.5kHz, and neuronal firing
events (spikes) were detected when the voltage exceeded a ``crossing
threshold'' set at 5.5 standard deviations away from the root mean
squared (RMS) of the background potential calculated over a 10 ms moving
window. All recordings were performed for 30min unless otherwise
specified.

\paragraph{MEA analysis}\label{mea-analysis}

Raw voltage, timestamp, value of crossing threshold for each spike event
were extracted from the \textsf{.spk} files of MEA recordings produced
AxIS acquisition software, using custom MatLab scripts (MathWorks,
Natick MA) using extractor functions provided with AxIS version 2.4.
Following extraction of the raw recording data, all analyses and
simulations were performed using the R statistical programming language
(R Core Team 2017). All figures were generated using the R
\textsf{ggplot2} and accompanying \textsf{ggpubr} libraries (Wickham
2016; Kassambara 2018).

\textit{Mean-firing rate calculation} In order to remove spurious spike
events arising from by `high-noise' electrodes, an upper limit to the
crossing threshold was established by examining the crossing threshold
(\(\mu\)V) for all spike events detected and calculated the value
corresponding to 3SD greater than the mean crossing threshold, all
events detected at a crossing threshold greater than this upper limit
were excluded from the analysis. The mean firing rate (MFR, Hz) was
calculated as the ratio of the total number of spikes record, \(n\), and
the duration of recording in seconds, \(s\), \(MFR = \frac{n}{s}\). The
log transform of the MFR was calculated as \(log_{10}(\frac{n+1}{s})\),
to account for instances of \(n = 0\), the log of which is undefined.

\textit{Treatment group assignment} A pool of \emph{active} arrays from
a mutli-well MEAs is established by selecting those arrays that are no
more than 2 SD below the median of the sample set. A panel of \(i\)
possible treatment assignments is generated by randomly assigning arrays
to treatment groups \(g\), each with \(n\) members. For the purposes of
this study, \(i\) = \(10^{4}\). For each instance of \(i\), a one-way
ANOVA was performed, assessing \(log_{10}\)Hz as a function of \(g\).
The instance of \(i\) resulting in the lowest value of the F-statistic,
was used as the treatment group assignment.

\textit{Tetrodotoxin treatment} Tetrodotoxin (TTX, Sigma Aldrich) was
reconstituted in sterile water, and diluted to the specified
concentration in BrainPhys culture media containing SM-1 neuronal
supplement (StemCell Technologies). 30 minute baseline recordings of
mature (\textgreater{}DIV21) rat primary cortical cultures were
performed, and arrays were assigned to cohorts with comparable activity
(6-8 replicates per group) using the technique described above. Cultures
were treated with a titration of TTX (0.001-1\(\mu\)M), and immediately
recorded again for 30 minutes. The magnitude of effect of each
concentration of TTX was determined from the coefficients of a linear
mix-effect model aggregating the data from three separate experiments,
and estimating the effect on firing frequency from the \emph{fixed
effects} of dose and recording (\emph{pre}/\emph{post}), and treating
experiment as a \emph{random effect}, using the \texttt{lme} function
within the R \textsf{nlme} library (Pinheiro et al. 2018; Pinheiro and
Bates 2000).

\textit{Power Calculation} A dataset of 30 minute recordings of 1272
unique, untreated MEAs was taken to represent spontaneous firing
activity \(log_{10}\)Hz at time (\(t_0\)), this was defined as vector
\(A\). The correlation coefficient between the firing frequencies of a
population MEAs recorded at separate times was estimated to be
\(\rho\)=0.8, based on the repeated recordings of multiple culture
preparations at DIV20 and 21. To simulate the expected variance in
firing frequencies between recordings, a second vector \(B\) was
calculated by the formula: \[
B = \rho A \ + \ A^\perp \sqrt{\ 1\ -\ \rho^2} 
\] Where \(A^\perp\) represents the residuals of a linear regression
between \(A\) and and a sample of equal length drawn from a random
normal distribution. The resulting value of \(B\) was examined to
confirm that: \[cor(\vec{A},\vec{B}) = \rho \] \(B\) is then taken to
represent spontaneous firing activity \(log_{10}\)Hz at time (\(t_1\)).
For each simulated experiment, \emph{control} and \emph{treatment}
groups were generated by drawing the paired \(log_{10}\)Hz\(_{t0}\) and
\(log_{10}\)Hz\(_{t1}\) values for random arrays for sample sizes
ranging 3-16. The \(log_{10}\)Hz\(_{t1}\) values within the
\emph{treatment} group were offset by effect sizes ranging from 0.1-2.0
\(log_{10}\)Hz. \(5000\) iterations were preformed for each sample
size:effect size pairing, for a total of \(1.4\times 10^{6}\) total
simulated treatments. For each iteration, an ANCOVA was performed by
fitting an ANOVA model to linear regression for \(log_{10}\)Hz as a
function of group (\emph{control}/\emph{treatment}) and time
(\emph{pre}/\emph{post}) allowing for interaction between the group and
time variables. The coefficients for the model were compared using
Tukey's test for honest significant difference, and the \(p\)-value for
the comparison of \emph{control} verse \emph{treatment} at time \(t_1\)
was extracted. The linear regression, fitting of the ANOVA, and Tukey's
test were performed using the \texttt{lm}, \texttt{aov}, and
\texttt{TukeyHSD} functions within the base R \textsf{stats} library.
Power was calculated as the proportion of iterations within each sample
size:effect size pairing for which the difference between \emph{control}
and \emph{treatment} was calculated to have a \emph{p}-value \textless{}
0.05. The simulation was performed on the O2 high performance computing
cluster (Research Computing Group, Harvard Medical School, Boston MA).

\FloatBarrier
\newpage

\singlespacing

\section{\texorpdfstring{Chapter 3: Investigating the influence of
patient-derived amyloid-\(\beta\) on neuronal
firing}{Chapter 3: Investigating the influence of patient-derived amyloid-\textbackslash{}beta on neuronal firing}}\label{chapter-3-investigating-the-influence-of-patient-derived-amyloid-beta-on-neuronal-firing}

\doublespacing

\subsection{Introduction}\label{introduction-1}

Alzheimer's disease (AD) is the most common form of age-related dementia
with an estimated 5.7M cases in the United States, and expected to grow
to 13.8M in the U.S. and 50M worldwide by 2050. The cost for caring for
AD sufferers is predicted to reach \$277B per annum in 2018 (Alzheimer's
Association 2018). Unfortunately, progress on the development of
therapies for AD has been lagging, with the last FDA approval for an AD
therapy, memantine, occurring in 2003. Of the four approved therapies:
donepezil, rivastigmine, and galantamine, and memantine, none target the
underlying disease mechanism, and in best cases only serve to delay the
progress of symptoms on the order of months. In the last 15 years, the
development novel therapies for AD has been marked by a number of
disappointing failures in late-stage clinical trials due to lack of
efficacy. Between 2002-2012, of the 244 agents test in clinical trials
registered with the NIH, only a single therapy (memantine) ultimately
received regulatory approval (Cummings, Morstorf, and Zhong 2014;
Alzheimer's Association 2018). These events have resulted in
reconsideration of the design of AD clinical trials with the assertion
that patients may have been recruited to the trials too late into
disease progression for even an efficacious therapy to show benefit
(Schaper 2018). While reassessing trial design is critical, clinical
trials are expense in terms of monetary cost, time, and human capital.
Therefore, it is imperative to improve the disease models used in the
pre-clinical setting to identify candidate therapies with the highest
probability for success before promoting them to later stage
development.

Within the field of therapeutic discovery there is a movement toward
increased use of representative phenotypic assays that: \emph{i} use the
most relevant cell type, \emph{ii} employ a disease relevant
perturbation, \emph{iii} and rely on an assay metric that is
representative of the disease symptomology (Vincent et al. 2015).
Incorporating this paradigm in the context of AD would translate to a
model in which the behavior of neurons could be monitored while
experiencing conditions of AD pathology.

In order to model AD pathology, the `amyloid hypothesis' posits that
overproduction of amyloid-\(\beta\) peptide (A\(\beta\)) and a shift in
the ratio A\(\beta_{42}\):A\(\beta_{40}\) toward the more hydrophobic
A\(\beta_{42}\), results in the accumulation of soluble A\(\beta_{42}\)
oligomers which impede synaptic function leading to eventual cell death
(Hardy and Selkoe 2002; Haass and Selkoe 2007; Sheng, Sabatini, and
Südhof 2012). While these soluble oligomers are considered to be
primarily responsible for pathogenic effects of A\(\beta\), reports
studying the mechanism of A\(\beta_{42}\) on neurons have utilized
different preparations of aggregates comprised of synthetic peptide and
recombinant protein (Walsh et al. 2002; Shankar et al. 2007; Lacor et
al. 2007; Rönicke et al. 2011; Li et al. 2011). However, tris-buffer
saline (TBS) soluble extract of AD brain is considered to contain the
most representative form of A\(\beta\) as this presumably comprises the
endogenous disease causing species (Shankar et al. 2008; Jin et al.
2011; Barry et al. 2011; Wang et al. 2017; Yang et al. 2017).

Post mortem examination of AD brain has found that synaptic density is
the metric most closely correlated with cognitive decline, even more so
than amyloid plaque density (Terry et al. 1991; Scheff and Price 2006;
Scheff et al. 2015; Chen et al. 2018). Therefore, using an assay metric
that is closely related to synaptic density would represent a relevant
disease-related phenotype. The model should incorporate mixed cultures
of neurons and glia given the myriad of roles glia have in neuronal
homeostasis and AD pathology including maintenance of synapses,
A\(\beta\) catabolism, and regulating immune response (Clarke and Barres
2013; Chung et al. 2015; De Strooper and Karran 2016). Further, studies
from oncology drug development have shown the importance of testing
candidate therapeutics in the presence of tissue relevant accessory
cells in order to capture non-cell-autonomous dynamics of response to or
attenuation of treatment (McMillin, Negri, and Mitsiades 2013).

Assessing the spontaneous firing of neurons cultured on multi-electrode
arrays (MEAs) is an assay platform that fulfills these requirements for
an AD experimental model. Spontaneous activity in neuronal networks has
been shown to be correlated with synaptic density (Brewer et al. 2009).
Further, by virtue of being a label-free system relying on detection of
endogenously generated potentials, MEA-based experiments can be
performed with dense, mixed cultures without confounding signal from
non-neuronal accessory cells. This is an important consideration since
the density of \emph{in vitro} cultures has been show to influence
synaptic density, complexity of dendritic arbors, and electrical
behaviors of neurons (Cullen et al. 2010; Ivenshitz and Segal 2010).

In order to characterize the behavior of neurons under conditions
mimicking the AD brain, the spontaneous firing of neurons was monitored
within mixed cortical cultures by MEA recording following treatment with
TBS-soluble extracts from a panel of human brains as well as those from
AD mouse models. Additional experiments were preformed to determine
whether the presence of A\(\beta\) within these extracts is necessary
for eliciting the observed changes in firing activity, and whether
aggregated forms of proteins associated with AD pathology,
A\(\beta_{42}\) and tau, are alone sufficient to influence spontaneous
firing. The results of these studies point to the varied responses seen
when testing primary patient samples, even amongst those with a common
clinical diagnosis, and indicate areas of consideration for further
refinement of \emph{in vitro} modeling of AD.

\newpage

\blandscape

\begin{table}

\caption{\label{tab:brainExtractCompTable}\label{brainExtractTab} Human brain samples used in this study}
\centering
\resizebox{\linewidth}{!}{
\begin{tabular}[t]{llrlllllrr}
\toprule
ID & Class & Age & Gender & Clinical Dx & Pathology Dx & A$\beta_{42}$/A$\beta_{40}$ -GuHCl & A$\beta_{42}$/A$\beta_{40}$ +GuHCl & N \textit{exper.} & n \textit{arrays}\\
\midrule
BWH02 & LP-NCI & 76 & F & NA & NA & 4.9 / ND & 39.2 / ND & 3 & 30\\
BWH04 & LP-NCI & 53 & M & NA & NA & 7.5 / 25.3 & 33.1 / ND & 2 & 12\\
MGH1837 & LP-NCI & 68 & M & NA & NA & 1.7 / 12.2 & 40.7 / ND & 6 & 62\\
MGH1887 & LP-NCI & 60 & M & NA & NA & 12.6 / 72.8 & 49.4 / ND & 4 & 50\\
MGH2018 & LP-NCI & 92 & F & NA & NA & 10.1 / 25.3 & 77.9 / ND & 2 & 19\\
\addlinespace
MGH2038 & LP-NCI & 78 & F & mild CI & NA & 14.5 / 72.8 & 49.4 / ND & 3 & 37\\
MGH2068 & LP-NCI & 79 & F & NA & NA & 2.6 / 40.6 & 33.1 / ND & 2 & 19\\
BWH01 & HP-NCI & 76 & F & NA & NA & 447.9 / 273.3 & 1964.1 / 748.0 & 3 & 28\\
BWH03 & HP-NCI & 77 & M & CI & mild AD & 58.0 / ND & 915.5 / ND & 3 & 29\\
MGH1846 & HP-NCI & 92 & M & NA & mild AD & 154.2 / 140.2 & 2439.0 / ND & 3 & 34\\
\addlinespace
MGH1965 & HP-NCI & 76 & F & NA & mild AD & 333.0 / 65.4 & 621.8 / ND & 3 & 34\\
ROSMAP21408940 & HP-NCI & 80 & F & NA & mild AD & 230.1 / 202.3 & 2233.5 / 516.8 & 1 & 9\\
ALB01 & AD & 68 & F & AD & AD & 123.1 / 83.83 & 5333.8 / 298.6 & 9 & 98\\
MGH1892 & AD & 67 & M & AD & AD & 167.0 / 606.3 & 5987.0 / 2841.5 & 2 & 20\\
MGH2031 & AD & 70 & F & AD & AD & 127.0 / 57.7 & 6996.8 / ND & 3 & 28\\
\addlinespace
MGH2037 & AD & 73 & F & AD & AD & 120.9 / 325.8 & 2416.1 / 717.9 & 3 & 34\\
MGH2039 & AD & 74 & M & AD, FTD & AD, FTD & 324.9 / 17632.7 & 3343.8 / 28040.1 & 3 & 37\\
ROSMAP20207437 & AD & 87 & F & AD & AD & 176.1 / 114.8 & 3337.3 / ND & 1 & 9\\
ROSMAP20254452 & AD & 87 & F & AD & AD & 126.1 / 411.2 & 5100.0 / 1307.7 & 1 & 9\\
\bottomrule
\multicolumn{10}{l}{\textbf{Abbreviations}}\\
\multicolumn{10}{l}{\textbf{LP-NCI} - Low Pathology Non-Cognitively Impaired,
      \textbf{HP-NCI} - High Pathology Non-Cognitively Impaired,
      \textbf{AD} - Alzheimer's Disease}\\
\multicolumn{10}{l}{\textbf{FTD} - Frontotemporal Dementia,
      \textbf{CI} - Cognitive Impairment,
      \textbf{NA} - Not Applicable}\\
\multicolumn{10}{l}{}\\
\multicolumn{10}{l}{\textbf{GuHCl} - Guanidine Hydrochloride,
      \textbf{ND} - Not Detected}\\
\multicolumn{10}{l}{}\\
\multicolumn{10}{l}{\textit{Note} A$\beta$ concentration: pg/mL}\\
\end{tabular}}
\end{table}

\elandscape

\subsection{Results}\label{results-1}

\subsubsection{Effect of human brain extract on spontaneous
firing}\label{effect-of-human-brain-extract-on-spontaneous-firing}

In order to test the effects of patient derived A\(\beta\) species on
the spontaneous firing of cultured neurons, primary rat cortical
cultures were treated with human brain extract. TBS-soluble brain
extracts containing 1mg/mL of brain derived protein were prepared from
tissue samples of an unaffected, non-cognitively impaired individual
MGH1887 and an AD diagnosed individual ALB01. These extracts were used
to treat mature primary rat cortical cultures grown on MEAs. While
similar AD patient derived brain extracts have been shown to inhibit LTP
within rodent brain organotypic slice cultures within minutes to hours
(Shankar et al. 2008; Li et al. 2011; Yang et al. 2017), the maximal
effect on spontaneous firing induced from the AD brain extract emerged
over 4 days (Figure \ref{singleExpPlot}A). As expected given the
reported role of A\(\beta\) in disrupting synaptic transmission and
synaptic structures, after 4 days treatment with extract from the AD
diagnosed individual, we observed significantly decreased spontaneous
firing by approximately an order of magnitude (-0.993
\(\Delta log_{10}\)Hz, p = \(2.07\times 10^{-5}\)) compared to the
unaffected individual, MGH1887 (Figure \ref{singleExpPlot}B).

Following the removal of the brain extract treatment, there was a
partial recovery in spontaneous firing within the AD-extract treated
cultures, peaking 2-days following the exchange of extract containing
media with fresh, untreated culture media (Figure \ref{singleExpPlot}A).
While the firing activity within the AD extract treated culture 2-days
following the \emph{washout} of the treatment was still significantly
diminished compared to the activity within those cultures previously
treated with brain extract from the unaffected individual (0.646
\(\Delta log_{10}\)Hz,p = \(0.0158\)), the extent of recovery within the
AD extract treated cultures was itself significant (0.648
\(\Delta log_{10}\)Hz, p = \(0.0152\)). This recovery in spontaneous
firing activity is consistent with previous reports that synaptic
structures within cultured neurons are able to regenerate following
exposure to recombinant A\(\beta\) species (Shankar et al. 2007).
Further, examination of the cultures after 4-days of treatment by
immunocytochemistry showed that the integrity of cells within AD extract
treatment condition was largely intact (Figure \ref{singleExpPlot}D).

Subsequent experiments showed that the potency of the effects on
spontaneous firing induced by the AD brain extract extended over a
fairly narrow range, as shown by the titration in Figure
\ref{singleExpPlot}C. This was determined by treating cultures with
brain extract that had been further diluted in culture media using a
half-log (1:3) dilution series. Within a 10-fold dilution of the AD
brain extract, the difference in firing between the AD extract treated
cultures, and those treated with the extract from the unaffected
individual was completely attenuated (Figure \ref{singleExpPlot}C).

\begin{figure}

{\centering \includegraphics{figures/singleExp_plot-1} 

}

\caption{\label{singleExpPlot}{\small\textbf{Effect of human brain extract on spontaneous firing of cultured cortical neurons.} \textbf{A} Time course of firing activity within rat cortical cultures prior to, during, and following treatment with human brain extract. Each dot represents a single multi-electrode array during each recording. Solid line tracks the mean firing frequency per condition at each recording, dashed lines indicate $\pm$ SE. \textbf{B} Comparison of firing frequencies across treatment conditions prior to, 4-days post-treatment, and 2-days following washout. Bars represent mean $\pm$ SE. Significant differences between conditions indicated by brackets, * p < 0.05, ** p < 0.01, *** p < 0.001 \textbf{C} Titration of AD brain extract (ALB01, red) compared to an LP-NCI brain extract (MGH1887, blue) at 4-days post-treatment. \textbf{D} Images of rat cortical cultures stained for neuronal markers MAPT and NeuN following 4-day treatment with vehicle (media alone) or AD brain extract (ALB01).}}\label{fig:singleExp_plot}
\end{figure}

\paragraph{Diversity of effect across
individuals}\label{diversity-of-effect-across-individuals}

In order to understand whether these results were representative of AD
and unaffected patient populations as whole, these experiments were
extended to test a panel of human brain extracts from 19 individuals
(see Table \ref{brainExtractTab}). The individuals within this panel
were designated to one of three classes defines by pathology and
diagnosis. Low-pathology, non-cognitively impaired (LP-NCI): those
unaffected individuals who were not clinically diagnosed with AD nor
exhibited AD pathology during post mortem examination. High-pathology,
non-cognitively impaired (HP-NCI): individuals who did not receive a
clinical diagnosis of cognitive impairment, but were found to exhibit
AD-associated pathology post mortem and/or expressed high levels of
A\(\beta\) in their brain tissue. Lastly, AD patients: individuals who
exhibited cognitive impairment while alive, receiving a clinical
diagnosis of AD, which was subsequently confirmed by pathological
examination post mortem. This panel consisted of 7 LP-NCI, 5 HP-NCI, and
7 AD individuals.

As shown in Table \ref{brainExtractTab}, examination the TBS-soluble
extracts prepared from these individuals shows that the concentration of
aqueous soluble A\(\beta_{42}\) is in the range of 10\(^2\) pg/mL. These
values are similar to the CSF concentrations of A\(\beta_{42}\) observed
within a similarly aged population (Morris et al. 2010). Additionally,
the aqueous insoluble A\(\beta_{42}\) concentration, especially amongst
the HP-NCI and AD individuals, falls within the low ng/mL range, as
determined by treating extracts with the denaturing agent guanine
hydrochloride (GuHCl). This also mirrors the concentration of
A\(\beta_{42}\) found within the tissue of symptomatic AD individuals
processed using a denaturing extraction protocol (Näslund et al. 2000).

The primary samples within this panel were acquired periodically over a
period of nearly two years, and the amount of tissue obtained from each
individual ranged from a few grams to an entire hemisphere of the brain.
The consequence of this is that it was not possible to test all subjects
in all experiments in a head-to-head fashion, or test all individuals
the same number of times given the disparities in the material
available. The result is highly \emph{unbalanced} study with the
different individuals represented by differing numbers of replicates in
differing numbers of experiments. To date, the brain extracts from the
19 individuals have been tested across 22 experiments.

In order to make inferences about the effect of each extract brain
extract on spontaneous firing, a linear mixed effect model (Pinheiro and
Bates 2000) was constructed comprising of the data from all experiments
to estimate the firing frequencies observed in the treated cultures as
function of individual extract and time. As shown in Figure
\ref{indExtractDxPlot}A, there are significant differences in the firing
frequencies between cultures treated with extracts from different
individuals, both acutely after 1-day post-treatment and 4-day
post-treatment. Interestingly, despite the brain extracts representing 3
distinct patient populations, the degree of response is fairly
continuous across the panel of individuals. In fact when the model was
refit to predict the effect of extract diagnosis and treatment time on
firing frequencies, with individual extract handled as random effects,
it was found that there is no difference in the amount of spontaneous
firing within cultures treated with brain extracts from different
diagnosis classes (Figure \ref{indExtractDxPlot}B). This finding
contradicts the expected result that AD and HP-NCI brain extracts would
preferentially cause a decrease in spontaneous firing due to the
elevated levels of A\(\beta_{42}\) within these samples.

\begin{figure}

{\centering \includegraphics{figures/indExtract_Dx_plot-1} 

}

\caption{\label{indExtractDxPlot}{\small\textbf{Effects on spontaneous firing ellicited across a panel of human brain extracts.} \textbf{A} Estimated effect on spontaneous firing of individual human brain extracts at 1-day and 4-days following treatment with human brain extracts. Bars represent mean $\pm$ SE as predicted by linear-mixed effect model based on treatment data from 19 individuals across 1 to 9 replicate experiments per individual. \textit{Below} pairwise comparisons indicating significant differences (p < 0.05) in firing frequencies within cultures treated with each extract at each time point. \textbf{B} Estimated effect on spontaneous firing by diagnosis class at 1-day and 4-days following treatment with human brain extract. Bars represent mean $\pm$ SE as predicted by linear-mixed effect model based on treatment data from 19 individuals. Each dot represents the estimated effect of each individual. No significant differences were found between groups at any time point.}}\label{fig:indExtract_Dx_plot}
\end{figure}

\paragraph{Magnitude of recovery following removal of brain extract
treatment}\label{magnitude-of-recovery-following-removal-of-brain-extract-treatment}

In order to assess the robustness of the recovery of spontaneous firing
following the removal of the human brain extract treatment, the firing
frequencies were compared between the 4-day post-treatment and 2-day
\emph{washout} recordings from treatments with 10 individuals, each
tested across multiple culture preparations (2-6 experiments per
individual). As shown in Figure \ref{indExtractWashoutPlot}, while all
cultures show some degree of restoration in firing activity, the extent
of recovery is only significant in a few cases. However, amongst those
instances in which there is significantly higher firing following
washout of treatment, are cultures treated with brain extract from AD
patient ALB01, the sample in which this behavior was originally observed
(Figure \ref{singleExpPlot}B). While tempting to interpret this recovery
in firing activity, particularly in the case of ALB01, as evidence of
regenerative capacity within these neuronal cultures following exposure
to a milieu mimicking the AD brain, it is important to note that the
amount of firing activity during the washout period never fully matches
that observed prior to treatment. However, intervening earlier in the
culture time may have resulted in full recovery of firing.

\begin{figure}

{\centering \includegraphics{figures/indExtractWashout_plot-1} 

}

\caption{\label{indExtractWashoutPlot}{\small\textbf{Recovery of spontaneous firing following removal of human brain extract treatment.} Estimated effect on spontaneous firing prior to treatment, 4-day post-treatment, and 2-day following washout of human brain extracts. Bars represent mean +/- SE as predicted by linear-mixed effect model based on treatment data from 10 individuals across 2 to 6 replicate experiments per individual. Significant differences between conditions indicated by brackets, * p < 0.05, ** p < 0.01, *** p < 0.001}}\label{fig:indExtractWashout_plot}
\end{figure}

\subsubsection{\texorpdfstring{Interrogating the influence of
amyloid-\(\beta\) on spontaneous
firing}{Interrogating the influence of amyloid-\textbackslash{}beta on spontaneous firing}}\label{interrogating-the-influence-of-amyloid-beta-on-spontaneous-firing}

To estimate the extent that changes in spontaneous firing could be
attributed to A\(\beta\) mediated activity, the estimated decrease in
firing activity within treated cultures for each individual obtained
from the mixed effect model of all experiments (Figure
\ref{indExtractDxPlot}A) was compared to the concentration of soluble
(-GuHCl) and insoluble (+GuHCl) A\(\beta_{42}\) detected within each
extract (Table \ref{brainExtractTab}). While soluble A\(\beta_{42}\)
species are believed to be predominately responsible for the
neurodegenerative effects associated with amyloid pathology (Selkoe and
Hardy 2016), the concentration of soluble A\(\beta_{42}\) within an
extract is a poor predictor of its effect on spontaneous firing within
these primary cultures (Figure \ref{deltalogHzAb42}). A linear
regression model of these data indicates that the concentration of
soluble A\(\beta_{42}\) does not account for a significant amount of the
observed variance in spontaneous firing activity (p = 0.113, R\(^2\) =
0.141). Adding the concentration of insoluble A\(\beta_{42}\) to the
model still does not significantly account for the observations of
firing activity across the extracts (p = 0.162), and only accounts for
another 6.2\% of the total variance (R\(^2\) = 0.203).

\begin{figure}

{\centering \includegraphics{figures/deltalogHzAb42_plot-1} 

}

\caption{\label{deltalogHzAb42}{\small\textbf{A$\beta_{42}$ concentrations within human extracts poorly predict effects on spontaneous firing.} Comparison of \textbf{A} soluble A$\beta_{42}$ (-GuHCl) or \textbf{B} insoluble A$\beta_{42}$ (+GuHCl) and the difference in firing frequency, $\Delta$log$_{10}$Hz, between day-0 and day-4 treatment for each human brain extract. Line indicates predicted values from linear regression estimating $\Delta$log$_{10}$Hz as a function of soluble and insoluble A$\beta_{42}$, R$^2=$} 0.203}\label{fig:deltalogHzAb42_plot}
\end{figure}

Since the brain extracts being evaluated are complex mixtures of
proteins, in order to directly test the hypothesis that A\(\beta\)
within the brain extracts was influencing the firing phenotype within
these cultures, extracts depleted of A\(\beta\) were tested in the MEA
assay. Two AD brain extracts, ALB01 and MGH1892, and an HP-NCI extract,
MGH1965, which showed both high concentrations of A\(\beta\) and strong
inhibition of spontaneous firing, were immuno-depleted of A\(\beta\)
using the \(\alpha\)-A\(\beta\) polyclonal antibody S97 (provided by
Dr.~Dominic Walsh). As shown in Figure \ref{immunoDepPlot}, after 4-days
of treatment with complete extract, mock depleted, or the A\(\beta\)
depleted extract; removal of A\(\beta\) failed to rescue the effects of
the brain extract on spontaneous firing within the cultures. The
concentration of A\(\beta\) within the immuno-depleted extracts was
examined by ELISA to confirm that A\(\beta\) had been effectively
removed (Figure \ref{immunoDepPlot}B). The experiment was repeated using
the AD brain extract ALB01 and an alternative \(\alpha\)-A\(\beta\)
monoclonal antibody (IC22, provided by Dr.~Dominic Walsh) to perform the
immunodepletion, but this too failed to achieve significant rescue of
the firing phenotype (\emph{not shown}).

\begin{figure}

{\centering \includegraphics{figures/immunoDep_plot-1} 

}

\caption{\label{immunoDepPlot}{\small\textbf{Immunodepletion of} A$\beta$ \textbf{from extracts does not rescue spontaneous firing activity.}\textbf{A} Spontaneous firing activity within rat cortical cultures following 4-day treatment with human brain extract from AD (ALB01, MGH1892) and HP-NCI (MGH1965) individuals. Comparison between complete extract (open points), mock immuno-depletion with non specific poly-clonal antibody (shaded points), or $\alpha$ A$\beta$ poly-clonal antibody S97. Each point represents a single multi-electrode array, bars represent mean $\pm$ SE. No significant differences were found between conditions. \textbf{B} Percentage of A$\beta_{42}$ within $\alpha$A$\beta$ immuno-depleted compared to mock-depleted extracts, quantified by ELISA, mean $\pm$ SE.}}\label{fig:immunoDep_plot}
\end{figure}

All of the primary brain extracts tested were obtained from post mortem
tissue. For those individuals exhibiting AD pathology, the amyloid
deposition had likely been present for years if not decades prior, since
amyloid pathology is known to proceed the onset of memory impairment
(Jack et al. 2013). While the immunodepletion experiments were unable to
confirm that A\(\beta\) species themselves are solely responsible for
influencing the ability for neurons to fire, an alternative hypothesis
was considered that amyloid pathology may contribute to a protein
microenvironment that is detrimental to spontaneous neuronal activity.
In order to test this, brain extracts from an AD mouse model and
isogenic wildtype control animals were evaluated for their ability to
influence spontaneous firing of cortical cultures. Since the only
difference between the brain extracts of the two genotypes is the
presence of the mutant human APP transgene, any difference the firing
activity elicited between the two should be directly attributable to
human APP, the precursor of A\(\beta\).

The AD mouse model hAPP-J20 over-expresses a single copy of the human
amyloid-precursor-protein gene (APP) harboring two mutations,
KM670/671NL and V717F, both of which are associated with familial forms
of AD identified in a Swedish and Indiana pedigree respectively (Mullan
et al. 1992; Murrell et al. 1991). The Swedish mutation elevates
cleavage of APP by BACE (increasing A\(\beta\) generation), and the
Indiana mutation increases the A\(\beta_{42:40}\) ratio (Mullan et al.
1992; Murrell et al. 2000). Thus, the brains of these mice have been
shown to contain elevated human A\(\beta_{42}\), and exhibit amyloid
plaques as well as decreased synaptic density by 6-months of age (Mucke
et al. 2000). TBS-soluble brain extracts were prepared from 6-month-old
hAPP-J20 and wildtype littermates. In order to assess differences in
these extracts to influence spontaneous firing, these extracts were used
to treat primary mouse cortical cultures with a 2-fold titration with a
maximum dose of 3.5mg/mL of brain extract protein. As shown in Figure
\ref{ADmousePlot}A, at the 0.5 dilution (1.75mg/mL of protein), the
hAPP-J20 extract significantly inhibits firing activity compared to the
wildtype extract (\(\Delta log_{10}\)Hz = 2.14, p =
\(7.04\times 10^{-10}\)). However, like human brain extract, the range
of this effect is narrow, as the difference between the wt and hAPP-J20
extracts is negated following a further 2-fold dilution.

In order to confirm this finding, the experiment was repeated using
brain extracts from an alternative AD mouse model. The NL-F mouse is
similar to the hAPP-J20 mouse, in that it too expresses a human APP
transgene harboring two mutations associated with familial forms of AD
and known to promote the production of A\(\beta_{42}\), KM670/671NL
(Swedish), and I716F found in an Iberian pedigree (Guerreiro et al.
2010; Saito et al. 2014). Yet, unlike the hAPP-J20 mouse in which the
APP transgene is over expressed under the control of a PDGF-\(\beta\)
promoter, the NL-F mouse model utilizes a knock-in approach where the
mutant APP is expressed under the endogenous promoter resulting in
expression at typical cellular levels, providing greater confidence that
the effects observed between the extracts of AD and wild type mice are
due to the presence of A\(\beta\) and not an artifact of APP
over-expression. TBS-soluble brain extracts were prepared from
6-month-old homozygous and heterozygous NL-F animals and wildtype
littermates, which used to treat primary mouse cortical cultures with a
2-fold titration with a maximum dose of 3.5mg/mL of brain extract
protein. Despite diluting the brain extracts to the same protein
concentration used in the previous titration, the wildtype brain
extracts showed a 4-fold higher potency for inhibiting spontaneous
firing, largely attenuating activity out to the 0.25 dilution (0.875
mg/mL of protein). However, more importantly at the subsequent dilution
(0.125, 0.437 mg/mL) in which firing activity is observed in all
treatment groups, there is no decrease in activity amongst the cultures
treated with NL-F extracts than those treated with wildtype brain
extract (Figure \ref{ADmousePlot}B).

\begin{figure}

{\centering \includegraphics{figures/ADmouseModel_plot-1} 

}

\caption{\label{ADmousePlot}{\small\textbf{Treatment with extracts from AD mouse models ellicit differing effects on spontaneous firing phenotype.}Spontaneous firing activity within mouse cortical cultures following 2-day treatment with titration of brain extracts from aged \textit{wildtype} or hAPP-J20 mice \textbf{A}; \textit{wildtype}, heterozygous NLF, or homozygous NLF mice \textbf{B}. Each point represents a single multi-electrode array, bars represent mean +/- SE. Significant differences between conditions indicated by brackets and associated p-value.}}\label{fig:ADmouseModel_plot}
\end{figure}

Lastly, to test whether proteins associated with AD pathology are
sufficient to induce changes in firing activity within this assay,
cultures were treated with recombinant forms of A\(\beta_{42}\) and tau.

Treatment with synthetic A\(\beta_{42}\) has previously been reported to
inhibit spontaneous firing within primary neuron cultures as assessed by
MEA recordings (Kuperstein et al. 2010; Varghese et al. 2010; Charkhkar
et al. 2015). The recombinant A\(\beta_{42}\) tested here had been
aggregated to the half maximal Thioflavin T positive state, a form that
has been shown to inhibit LTP \emph{in vivo} at high pM concentrations
(O'Malley et al. 2014). However, despite treatment with
supra-physiological concentration of A\(\beta_{42}\), up to and
including the 1\(\mu\)M dose reported by Kuperstein and colleagues
(2010), no appreciable decrease in firing was observed in cultures after
4-days of treatment (Figure \ref{recombProteinPlot}A).

Hyper-phosphorylated aggregates of the microtubule associated protein
tau make up the neurofibrillary tangles which are a well characterized
intra-cellular pathology associated with AD (reviewed in Querfurth and
LaFerla 2010; Morris et al. 2011). In recent years, studies have found
aggregation-competent tau in neuronal-derived extracellular vesicles
(Guix et al. 2018). Further, studies have found that tau aggregates
added extracellularly can enter cells, and promote seeding of tau
aggregation within the recipient cell (Frost, Jacks, and Diamond 2009;
Sanders et al. 2014). Thus extracellular to intracellular migration of
tau is hypothesized to be a mechanism by which tauopathies propagate
throughout the brain (Frost and Diamond 2010). Exogenously added
recombinant aggregated tau species have previously been shown to inhibit
LTP in organotypic slice cultures and \emph{in vivo} (Fá et al. 2016;
Ondrejcak et al. 2018), as well as induce the degradation of neurites in
cultured neurons (Corbett et al. \emph{in preparation}). To test whether
tau aggregates are capable of inducing changes in spontaneous firing,
cultures were treated with a titration of recombinant soluble tau
aggregates, and compared with a control treatment of recombinant
monomeric tau. However, despite these other finding, no changes in
spontaneous firings were observed in recordings taken over three hours
immediately following treatment (\emph{not shown}) or out to 4-days post
treatment (Figure \ref{recombProteinPlot}B).

The complete lack of activity resulting from both the recombinant
A\(\beta\) and tau species is surprising as the same preparation of
these materials has been shown to inhibit LTP (O'Malley et al. 2014;
Ondrejcak et al. 2018).

\begin{figure}

{\centering \includegraphics{figures/recombProtein_plot-1} 

}

\caption{\label{recombProteinPlot}{\small\textbf{Treatment with recombinant forms of AD associated proteins insufficient to affect spontaneous firing phenotype.}Spontaneous firing activity within rat cortical cultures following 4-day treatment with titration recombinant A$\beta_{42}$ \textbf{A}; mononmers or soluble aggregates of recombinant tau \textbf{B}. Each point represents a single multi-electrode array, bars represent mean +/- SE. No significant differences were found between conditions.}}\label{fig:recombProtein_plot}
\end{figure}

\subsubsection{Determining whether effects on spontaneous firing arise
from proteinaceous species in brain
extracts}\label{determining-whether-effects-on-spontaneous-firing-arise-from-proteinaceous-species-in-brain-extracts}

Having been unable to demonstrate that A\(\beta\) species within the
brain extracts are necessary for the observed effects on firing activity
(Figure \ref{immunoDepPlot}), or that treatment with recombinant
aggregates of A\(\beta_{42}\) and tau are sufficient to inhibit
spontaneous firing (Figure \ref{recombProteinPlot}), this leaves the
possibility that the effect following treatment with the brain extracts
is being mediated by some other molecular species. While the brain
extracts were dialyzed over \(10^{4}\) fold during processing in order
to dilute out bioactive small molecules within the tissue prior to
treatment of cultures; there remains the chance that residual
pharmacological agents, metabolites, or neurotransmitters could be
responsible to the inhibition of firing. In order to determine whether
the effect was likely due to proteinaceous species within the extracts,
cultures were treated with brain extracts (2 AD, 2 HP-NCI) in either
their native form or following denaturation by boiling at 100C for
5-minutes. As shown in Figure \ref{boiledPlot}, after a 4-day treatment
activity remained higher in the cultures treated with boiled extract
compared to those treated with the native extract. In three of the four
extracts tested, the difference in firing activity was significant
(ALB01: \(\Delta log_{10}\)Hz = 1.04, p = \(1.67\times 10^{-6}\) ;
MGH1892: \(\Delta log_{10}\)Hz = 0.804, p = \(0.0364\); MGH1965:
\(\Delta log_{10}\)Hz = 0.807, p = \(0.0122\)), while in the fourth
there was greater activity in the boiled condition although the
difference did not reach the level of significance (MGH1846:
\(\Delta log_{10}\)Hz = 0.63, p = \(0.127\)). From this result it is not
possible to definitively conclude that protein specie(s) within the
brain extracts were responsible for the inhibition of firing activity
observed. However, it does indicate that the active species is heat
labile.

\begin{figure}

{\centering \includegraphics{figures/boiled_plot-1} 

}

\caption{\label{boiledPlot}{\small\textbf{Denaturing extracts by boiling rescues effects on spontaneous firing} Spontaneous firing activity within rat cortical cultures following 4-day treatment with native (open points) or boiled (shaded points) human brain extracts. Each point represents a single multi-electrode array, bars represent mean $\pm$ SE. Significant differences between conditions indicated by brackets and associated p-value.}}\label{fig:boiled_plot}
\end{figure}

In order to further refine what species within the brain extracts may be
contributing to the decrease in spontaneous firing, cultures were
treated with extracts that had been sequentially fractionated by
molecular weight. As part of the standard preparation, the dialyzed
extract was passed over a 3kD centrifugation filter with the retentate
being retained and resuspended in culture media. For the alternative
preparation, extracts were sequentially passed through 100kD, 30kD, and
3kD filters, with the retenate from each instance retained and
resuspended. The alternative preparation resulted in three fractions:
\emph{3-30kD}, \emph{30-100kD}, and \emph{\textgreater{}100kD}, while
the standard preparation resulted in a single \emph{whole} fraction of
all material \textgreater{}3kD.

As shown in Figure \ref{fractionPlot}A, after a 4-day treatment activity
remained higher in the cultures treated with \emph{3-30kD} and
\emph{30-100kD} fractions compared to those treated with either the
\emph{whole} or \emph{\textgreater{}100kD} fractions. In two of the
three extracts tested, the difference in firing activity between the
\emph{whole} and \emph{3-30kD}, and \emph{30-100kD} fractions was
significant (ALB01: \emph{3-30kD:whole} \(\Delta log_{10}\)Hz = 1.46, p
= \(1.66\times 10^{-10}\), \emph{30-100kD:whole} \(\Delta log_{10}\)Hz =
0.991, p = \(3.04\times 10^{-5}\); MGH1846: \emph{3-30kD:whole}
\(\Delta log_{10}\)Hz = 1.06, p = \(0.00369\), \emph{30-100kD:whole}
\(\Delta log_{10}\)Hz = 0.993, p = \(0.00783\)), while in the third
there was greater activity in the \emph{3-30kD} and \emph{30-100kD}
fraction treated conditions, although the difference did not reach the
level of significance (MGH1965: \emph{3-30kD:whole}
\(\Delta log_{10}\)Hz = 0.76, p = \(0.0992\), \emph{30-100kD:whole}
\(\Delta log_{10}\)Hz = 0.81, p = \(0.064\)).

Examining the protein context of the fractions showed that the largest
proportion of protein remained within the \emph{\textgreater{}100kD}
fraction (Figure \ref{fractionPlot}B). While the protein concentration
of the \emph{whole} and \emph{\textgreater{}100kD} fractions within the
ALB01 and MGH1965 preparations greatly exceed 1mg/mL, these were back
diluted to 1mg/mL prior to treatment of cultures as was standard of all
extract treatments. Further assessing the make up of the proteins within
each fraction by SDS-PAGE showed that the fractionation was effective at
excluding peptides exceeding the cutoff (i.e.~species
\textgreater{}100kD from the \emph{30-100kD} fraction, and
\textgreater{}30kD from the \emph{3-30kD} fraction), species captured
within the larger molecular weight fractions are likely made up of
complexes comprised of constituent proteins of small molecular weights,
as seen by the broad array of peptides sizes retained within the
\emph{\textgreater{}100kD} fraction (Figure \ref{fractionPlot}C, lane
5). So while the \emph{\textgreater{}100kD} fraction appears to contain
the causal agent(s) for inhibiting firing activity, it can not be
concluded that the agent(s) themselves are large \textgreater{}100kD
proteins, since this fraction contained the full spectrum of proteins
within the brain extracts.

\begin{figure}

{\centering \includegraphics{figures/fraction_plot-1} 

}

\caption{\label{fractionPlot}{\small\textbf{Spontaneous firing inhibited by high molecular weight fraction of human brain extracts.} \textbf{A} Spontaneous firing activity within rat cortical cultures following 4-day treatment with human extracts fractionated by centrifugal filtration: whole (>3kD), >100kD, 30-100kD, and 3-30kD. Each point represents a single multi-electrode array, bars represent mean $\pm$ SE. Significant differences between conditions indicated by brackets and associated p-value. \textbf{B} Estimated protein concentration within each extract fraction, bars represent mean $\pm$ 95 CI. \textbf{C} Silver stain of SDS-PAGE separation of extract fractions from individual ALB01.}}\label{fig:fraction_plot}
\end{figure}

\newpage

\subsection{Discussion}\label{discussion-1}

In order to assess how the microenvironment of the AD brain may
influence the spontaneous activity of neurons, primary rat cortical
cultures were grown on multi-electrode arrays and treated with a panel
of TBS-soluble human brain extracts from 19 individuals. This panel
consisted of 7 LP-NCI extracts, individuals who were not diagnosed with
cognitive impairment while alive nor exhibited AD pathology upon post
mortem examination; 5 HP-NCI extracts, individuals who were not
cognitively impaired, yet were found to have AD pathology upon post
mortem examination and/or elevated levels of A\(\beta\) within their
brain tissue; and 7 AD extracts, individuals who were clinically
diagnosed with AD and confirmed by pathological assessment post mortem.

From a preponderance of the data it is difficult to conclude that the
effects on firing activity within the treated cultures were the
consequence of AD pathology within the brain extracts, or mediated by
the presence of A\(\beta\) specifically. While comparison across
individual extracts showed significantly different effects on
spontaneous firing activity (Figure \ref{indExtractDxPlot}A), the
diagnosis of the individuals was not predictive of effects of the
extracts in the assay (Figure \ref{indExtractDxPlot}B). While TBS
soluble extracts from AD brain are considered to represent the most
disease-relevant form of non-fibrillar A\(\beta\) and repeatedly have
been shown to inhibit LTP \emph{in vitro} and \emph{in vivo} in an
A\(\beta\)-dependent fashion (Shankar et al. 2008; Barry et al. 2011;
Freir et al. 2011; Wang et al. 2017), amongst the extracts examined
here, the concentration of A\(\beta\) was not well correlated with the
extent of inhibition of spontaneous firing in cultures on MEAs (Figure
\ref{deltalogHzAb42}), nor did immunodepletion of A\(\beta\) from the
brain extracts rescue the effects on firing activity (Figure
\ref{immunoDepPlot}). Treatment with brain extracts from a the J20 hAPP
mouse model did exhibit higher potency in inhibiting spontaneous firing
compared to paired extract from wildtype animals, suggesting that the
presence of A\(\beta\) pathology could contribute to a decrease in
firing activity (Figure \ref{ADmousePlot}A). However, this finding could
not be confirmed using brain extracts from an alternative AD animal
model, the NL-F mouse, which does not rely on a highly over-expressed
APP transgene (Figure \ref{ADmousePlot}B).

Further, treatment with aggregated forms of recombinant AD associated
proteins A\(\beta_{42}\) and tau were insufficient to cause a decrease
in spontaneous firing (Figure \ref{recombProteinPlot}). This was
somewhat surprising given that the same forms A\(\beta_{42}\) and tau
have been reported to inhibit LTP in rodents \emph{in vivo} (O'Malley et
al. 2014; Ondrejcak et al. 2018). However, LTP assays and this MEA assay
are measuring different electrophysiological characteristics on a
different time scale. Perhaps more surprising was the lack of
concordance between these results and those studies that used MEAs:
treatment with synthetic A\(\beta_{42}\) previously has been shown to
decrease the spontaneous firing of primary neurons cultured on MEAs
(Kuperstein et al. 2010; Varghese et al. 2010; Charkhkar et al. 2015).
Possible explanations for the inconsistency between the reports of
A\(\beta_{42}\)-mediated inhibition in MEA based assays and the results
in this study could be due to differences in the form of A\(\beta_{42}\)
used (synthetic peptide versus a recombinant protein), or due to the
culture conditions under which the experiments were performed. While
Charkhkar and colleagues used mixed cortical cultures similar to those
used here, Kuperstein et al. and Varghese et al. used cultures of
hippocampal neurons. Additionally, while use of mitotic inhibitors is
common in assays involving primary cultures to suppress proliferation of
glia (Rönicke et al. 2011; Vincent et al. 2013; Black et al. 2018), no
mitotic inhibitors were used in these cultures. Charkhkar et al
maintained cultures under serum-free conditions in order to minimize the
expansion of glia, while the methods reported Kuperstein et al. and
Varghese et al. did not specify whether mitotic inhibitors or other
means were employed to suppress the proliferation of glia within the
primary cultures used in those studies. Since synthetic A\(\beta\) has
been shown to be degraded by microglia and astrocytes in culture
(Shaffer et al. 1995; Basak et al. 2012; Jones et al. 2013), the
presence of glia in these cultures may have mitigated the effects of the
exogenously added protein aggregates through catabolism. However,
excluding glia from a functional experimental model of neuronal behavior
in AD seems counter intuitive given that glia have been shown to promote
the density and strength of synapses in \emph{in vitro} cultures
(Faissner et al. 2010; Allen 2013), and a growing recognition that glia
- neuron signaling maybe a critical dynamic in the maintenance of
synapses in the context of cognitive impairment (Chung et al. 2015). In
addition to whether or not glia were maintained in the cultures, the
cultures used in these other studies were much sparser than those used
here. Kuperstein and colleagues used cultures less than half as dense,
\(10^{5}\) cell/cm\(^2\) verse \(2.4\times 10^{5}\) cell/cm\(^2\)
(\emph{this study}), Charkhkar et al. and Varghese et al. by nearly an
order of magnitude less dense (\(3.5\times 10^{4}\) cell/cm\(^2\) and
\(2\times 10^{4}\) cell/cm\(^2\) respectively). While the highest
concentration of recombinant A\(\beta\) used here (1\(\mu\)M) compared
with that used by Kuperstein and colleagues, the reports by Charkhkar et
al. and Varghese et al. reported effect of A\(\beta\) oligomers at
5\(\mu\)M and higher. For comparison, the concentrations of A\(\beta\)
detected in the human brain extracts after treatment with a denaturing
agent (GuHCl) were in the low pM range, indicating these concentrations
of recombinant A\(\beta\) are super-physiological by several orders of
magnitude. These other studies also used no more than half the number of
replicates used in the experiments here and did not report accounting
for the skewed distribution of firing frequencies (Kuperstein et al.
2010; Varghese et al. 2010; Charkhkar et al. 2015).

A review of the literature found no other studies which tested the
activity of TBS-soluble brain extracts in an MEA-based spontaneous
firing assay, so there is no prior art with which to compare the
experimental results generated here. Nevertheless, if MEAs are to serve
a role in preclinical modeling of AD and other neurodegenerative
disease, incongruities between the observations of neuronal activity
made using these instruments and other experimental modalities such as
LTP recordings, patch clamping, and calcium imaging in response to
similar perturbations will need to be rectified. A recent study by
Ondrejcak and colleagues found that while treatment with soluble tau
aggregates inhibited LTP \emph{in vivo} the same treatment had no effect
on the generation of spontaneous action potentials, suggesting that
these tau species influence spontaneous and evoked potentials
differently. The same study also reported instances where inhibition of
LTP by AD TBS-soluble brain extracts could not be rescued by
immunodepletion of A\(\beta\), indicating that the eletrophysiological
phenotypes elicited by these extracts may not always be
A\(\beta\)-mediated (2018).

While monitoring the spontaneous firing of neurons cultured on MEAs
following treatment with material obtained from patient brains still
seems like a rational approach for modeling AD in an \emph{in vitro}
context, further development is required to determine whether this
system can serve as a fair approximation of certain \emph{in vivo}
activities. This includes better understanding the variability observed
across extracts obtained from different individuals, and whether or not
the assay activity is a reflection of the physiological state from which
the brain extract is derived. Experiments demonstrating that denaturing
the brain extracts by boiling rescues the effects on spontaneous firing
(Figure \ref{boiledPlot}), and that the inhibition of firing activity is
primarily mediated by higher molecular weight complexes (Figure
\ref{fractionPlot}). This provides some, albeit oblique, clues to
identifying the causal factor(s) influencing firing activity. While
these findings are suggestive that the causal agent may be
proteinaceous, additional experiments would be required to draw this
conclusion. Following in the vein of Prusiner's landmark studies
demonstrating the proteinaceous nature of prion particles (1982), these
experiments could include demonstrating that the effect on the firing
phenotype are prevented by pretreating extracts with a proteinase, such
as proteinase K. Or further, by showing that the effects of the extracts
are insensitive to treatment with nucleases, UV radiation, or dication
catalyzed hydrolysis. Further characterization of these soluble brain
extracts may provide more insight to the species, proteinaceous or not,
that are responsible for inhibition of activity. These species may in
turn represent novel candidates that are involved in the propagation of
AD pathology, or could simply be confounding contaminants whose
elimination should be addressed in refining the protocols for
preparation of human brain material for \emph{in vitro} and \emph{in
vivo} experiments.

\newpage

\subsection{Methods}\label{methods-1}

\paragraph{Tissue Culture}\label{tissue-culture-1}

Preparation of 96 well MEA plates and primary rat cortical cultures was
performed as previously described (Chapter 2 Methods). Primary mouse
cortical cultures were established from E18 C57BL/6 (Charles River
Laboratories, Wilmington MA) using the same protocol and plating
densities as was used for the primary rat cortical cultures. Both rat
and mouse cortical cultures were maintained in BrainPhys culture media
containing SM-1 neuronal supplement (StemCell Technologies, Vancouver
BC), with semi-weekly half-volume media changes.

\paragraph{Brain Extract Preparation}\label{brain-extract-preparation}

Human brain material was obtained from the neuropathology core facility
at Massachusetts General Hospital (Boston MA) and generously provided by
Dr.~Cindy Lemere of Brigham and Women's Hospital (Boston MA), Dr.~David
Bennett of Rush University Medical Center (Chicago IL). Tris-buffered
saline (TBS) brain extracts were prepared using the method described by
Shankar et al. (2008). Briefly, the tissue was dissected to isolate the
gray matter, which is homogenized in TBS-buffer (ice-cold TBS consisting
of 20 mM Tris-HCl, 150 mM NaCl, pH 7.4) at a ratio of 1:4 tissue weight
to buffer volume within a Dounce homogenizer. This suspension was then
subject to ultra-centrifugation at \(1.75\times 10^{5}\)g in a TL100
centrifuge (Beckman Coulter, Brea CA) to pellet cellular debris. The the
supernatant was place in a 2kD dialysis cassette (Slide-A-Lyzer,
ThermoFisher, Waltham MA) and dialyzed 1:\(10^{4}\) volume:volume in
artificial cerebrospinal fluid (aCSF: 124mM NaCl, 2.5mM KCl, 2.0mM
MgSO\(_4\), 1.25mM KH\(_2\)PO\(_4\), 26mM NaHCO\(_3\), 10mM glucose, 4mM
sucrose, 2.5mM CaCl\(_2\)). Following dialysis, the TBS brain extracts
were aliquoted and stored at -80C. Prior to treatment of cultures, the
TBS brain extracts were `buffer-exchanged' into culture media by placing
extract in a 3kD centrifugation filter (Amicon Ultra, EMD Millipore,
Burlington MA) and performing a 90 minute centrifugation at \(3000\)g in
a RC-5C centrifuge (Sorvall, ThermoFisher). For the generation of
fractionated extracts, samples were processed sequentially through
100kD, 30kD, and 3kD centrifugation filters. Following centrifugation,
the retentate was reconstituted to 1X volume in BrainPhys Media with
SM-1 neuronal supplement (StemCell Technologies, Vancouver BC). The
protein concentration within the reconstituted extracts was determined
by performing a Bicinchoninic acid (BCA) assay (ThermoFisher) on an
8-step, 2-fold dilution series of reconstituted extract and media in
order to determine the proportion of protein contributed by the brain
extract. The reconstituted extract was then back diluted in BrainPhys
media to a concentration of 1mg/mL brain extract protein and used for
treatment of primary cultures.

The preparation of mouse brain extracts was performed in a similar
manner, with the following exceptions. C57BL/6 mice harboring a
wildtype, J20-hAPP, or APP\(^{NLF}\) genotype were housed in an vivarium
under a protocol approved by the IACUC of Brigham and Women's Hospital.
Animals were aged 6-9 months prior to extracting brain tissue for
experiments. Following euthanasia, the cortex was dissected and
homogenized in TBS-buffer as described above, without further dissection
of gray matter from white matter. Due to the smaller volumes of extract
no centrifugation-based buffer exchange was performed, rather the
dialysis was performed sequentially, 1:1000 volume:volume in aCSF
followed by 1:10 in BrainPhys media for a final dialysis volume of
1:\(10^{4}\), after which SM-1 neuronal supplement was added to the
dialyzed extract. A BCA assay was performed to quantify the protein
concentration, and extracts were back diluted in culture media to a
concentration of 3.5mg/mL of brain extract protein, the maximum
concentration used for titration of mouse brain extracts in MEA
experiments.

\paragraph{\texorpdfstring{Amyloid-\(\beta\)
Immunodepletion}{Amyloid-\textbackslash{}beta Immunodepletion}}\label{amyloid-beta-immunodepletion}

Brain extracts reconstituted in culture media were incubated with 4.4
\(\mu\)g/mL of S97, an \(\alpha\)A\(\beta\) rabbit poly-clonal antibody
under agitation for a minimum of 1 hour at 4C. For mock depletion,
extracts were incubated with 4.4 \(\mu\)g/mL of a non-specific rabbit
poly-clonal IgG (EMD Millipore). The 4.4 \(\mu\)g/mL of S97 used was
determined to be triple the concentration required to completely deplete
culture media of 100pg/mL of recombinant A\(\beta_{42}\) under similar
conditions. Following the 1 hour incubation, a 1:1 mixture of Protein A
and Protein G coated agarose beads (Roche, Basel Switzerland) was added
a ratio of 25\(\mu\)L/mL and further incubated overnight with agitation
at 4C. The antibody bead complexes were removed from suspension by
centrifugaton for 1 minute at \(1000\)g, and the media supernant was
collected and used for treatment of cultures.

\paragraph{Recombinant Proteins}\label{recombinant-proteins}

Recombinant A\(\beta_{42}\) and tau species were generously provided by
the laboratory of Dr.~Dominic Walsh. The recombinant A\(\beta_{42}\) and
tau species received were generated and purified from \emph{E.coli}
expressing either the A\(\beta_{1-42}\) coding sequence or full length
tau441. The A\(\beta_{42}\) as aggregated to the half maximal Thioflavin
T positive state (Walsh et al. 2009; O'Malley et al. 2014), while the
recombinant tau was either maintained as monomers or converted to
soluble aggregates by allowing it to aggregate into fibrils then
dissociated by sonication (Ondrejcak et al. 2018). The A\(\beta_{42}\)
and tau species were diluted to the specified concentrations in
BrainPhys media with SM-1 neuronal supplement and added to cultures as
1X solutions by a complete media change.

\paragraph{MEA Treatment and
Recordings}\label{mea-treatment-and-recordings}

MEA recordings and analysis were performed as previously described in
Chapter 2. Briefly, prior to treatment with prepared brain extract or
recombinant proteins, a 30 minute baseline recording was performed and
arrays were assigned to treatment cohorts with comparable firing
frequencies based on mean-firing-rate, using a bootstrapping simulation
technique. All treatments were prepared at 1X concentrations in
BrainPhys culture media and added to MEAs by a complete media change.
Daily 30 minute recordings were preformed for the duration of the
experiment. For \emph{`washout'} experiments, immediately following the
final treatment recording (day-4 for brain extract experiments) the
treated media was aspirated and replace with fresh culture media,
recording recommenced the following day.

\paragraph{\texorpdfstring{Amyloid-\(\beta\)
ELISA}{Amyloid-\textbackslash{}beta ELISA}}\label{amyloid-beta-elisa}

Quantification of A\(\beta\) within the dialyzed human brain extracts
was performed using a multiplex ELISA for A\(\beta_{38}\),
A\(\beta_{40}\), and A\(\beta_{42}\) (V-Plex Plus A\(\beta\) Panel,
Mesoscale Discovery, Rockville MD). The A\(\beta\) detected within the
native extract was designated as \emph{Soluble} A\(\beta\). In order to
quantify the additional, aggregated A\(\beta\), extracts were treated
with 6M guanidine hydrochloride (GuHCl) to denature the proteins in the
mixture prior to performing the ELISA. Native extract samples were
diluted 1:10, and GuHCl treated extracts were diluted 1:35, prior to
being added to the ELISA plate. A\(\beta_{38}\) levels were below the
detectable range of the assay for most samples and thus were not
reported.

\paragraph{Immunocytochemistry}\label{immunocytochemistry}

Rat primary cortical cultures were established in black-walled, glass
bottom 96-well plates (\(\mu\)Clear, Griener Bio-One, Monroe NC) with
the same cell density and culture conditions as described for MEA
experiments. Following a 4-day treatment with extract from AD patient,
ALB01, or vehicle (media alone), the cultures were washed with ice cold
1X PBS and fixed with 4\% paraformaldehyde in 1X PBS. Following
fixation, the cell were permeablized with 1\% Triton X-100 in donkey
serum (Jackson Immunoresearch, West Grove PA). The cells were probed
with primary antibodies for MAPT(1:200, A0024, Dako, Santa Clara CA),
GFAP(1:1000, ab4674, Abcam, Cambridge UK) and NeuN (1:1000, MAB377,
Millipore), followed by \(\alpha\)-chick Cy2, \(\alpha\)-rabbit Cy3, and
\(\alpha\)-mouse Cy5 secondary antibodies (Jackson Immunoresearch) and
1\(\mu\)g/mL DAPI (ThermoFisher). Images were acquired at 40X using an
LSM710 confocal microscope (Zeiss, Jena Germany).

\paragraph{Statistical Analysis}\label{statistical-analysis}

Spontaneous firing frequencies of cultures were assessed using the array
mean-firing-rate during each 30 minute recording. For comparing the
effects of treatment conditions within single experiments (e.g.
\emph{extract immuno-depletion}, \emph{boiling}, \emph{fractionation}),
an ANCOVA was performed by fitting a linear regression for
\(log_{10}\)Hz as a function of treatment group and time allowing for
interaction between the group and time variables, and treating time as
the covariate. For comparing the effects of treatments across several
experiments (e.g. \emph{individual extract} or \emph{diagnosis}), a
linear mixed effect (LME) model was fit to the data. For the model
estimating the effect of diagnosis, diagnosis and time were treated as
fixed effects and individual, experiment, and array as nested random
effects. For the model estimating the effect of individual exact,
individual and time were treated as fixed effects, and experiment and
array were treated as nested random effects. In both cases, interaction
between the fixed effects (diagnosis/individual and time) was modeled,
since the different diagnoses or individuals would be expected to have
differing effects over time. Significance between the model coefficients
from the ANCOVA and LME models was determined using a general linear
hypothesis test. To estimate the effect of soluble and insoluble
A\(\beta_{42}\) on firing frequency, a multivariant linear regression
model was fit. The linear regressions were fit using the \texttt{lm}
function within the base R \textsf{stats} library, while the LME were
fit using the \texttt{lme} function within the \textsf{nlme} library (R
Core Team 2017; Pinheiro et al. 2018; Pinheiro and Bates 2000). The
general linear hypothesis tests were performed using the \texttt{glht}
function within the \textsf{multcomp} library after generating a
contrast matrix for the comparisons of interest, multiple comparison
were accounted for using the default single-step multivariant t
comparison test (Hothorn, Bretz, and Westfall 2008). All figures were
generated using the R \textsf{ggplot2} and accompanying \textsf{ggpubr}
libraries (Wickham 2016; Kassambara 2018).

\FloatBarrier
\newpage

\singlespacing

\section{Chapter 4: Identification of candidate proteins influencing \\ AD relevant phenotypes through unbiased
proteomics}\label{chapter-4-identification-of-candidate-proteins-influencing-ad-relevant-phenotypes-through-unbiased-proteomics}

\doublespacing

\subsection{Introduction}\label{introduction-2}

This study was predicated on the hypothesis that human brain extract
from individuals with Alzheimer's disease (AD) amyloid-pathology would
reduce spontaneous firing in cultured neurons resulting from the
presence of soluble, synaptotoxic A\(\beta\) species. In Chapter 3 a
series of experiments were presented in which soluble brain extracts
from a panel of individuals were used to treat primary rodent cortical
cultures in a multi-electrode array based spontaneous firing assay. The
individuals comprising this panel represented 3 diagnosis classes;
Low-pathology, not cognitively impaired (LP-NCI): those unaffected
individuals who were not clinically diagnosed with AD nor exhibited AD
pathology during post mortem examination. High-pathology, not
cognitively impaired (HP-NCI): individuals who did not receive a
clinical diagnosis of cognitive impairment, but were found to exhibit
AD-associated pathology post mortem and/or expressed high levels of
A\(\beta\) in their brain tissue. Lastly, AD patients: individuals who
exhibited cognitive impaired, receiving a clinical diagnosis of AD,
which was subsequently confirmed by pathological examination post
mortem. The results of these experiments showed that while treatment
with these extracts elicited varied effects on spontaneous firing in the
treated cultures, the induced changes could not be directly attributed
to A\(\beta\). However, additional experimentation suggested that the
effects of the brain extracts may be mediated by a proteinaceous
species.

In order to identify candidate proteins that may be influencing the
spontaneous firing phenotype, proteomic profiling was performed on these
extracts to evaluate their constituents in an unbiased fashion. To
improve the power of this analysis brain extracts from a second cohort
of LP-NCI, HP-NCI, and AD individuals were included in the examination.
The extracts from this second cohort were used to treat human
iPS-derived neurons in a separate assay measuring changes in neurite
structures. Examining this larger group allows for a more thorough
assessment of the the protein landscape across individuals exhibiting
differing levels of AD pathology and cognitive impairment. Additionally,
pairing the proteomic profiles with the changes measured in these
phenotypic assays may reveal proteins influencing structural and
functional changes in neurons ultimately contributing to AD
symptomology.

\newpage

\subsection{Results and Discussion}\label{results-and-discussion}

For this study a panel of brain extracts from 43 individuals was
examined. These extracts included: 15 AD, 14 HP-NCI, and 14 LP-NCI. In
order to assess the profile of proteins expressed in each individuals,
samples of all 43 extracts were examined by unlabeled proteomic
analysis. To correlate the influence of these extracts on structural and
functional neuronal phenotypes, each extract was tested in either an
imaging-based morphological neurite integrity assay and/or a MEA-based
spontaneous firing assay. Details of each individual and the assay(s)
each extract was evaluated in are detailed in Table
\ref{brainExtractAssayTab}.

\rowcolors{2}{gray!6}{white}

\begin{table}

\caption{\label{tab:brainExtractTable}\label{brainExtractAssayTab} Human brain samples used in this study}
\centering
\resizebox{\linewidth}{!}{
\fontsize{7}{9}\selectfont
\begin{tabular}[t]{llrllllll}
\hiderowcolors
\toprule
ID & Class & Age & Gender & Clinical Dx & Pathology Dx & A$\beta_{42}$/A$\beta_{40}$ -GuHCl & A$\beta_{42}$/A$\beta_{40}$ +GuHCl & \textit{in vitro} Assay\\
\midrule
\showrowcolors
BWH02 & LP-NCI & 76 & F & NA & NA & 4.9 / ND & 39.2 / ND & MEA\\
BWH04 & LP-NCI & 53 & M & NA & NA & 7.5 / 25.3 & 33.1 / ND & MEA\\
MGH1837 & LP-NCI & 68 & M & NA & NA & 1.7 / 12.2 & 40.7 / ND & MEA\\
MGH1887 & LP-NCI & 60 & M & NA & NA & 12.6 / 72.8 & 49.4 / ND & MEA\\
MGH2018 & LP-NCI & 92 & F & NA & NA & 10.1 / 25.3 & 77.9 / ND & MEA\\
\addlinespace
MGH2038 & LP-NCI & 78 & F & mild CI & NA & 14.5 / 72.8 & 49.4 / ND & MEA\\
MGH2068 & LP-NCI & 79 & F & NA & NA & 2.6 / 40.6 & 33.1 / ND & MEA\\
ROSMAP10260309 & LP-NCI & 88 & M & NA & NA & 2.1 / ND & ND / ND & NI\\
ROSMAP11190734 & LP-NCI & 76 & M & NA & NA & 113.7 / 310.9 & 10.2 / ND & NI\\
ROSMAP11389397 & LP-NCI & 82 & M & NA & NA & 10.2 / 128 & 107.8 / ND & NI\\
\addlinespace
ROSMAP15138884 & LP-NCI & 85 & M & NA & NA & 3.1 / 22.3 & ND / 415.5 & NI\\
ROSMAP20956867 & LP-NCI & 79 & F & NA & NA & 3.6 / 18 & 122.8 / 1357.6 & NI\\
ROSMAP21274866 & LP-NCI & 75 & F & NA & NA & 8.9 / ND & 107.8 / ND & NI\\
ROSMAP21412626 & LP-NCI & 91 & F & NA & NA & 6.1 / 17.4 & ND / ND & NI\\
BWH01 & HP-NCI & 76 & F & NA & NA & 447.9 / 273.3 & 1964.1 / 748.0 & MEA\\
\addlinespace
BWH03 & HP-NCI & 77 & M & CI & mild AD & 58.0 / ND & 915.5 / ND & MEA\\
MGH1846 & HP-NCI & 92 & M & NA & mild AD & 154.2 / 140.2 & 2439.0 / ND & MEA\\
MGH1965 & HP-NCI & 76 & F & NA & mild AD & 333.0 / 65.4 & 621.8 / ND & MEA\\
ROSMAP10394182 & HP-NCI & 86 & M & NA & mild AD & 514.9 / 432.2 & 12102.8 / 3077.6 & NI\\
ROSMAP20153984 & HP-NCI & 78 & F & NA & mild AD & 249.9 / ND & 5877 / ND & NI\\
\addlinespace
ROSMAP20187204 & HP-NCI & 88 & F & NA & mild AD & 63.4 / 129 & 13471.9 / 1677 & NI\\
ROSMAP20254588 & HP-NCI & 86 & F & NA & mild AD & 246.1 / 91.7 & 8559.6 / 1821.4 & NI\\
ROSMAP20779834 & HP-NCI & 82 & F & NA & mild AD & 232.4 / 409.1 & 6203.7 / ND & NI\\
ROSMAP21101658 & HP-NCI & 83 & F & NA & mild AD & 254.8 / 41.4 & 7892 / ND & NI\\
ROSMAP21151608 & HP-NCI & 85 & F & NA & mild AD & 227.3 / 685.4 & 20939.7 / ND & NI\\
\addlinespace
ROSMAP21404450 & HP-NCI & 89 & F & NA & mild AD & ND / ND & 9445.2 / ND & NI\\
ROSMAP21408940 & HP-NCI & 80 & F & NA & mild AD & 230.1 / 202.3 & 2233.5 / 516.8 & MEA / NI\\
ROSMAP21408940 & HP-NCI & 80 & F & NA & mild AD & 227.1 / 250.2 & 3754.5 / 2130.7 & MEA / NI\\
ROSMAP21411323 & HP-NCI & 87 & F & NA & mild AD & 225.3 / 269.4 & 8699.9 / ND & NI\\
ALB01 & AD & 68 & F & AD & AD & 123.1 / 83.83 & 5333.8 / 298.6 & MEA / NI\\
\addlinespace
MGH1168 & AD & 89 & F & AD & AD & ND / ND & ND / ND & NA\\
MGH1892 & AD & 67 & M & AD & AD & 167.0 / 606.3 & 5987.0 / 2841.5 & MEA\\
MGH2031 & AD & 70 & F & AD & AD & 127.0 / 57.7 & 6996.8 / ND & MEA\\
MGH2037 & AD & 73 & F & AD & AD & 120.9 / 325.8 & 2416.1 / 717.9 & MEA\\
MGH2039 & AD & 74 & M & AD, FTD & AD, FTD & 324.9 / 17632.7 & 3343.8 / 28040.1 & MEA\\
\addlinespace
ROSMAP10222853 & AD & 85 & M & AD & AD & 126.3 / 345.4 & 4722.2 / ND & NI\\
ROSMAP10235948 & AD & 81 & M & AD & AD & 132.5 / ND & 12576.6 / 639.9 & NI\\
ROSMAP11336574 & AD & 89 & M & AD & AD & 359 / 14978.1 & 20966.8 / 44974.3 & NI\\
ROSMAP20139562 & AD & 87 & F & AD & AD & 165.3 / 2974.3 & 2168.6 / 6331.7 & NI\\
ROSMAP20207437 & AD & 87 & F & AD & AD & 176.1 / 114.8 & 3337.3 / ND & MEA / NI\\
\addlinespace
ROSMAP20254452 & AD & 87 & F & AD & AD & 126.1 / 411.2 & 5100.0 / 1307.7 & MEA / NI\\
ROSMAP20371236 & AD & 90 & F & AD & AD & 199.5 / 831.8 & 12259.1 / 2294 & NI\\
ROSMAP20561642 & AD & 91 & F & AD & AD & 436.9 / 1545.7 & 1128.2 / ND & NI\\
ROSMAP20898476 & AD & 98 & F & AD & AD & 165.5 / 478.7 & 19833.4 / 818.3 & NI\\
\bottomrule
\multicolumn{9}{l}{\textbf{Abbreviations}}\\
\multicolumn{9}{l}{\footnotesize{\textbf{LP-NCI} - Low Pathology Non-Cognitively Impaired}}\\
\multicolumn{9}{l}{\footnotesize{\textbf{HP-NCI} - High Pathology Non-Cognitively Impaired,
      \textbf{AD} - Alzheimer's Disease}}\\
\multicolumn{9}{l}{\footnotesize{\textbf{FTD} - Frontotemporal Dementia,
      \textbf{CI} - Cognitive Impairment,
      \textbf{NA} - Not Applicable}}\\
\multicolumn{9}{l}{\footnotesize{\textbf{GuHCl} - Guanidine Hydrochloride,
      \textbf{ND} - Not Detected}}\\
\multicolumn{9}{l}{\footnotesize{\textbf{MEA} - Multi-Electrode Array,
      \textbf{NI} - Neurite Integrity}}\\
\multicolumn{9}{l}{\footnotesize{\textit{Note} A$\beta$ concentration: pg/mL}}\\
\end{tabular}}
\end{table}

\rowcolors{2}{white}{white}

\subsubsection{\texorpdfstring{\textit{In vitro} profiling of brain
extracts on structural and functional neuronal
phenotypes}{ profiling of brain extracts on structural and functional neuronal phenotypes}}\label{profiling-of-brain-extracts-on-structural-and-functional-neuronal-phenotypes}

The loss of synapse and disruptions to neuronal structural integrity
have long been established as the best correlates to the clinical
manifestations of AD, more highly correlated with cognitive decline than
amyloid burden (Terry et al. 1991; Sheng, Sabatini, and Südhof 2012). It
has recently been shown that is possible to monitor changes to neuronal
structures in a dynamic fashion \emph{in vitro} in response to exposure
to aqueous-soluble brain extracts using a live-cell, morphological
neurite integrity assay (Jin et al. 2018). Using the same experimental
procedure as reported by Jin and colleagues, neurite integrity in
cultured human iPS-derived neurons was monitored at 2 hour intervals for
78 hours following treatment with aqueous-soluble human brain extracts
from 27 individuals (Figure \ref{inVitroAssay}). A regression model was
fit to the time-series data in order to obtain an estimate of the
proportion of neurite integrity in cultures after 72 hours of exposure
to each brain extract. As shown in Figure \ref{inVitroAssay}B, there is
a fairly continuous response to the brain extract treatment, with some
extracts having negligible effect, while others cause a near complete
atrophy of neurites, yet with no clear stratification of groups.
Examining the effects on neurites in the context of diagnosis shows that
while extracts from AD and HP-NCI individuals tended to induce a greater
decrease in neuritic integrity than those of extracts of LP-NCI
individuals, the differences are not significant (LP-NCI : AD p = 0.112;
LP-NCI : HP-NCI p = 0.0515), suggesting that diagnosis classification of
the individual is not a perfect predictor of whether the extract will
affect neurites (Figure \ref{inVitroAssay}C).

The generation of action-potentials in neuronal cultures can be assessed
using a multi-electrode array (MEA) based spontaneous firing assay
(discussed in Chapter 2). As described in Chapter 3, MEA recordings were
used to assess the effect of aqueous-soluble brain extract from 19
individuals on spontaneous firing of primary rat neurons in dense,
mixed-cortical cultures. To obtain a single metric for comparing effects
in the MEA assay with those from the neurite integrity assay, the change
in the rate of spontaneous firing between time 0 (untreated) and 96
hours post-treatment was calculated for each extract, expressed as
\(\Delta log_{10}\)Hz. Similar to the effects observed in the neurite
integrity assay, the changes in spontaneous firing induced by these
extracts occur across a continuous range (Figure \ref{inVitroAssay}D).
Additionally, examining effects on spontaneous firing in the context of
diagnosis shows that while extracts from AD and HP-NCI individuals
tended to induce a greater decrease in firing activity than those of
extracts of LP-NCI individuals, the differences are not significant
(LP-NCI : AD p = 0.185; LP-NCI : HP-NCI p = 0.138) (Figure
\ref{inVitroAssay}E). Again as with neurite integrity, suggesting that
the diagnosis class does not account for all the variability seen in
this phenotype.

\begin{figure}

{\centering \includegraphics{figures/inVitroAssayPlot-1} 

}

\caption{\label{inVitroAssay}{\small\textbf{Influence of human brain extracts on structural, functional neuronal phenotypes \textit{in vitro}.}
\textbf{A} Neurite integrity in human iPS-derived neurons expressed as percent of baseline, examined at 2hr intervals over 78hr treatment with human brain. Color lines represent replicate wells (n = 6, media alone: n = 36), black line represents estimate from regression model fit. Estimated neurite integrity following 72hr treatment by individual extract (\textbf{B}), and by diagnosis class (\textbf{C}). Estimated change in spontaneous neuronal firing from baseline expressed as $\Delta$ log${10}$Hz following 96hr treatment with human brain by individual extract (\textbf{D}), and by diagnosis class (\textbf{E}). Each point corresponds to an individual extract, bars represent mean $\pm$ SE.}}\label{fig:inVitroAssayPlot}
\end{figure}

\subsubsection{Proteomic profiling of soluble brain
extracts}\label{proteomic-profiling-of-soluble-brain-extracts}

The protein content in these brain extracts was examined by unbiased
proteomic analysis consisting of liquid chromatography/tandem
mass-spectroscopy (LS/MS-MS) followed by label-free quantification (LFQ)
(Cox et al. 2014; Seyfried et al. 2017).

The samples of brain extracts were processed in two separate LS/MS-MS
runs. Of these 43 individuals examined, samples of 20 individuals were
analyzed exclusively in the first round, 16 were analyzed exclusively in
the second round, and 7 were analyzed in both rounds. In order to
account for non-biological variability introduced by sample preparation
and instrument detection, the LFQ values were normalized using variance
stabilization normalization (VSN) as recommended by Valikangas and
colleagues (Huber et al. 2002; Välikangas, Suomi, and Elo 2018), common
to most methods of normalization for proteomic data this results in
normalized LFQ values that have been log2 transformed in order to accord
for the highly skewed distributions of raw LFQ values. Any residual
effects of the separate analysis rounds were accounted for by
calculating and removing the effect of run on each protein using linear
regression.

A known short-coming of label-free proteomic analysis is the occurrence
of failed or missed quantification of proteins, in particular amongst
those proteins with lower expression (Karpievitch, Dabney, and Smith
2012; Seyfried et al. 2017). Instances in which the quantification of a
given protein was missed in an excessive number of samples presents a
challenge when attempting to make inferences about the effect of that
protein on a particular phenotype. Therefore, any protein in which
quantification was missed in 5 or more samples was excluded for the
purposes of this analysis. This translates to a requirement that a given
protein needed to be quantified in 39 of 43 individuals (90.6\%), a
threshold similar to that employed by Seyfried and colleagues for
analyzing a similar cohort of AD-related human brain extracts (2017).
Additionally, a set of highly expressed ribosomal proteins unlikely to
be related to the phenotypes of interest and common contaminates that
arise from sample processing were removed. After the application of
these inclusion criteria, the data set contained 1841 proteins for
further analysis.

\subsubsection{Top-down assessment of protein expression profiles in
human brain
extracts}\label{top-down-assessment-of-protein-expression-profiles-in-human-brain-extracts}

In order to assess the relationship of all 43 individuals based on the
complete profile of protein expression, agglomerative hierarchical
clustering was performed based on the correlation of expression across
the 1841 proteins detected.

Examining the dendrogram depicting the results of the hierarchical
clustering shows as uniformly diverging arbor (Figure
\ref{indDendroVolcano}A). While there is no definitely correct method
for determining where to `cut' hierarchically clustered elements to form
discrete groupings, one method is to evaluate the step sizes within the
hierarchy and segment clusters arising from large steps. Hierarchical
clustering of \(n\) elements will always result in \(n-1\) steps. Large
steps within the hierarchy are indicative of greater dissimilarity
between the branches arising from that node. Employing that strategy
here results in the formation of two large clusters: cluster 1
containing 20 members, and cluster 2 containing 23 members.

Assessing the diagnosis class of the members of each cluster shows that
there are far fewer AD and HP-NCI individuals in cluster 1 (10 LP-NCI, 5
AD, 5 HP-NCI ) than in cluster 2 ( 4 LP-NCI, 10 AD, 9 HP-NCI). Although
the disparity of diagnosis classes between the two clusters does not
reach statistical significance (\(\chi^2\) = 5.2, d.f. = 2 p = 0.0744).

To understand what accounts for the difference between the two groups of
individuals suggested by the hierarchical clustering, differential
expression analysis was performed to determine what proteins exhibited
divergent levels of expression across the two clusters of extracts. The
differential expression analysis was performed comparing the normalized,
log\(_2\) transformed LFQ values for each protein across clusters using
one-way ANOVA. This analysis revealed that 323 of the 1841 proteins
examined (17.5\%) are differentially expressed (\emph{adjusted p}
\textless{} 0.05) between the brain extracts within clusters 1 and 2,
after accounting for the multiple comparisons performed (Figure
\ref{indDendroVolcano}B). For comparison of fold-change in expression
between the two groups, those proteins exhibiting higher expression in
cluster 1 and lower expression in cluster 2 were designated as
\emph{negative} fold-change, while those exhibiting lower expression in
cluster 1 and higher expression in cluster 2 were designated as
\emph{positive} fold-change.

\begin{figure}

{\centering \includegraphics{figures/individualDendroVolcano-1} 

}

\caption{\label{indDendroVolcano}{\small\textbf{Profile of protein expression across human brain extracts.} \textbf{A} Agglomerative hierarchical clustering based on correlation of expression of 1841 proteins across 43 individuals. Dashed line indicates segmentation of dendrogram at largest step size. \textit{Insert} Color-bar indicating diagnosis class of each individual extract. Table indicating instances of each diagnosis class present in the resulting clusters following segmentation of the dendrogram. \textbf{B} Differential expression of 1841 proteins between the two clusters of brain extracts. Each point corresponds to an individual protein. Fold change represents the difference in expression between cluster 1:cluster 2 as determined by normalized LFQ values; \textit{positive} cluster 1 < cluster 2, \textit{negative} cluster 1 > cluster 2. Dashed line indicates log${10}$ \textit{p}-value corresponding to \textit{p}=0.05 after accounting for multiple comparisons. Identity of select, highly significant proteins indicated by gene symbol.}}\label{fig:individualDendroVolcano}
\end{figure}

In order to explore what common biological processes might be shared
amongst this comparatively large number of differentially expressed
proteins, Gene Ontology (GO) enrichment analysis was performed using
these 323 proteins as the query set. Of the 323 proteins, 317 could be
associated with GO terms, and of the 3586 GO terms examined, the
proteins within this query set were significantly enriched amongst 22
terms (Table \ref{signifGOTab}). Amongst the significantly enriched GO
terms, several can be broadly related to functions of: regulation of
apoptosis, protein homeostasis, and vesicle processing. The constituent,
differentially expressed proteins mapping to several of these GO terms
are shown in Table \ref{selectGOproteinTab}.

\rowcolors{2}{gray!6}{white}

\begin{table}

\caption{\label{tab:table_signifGO}\label{signifGOTab} Gene Ontology terms significantly enriched amongst proteins differetially expressed between brain extact clusters }
\centering
\resizebox{\linewidth}{!}{
\fontsize{7}{9}\selectfont
\begin{tabular}[t]{l>{\raggedright\arraybackslash}p{30em}rrrl}
\hiderowcolors
\toprule
GO ID & Description & \textit{n} Members & \textit{n} Diff. Expressed & \textit{n} Expected & \textit{p}-value\\
\midrule
\showrowcolors
GO:0000338 & protein deneddylation & 8 & 7 & 1.43 & 3.7e-05\\
GO:0032435 & negative regulation of proteasomal ubiquitin-dependent protein catabolic process & 7 & 6 & 1.25 & 0.00018\\
GO:0006283 & transcription-coupled nucleotide-excision repair & 18 & 10 & 3.22 & 0.00032\\
GO:0019886 & antigen processing and presentation of exogenous peptide antigen via MHC class II & 38 & 15 & 6.79 & 0.00124\\
GO:0097711 & ciliary basal body-plasma membrane docking & 24 & 11 & 4.29 & 0.00134\\
\addlinespace
GO:0051131 & chaperone-mediated protein complex assembly & 9 & 6 & 1.61 & 0.00159\\
GO:0051973 & positive regulation of telomerase activity & 12 & 7 & 2.15 & 0.00187\\
GO:0000715 & nucleotide-excision repair, DNA damage recognition & 16 & 8 & 2.86 & 0.00319\\
GO:0047496 & vesicle transport along microtubule & 10 & 6 & 1.79 & 0.00338\\
GO:0010389 & regulation of G2/M transition of mitotic cell cycle & 68 & 20 & 12.16 & 0.00400\\
\addlinespace
GO:0008637 & apoptotic mitochondrial changes & 34 & 14 & 6.08 & 0.00402\\
GO:0007032 & endosome organization & 15 & 6 & 2.68 & 0.00425\\
GO:0016574 & histone ubiquitination & 5 & 4 & 0.89 & 0.00427\\
GO:0032402 & melanosome transport & 5 & 4 & 0.89 & 0.00427\\
GO:0071539 & protein localization to centrosome & 5 & 4 & 0.89 & 0.00427\\
\addlinespace
GO:0045956 & positive regulation of calcium ion-dependent exocytosis & 5 & 4 & 0.89 & 0.00427\\
GO:0090148 & membrane fission & 5 & 4 & 0.89 & 0.00427\\
GO:0051702 & interaction with symbiont & 12 & 5 & 2.15 & 0.00560\\
GO:0016050 & vesicle organization & 72 & 24 & 12.87 & 0.00561\\
GO:0032388 & positive regulation of intracellular transport & 66 & 18 & 11.80 & 0.00610\\
\addlinespace
GO:0008033 & tRNA processing & 14 & 6 & 2.50 & 0.00612\\
GO:0032502 & developmental process & 724 & 118 & 129.45 & 0.00689\\
\bottomrule
\end{tabular}}
\end{table}

\rowcolors{2}{white}{white}

\rowcolors{2}{gray!6}{white}

\begin{table}

\caption{\label{tab:table_selectGOProteins}\label{selectGOproteinTab} differential expression of proteins amongst select, significantly enriched GO terms}
\centering
\resizebox{\linewidth}{!}{
\fontsize{7}{9}\selectfont
\begin{tabular}[t]{>{\raggedright\arraybackslash}p{25em}llrr>{\raggedright\arraybackslash}p{12em}}
\hiderowcolors
\toprule
Name & Gene & Uniprot ID & Fold Change & \textit{p}-value (\textit{adjusted}) & GO ID\\
\midrule
\showrowcolors
\addlinespace[0.3em]
\multicolumn{6}{l}{\textbf{apoptosis (GO:0008637)}}\\
\hspace{1em}14-3-3 protein eta & YWHAH & Q04917 & -1.890 & 6.00e-07 & GO:0008637\\
\hspace{1em}Glycogen synthase kinase-3 beta & GSK3B & P49841 & -1.370 & 1.17e-04 & GO:0008637\\
\hspace{1em}Vacuolar protein sorting-associated protein 35 & VPS35 & Q96QK1 & -2.780 & 2.11e-04 & GO:0008637\\
\hspace{1em}14-3-3 protein zeta/delta & YWHAZ & P63104 & -0.943 & 3.89e-04 & GO:0008637\\
\hspace{1em}14-3-3 protein epsilon & YWHAE & P62258 & -0.777 & 8.50e-05 & GO:0008637\\
\hspace{1em}14-3-3 protein beta/alpha & YWHAB & P31946 & -1.350 & 1.36e-05 & GO:0008637\\
\hspace{1em}Glutamate--cysteine ligase catalytic subunit & GCLC & P48506 & -2.530 & 3.74e-03 & GO:0008637\\
\hspace{1em}14-3-3 protein theta & YWHAQ & P27348 & -1.160 & 8.86e-03 & GO:0008637\\
\hspace{1em}14-3-3 protein gamma & YWHAG & P61981 & -2.110 & 4.00e-07 & GO:0008637\\
\hspace{1em}ATPase inhibitor, mitochondrial & ATPIF1 & Q9UII2 & 0.824 & 1.94e-02 & GO:0008637\\
\hspace{1em}Glutathione peroxidase 1 & GPX1 & P07203 & 0.485 & 4.20e-03 & GO:0008637\\
\hspace{1em}Cell cycle and apoptosis regulator protein 2 & CCAR2 & Q8N163 & -1.460 & 5.70e-03 & GO:0032435; GO:0008637\\
\hspace{1em}Dynamin-1-like protein & DNM1L & O00429 & -1.510 & 1.25e-04 & GO:0045956; GO:0090148; GO:0008637\\
\hspace{1em}60 kDa heat shock protein, mitochondrial & HSPD1 & P10809 & 0.434 & 1.21e-02 & GO:0051131; GO:0008637\\
\addlinespace[0.3em]
\multicolumn{6}{l}{\textbf{protein homeostasis (GO:0000338; GO:0032435; GO:0051131; GO:0016574)}}\\
\hspace{1em}COP9 signalosome complex subunit 6 & COPS6 & Q7L5N1 & -0.754 & 2.95e-02 & GO:0000338\\
\hspace{1em}COP9 signalosome complex subunit 4 & COPS4 & Q9BT78 & -0.844 & 1.10e-04 & GO:0000338\\
\hspace{1em}COP9 signalosome complex subunit 1 & GPS1 & Q13098 & -0.852 & 1.69e-04 & GO:0000338\\
\hspace{1em}COP9 signalosome complex subunit 5 & COPS5 & Q92905 & -0.621 & 3.46e-03 & GO:0000338\\
\hspace{1em}COP9 signalosome complex subunit 7a & COPS7A & Q9UBW8 & -0.843 & 1.15e-02 & GO:0000338\\
\hspace{1em}COP9 signalosome complex subunit 8 & COPS8 & Q99627 & -0.665 & 8.11e-04 & GO:0000338\\
\hspace{1em}COP9 signalosome complex subunit 3 & COPS3 & Q9UNS2 & -1.020 & 3.04e-02 & GO:0000338\\
\hspace{1em}E3 ubiquitin-protein ligase HUWE1 & HUWE1 & Q7Z6Z7 & -1.240 & 2.32e-04 & GO:0016574\\
\hspace{1em}S-phase kinase-associated protein 1 & SKP1 & P63208 & 0.431 & 6.15e-04 & GO:0016574\\
\hspace{1em}DNA damage-binding protein 1 & DDB1 & Q16531 & 0.412 & 6.84e-04 & GO:0016574\\
\hspace{1em}Ubiquitin thioesterase OTUB1 & OTUB1 & Q96FW1 & -2.800 & 4.12e-03 & GO:0016574\\
\hspace{1em}Small glutamine-rich tetratricopeptide repeat-containing protein alpha & SGTA & O43765 & -0.740 & 3.64e-02 & GO:0032435\\
\hspace{1em}Protein/nucleic acid deglycase DJ-1 & PARK7 & Q99497 & 0.386 & 2.23e-02 & GO:0032435\\
\hspace{1em}Ubiquitin carboxyl-terminal hydrolase 14 & USP14 & P54578 & -1.410 & 3.73e-03 & GO:0032435\\
\hspace{1em}Bis(5'-adenosyl)-triphosphatase & FHIT & P49789 & 0.488 & 3.16e-03 & GO:0032435\\
\hspace{1em}Heat shock protein HSP 90-beta & HSP90AB1 & P08238 & -2.240 & 3.00e-07 & GO:0032435; GO:0051131\\
\hspace{1em}T-complex protein 1 subunit beta & CCT2 & P78371 & -0.655 & 1.17e-02 & GO:0051131\\
\hspace{1em}Prostaglandin E synthase 3 & PTGES3 & Q15185 & -3.050 & 9.47e-05 & GO:0051131\\
\hspace{1em}Heat shock protein HSP 90-alpha & HSP90AA1 & P07900 & -1.020 & 2.04e-04 & GO:0051131\\
\hspace{1em}Heat shock 70 kDa protein 4 & HSPA4 & P34932 & 0.270 & 4.21e-02 & GO:0051131\\
\addlinespace[0.3em]
\multicolumn{6}{l}{\textbf{vesicle processing (GO:0000338; GO:0032435; GO:0051131; GO:0016574)}}\\
\hspace{1em}Ras-related protein Rab-5B & RAB5B & P61020 & 0.529 & 1.24e-03 & GO:0016050\\
\hspace{1em}Protein Hook homolog 3 & HOOK3 & Q86VS8 & -1.020 & 4.56e-04 & GO:0016050\\
\hspace{1em}Sorting nexin-1 & SNX1 & Q13596 & -1.780 & 4.15e-03 & GO:0016050\\
\hspace{1em}Sorting nexin-12 & SNX12 & Q9UMY4 & -1.820 & 2.23e-05 & GO:0016050\\
\hspace{1em}Sorting nexin-6 & SNX6 & Q9UNH7 & -1.410 & 2.56e-03 & GO:0016050\\
\hspace{1em}Arf-GAP domain and FG repeat-containing protein 1 & AGFG1 & P52594 & -0.634 & 1.30e-02 & GO:0016050\\
\hspace{1em}Coronin-1A & CORO1A & P31146 & -2.010 & 2.70e-03 & GO:0016050\\
\hspace{1em}Vacuolar protein sorting-associated protein VTA1 homolog & VTA1 & Q9NP79 & -3.330 & 1.37e-02 & GO:0016050\\
\hspace{1em}AP-1 complex subunit gamma-1 & AP1G1 & O43747 & -3.170 & 5.50e-06 & GO:0016050\\
\hspace{1em}Disks large homolog 4 & DLG4 & P78352 & -1.650 & 7.58e-03 & GO:0016050\\
\hspace{1em}Calreticulin & CALR & P27797 & -1.540 & 3.19e-03 & GO:0016050\\
\hspace{1em}Syntaxin-7 & STX7 & O15400 & 0.653 & 2.51e-03 & GO:0016050\\
\hspace{1em}Calcium-dependent secretion activator 1 & CADPS & Q9ULU8 & -3.400 & 1.91e-04 & GO:0016050\\
\hspace{1em}Ras-related protein Rab-7a & RAB7A & P51149 & 0.346 & 1.01e-02 & GO:0016050\\
\hspace{1em}AP-1 complex subunit mu-1 & AP1M1 & Q9BXS5 & -1.850 & 1.45e-03 & GO:0016050\\
\hspace{1em}General vesicular transport factor p115 & USO1 & O60763 & -2.940 & 1.30e-05 & GO:0016050\\
\hspace{1em}Synaptotagmin-7 & SYT7 & O43581 & 1.050 & 2.69e-03 & GO:0016050; GO:0045956\\
\hspace{1em}Syntaxin-binding protein 1 & STXBP1 & P61764 & -2.410 & 7.15e-03 & GO:0016050; GO:0045956\\
\hspace{1em}Syntaxin-1A & STX1A & Q16623 & 0.726 & 2.28e-02 & GO:0016050; GO:0045956\\
\hspace{1em}AP-3 complex subunit delta-1 & AP3D1 & O14617 & -2.160 & 5.86e-03 & GO:0016050; GO:0047496\\
\hspace{1em}Platelet-activating factor acetylhydrolase IB subunit alpha & PAFAH1B1 & P43034 & -1.070 & 6.06e-03 & GO:0016050; GO:0047496\\
\hspace{1em}Charged multivesicular body protein 4b & CHMP4B & Q9H444 & 1.120 & 1.10e-04 & GO:0016050; GO:0090148\\
\hspace{1em}Dynamin-1 & DNM1 & Q05193 & -2.540 & 1.88e-02 & GO:0016050; GO:0090148\\
\hspace{1em}Dynactin subunit 2 & DCTN2 & Q13561 & -0.711 & 8.56e-05 & GO:0032402\\
\hspace{1em}Dynactin subunit 1 & DCTN1 & Q14203 & -1.270 & 3.00e-02 & GO:0032402\\
\hspace{1em}Sulfite oxidase, mitochondrial & SUOX & P51687 & 0.407 & 1.00e+00 & GO:0032402\\
\hspace{1em}Ras-related protein Rab-11B & RAB11B & Q15907 & 0.511 & 2.15e-03 & GO:0032402\\
\hspace{1em}Ras-related protein Rab-1A & RAB1A & P62820 & 0.373 & 3.56e-02 & GO:0032402; GO:0016050; GO:0047496\\
\hspace{1em}AP-3 complex subunit beta-2 & AP3B2 & Q13367 & -4.460 & 0.00e+00 & GO:0047496\\
\hspace{1em}Cytoplasmic dynein 1 heavy chain 1 & DYNC1H1 & Q14204 & -2.170 & 2.22e-04 & GO:0047496\\
\hspace{1em}AP-3 complex subunit mu-2 & AP3M2 & P53677 & -1.660 & 7.45e-05 & GO:0047496\\
\hspace{1em}Endophilin-B1 & SH3GLB1 & Q9Y371 & -1.350 & 3.29e-03 & GO:0090148\\
\bottomrule
\end{tabular}}
\end{table}

\rowcolors{2}{white}{white}

Of differentially expressed proteins related to regulation of apoptosis,
several isoforms of 14-3-3 are down regulated in cluster 2 compared with
cluster 1. This is relevant given that 14-3-3 proteins have been shown
to negative regulators of apoptosis by directly inhibiting the
pro-apoptotic Bcl-2 family members, BAD and Bax (Yaffe et al. 1997;
Zhang, Chen, and Fu 1999; Nomura et al. 2003). Moreover, 14-3-3 have
been shown to promote neurite outgrowth (Kaplan et al. 2017; Lavalley et
al. 2016) and neuronal survival (Slone et al. 2015). While 14-3-3
proteins have been associated with tau aggregates in tauopathies,
including AD, they are believed to play a chaperone-like role
facilitating the clearance of tau aggregates (Sluchanko and Gusev 2017;
Papanikolopoulou et al. 2018).

Additionally, several proteins responsible for maintaining protein
homeostasis in cells are differentially expressed between clusters 1 and
2. A indicated by the significant enrichment of the GO term
`\url{GO:000038} - \emph{protein deneddylation}', 7 of the 8 sub units
of the COP9 signalosome complex are significantly higher amongst cluster
1 extracts compared with cluster 2. The COP9 signalosome complex, along
with ubiquitin E3 ligases and the 26S proteasome make up the 3
macro-molecules of the ubiquitin-proteasome pathway, a central mechanism
for protein catabolism in all cells, including neurons (Tai and Schuman
2008; Lingaraju et al. 2014; Meister et al. 2016). Also, while annotated
by a somewhat curious GO term `\url{GO:0010389} - \emph{regulation of
G2/M transition of mitotic cell cycle}', several of the 26S proteasome
sub-units are also down regulated in cluster 2 compared to cluster 1. Of
the twenty 26S proteasome sub unit proteins within Uniprot human
proteome database, 16 were detected in this analysis. As shown in Table
\ref{diffExp26STab}, all but 1 of the 16 sub-units are decreased in
cluster 2 compared with cluster 1, including the 8 that are
significantly decreased. Given the greater number of AD and HP-NCI
individuals within cluster 2, this is consistent with findings that
proteasome activity is decreased in the AD brain and that patterns of
ubiquitination are disrupted across the proteome in AD brains verse
unaffected controls (Keller, Hanni, and Markesbery 2000; Abreha et al.
2018).

\begin{table}

\caption{\label{tab:table_26Sproteosome}\label{diffExp26STab} differential expression of 26S proteosome subunits}
\centering
\begin{tabular}[t]{llrr}
\toprule
26S proteasome subunit & Gene & Fold Change & \textit{p}-value (\textit{adjusted})\\
\midrule
non-ATPase regulatory subunit 13 & PSMD13 & -1.810 & 0.000552\\
non-ATPase regulatory subunit 6 & PSMD6 & -0.974 & 0.001250\\
non-ATPase regulatory subunit 2 & PSMD2 & -0.872 & 0.001380\\
non-ATPase regulatory subunit 3 & PSMD3 & -0.879 & 0.003180\\
regulatory subunit 7 & PSMC2 & -1.300 & 0.004380\\
\addlinespace
regulatory subunit 6A & PSMC3 & -1.190 & 0.004880\\
regulatory subunit 4 & PSMC1 & -1.580 & 0.013400\\
non-ATPase regulatory subunit 7 & PSMD7 & -1.060 & 0.014400\\
\hline
non-ATPase regulatory subunit 8 & PSMD8 & -0.699 & 0.067800\\
non-ATPase regulatory subunit 12 & PSMD12 & -1.200 & 0.068500\\
\addlinespace
non-ATPase regulatory subunit 11 & PSMD11 & -1.600 & 0.084200\\
non-ATPase regulatory subunit 9 & PSMD9 & 0.578 & 0.095100\\
regulatory subunit 10B & PSMC6 & -1.390 & 0.140000\\
non-ATPase regulatory subunit 4 & PSMD4 & -0.577 & 0.380000\\
regulatory subunit 8 & PSMC5 & -1.160 & 1.000000\\
non-ATPase regulatory subunit 1 & PSMD1 & -0.658 & 1.000000\\
\bottomrule
\end{tabular}
\end{table}

Interestingly of the 323 proteins differentially expressed between the
two groups of brain extracts, there is an asymmetry in the
directionality of the changes. As seen by the greater number of events
in the upper left compared with the upper right of Figure
\ref{indDendroVolcano}B, more than two-thirds (229 of 323) of the
differentially expressed proteins are down-regulated in cluster 2 verse
cluster 1 (\emph{negative} fold-change). While there is no immediate
explanation for this disparity, within the minority of 94 proteins that
are up-regulated in cluster 2 compared with cluster 1, 8 are members of
the Ras-related GTPase family, or Rabs. Of the 26 Rab family members
detected in the data set all but 3 are show higher expression in cluster
2, including the 8 that significantly higher (Table
\ref{diffExpRabTab}). In particular all three isoforms of Rab-5 show
significantly higher expression in cluster 2. This is relevant since
Rabs play an integral role in vesicle processing in neurons and have
previously been shown to be highly expressed in AD brain tissue
(Ginsberg:2011eg; Kiral et al. 2018). Especially Rab-5, which is
involved in trafficking of endosomes, and has been found to be present
within enlarged endosome enriched with A\(\beta\) found in AD brain.
This has lead to speculation that over-active Rab-5 leads to aberrant
endosome sorting and diminished fusion with lysosomes, leading to
increased A\(\beta\) aggregation (Whyte et al. 2017; Xu et al. 2018; X.
Zhang et al. 2018).

\begin{table}

\caption{\label{tab:table_Rabs}\label{diffExpRabTab} differential expression of Rab GTPases}
\centering
\begin{tabular}[t]{llrr}
\toprule
Rab protein & Gene & Fold Change & \textit{p}-value (\textit{adjusted})\\
\midrule
Rab-5B & RAB5B & 0.5290 & 0.00124\\
Rab-11B & RAB11B & 0.5110 & 0.00215\\
Rab-5A & RAB5A & 0.4620 & 0.00225\\
Rab-21 & RAB21 & 0.7060 & 0.00948\\
Rab-7a & RAB7A & 0.3460 & 0.01010\\
\addlinespace
Rab-5C & RAB5C & 0.5530 & 0.01360\\
Rab-10 & RAB10 & 0.5010 & 0.01640\\
Rab-1A & RAB1A & 0.3730 & 0.03560\\
\hline
Rab-18 & RAB18 & 0.2690 & 0.13400\\
Rab-14 & RAB14 & 0.3140 & 0.26700\\
\addlinespace
Rab-2A & RAB2A & 0.4520 & 0.35300\\
Rab-3D & RAB3D & 0.2620 & 1.00000\\
Rab-3A & RAB3A & 0.1650 & 1.00000\\
Rab-4A & RAB4A & 0.2180 & 1.00000\\
Rab-6A & RAB6A & -0.1190 & 1.00000\\
\addlinespace
Rab-8A & RAB8A & 0.2950 & 1.00000\\
Rab-4B & RAB4B & 0.3900 & 1.00000\\
Rab-35 & RAB35 & -0.2480 & 1.00000\\
Rab-like protein 6 & RABL6 & 0.1390 & 1.00000\\
Rab-12 & RAB12 & 0.2990 & 1.00000\\
\addlinespace
Rab-2B & RAB2B & 0.3360 & 1.00000\\
Rab-8B & RAB8B & 0.2970 & 1.00000\\
Rab-3C & RAB3C & 0.2370 & 1.00000\\
Rab-1B & RAB1B & 0.1450 & 1.00000\\
Rab-6B & RAB6B & 0.0678 & 1.00000\\
Rab-23 & RAB23 & -0.0572 & 1.00000\\
\bottomrule
\end{tabular}
\end{table}

To determine whether the differences between these two clusters of
extracts as determined by their protein expression profiles translated
to changes in neuronal phenotypes, effects in the neurite integrity and
spontaneous firing assays were examined based on cluster membership. As
shown in Figure \ref{assayPhenotypeCluster}, the extracts within cluster
2 had a significantly greater effect on neuritic atrophy when added to
cultured human iPS-derived neurons, than those in cluster 1 (p =
\(1.73\times 10^{-4}\)). Conversely, while the extracts in cluster 2
tended to cause a greater decrease in spontaneous firing on average
within primary rat cortical cultures, the difference was not significant
(p = 0.319).

\begin{figure}

{\centering \includegraphics{figures/assayPhenotypeClusterPlot-1} 

}

\caption{\label{assayPhenotypeCluster}{\small\textbf{Correspondence between protein expression profiles and influence on neuronal phenotypes.} Effect of brain extracts segregated by protein expression cluster membership on neurite integrity (\textbf{A}) or spontaneous firing (\textbf{B}). Each point corresponds to an individual extract, bars represent mean $\pm$ SE. Significant differences between conditions indicated by brackets and associated \textit{p}-value.}}\label{fig:assayPhenotypeClusterPlot}
\end{figure}

To assess what proteins may be contributing to differences in the firing
activity of neurons, the extracts tested in the spontaneous firing assay
were manually segregated into those exhibiting a greater or lesser
effect on spike frequency (Figure \ref{deltaLogHzVolcano}A).
Differential expression analysis was then repeated to determine which if
any proteins exhibited different expression levels between the two
groups. While several proteins exhibited some difference between the
groups, none of these remained statistically significant after adjusting
for the multiple comparisons performed (Figure
\ref{deltaLogHzVolcano}B). While not significant, amongst the proteins
showing higher expression within extracts with higher residual firing
activity are prefoldin (PFDN) and WASF3. PFDN, is a chaperone shown to
prevent aggregation of Huntingtin, \(\alpha\)-synuclein, and A\(\beta\),
and a recent GWAS study of late-onset AD in a trans-ethnic population
identified 2 SNPs in the vicinity of PFDN (Tashiro et al. 2013; Sörgjerd
et al. 2013; Takano et al. 2014; Jun et al. 2017). WASF3 is an actin
binding protein that facilitates neurite outgrowth by promoting actin
polymerization through cooperative action with the Rac1 GTPase (Sainath
and Gallo 2015; Zhang et al. 2017).

\begin{figure}

{\centering \includegraphics{figures/deltaLogHzVolcanoPlot-1} 

}

\caption{\label{deltaLogHzVolcano}{\small\textbf{Differential expression of proteins between brain extracts eliciting lesser or greater effect on spontaneous firing.} \textbf{A} segregating brain extracts into groups with lesser or greater activity, demarcation indicated by dashed line. \textbf{B} Differential expression of proteins between brain extracts across activity levels. Each point corresponds to an individual protein. Fold change represents the difference in expression between less active:more active as determined by normalized LFQ values;
\textit{positive} less active > more active, \textit{negative} less active < more active. Dashed lines indicates log${10}$ \textit{p}-values corresponding to \textit{p}=0.05 without (\textit{lower}) and with (\textit{upper}) accounting for multiple comparisons. Identity of select proteins indicated by gene symbol.}}\label{fig:deltaLogHzVolcanoPlot}
\end{figure}

\subsubsection{Bottom-up examination of individual protein expression
and effect on neuronal
phenotypes.}\label{bottom-up-examination-of-individual-protein-expression-and-effect-on-neuronal-phenotypes.}

As an alternative to the previous analysis assessing differences in
holistic profiles of protein expression, instead individual proteins
were examined to identify those whose expression is correlated with the
changes in the structural and functional phenotypes examined \emph{in
vitro}.

\paragraph{\texorpdfstring{Influence of A\(\beta\) and tau expression on
\textit{in vitro}
phenotypes}{Influence of A\textbackslash{}beta and tau expression on  phenotypes}}\label{influence-of-abeta-and-tau-expression-on-phenotypes}

The hallmarks of AD pathology are extracellular plaques of A\(\beta\)
and intracellular neurofibrillary tangles of hyperphosphorylated tau.
Therefore, expression of A\(\beta\) and tau were examined for their
expression across the diagnosis classes of extracts and the correlation
of expression with effects of in the in vitro assays.

A\(\beta\) itself is not directly quantified in standard LFQ analysis,
rather peptides corresponding to A\(\beta\) were mapped to the full
length Amyloid Precursor Protein (APP) from which A\(\beta\) is derived.
Therefore, quantification of APP is used as a surrogate for A\(\beta\)
expression in these data as previously reported by Seyfried et al.
(2017).

\begin{figure}

{\centering \includegraphics{figures/ADproteinPlot-1} 

}

\caption{\label{ADproteinPlot}{\small\textbf{Differential expression of AD related proteins with respect to diagnosis class.} Expression of APP (\textbf{A}) and tau (\textbf{B}) within human brain extracts examined by LC-MS/MS proteomic analysis and reported as normalized LFQ values. Effect of APP and tau expression on neurite integrity (\textbf{C}, \textbf{D}), and $\Delta log_{10}$Hz (\textbf{E}, \textbf{F}). Each point corresponds to an individual extract, bars represent mean $\pm$ SE. Significant differences between conditions indicated by brackets and associated \textit{p}-value.}. Line indicate fit of linear regression for assay phenotype as function of LFQ values.}\label{fig:ADproteinPlot}
\end{figure}

Comparing extracts by diagnosis class there is no difference in the
levels of APP detected (Figure \ref{ADproteinPlot}A). Conversely, tau
levels are significantly lower in the extracts of AD diagnosed
individuals than in those of LP-NCI (p = 0.0223), and decreased relative
to HP-NCI (Figure \ref{ADproteinPlot}B). This result is somewhat
paradoxical, given that tau expression has been repeatedly shown to
positively correlated with Alzheimer's progression, even more so than
amyloid-beta levels (reviewed in Nelson et al. 2012). A possible caveat
is, this analysis was performed using TBS-soluble brain extracts, so
only tau within the aqueous soluble portion of the tissue is being
examined. In order to solubilize most neurofibrillary associated tau
species, much more stringent detergent-based extraction methods are
required (Goedert et al. 1992; Köpke et al. 1993). Given the mild
digestion protocol used in the preparation of these brain tissues, most
neurofibrillary species would have remained insoluble and been
precipitated during centrifugation along with other cellular debris. For
this reason, the lower expression of tau in the extracts of AD
individuals may be due to a large portion of total tau species being
sequestered within insoluble neurofibrillary species. However, since the
detergent soluble fractions of these tissues were not assayed, this can
not be definitively concluded.

Examining the correlation of APP and tau expression on the assay
phenotypes of neurite integrity and spontaneous show conflicting
responses with neurite integrity tending to decrease with increasing
expression of APP and tau (Figure \ref{ADproteinPlot}B,C) and
spontaneous firing increasing (Figure \ref{ADproteinPlot}E,F). However,
in all cases the strength of the relationship is fairly weak, thus
lending poor predictive value to the changes in these phenotypes based
APP and tau expression ( \(R^2_{LFQ_{APP}:\%NI}\) = 0.162,
\(R^2_{LFQ_{tau}:\%NI}\) = 0.238, \(R^2_{LFQ_{APP}:\Delta log_{10}Hz}\)
= 0.0778, \(R^2_{LFQ_{tau}:\Delta log_{10}Hz}\) = 0.085).

\paragraph{\texorpdfstring{Correlation of effect on \textit{in vitro}
phenotypes across
proteome}{Correlation of effect on  phenotypes across proteome}}\label{correlation-of-effect-on-phenotypes-across-proteome}

In order to assess the relationship between each of the 1841 proteins
detected in this analysis and the \emph{in vitro} phenotypes, Spearman's
correlation coefficient (\(\rho\)) was calculated between the normalized
LFQ value for each protein by extract and the effect of each extract in
both the neurite integrity and spontaneous firing assays. Spearman's
correlation was chosen for this purpose as it is less sensitive than
Pearson's \(\rho\) to the effect of~leverage~from outlying data-points
which are difficult to account for given the large number of comparisons
being performed. To determine the probability of observing a
statistically significant positive or negative correlation, the
resulting value of \(\rho\) was converted to a z-score using a Fisher
transformation and the z-score level corresponding to a probability of p
= 0.05 was adjusted for multiple comparisons. A \emph{positive}
correlation represents proteins that exhibit higher expression in
extracts with less effect on neurite integrity or spontaneous firing,
while a \emph{negative} correlation represents proteins that exhibit
higher expression in extracts with greater effect on these phenotypes.
The resulting distributions of correlations for all proteins in each
assay are shown in Figure \ref{assayCorrelation}A.

\begin{figure}

{\centering \includegraphics{figures/assayCorrPlot-1} 

}

\caption{\label{assayCorrelation}{\small\textbf{Correlation of individual protein expression with neuronal phenotypes.} Distribution of \textit{z}-scores for correlation between expression of individual protein within extracts and effect of extracts on neurite integrity (\textbf{A}) and spontaneous firing (\textbf{B}).
Positive correlation (\textit{red}) higher protein expression $\propto$ lesser effect on phenotype; negative correlation (\textit{blue}) higher protein expression $\propto$ greater effect on phenotype.\textit{Upper}, distribution for all proteins, \textit{lower} 10 proteins in the upper and lower tails of each distribution. Colored horizontal lines indicate value of strongest correlation observed, dashed lines indicate \textit{z}-score values corresponding to \textit{p}=0.05 accounting for multiple comparisons. \textbf{B} Distribution of random rank sums for vectors of \textit{n} proteins. Shaded regions indicate top, bottom 1$\%$ expected rank sums. \textit{Below} Proteins with sum of correlation rank across assay phenotypes within within top, bottom 1$\%$ of predicted rank sums.
}}\label{fig:assayCorrPlot}
\end{figure}

Sixty-three proteins showed statistically significant correlations with
effects on neurite integrity, with 48 positively correlated and 15
negatively correlated. Amongst the proteins whose expression correlated
with changes in neurite integrity, Calpastatin (CAST) showed the
strongest negative correlation, indicating that it is highly expressed
in extracts that induced a substantial loss of neurites following
treatment. This is somewhat unexpected given that CAST as an inhibitor
of the cysteine protease calpain, is typically seen as neuroprotective
in AD models (Rami 2003; Yamashima 2013). Inhibition of calpain by CAST
prevented tauopathy and neurodegeneration in Tau P301L mice (Rao et al.
2014). However, the elevated levels of CAST here maybe due to
compensatory up-regulation in brains exhibiting higher levels of
pathology rather than the result of CAST activity itself. Calpain
activity has been shown to be increased in AD brain cerebral-spinal
fluid (CSF), and CAST expression is increased in AD brain, though this
has been speculated to be the result of long term calpain activity
(Laske et al. 2015; Kurbatskaya et al. 2016).

Conversely, in comparisons with the spontaneous firing assay only a
single protein, Hornerin, was found to have a statistically significant
correlation showing a positive correlation with the spontaneous firing
phenotype. Hornerin, a pro-flaggrin like protein primarily expressed in
the epidermis (Makino et al. 2001) is not reported in the literature to
have a role in neuronal function.

In order to determine which proteins may be contributing to both
phenotypes, the rank sum of each protein was calculated based on its
rank 1-1841 from strongest positive to strongest negative correlation in
each assay. These rank sum values were compared to a null distribution
of rank sums generated from 10,000 iterations of adding vectors of
1-1841 in a random order (Figure \ref{assayCorrelation}B). From this
analysis, proteins exhibiting rank sums of correlation within the top
and bottom 1\% as expected by chance were selected. Thirty-nine proteins
met this criteria, of which 6 showed positive correlations across both
assay, while the remainder were negatively correlated. The effect in
each assay as a function of protein expression level is shown for 4 of
these 39 proteins in Figure \ref{signifRankCorr} while details of all 39
are listed in Table \ref{assayCorr_rankSumTab}.

\rowcolors{2}{gray!6}{white}

\begin{table}

\caption{\label{tab:table_assayCorrRankSum}\label{assayCorr_rankSumTab} Proteins exhibiting strong positive, negative correlation across spontaneous firing and neurite integrity assays}
\centering
\resizebox{\linewidth}{!}{
\fontsize{7}{9}\selectfont
\begin{tabular}[t]{>{\raggedright\arraybackslash}p{25em}llrrrr}
\hiderowcolors
\toprule
\multicolumn{1}{c}{ } & \multicolumn{1}{c}{ } & \multicolumn{1}{c}{ } & \multicolumn{3}{c}{Rank} & \multicolumn{1}{c}{ } \\
\cmidrule(l{2pt}r{2pt}){4-6}
Name & Gene & Uniprot ID & $\Delta log_{10}$Hz & $\%$ Neurite Integrity & Sum & \textit{p}-value\\
\midrule
\showrowcolors
\addlinespace[0.3em]
\multicolumn{7}{l}{\textbf{positive correlation}}\\
\hspace{1em}T-complex protein 1 subunit alpha & TCP1 & P17987 & 111 & 49 & 160 & 0.003740\\
\hspace{1em}Regulator of G-protein signaling 6 & RGS6 & P49758 & 184 & 8 & 192 & 0.005410\\
\hspace{1em}X-ray repair cross-complementing protein 5 & XRCC5 & P13010 & 148 & 81 & 229 & 0.007700\\
\hspace{1em}Tetratricopeptide repeat protein 9A & TTC9 & Q92623 & 91 & 153 & 244 & 0.008750\\
\hspace{1em}Abl interactor 1 & ABI1 & Q8IZP0 & 8 & 242 & 250 & 0.009200\\
\hspace{1em}L-aminoadipate-semialdehyde dehydrogenase-phosphopantetheinyl transferase & AASDHPPT & Q9NRN7 & 234 & 18 & 252 & 0.009340\\
\addlinespace[0.3em]
\multicolumn{7}{l}{\textbf{negative correlation}}\\
\hspace{1em}Transcription intermediary factor 1-beta & TRIM28 & Q13263 & 1767 & 1658 & 3425 & 0.009770\\
\hspace{1em}Alcohol dehydrogenase class-3 & ADH5 & P11766 & 1648 & 1781 & 3429 & 0.009470\\
\hspace{1em}ATP synthase subunit delta, mitochondrial & ATP5F1D & P30049 & 1771 & 1661 & 3432 & 0.009250\\
\hspace{1em}Dynein light chain 2, cytoplasmic & DYNLL2 & Q96FJ2 & 1794 & 1646 & 3440 & 0.008670\\
\hspace{1em}Bis(5'-adenosyl)-triphosphatase & FHIT & P49789 & 1619 & 1825 & 3444 & 0.008390\\
\hspace{1em}Protein S100-B & S100B & P04271 & 1746 & 1702 & 3448 & 0.008110\\
\hspace{1em}ATP synthase subunit beta, mitochondrial & ATP5F1B & P06576 & 1637 & 1813 & 3450 & 0.007970\\
\hspace{1em}NADH-cytochrome b5 reductase 3 & CYB5R3 & P00387 & 1693 & 1766 & 3459 & 0.007360\\
\hspace{1em}Hsp70-binding protein 1 & HSPBP1 & Q9NZL4 & 1654 & 1810 & 3464 & 0.007030\\
\hspace{1em}Hepatocyte cell adhesion molecule & HEPACAM & Q14CZ8 & 1719 & 1757 & 3476 & 0.006290\\
\hspace{1em}Glyoxylate reductase/hydroxypyruvate reductase & GRHPR & Q9UBQ7 & 1640 & 1836 & 3476 & 0.006290\\
\hspace{1em}Solute carrier family 12 member 2 & SLC12A2 & P55011 & 1834 & 1648 & 3482 & 0.005930\\
\hspace{1em}Dipeptidyl peptidase 3 & DPP3 & Q9NY33 & 1672 & 1814 & 3486 & 0.005700\\
\hspace{1em}Cathepsin B & CTSB & P07858 & 1726 & 1763 & 3489 & 0.005530\\
\hspace{1em}Glutathione peroxidase 1 & GPX1 & P07203 & 1673 & 1824 & 3497 & 0.005080\\
\hspace{1em}Ribosome-binding protein 1 & RRBP1 & Q9P2E9 & 1756 & 1744 & 3500 & 0.004910\\
\hspace{1em}Ester hydrolase C11orf54 & C11orf54 & Q9H0W9 & 1689 & 1827 & 3516 & 0.004100\\
\hspace{1em}Methyl-CpG-binding protein 2 & MECP2 & P51608 & 1751 & 1772 & 3523 & 0.003760\\
\hspace{1em}Ganglioside GM2 activator & GM2A & P17900 & 1836 & 1689 & 3525 & 0.003660\\
\hspace{1em}Echinoderm microtubule-associated protein-like 2 & EML2 & O95834 & 1708 & 1837 & 3545 & 0.002790\\
\hspace{1em}Cadherin-2 & CDH2 & P19022 & 1824 & 1721 & 3545 & 0.002790\\
\hspace{1em}RNA-binding protein EWS & EWSR1 & Q01844 & 1765 & 1788 & 3553 & 0.002480\\
\hspace{1em}Ras-related protein Rab-21 & RAB21 & Q9UL25 & 1753 & 1803 & 3556 & 0.002360\\
\hspace{1em}Aminoacylase-1 & ACY1 & Q03154 & 1791 & 1771 & 3562 & 0.002150\\
\hspace{1em}Quinone oxidoreductase-like protein 1 & CRYZL1 & O95825 & 1820 & 1761 & 3581 & 0.001530\\
\hspace{1em}Serine/arginine-rich splicing factor 3 & SRSF3 & P84103 & 1784 & 1805 & 3589 & 0.001290\\
\hspace{1em}Enhancer of rudimentary homolog & ERH & P84090 & 1806 & 1784 & 3590 & 0.001260\\
\hspace{1em}3-hydroxyisobutyrate dehydrogenase, mitochondrial & HIBADH & P31937 & 1770 & 1839 & 3609 & 0.000801\\
\hspace{1em}Ras-related protein Rab-4A & RAB4A & P20338 & 1776 & 1834 & 3610 & 0.000782\\
\hspace{1em}Persulfide dioxygenase ETHE1, mitochondrial & ETHE1 & O95571 & 1800 & 1817 & 3617 & 0.000641\\
\hspace{1em}39S ribosomal protein L12, mitochondrial & MRPL12 & P52815 & 1829 & 1807 & 3636 & 0.000326\\
\hspace{1em}Calpastatin & CAST & P20810 & 1802 & 1841 & 3643 & 0.000236\\
\hspace{1em}Acid ceramidase & ASAH1 & Q13510 & 1823 & 1829 & 3652 & 0.000141\\
\bottomrule
\end{tabular}}
\end{table}

\rowcolors{2}{white}{white}

The 6 proteins showing higher expression in those extracts exhibiting
greater residual neurite integrity and spontaneous firing were: TCP1,
ABI1, RGS6, XCCR5, TTC9, and AASDHPT. Of these, several has been shown
to serve functions that would mitigate the effects of AD pathology and
preserve these structural and functional phenotypes. T-complex protein 1
(TCP1) is a member of the Hsp60 family of chaperones and has been shown
to be down regulated in AD brain (Brehme et al. 2014). Interestingly,
TCP1 also has been shown to mitigate Huntingtin protein aggregates
\emph{in vitro} and remarkably has been shown to do so by entering cells
when added exogenously, possibly through a mechanism involving the
protein transduction domain within it apical region (Sontag et al. 2013;
Zhao et al. 2016). TCP1 has is also reported to be an agonist of TREM2
(Stefano et al. 2009). This is relevant for AD as diminished TREM2
activity is believed to be a risk factor for sporadic AD (Colonna and
Wang 2016). Abl interactor 1 (ABI1) has been shown to promote dendrite
morphogenesis and synapse formation through Rac1 dependent actin
remodeling (Proepper et al. 2007; Proepper et al. 2011; Park, Chi, and
Park 2012). RGS6 as been shown to be required for neuron survival of
dopaminergic neurons in aged animals, potentially due to its role in
regulating p27 (Ogawa et al. 2003; Bifsha et al. 2014).

Amongst the proteins negatively correlated across the assay phenotypes
is Ewing Scarcoma RNA binding protein (EWSR1). EWSR1 is among the RNA
binding proteins along with TDP-43 and FUS found to harbor mutation
associated with amyloid lateral sclerosis, and EWSR1 is found associated
with cytoplasmic inclusions in frontal-temporal lobe dementia (Neumann
et al. 2011; Li et al. 2013; Maziuk, Ballance, and Wolozin 2017).
Another RNA-binding protein, DHX9, was also showed a strong negative
correlation with effect in the spontaneous firing assay (Figure
\ref{assayCorrelation}A).

\begin{figure}

{\centering \includegraphics{figures/signifRankCorrPlot-1} 

}

\caption{\label{signifRankCorr}{\small\textbf{Correlation of activity across neuronal phenotypes.} Comparison of protein expression and effect on neurite integrity (\textit{left}) and spontaneous firing (\textit{right}) for TCP1 (\textbf{A}), ABI1 (\textbf{B}), CAST (\textbf{C}), and ASAH1 (\textbf{D}). Each point corresponds to an individual extract, line indicates fit of linear regression between LFQ and neuronal phenotype.
}}\label{fig:signifRankCorrPlot}
\end{figure}

As these proteins were selected purely on their correlation to the
observed effects in the in vitro assays, these 39 proteins were examined
to see if any are differentially expressed across the different
diagnoses of brain extracts. Eight of these proteins were found to show
significant differential expression across diagnosis class after
accounting for multiple comparisons (Figure \ref{signifRankDxDiffPlot}).

Of those showing higher expression in LP-NCI individuals were ABI1
(discussed above) and Tetratricopeptide repeat protein 9 (TTC9). TTC9 is
broadly expressed in the brain and shares homology with co-chaperones
for Hsp70 and 90 (Smith 2004; Shrestha, Cao, and Lin 2012). TTC9 was
recently shown to have decreased expression in microglia from an AD
mouse model (Rangaraju et al. 2018). Additionally, a novel mis-sense
mutation in a related protein, TTC3, was found through whole-exosome
sequencing in all affected members of a family exhibiting high instances
of late-onset AD (Kohli et al. 2016).

Two proteins that showed significantly higher expression in AD extracts
over both LP-NCI and HP-NCI individuals were Acid cerimidase (ASAH1) and
Ganglioside GM2 activator protein (GM2A). Both are involved in
sphingolipid metabolism and have been shown to be elevated in the brain
and cerebral-spinal fluid of AD patients (He et al. 2010; Heywood et al.
2015). Altered sphingolipid metabolism in AD has lead to the hypothesis
that increased flux in sphingolipid catabolism pathways may result in
decreased sphingomyelin in the brain, affecting membrane fluidity and
integrity (Han et al. 2002; Wong et al. 2017).

\begin{figure}

{\centering \includegraphics{figures/plot_signifRankDxDiffExp-1} 

}

\caption{\label{signifRankDxDiffPlot}{\small\textbf{Proteins highly correlated with changes in neuronal phenotypes \textit{in vitro} differentially expressed across diagnosis classes.} Expression levels of proteins strongly correlated with effects on neurite integrity and spontaneous firing phenotypes within LP-NCI, AD, and HP-NCI brain extracts. Each point corresponds to an individual extract, bars represent mean $\pm$ SE. Significant differences between conditions indicated by brackets and associated \textit{p}-value.}}\label{fig:plot_signifRankDxDiffExp}
\end{figure}

\newpage

\subsection{Conclusion}\label{conclusion}

In order to better understand the molecular changes that occur during
the developed and progress of AD numerous reports in the literature have
used proteomic analysis to analyze the expression profiles in the brain
tissue of AD patients and unaffected individuals (Andreev et al. 2012;
Seyfried et al. 2017; Abreha et al. 2018; Q. Zhang et al. 2018). It is
less common for the primary samples profiled to also be assessed in
experimental assays of neuronal structure or function. Here proteomic
analysis was performed on brain extracts of 43 AD, LP-NCI, and HP-NCI
individuals, which were also used as perturbations in \emph{in vitro}
assays of neurite integrity and spontaneous neuronal firing.

With no consideration given \textit{a priori} to diagnosis or annotated
pathology, clusters of extracts established from protein expression
profiles were shown to have significantly different effects on neurite
integrity. Further comparison of these two groups revealed differences
in major elements of the ubiquitin-proteasome pathway, as well as
proteins involved in vesicle processing and regulation of apoptosis.
Conversely, individually comparing protein expression with effects
across these in vitro assays identified a modulator of dendrite
morphology (ABI1) and proteins involved in lipid metabolism (ASAH1,
GM2A), which in turn were shown to be differentially expressed across
classes of diagnosis and pathology. Given the correlated nature of the
data, definitive conclusions can not be drawn regarding which proteins
may be driving the changes in these phenotypes and which may be merely
by-standers. Nevertheless, pairing unbiased molecular profiling with
phenotypic assays can prioritize the data obtain from `omics-scale'
evaluations. This is demonstrated here with an analysis which yielded
multiple candidates and classes of proteins that warrant further
investigation into how they may be perpetuating or mitigating the
physiological changes associated with AD.

\newpage

\subsection{Methods}\label{methods-2}

\paragraph{Brain extract preparation}\label{brain-extract-preparation-1}

Tris-buffer soluble brain extract from prepared from the dissected gray
matter of cortical tissue as described in Chapter 3 Methods, using the
techniques originally reported by Shankar et al. (2008). Prior to
treatment of cultures, the extract was dialyzed 1:\(10^{4}\) v/v in
artificial cerebral-spinal fluid (aCSF) and buffer-exchanged into
cultures media.

\paragraph{\texorpdfstring{\textit{In vitro}
assays}{ assays}}\label{assays}

\textit{Neurite Integrity} Human iPS-derived neurons are established
from the YZ1 1.4 cell line using a Neurogenin-2 induction (iN)
differentiation protocol (Muratore et al. 2014; Zhang et al. 2013).
Cells were plated at DIV4 of the iN differentiation protocol at a
density of \(`5000`\) cells/well and grown in mono-culture on 96-well
glass-bottom plates (\(\mu\)Clear, Griener Bio-One, Monroe NC) coated
with Matrigel substrate (Corning Life Sciences, Corning New York) and
grown until to DIV 21 post-induction or longer prior to treatment with
brain extract. Neurite structures are assayed by serial bright-field
imaging 78 hours at 2-hour intervals using an IncuCyte live-cell
microscope (Sartorius, Michigan USA) with quantification using the
IncuCyte NeuroTrack Software Module (Sartorius). Total neurite length
per well were normalized to the values from the first 6 hours of
imaging, and a nonlinear logistic regression model was fit to the data
from the entire time course using the \texttt{nlme} function within the
R \textsf{nlme} library (R Core Team 2017; Pinheiro et al. 2018;
Pinheiro and Bates 2000).

\textit{MEA-spontaneous firing} Primary rat cortical cultures were
established from E18 pups on 96-well multi-electrode array plates (Axion
Biosystems, Atlanta GA) at a density of \(`75000`\) cells/well and grown
until to DIV 21 prior to treatment with brain extract. MEA recordings
and analysis were performed as previously described in Chapter 2.
Estimates of \(\Delta log_{10}\)Hz were obtained from a linear mixed
model of multiple experiments comparing the day-0 and day-4 recordings
with respect to individual extracts. The model was fit using the
\texttt{lme} function within the R \textsf{nlme} library (R Core Team
2017; Pinheiro et al. 2018; Pinheiro and Bates 2000).

\paragraph{Proteomic analysis}\label{proteomic-analysis}

Unlabeled proteomic analysis was performed on the TBS-soluble brain
extracts following dialysis in aCSF. Liquid chromatography coupled to
tandem mass spectrometry (LC-MS/MS) was performed by the laboratory of
Dr.~Nicholas Seyfried, Emory University using a NanoAcquity UHPLC
(Waters, Milford PA) and Q-Exactive Plus mass spectrometer
(ThermoFisher, Waltham MA) using methods previously reported (Seyfried
et al. 2017; Abreha et al. 2018). Label-free quantification (LFQ) of
proteins was performed using the MaxQuant algorithm (Cox et al. 2014).
Variance stabilization was used to normalize LFQ values across samples
using the R \textsf{vsn} library (Huber et al. 2002).

\paragraph{Statistical analysis}\label{statistical-analysis-1}

Comparisons between group were performed using a one-way ANOVA followed
by Tukey post-hoc test, unless otherwise specified. Comparing instances
of diagnosis class across protein expression clusters was performed
using Pearson's Chi-squared test. In cases of multiple comparisons,
adjusted \(p\)-values were calculated using the methods described by
Holm (1979). All analysis was performed using the R statistical
programming language. Figures and tables were generated using the R
\textsf{ggplot2}, \textsf{ggpubr}, and \textsf{kableExtra} libraries
(Wickham 2016; Kassambara 2018; Zhu 2018).

\textit{Hierarchical Clustering} Agglomerative (bottom-up) hierarchical
clustering was performed using complete linkage based on pairwise
calculation of Spearman's correlation between the vectors of protein
expression for each individual extract (Jain and Dubes 1988). Missing
values in the LFQ matrix were imputed based on columns means, however
this was only necessary for 0.8\(\%\) of observations (646 of 79163).
Hierarchical clustering was performed using the \texttt{hclust} and
\texttt{cutree} functions within the base R \textsf{stats} library.

\textit{Differential Expression} Differential expression analysis was
performed using one-way ANOVA as described by (Oberg et al. 2008).

\textit{Gene Ontology} Gene Ontology enrichment analysis of biological
process terms was based on the BioConductor human gene and GO term
databases. The enrichment analysis performed using the R \textsf{topGO}
library utilizing the \textit{weight01} algorithm that accounts for the
hierarchical structure of the GO terms and down-weighting genes in
parent terms when significant enrichment was established in subsidiary
child terms (Alexa, Rahnenführer, and Lengauer 2006; Alexa and
Rahnenfuhrer 2016)

\textit{Correlation analysis} Spearman's correlation (Spearman's
\(\rho\)) was calculated between the two numerical vectors representing
\emph{i} the assay metric (\(\%\) neurite integrity or
\(\Delta Log_{10}Hz\)) for the \(n\) individuals examined in each assay
and \emph{ii} the \(n\) protein expression values (normalized LFQ) for a
given protein in those individuals. This calculation was repeated for
each protein reliably detected across the panel of brain extracts tested
in a given assay. In order to determine the probability of each observed
correlation, the \(\rho\) value was converted to a \(z\)-score under a
random normal distribution by applying Fisher's transformation. Fisher's
transformation was applied using the \texttt{FisherZ} function within R
\textsf{DescTools} library with modifications for Spearman's \(\rho\) as
shown below: \[
z=\sqrt{\frac{n-3}{1.06}}\times F(\rho)
\]

\FloatBarrier
\newpage

\singlespacing

\section{Chapter 5: Examination of single neuron frequency and potential
dynamics through the use of spike sorting
analysis}\label{chapter-5-examination-of-single-neuron-frequency-and-potential-dynamics-through-the-use-of-spike-sorting-analysis}

\doublespacing

\subsection{Introduction}\label{introduction-3}

Multi-electrode array (MEA) recordings provide a means of generating
rich data sets of electrophysiological activity from a large number of
neurons over comparatively long time intervals of days to weeks.
However, resolving the activity of individual neurons within this data
is challenging due to the poor spatial resolution of MEA data
acquisition. Performing recordings within dense neuronal cultures
results in a single recording electrode conflating the potentials
arising from multiple neurons. The fixed position of the recording
electrodes of MEAs within the culture substrate does not allow for
preferential positioning of the detector on or within a single neuron
which would enable exclusive detection of electrical potentials from
that cell (as is possible with other techniques such as patch-clamping
or shape-electrode recordings). Further, while MEAs technically are
arrays, the density of detectors is sparse on a cellular scale,
effectively rendering each electrode a point detector, compared to the
dense array of optical detectors within a digital camera. Therefore as
compared to microscopy based techniques such as fluorescence calcium
imaging where it is trivial to identify individual cells within the
Cartesian field of pixels, similar spatial segregation of neurons from
MEA recordings is not possible with currently available devices.

Despite these challenges, it is possible to deconvolute the mixed
signals of multiple neurons detected on each recording electrode of a
multi-electrode array through the implementation of spike sorting
analysis. This class of analyses is predicated on the fact that, if
voltage potentials within neuronal cultures are measured with
sufficiently high temporal frequency, it is possible to capture the
discrepancies between voltage waveforms arising from different neurons
recorded by a single external electrode due to the difference in spatial
orientation of the neurons relative to the electrode. The exercise of
spike sorting is inherently made more difficult by the absence of
\emph{ground-truth}, knowledge of exactly how many neurons are
contributing signal that is being recorded by given electrode.
Extraction of shape parameters from waveforms arising from a population
of neurons and binning them by similarity can allow for attribution of
individual waveforms to a cell of origin, has been established viable
method for identifying the activity of single neurons from
extra-cellular recordings. (Buzsáki, Anastassiou, and Koch 2012; Gibson
2012)

While spike sorting analyses are well established in disciplines of
computational neuroscience and neuro-prosthetics, (reviewed in Gibson
2012), their integration with studies using multi-well \emph{in vitro}
MEAs and experimental models of AD is less common. This is in part due
to the lack of integrated workflows allowing for processing of the large
datasets generated by these instruments, which consist of hundreds of
thousands to millions of voltage waveforms, with the computationally
intensive spike sorting algorithms.

The application of spike sorting analysis to MEA recordings is
attractive as it would allow for refinement of the metrics for the
frequency of action potential generation within populations of neurons
(such as those used thus far in this study) to be resolved to the level
of individual neurons. It potentially may allow for the additional
assessment of frequency-independent phenotypes, such as changes in the
amplitude of potentials emitted from firing neurons. While the data
obtained from MEA recordings are not capable of assessing resting
membrane potentials directly, the high temporal sampling rate of voltage
potentials (12.5kHz), is sufficient to detect decreases in action
potential amplitude. Measuring changes in the amplitude of voltage
potentials from the action potentials generated by a large number of
individual cells may allow for testing of hypotheses regarding the
pathophysiology of A\(\beta\) and other neurotoxic species that would be
difficult using other experimental techniques.

While the overwhelming preponderance of biochemical, genetic, cellular,
and clinical observations support the hypothesis that amyloid-beta
(A\(\beta\)) underlies the pathophysiology of Alzheimer's Disease (AD),
the specific mechanisms by which A\(\beta\) is toxic to neurons remains
an area of debate and on-going investigation. One of the many proposed
theories, termed the `channel hypothesis', posits that A\(\beta\) is
capable of forming ion channels across the cellular membrane, causing
the dysregulation of cation flow into neurons, which in turn alters
membrane potentials and promotes Ca\(^{2+}\)-dependent neuronal toxicity
(Kagan et al. 2002). While controversial, this theory is supported by a
broad body of evidence that oligomers of A\(\beta\) peptides form
pore-like structures in membranes similar to ion channels, and are
capable of causing a disruption of ionic equilibrium within cells
(Arispe, Rojas, and Pollard 1993; Arispe, Pollard, and Rojas 1993;
Lashuel et al. 2002; Micelli et al. 2004; Quist et al. 2005; Shafrir et
al. 2010; Di Scala et al. 2016; Serra-Batiste et al. 2016; Kandel et al.
2017). This model suggests an analogous mechanism of pathology between
that of A\(\beta\) and bacterial cytolysins, which A\(\beta\) oligomers
have been shown to share structural similarity with (Lashuel et al.
2002; Kayed et al. 2009). However, the majority of the evidence
supporting this pore-forming behavior of A\(\beta\) has been generated
in either non-neuronal cell models or synthetic membrane structures.
Thus, it is unclear how the proposed changes in membrane potentials
resulting from these ``amyloid-pores'' would manifest in functioning
neurons.

Electrophysiological studies by others have found that neurons in AD
mouse models exhibit partially depolarized resting membrane potentials
and lower action potential amplitudes (Minkeviciene et al. 2009; Perez,
Ziburkus, and Ullah 2016), consistent with a model of increased membrane
leakage (Perez, Ziburkus, and Ullah 2016). Further, the lower afferent
stimulations required to elicit action potentials in these partially
depolarized neurons could explain the increased prevalence of epileptic
symptoms observed in AD patients (Amatniek et al. 2006; Minkeviciene et
al. 2009). Additionally, animal models lack BACE-1, and thus unable to
generate A\(\beta\), show the opposite phenotype of increased action
potential amplitudes compared with wild type animals (Hu et al. 2010).

However, other studies examining the effects of A\(\beta\) on neuronal
membranes in dissociated cultures and organotypic slices have reported
that A\(\beta\) does not appear to compromise membrane integrity. This
studies conclude that there is no changes in intrinsic membrane
properties following application of A\(\beta\) as determined by
assessing parameters such as membrane potential and input resistance
(Nimmrich et al. 2008; Tamagnini et al. 2015).

Additionally, it is important to recognize that a number of mechanisms
influencing ionic gradients across the membrane that would result in
decreased action potential amplitude. In addition to increased
non-specific conductance of cations, other factors such as: \emph{i}
impedance of Na\(^+\) influx by blockage of voltage-gated sodium
channels, \emph{ii} decreases in extracellular Na\(^+\) concentration,
or \emph{iii} selective increase in K\(^+\) conductance would all result
in reduced action potential amplitudes, (reviewed in Purves et al.
2017).

To summarize, phenotypes of a partially depolarized resting membrane
potential and decreased action potential amplitude would appear
consistent with the expected behavior of a neuron with a perforated
membrane, due to the decreased gradient of cations between the cytosol
and extra-cellular space. Therefore, further refinement of MEA
recordings through the use of spike-sorting analysis could allow for
direct testing of whether exposure to A\(\beta\) species results in the
attenuation of action potential amplitudes predicted by a `pore-forming'
model of A\(\beta\), by being able to reconstruct changes in the
magnitude of voltage potentials recorded from individual neurons over
time, coinciding with exposure to A\(\beta\) species.

To this end, an open-source analysis pipeline for spike-sorting was
constructed, capable of scaling to accommodate the large numbers of
electrodes and quantity of voltage waveform data collected using
multi-well MEA systems. Using these tools, analyses of recording data
from neuronal cultures treated with a known pore-forming bacterial
cytolytic toxin, \(\alpha\)-hemolysin, were performed to determine
whether membrane perforation would result in decreased action potential
amplitudes. Finally, a meta-analysis of experiments assessing the effect
of human brain extracts and recombinant A\(\beta\) on spontaneous firing
of neurons was performed to determine whether A\(\beta\) species
elicited a change in action potential amplitudes.

\newpage

\subsection{Results}\label{results-2}

\subsubsection{Implementation of Unsupervised Spike Sorting for MEA
recordings}\label{implementation-of-unsupervised-spike-sorting-for-mea-recordings}

Before initiating development of the spike sorting analysis pipeline, a
review of commercially available software solutions capable of
performing this analysis was performed. The most suitable product
available at the time was \emph{Offline Sorter} (Plexon, Dallas TX) and
has been used in the analysis of other MEA studies (Slomowitz et al.
2015; Vertkin et al. 2015). This product was purchased and evaluated for
performance of analyzing MEA recording data. This product is intended
for use of analyzing electrophysiological data, including voltage
waveforms like those recorded by MEA instruments. Additionally, it has
the capability to implement similar clustering algorithms to those
ultimately employed in the analysis pipeline developed for this study.
Unfortunately, this software was \emph{not} designed for the analysis of
multi-channel recordings and as such had critical limitations that made
it non-viable for use in this study, both with regard to processing and
throughput of data analysis. First, this software is only capable of
running on a single CPU, precluding the ability to take advantage of
distributed computing resources available, and thus greatly increasing
the processing times of analysis. Consequently, the data from each of
the 768 electrodes present on the Axion MEA instrument needed to be
analyzed in a serial fashion, one after another. Second, the software
required the data to be analyzed in a recording centric fashion, which
as demonstrated in Figure \ref{singleVsMultiRecSortPlot} and discussed
in greater detail below, greatly affects the accuracy of the clustering.
Finally, the software demonstrated an inability to analyze recording
datasets with \(>10^4\) events. After determining that Plexon
\emph{Offline Sorter} was not an adequate solution for the requirements
of the analysis, and finding no other readily available appropriate
options, the decision was made to develop an analysis pipeline in-house
for this study.

The development criteria for the analysis pipeline were: \emph{i}
flexibility to analyze data collected during separate recording
sessions, \emph{ii} ability to perform clustering analysis in an
unsupervised fashion, \emph{iii} extendable to parallel processing in a
distributed computing environment such as high-performance computing
clusters, and \emph{iv} able to utilize open-source software to enable
distribution and use by other researchers.

For these analyses, all recordings were performed in 96-well MEA plates
using Maestro recording instrument and AxIS acquisition software (Axion
Biosciences, Atlanta GA). The recording sampling rate was 12.5kHz, and
the spike detection threshold was set at 5.5 standard deviations from
the root-mean square of the background voltage potential calculated over
a 10ms moving window. Upon recording of a voltage above the detection
threshold (spike event), 3ms of recording data was retained, 1msec
proceeding and 2msec following the timing of the threshold crossing
event. With a sampling rate of 12.5kHz, the 3ms window around each
threshold event results in the retention of 38 momentary voltage
recordings for each spike event. These 38 momentary voltage recordings
are reconstructed into a waveform. From the reconstructed waveform, an
array of shape parameters are calculated for each event, including:
maximum voltage (\emph{peak}), minimum voltage (\emph{valley}), waveform
amplitude (\emph{peak-valley range}), time interval between \emph{peak}
and \emph{valley}, area under the curve (\emph{auc}), and non-linear
energy (NLE), a parameter describing the `sharpness' around the maxima
and minima of the waveform (Kim and Kim 2000).

The spike sorting process begins by aggregating all of the spike events
detected by a given electrode during all recordings of an experiment, as
shown in Figure \ref{MSclusteringPlot}A. Based on the waveform
parameters of all spike events collected on a given electrode, principal
component analysis (PCA) is performed, to minimize the dimensionality of
the shape parameters being considered. PCA is commonly used in spike
sorting within retrospective analyses of recordings such as that being
performed here. This is because the variance between waveforms of
differing origins is usually largely explained by the first 2 or 3
principal components; and secondly, the prerequisite condition for PCA
of a complete dataset is met, as opposed to the alternative context of
real-time, \emph{online}, spike sorting required in most neuroprosthetic
applications (Gibson 2012). For this study, all spike events were
projected along the first two principal components as shown in Figure
\ref{MSclusteringPlot}B.

Once projected along principal components, clustering of the waveforms
is performed to group those exhibiting the highest degree of similarity.
Given the large number of recording channels (electrodes), 768 per
plate, with the potential of using multiple plates per experiment,
across multiple experiments, number of iterations of this analysis would
quickly expand into the 1000s or more. For that reason it proved
necessary to automate this process by performing the clustering in an
unsupervised process. To this end, the mean-shift algorithm was applied
for this clustering analysis, implemented through use of the
\textsf{MeanShift} library within the R programming language (Fukunaga
and Hostetler 1975; Comaniciu and Meer 2002; Ciollaro and Wang 2016).
The use of mean-shift clustering has particular advantages for the
analysis of MEA data over other non-hierarchical clustering techniques
such as, \(k\)-means, since mean-shift does not require an \emph{a
priori} estimate of the number of groups in the dataset. This is an
important practical consideration given that the number of cells that
will be recorded on a single electrode is unknown and variable. While
others have implemented semi-supervised clustering methods relying on
\(k\)-means for the purpose of spike sorting in MEA recordings
(Slomowitz et al. 2015), this requires manual intervention to indicate
the number of distinct waveform shapes present on each channel. Instead,
the mean-shift algorithm operates by applying a density function to the
observed data and identifying the local maxima across the distribution
of the observations, all observations within the dataset are then mapped
or `\emph{shifted}' to the nearest local maxima. The only variable that
needs to be supplied to the algorithm is the bandwidth term (\(h\)) to
the kernel density estimator (KDE) function to determine the degree of
curvature supplied to the density function. For this analysis, a
Gaussian density function was applied, while the value of the KDE was
determined empirically by applying a range of values to sample
clusterings of a teach-set of electrodes to establish a value which
provided accurate segregation of the waveforms, resulting in the use of
\(h = 1.5\). The influence of the KDE in the mean-shift algorithm is
demonstrated in Figure \ref{MSclusteringPlot}C, in which lower values of
\(h\) result in insufficient smoothing of the density function,
resulting in an over proliferation of local maxima, while higher values
of \(h\) result in over smoothing of the density function resulting in
only a single maximum.

While the cluster centroids, as shown in Figure \ref{MSclusteringPlot}D,
occupy a finite position with respect to the eigenvectors describing the
principal components describing the waveforms shape they may or may not
coincide with any of the points representing individual waveforms.
Therefore in order to map the cluster centroid back to the original
shape parameters used in deriving the clusters themselves, the waveform
closest to the centroid is determined by calculating the minimum
Euclidean distance Figure \ref{MSclusteringPlot}E. The shape parameters
of this waveform closest to the centroid can thus be used for evaluating
the mean shape differences between clusters.

\begin{figure}

{\centering \includegraphics{figures/mean-shift_clustering_plots-1} 

}

\caption{\label{MSclusteringPlot} {\small \textbf{Demonstration of Spike Sorting.} \textbf{A} Waveforms of } 269 {\small spike events captured by a single electrode across } 7 {\small, 30min recordings. \textbf{B} Projection of } 269 {\small spike events along principle first two principal components calculated from waveform shape parameters, blue lines indicate density contours of events. \textbf{C} Varying number of cluster centroids identified by mean-shift clustering as a function of bandwidth value \textit{h} of KDE. Cluster centroids indicated in red, value of kernel density estimate denoted above each panel. \textbf{D} Spike events colored by cluster membership as determined by 
mean-shift clustering performed with a KDE \textit{h}=1.5, cluster centroids are indicated in 
black. \textbf{E} Mapping of cluster centroids (black circle) to nearest spike (filled circle) event by minimum Euclidean distance.
\textbf{F} Waveforms of spike events colored by cluster membership. }}\label{fig:mean-shift_clustering_plots}
\end{figure}

\paragraph{Spike sorting in a recording-dependent versus
recording-independent
fashion}\label{spike-sorting-in-a-recording-dependent-versus-recording-independent-fashion}

Given the longitudinal nature of the recordings performed in this study,
it was important to assess the effect of performing the mean-shift
clustering in a \emph{recording-dependent} fashion, in which the
clustering is performed several times, once for each set of single
recording data, or in a \emph{recording-independent} fashion, in which
the clustering is performed once on the data from all recordings. In the
latter case, the meta-data describing the recording origin of each event
is maintained, so that the events can still be parsed by recording. The
result of these two approaches is shown in Figure
\ref{singleVsMultiRecSortPlot} in which 269 spikes recorded by a single
electrode across 7 recordings were clustered within each recording
(\emph{top row}), or across all recordings (\emph{bottom row}). The
limitation of the \emph{recording-dependent} approach is exposed as a
consequence of the uneven distribution of spike events across
recordings. During the fifth recording in which 92 spikes are recorded,
the \emph{recording-dependent} method identifies the same number of
clusters, three, as identified in the \emph{recording-independent}
analysis. However, during the third recording in which only 35 spikes
are recorded, sporadic events that are otherwise aggregated into cluster
\#3 (\emph{green}) in the \emph{recording-independent} analysis are
over-segregated in the clusters \#4 (\emph{purple}), \#5 (\emph{dark
green}), \#6 (\emph{orange}) in the \emph{recording-dependent} method.
The other limitation to the \emph{recording-dependent} approach is that
since the values assigned denoting cluster membership are arbitrary,
clusters that occupy the same position in principal component space are
assigned to different cluster values in different recordings, presenting
an additional challenge of correctly associating clusters hosting
similarly shaped waveforms based on the cluster identifier alone.

\pagebreak
\blandscape

\begin{figure}

{\centering \includegraphics{figures/spike_sorting_single_vs_multiple_rec_plot-1} 

}

\caption{\label{singleVsMultiRecSortPlot}{\small\textbf{Spike sorting within single recordings, verse independent of recording.} Result of cluster assignment of spike events recorded from a single electrode across} 7 {\small, 30 minutes recordings. Mean-shift clustering performed independently on spike events from each recording (\textit{above})
or across pooled spike events from all recordings (\textit{below}).}}\label{fig:spike_sorting_single_vs_multiple_rec_plot}
\end{figure}

\elandscape
\newpage

\subsubsection{Stochastic nature of firing patterns of individual spike
clusters}\label{stochastic-nature-of-firing-patterns-of-individual-spike-clusters}

Having implemented the spike sorting analysis, it is possible to
evaluate the firing frequencies of individual spike clusters, providing
a higher resolution of the spontaneous firing than obtained with the
well-level described in Chapters 2 and 3.

The firing frequencies of 237 spike clusters identified within 32 wells
across 4 untreated culture preparations, are shown in Figure
\ref{stochasticFiringPlot}A. These data show the firing frequencies as
average \(\frac{spikes}{sec}\) (Hz) from 30min recordings taken on three
consecutive days. Across the 4 experiments, the timing of culture media
changes was coordinated with respect to the timing of recordings to
minimize differences in firing across experiments as a consequence of
the state of media exhaustion.

Examining firing frequencies at the level of individual spike clusters
in Figure \ref{stochasticFiringPlot}A, the patterns of firing over time
appear largely stochastic. First, the firing frequencies, as indicated
by increasing values from \emph{white} \(\rightarrow\) \emph{black} of
clusters differ dramatically with respect to each other across clusters
within recordings (\emph{along rows}) and within the same cluster across
recordings (\emph{down columns}), from a minimum of 0 Hz to a maximum of
15.96 Hz. Additionally, while all clusters are observed firing in at
least one of the recordings, the number of instances in which clusters
are inactive during any one 30min recording interval in which the
culture is observed is substantial. As shown in the cumulative density
plot in Figure \ref{stochasticFiringPlot}B, the proportion of inactive
clusters during a given recording ranges from 24\% to 41\%.
Additionally, while the majority of spike clusters exhibit firing
frequencies are \textless{} 1Hz, there are a limited number of highly
active clusters. The combination of a large proportion of inactive
clusters and a limited number of highly active clusters results in a
highly skewed distribution of firing frequencies as a shown in the left
panel of Figure \ref{stochasticFiringPlot}C.

The \emph{zero-inflated} nature of the cluster firing frequencies
presents a challenge when aiming to describe differences in
distributions of cluster frequencies, such as in response to
experimental conditions. The strategy of applying a log transformation
to the frequency values as described in Chapter 2 for well-level
frequency data is not applicable in this instance, since the result is
not a symmetrical normal distribution as is achieved in that case, but
rather an asymmetrical bi-modal distribution as shown in the right panel
of Figure \ref{stochasticFiringPlot}C. However, since the values of the
firing frequencies can theoretically assume values from 0
\(\rightarrow\) \(\infty\), an alternative approach is to model the
distribution of cluster firing frequencies with a Gamma (\(\Gamma\))
distribution. Using this method, changes in the firing frequencies
across distributions of clusters under different experimental conditions
can be assessed using Gamma-distributed generalized linear models
(\(\Gamma\)-GLMs) in order to obtain estimates for the average firing
frequency in each condition (McCullagh and Nelder 1989). This approach
is demonstrated in Figure \ref{stochasticFiringPlot}D, in which a
\(\Gamma\)-GLM was fit to the data model frequency as a function of
recording, and the resulting \(\Gamma\) distributions predicted by the
model are overlaid on the distribution of clustering firing frequencies
observed in each recording. The differences in firing frequencies across
recordings are shown in Figure \ref{stochasticFiringPlot}E comparing the
\(\Gamma\) distributions predicted for each recording and in Figure
\ref{stochasticFiringPlot}F comparing the differences in average Hz
\(\pm\) SE between recordings based on the estimates obtained from the
fitted model.

\begin{figure}

{\centering \includegraphics{figures/stochastic_firing_of_clusters_plot-1} 

}

\caption{\label{stochasticFiringPlot}{\small\textbf{Stochastic firing patterns of spike clusters.} \textbf{A} Firing frequencies (Hz) of } 237 {\small spike clusters identified in recordings of} 156 {\small electrodes in} 32 {\small replicate wells across} 4 {\small mature, untreated cultures (DIV20-45). Data from 30min recordings across three consecutive days. Spike clusters within common wells and experiments indicated by hash bars. \textbf{B} Cumulative distribution of spike cluster firing frequencies across recordings, arrows denote y-intercept, indicating proportion of inactive clusters during each recording. \textbf{C} Distribution of firing frequencies Hz across all 3 recordings on a linear (\textit{left}) or log$_{10}$-transformed scale (\textit{right}). \textbf{D} Distribution of firing frequencies across each recording overlaid with estimated distribution of firing frequencies obtained from $\Gamma$-GLM. \textbf{E} Overlay comparing estimated distribution of firing frequencies from each recording. \textbf{F} Average firing frequency per recording estimated from $\Gamma$-GLM, presented as mean $\pm$ SE.}}\label{fig:stochastic_firing_of_clusters_plot}
\end{figure}

\paragraph{Paradigms of spike cluster firing
patterns}\label{paradigms-of-spike-cluster-firing-patterns}

Given the stochasticity observed in the firing patterns of spike
clusters over successive recordings, the firing pattern of any one spike
cluster can be thought of as adhering to one of four paradigms:
\emph{Persistent}, \emph{Lost}, \emph{Recovering}, or \emph{Emergent},
as depicted in Figure \ref{clusterParadigmPlot}: \emph{i}
\emph{Persistent}, pertaining to clusters active during all recordings
of interest; \emph{ii} \emph{Lost}, pertaining to those that are active
during initial recordings but not observed in subsequent recordings,
either as the result of chance or the consequence of an experimental
perturbation; \emph{iii} \emph{Recovering}, pertaining to those that are
active during initial recordings, inactive during interim recordings,
yet are observed again in subsequent recordings, similarly either as the
result of chance or the consequence of an experimental perturbation; and
lastly, \emph{iv} \emph{Emergent}, pertaining to those clusters that are
absent during initial recordings but observed during subsequent
recordings. The \emph{Emergent} class harbors the caveat that these
clusters may have been absent during initial recordings due to chance,
or that the cluster represent waveforms from a cell active during an
initial recording but then as a result of experimental perturbation
produces waveforms with sufficiently different shape characteristics as
to result in the identification of a \emph{distinct} cluster than those
produced originally. In this potential scenario, the analysis would
identify two distinct clusters, mutually exclusive with respect to time,
in which one is \emph{Lost} and the other is \emph{Emergent}. These
classifications of firing patterns are not purely semantic, since there
are practical implications for which classes can contribute to the
analysis of different phenotypes. For frequency-\emph{dependent}
phenotypes, such as rates of firing, the absence of activity is itself
relevant. However, for frequency-\emph{independent} phenotypes, such as
waveform amplitude (discussed below), the phenotype is dependent on
activity such that the analysis needs to be restricted to those
\emph{Persistent} clusters that present during all recordings when these
frequency-\emph{independent} phenotypes are being assessed.

\begin{figure}

{\centering \includegraphics{/Volumes/GoogleDrive/My Drive/Young-Pearse Lab/Manuscripts/Dissertation/20180807_ClusterClass_Diagram} 

}

\caption{\label{clusterParadigmPlot}{\small \textbf{Paradigms of spike cluster firing patterns.} Actively firing clusters indicated by filled circles, and inactive clusters by open circles}}\label{fig:paradigms_of_cluster_activity}
\end{figure}

\subsubsection{Effect of pore-forming toxins on cell
viability}\label{effect-of-pore-forming-toxins-on-cell-viability}

In order to model the effects of membrane perforation on the spontaneous
firing frequency and magnitude of voltage potentials generated by
neurons, cultures were treated with the well characterized pore-forming
toxin, \(\alpha\)-hemolysin (\(\alpha\)HL) derived from
\emph{Staphylococcus aureus} (Parker and Feil 2005). The non
pore-forming, \(Na^+\)-channel blocker tetrodotoxin (TTX) was used for
comparison (Narahasi, Moore, and Scott 1964). In order to determine the
maximum tolerated dose of these two toxins, a cell viability assay was
performed to assess the induction of cell death in cultures following
treatment with a titration of each agent. Cortical cultures were treated
with titrations of \(\alpha\)HL (0.25 - 16 \(\mu\)g/mL) and TTX (0.001 -
1 \(\mu\)M) for 24hr, after which viability was assessed by detection of
lactate dehydrogenase (LDH) released into the culture media. As shown in
Figure \ref{TTXalphaViabilityPlot}, \(\alpha\)HL was tolerated up to
4\(\mu\)g/mL causing an \textasciitilde{} 30\% reduction in viability,
while TTX had negligible effect on viability even at the highest dose
tested. Based on these data, \(\alpha\)HL was used at a maximum
concentration of 4\(\mu\)g/mL in all subsequent experiments.

\begin{figure}

{\centering \includegraphics{figures/TTXalphaHL_percent_viability_plot-1} 

}

\caption{\label{TTXalphaViabilityPlot}{\small\textbf{Viability of TTX and $\alpha$HL treated cultures.} Percent viability within cortical cultures following 24hr treatment with TTX (\textit{left}), or $\alpha$HL (\textit{right}) as assessed by LDH release. Data presented as mean $\pm$ SE, n = 5 per condition.}}\label{fig:TTXalphaHL_percent_viability_plot}
\end{figure}

\subsubsection{\texorpdfstring{Examining changes in firing frequency in
response to treatment with pore-forming toxin, recombinant
A\(\beta_{42}\), and human brain
extract}{Examining changes in firing frequency in response to treatment with pore-forming toxin, recombinant A\textbackslash{}beta\_\{42\}, and human brain extract}}\label{examining-changes-in-firing-frequency-in-response-to-treatment-with-pore-forming-toxin-recombinant-abeta_42-and-human-brain-extract}

\paragraph{\texorpdfstring{Effect of TTX and
\(\alpha\)HL}{Effect of TTX and \textbackslash{}alphaHL}}\label{effect-of-ttx-and-alphahl}

In order to assess whether these concentrations of TTX and \(\alpha\)HL
were sufficient to cause an effect on the generation of action
potentials, the frequency of spontaneous firing was assessed in cultures
treated with a titration of TTX or \(\alpha\)HL at 24hr following
treatment, and again at 24hr following the removal of treatment
(\emph{washout}). The data were modeled using a \(\Gamma\)-GLM,
estimating the average Hz across the spike clusters within each
condition as a function of treatment and recording. Differences in
firing frequencies between conditions were assessed using a general
linear hypothesis test (Hothorn, Bretz, and Westfall 2008). As shown in
Figure \ref{TTXalphaHLdoseHzPlot}, treatment with 0.1\(\mu\)M TTX
(0.0266 \(\pm\) 0.00121 Hz ) and 1\(\mu\)M TTX (0.000556 \(\pm\) 2.2e-05
Hz ) as well as 4\(\mu\)g/mL \(\alpha\)HL (0.0586 \(\pm\) 0.00225 Hz)
resulted in a significant decrease in firing frequency compared to
treatment with media alone (0.24 \(\pm\) 0.00809 Hz), p = 0.0148,
0.000135, and 0.0088 respectively. While 2\(\mu\)g/mL \(\alpha\)HL
(0.127 \(\pm\) 0.00435 Hz) also appeared to induce a decrease in firing
relative to control, the difference was not significant, p = 0.234.
Following the washout of treatment, the TTX treated conditions show a
complete restoration of firing activity, returning to levels comparable
with that of media alone, while firing within the 4\(\mu\)g/mL
\(\alpha\)HL treatment condition was still significantly reduced
compared to media alone, p = 0.0139. While the firing frequency within
the 4\(\mu\)g/mL \(\alpha\)HL condition increased following the removal
of treatment, to 0.0984 \(\pm\) 0.00378 Hz from 0.0586 \(\pm\) 0.00225
Hz, suggestive of some recovery, this increase was not significant, p =
0.8201

The observed decrease in firing frequency amongst the 4\(\mu\)g/mL
\(\alpha\)HL treated condition following treatment could be partially
attributable to the effect on cell viability at this concentration
(Figure \ref{TTXalphaViabilityPlot}), given that the effect was seen
both in the presence of the toxin and sustained following its removal.
This analysis examined the average firing frequency across all clusters
active during each recording, and not specifically the change in firing
frequency amongst cluster that showed \emph{persistent} activity across
recordings. Performing the analysis on the aggregate of all clusters is
necessary to capture the effect of a treatment with the magnitude of
effect of 1\(\mu\)M TTX, in which there are no active clusters in the
treated condition in which the comparison can be made.

\begin{figure}

{\centering \includegraphics{figures/TTXalphaHL_cluster_analysis_Hz_dose_response_barplot-1} 

}

\caption{\label{TTXalphaHLdoseHzPlot}{\small \textbf{Spike cluster firing frequencies within TTX, $\alpha$HL treated cultures.} Firing frequencies (Hz) of spike clusters within cortical cultures treated with titration of TTX (green) or $\alpha$HL (blue). Frequencies from recordings across 3 times points: untreated (open bars), 24hr post treatment (filled bars), and washout - 24hr following removal of treatment (half-filled bars). Treatment of } 22 - 39 {\small replicate wells per condition across four experiments. Data presented as mean $\pm$ SE Hz obtained from estimates of $\Gamma$-GLM modeling frequency as function of treatment and recording. Differences in frequencies assessed by general-linear hypothesis test, significance indicated by brackets and associated p-values.}}\label{fig:TTXalphaHL_cluster_analysis_Hz_dose_response_barplot}
\end{figure}

\paragraph{\texorpdfstring{Effect of recombinant oligomers of
A\(\beta_{42}\)}{Effect of recombinant oligomers of A\textbackslash{}beta\_\{42\}}}\label{effect-of-recombinant-oligomers-of-abeta_42}

As previously discussed in Chapter 3, treatment of cultures with
super-physiological concentrations of recombinant A\(\beta_{42}\) showed
no effect on spontaneous firing examined at the well-level in this
culture model. Previous studies reporting that recombinant
A\(\beta_{42}\) did reduce spontaneous firing frequency in a MEA-based
culture model, assessed the effect at the individual electrode level
rather than aggregated frequency of events across the electrode array
(Kuperstein et al. 2010; Charkhkar et al. 2015). While the effect size
on firing frequency reported in these studies was sufficiently
substantial that it would likely have been apparent when integrated
across the entire array, it is possible that a higher level of
resolution is necessary to detect the influence of recombinant
A\(\beta_{42}\) on spontaneous firing in our experimental system. To
test that possibility, the data from that experiment was re-examined
following spike sorting, to determine whether there was a change in the
firing frequency at the cellular level (spike cluster), a degree of
resolution even greater than that of the individual electrode. However,
as shown in Figure \ref{rAb42doseHzPlot}, even when examined at this
level, there is no significant decrease in spontaneous firing amongst
the A\(\beta_{42}\) treated conditions.

\begin{figure}

{\centering \includegraphics{figures/recombinant_Ab42_cluster_analysis_Hz_barplot-1} 

}

\caption{\label{rAb42doseHzPlot}{\small Spike cluster firing frequencies within recombinant A$\beta_{42}$ treated cultures. Firing frequencies (Hz) of spike clusters within cortical cultures treated with titration of recombinant A$\beta_{42}$. Recordings of two times points: day-0 untreated (open bars) and day-4 post treatment (filled bars). Treatment of} 12 {\small replicate wells per condition. Data presented as mean $\pm$ SE Hz obtained from estimates of $\Gamma$-GLM modeling frequency as function of treatment and recording. Differences in frequencies across coniditons assessed by general-linear hypothesis test, no significant differences found.} }\label{fig:recombinant_Ab42_cluster_analysis_Hz_barplot}
\end{figure}

\paragraph{Effect of human brain
extract}\label{effect-of-human-brain-extract}

Lastly, the effect of human brain extract on spontaneous firing at the
level of spike clusters was examined at 4 days following the addition
treatment, and again 2 days following the removal of treatment
(\emph{washout}). As shown in Figure \ref{brainExtractHzPlot}, within 4
days of treatment the firing frequency of spike clusters within cultures
treated with extract from an AD individual ALB01 (0.059 \(\pm\) 0.0013
Hz) is significantly deceased compared to treatment with either media
alone (0.455 \(\pm\) 0.0086) or extract from Low-pathology,
non-cognitively impaired (LP-NCI) individual MGH1887 (0.296 \(\pm\)
0.0075), p = (4.72e-10) and (6.54e-09) respectively. Meanwhile,
treatment with extract from individual MGH1887 does not induce a
significant decrease in firing compared with media alone, p = (0.843)

Interestingly, following the removal of treatment, there is a
significant increase in firing frequency within the AD extract treated
condition, 0.201 \(\pm\) 0.0042Hz 2-day following washout compared with
0.059\(\pm\) 0.0012Hz following 4-days in the presence of brain extract
treatment, p = 5.8e-06. Of the 563 unique spike clusters identified
amongst the ALB01 treated cultures across all recordings, 211 clusters
were active during the washout recording, as opposed to only 136 in
during exposure (p = 1.67e-06, Fisher's test). This suggests that the
increase in firing frequency with the ALB01 condition during washout is
due to restoration of activity with cells \emph{recovering} from
exposure to AD brain extract rather than an increase in firing frequency
amongst residual surviving neurons.

\begin{figure}

{\centering \includegraphics{figures/brain_extract_cluster_analysis_Hz_barplot-1} 

}

\caption{\label{brainExtractHzPlot}{\small Spike cluster firing frequencies within human brain extract treated cultures. Firing frequencies (Hz) of spike clusters within cortical cultures treated with human brain extract from AD individual ALB01 (\textit{dark green}) and LP-NCI individual MGH1887 (\textit{orange}). Recordings from three times points: untreated (open bars), day-4 post treatment (filled bars), and day-2 following removal of treatment, washout (half-filled bars). Treatment of (} 104 {\small replicate wells, across} 9 {\small experiments) for media alone, (} 98 , 9 {\small) for ALB01, and (} 50 , 4 {\small) MGH1887. Data presented as mean $\pm$ SE Hz obtained from estimates of gamma-GLM modeling frequency as function of treatment and recording. Differences in frequencies assessed by general-linear hypothesis test, significance indicated by brackets and associated p-values. Significant differences between media alone:ALB01 (\textit{p =}} 1.3e-05 {\small) and MGH1887:ALB01 (\textit{p =}} 0.0062 {\small) at washout not indicated on plot.}}\label{fig:brain_extract_cluster_analysis_Hz_barplot}
\end{figure}

\blandscape

\begin{figure}

{\centering \includegraphics{figures/cluster_freq_amp_comparison_plot-1} 

}

\caption{\label{freqAmpComp}{\small \textbf{Comparison of firing frequency and waveform amplitude.} \textit{Left}, Firing frequency (Hz) and waveform amplitude ($\mu$V) for } 5506 {\small spike clusters within untreated cortical cultures. Waveform amplitude determined by cluster centroid.\textit{Center}, distribution of spike cluster waveform amplitudes, dash line indicating 30 $\mu$V threshold used for selecting clusters for assessing changes in waveform amplitude in response to treatment.\textit{Right}, examples of low amplitude (\textit{teal}), mid amplitude (\textit{gold}) and high amplitude (\textit{persimmon}) amplitude waveforms.}}\label{fig:cluster_freq_amp_comparison_plot}
\end{figure}

\elandscape
\newpage

\subsubsection{Investigating the effect of pore-forming toxin and human
brain extract on voltage potential
amplitudes}\label{investigating-the-effect-of-pore-forming-toxin-and-human-brain-extract-on-voltage-potential-amplitudes}

Having examined changes in firing frequency at the cluster level, the
next question was whether it is possible to assess disruptions in
membrane integrity by assessing changes in the amplitude of voltage
waveforms emitted from the generation of action potentials.

The relationship between the firing frequency and waveform amplitude of
spike clusters is shown in the left panel of Figure \ref{freqAmpComp}.
As expected there is minimal correlation between the rate at which a
neuron fires and the detected amplitude of the resulting voltage
potential. This lack of correlation stems from the different factors
underlying the generation of theses properties and the manner in which
they are detected in this system. The firing frequency of a neuron,
absent any exogenous experimental stimulus, arises from the intrinsic
properties of that particular class of neuron and the excitatory and
inhibitory inputs it receives from other neurons that have formed
synapses on it (Bean 2007). Conversely, while the action potentials of
all neurons are expected to be of the magnitude of \textasciitilde{}
70-100mV with respect to the charge disparity between the intra-cellular
and extra-cellular space, the potential that is detected by the
extra-cellular recording electrode of the MEA is dramatically influenced
by the physical constraints of the system. If a firing neuron is modeled
as a point source of charge, the extra-cellular potential \(V_e\) could
be approximated from Coulomb's law, such that:

\[
V_e = \frac{I}{4\pi d \sigma}
\]

where I is amplitude of a point source of current (A), and d is the
distance from the source (meters), and \(\sigma\) is the conductivity
(Siemens/meters) of extracellular space (Gold, Henze, and Koch 2007).
However, since neurons are better approximate by cylinders whose length
is much greater than their transectional radius, the model is refined by
the Linear Source Approximation (LSA) described by Holt and Koch (Holt
and Koch 1999). Thereby approximation of the extra-cellular potential is
instead modeled as:

\[
V_e = \frac{I}{4\pi \sigma \Delta s}log\left| \frac{\sqrt{h^2+r^2}-h}{\sqrt{\textit{l}^2-r^2}-\textit{l}} \right|
\]

where \(\Delta s\) is the length of the cylinder, \(r\) is the radial
distance from the cylinder, \(h\) is the longitudinal distance from the
end of the cylinder, and \textit{l} = \(h + \Delta s\) representing the
longitudinal distance from the beginning of the cylinder (Holt and Koch
1999; Gold et al. 2006; Gold, Henze, and Koch 2007). The practical
consequence of this is that the waveform of extra-cellular voltage
potential will vary drastically depending on the spatial orientation
with respect to \emph{and} distance between the firing neuron and the
recording electrode, with an amplitude in the range of
(\textasciitilde{}10 - 100\(\mu\)V), 2 to 3 orders of magnitude below
the potential across the cell membrane (Gibson 2012; Buzsáki,
Anastassiou, and Koch 2012). Taken together, these considerations
explain the wide range in firing frequencies (Hz, x-axis) and voltage
amplitude (\(\mu\)V, y-axis) observed across this population of spike
clusters.

Since an increase in conductance across the membrane resulting from
perforation by \(\alpha\)HL or A\(\beta_{42}\) would be expected to
cause a decrease in waveform amplitude, the analysis was limited to
spike clusters exhibiting a potential of at least 30\(\mu\)V prior to
treatment to allow for a sufficient dynamic range of response. This
threshold is indicated by the dashed line on the histogram in center of
Figure \ref{freqAmpComp}. Additionally, in an attempt to estimate the
magnitude of the change in amplitude on individual cells, the analysis
was limited to those \emph{persistent} clusters that were active during
both the untreated and treated recordings.

The estimate of the effect of treatment on waveform amplitude has to
account for several factors within the data. First, the estimate of the
amplitude of a given neuron during a recording period is derived from
the random sample of firing events detected from that neuron, which will
exhibit a certain degree of variability. Next, the amplitude across
spike clusters varies greatly both prior to the initiation of treatment,
and likely in response to treatment. Additionally, multiple spike
clusters can be detected by a single electrode. Each electrode will
exhibit a range of sensitivity due to either the micro-environment
within the culture in which they reside, and/or in the physical
circuitry connecting them to the recording device. Finally, these data
were collected across several experiments relying on separate
preparations of cultures and reagents, which can contribute to
additional variability. Therefore, in order to obtain an estimate for
the effect of each treatment on waveform amplitude while accounting for
these sources of variability, a linear mixed-effect model (LME) was
constructed to predict \(log_{10}\mu\)V as a function of the fixed
effects of treatment and recording (untreated and treated), while
controlling for cluster, electrode, and experiment as nested-random
effects (Pinheiro and Bates 2000). The construction of the model is
demonstrated in Figure \ref{wfMixedModel} for two treatments, media
alone and 4\(\mu\)g/mL \(\alpha\)HL. Based on the raw data of individual
waveforms, a series of linear regressions are fit, first based on
waveforms within an individual spike cluster, then for all clusters
detected by a common electrode, all electrodes within a single
experiment, and finally for the effect of treatment across all
experiments.

\blandscape

\begin{figure}

{\centering \includegraphics{figures/TTXalphaHL_cluster_analysis_waveform_mixed_model_plot-1} 

}

\caption{\label{wfMixedModel}{\small \textbf{Determining change in waveform amplitude using linear-mixed model.} Modeling effect on waveform amplitude in response to treatment media alone (\textit{top row}) or 4$\mu$g/mL $\alpha$HL (\textit{bottom row}) across untreated and treated recordings. Estimates derived from amplitude values of individual waveforms (closed points, sample of } 2000 {\small shown per condition), and refined at level of spike cluster (open circles), electrodes, experiment, and treatment, indicated above each panel. Estimates at each level shown in red, with estimates of subsidiary level shown in gray.}}\label{fig:TTXalphaHL_cluster_analysis_waveform_mixed_model_plot}
\end{figure}

\elandscape
\newpage

\paragraph{Effect of pore-forming toxin on voltage
amplitudes}\label{effect-of-pore-forming-toxin-on-voltage-amplitudes}

This method was applied to the recording data from treatment of cultures
with a titration of TTX and \(\alpha\)HL, in order to determine whether
an increase in membrane conductance resulting from exposure to a
pore-forming toxin would result in a decrease in the amplitude of the
voltage waveforms detected. The results in Figure
\ref{TTXalphaHLuVslopeComp} show that treatment with either 2\(\mu\)g/mL
\(\alpha\)HL (-0.068 \(\pm\) 0.034 \(\Delta log_{10}\mu V\)) or
4\(\mu\)g/mL \(\alpha\)HL (-0.074 \(\pm\) 0.034\(\Delta log_{10}\mu V\))
induced a significant decrease in waveform amplitude compared to
treatment with media alone, p = 0.006 and 0.003 respectively. While the
media alone condition appeared have a slight increase in waveform
amplitude (0.046 \(\pm\) 0.027 \(\Delta log_{10}\mu V\)), this estimate
is not significantly different from zero, p = 0.09. While treatment with
0.1\(\mu\)M TTX (-0.02 \(\pm\) 0.04 \(\Delta log_{10}\mu V\)) showed a
slight decrease in waveform amplitude as well, this difference was not
significant, p = 0.1. Treatment with 1\(\mu\)M TTX could not be included
in this analysis, since this concentration caused complete inhibition of
firing activity (there were no active spike clusters during the
\emph{treated} recording with which to perform the comparison with
waveform amplitudes prior to treatment).

\begin{figure}[h]

{\centering \includegraphics[ ]{figures/TTXalphaHL_cluster_analysis_delta_waveform_plot-1} 

}

\caption{\label{TTXalphaHLuVslopeComp}{\small \textbf{Change in waveform amplitude following treatment with pore-forming toxin.} Estimated of change in spike waveform amplitude ($\Delta$\textit{log}$_{10}$ $\mu$V) of spike clusters within cortical cultures treated with titration of TTX (\textit{green}) or $\alpha$HL (\textit{blue}), across untreated (0hr) and treated (24hr) recordings. Treatment of } 22 - 39 {\small replicate wells per condition across four experiments. Data presented as mean $\pm$ SE Hz obtained from linear mixed-effect model fitting waveform amplitude as function of treatment and recording. Differences in  waveform amplitude assessed by general-linear hypothesis test, significant differences indicated by brackets and associated p-values. Table indicates number of spike clusters, and spike events available for analysis in each condition.}}\label{fig:TTXalphaHL_cluster_analysis_delta_waveform_plot}
\end{figure}

\nopagebreak

\begin{table}[!h]

\caption{\label{tab:unnamed-chunk-12}neurotoxin treatment, waveform analysis}
\centering
\begin{tabular}[t]{lrr}
\toprule
treatment & \textit{n} Clusters & \textit{n} Spikes\\
\midrule
media alone & 43 & 47644\\
0.01$\mu$M TTX & 17 & 21368\\
0.1$\mu$M TTX & 13 & 9893\\
1$\mu$g/mL $\alpha$HL & 27 & 26258\\
2$\mu$g/mL $\alpha$HL & 16 & 25113\\
4$\mu$g/mL $\alpha$HL & 13 & 25570\\
\bottomrule
\end{tabular}
\end{table}

It is worth noting that the significant effects on waveform amplitude
are somewhat modest. The estimated change of -0.074 \(\Delta log\mu V\)
within the 4\(\mu\)g/mL \(\alpha\)HL treated cultures translates to a
potential difference on average of -5.74\(\mu\)V after accounting for
the baseline amplitude amongst the spike clusters. That said, due to the
attenuation of voltage potentials detected by extra-cellular electrodes
as a result of distance from the neuron and resistance of the
extra-cellular milieu (\emph{discussed above}), the decrease in
potential across the cell membrane is probably a 100X greater or more.
Despite this attenuation of signal, the resulting decrease in amplitude
is still apparent. Figure \ref{prePostWaveforms} shows waveforms from
the spike cluster within the 4\(\mu\)g/mL \(\alpha\)HL treatment
condition exhibiting the largest change in amplitude (\(\Delta\)
14.2\(\mu\)V) during the \emph{pre} and \emph{post} treatment
recordings.

Lastly, is important to emphasize that despite treatment with
4\(\mu\)g/mL \(\alpha\)HL inducing a slight decrease in cell viability
(Figure \ref{TTXalphaViabilityPlot}), any such decrease in viability
would not be consequential for the effect on waveform amplitude, since
these estimates were derived purely from those clusters active during
both the untreated and treated recordings.

\begin{figure}[h]

{\centering \includegraphics[ ]{figures/TTXalphaHL_cluster_analysis_pre_post_alphaHL_treatment_waveform_plot-1} 

}

\caption{\label{prePostWaveforms}{\small \textbf{Magnitude of effect on spike amplitude following treatment with $\alpha$HL.} Comparison of waveforms within spike cluster demonstrating the largest change in amplitude ($\mu$V) amongst the 4$\mu$g/mL $\alpha$HL treatment condition. Sample of 50 waveforms per recording shown, untreated (\textit{light blue}) and treated (\textit{dark blue}).}}\label{fig:TTXalphaHL_cluster_analysis_pre_post_alphaHL_treatment_waveform_plot}
\end{figure}

\paragraph{Effect of human brain extract on voltage
amplitudes}\label{effect-of-human-brain-extract-on-voltage-amplitudes}

Finally, the waveforms of spike clusters within cultures treated with
human brain extracts from an LP-NCI and AD individuals were examined to
determine whether the presence of A\(\beta_{42}\) contributes to a
decrease in amplitude of voltage. As shown in Figure
\ref{brainExtractuVslopeComp}, while change in waveform amplitude is
significantly decreased in response to treatment with AD individual
ALB01 extract (-0.091 \(\pm\) 0.022\(\Delta log_{10}\mu V\)) compared to
media alone (0.0061 \(\pm\) 0.014\(\Delta log_{10}\mu V\)), p = 0.00085,
there is no significant difference between the decrease in elicited by
the extract of the AD individual ALB01 and that of the LP-NCI individual
MGH1887 (-0.043 \(\pm\) 0.02\(\Delta log_{10}\mu V\)), p = 0.094.
Notably, the decrease in amplitudes within the brain extract treatments,
for ALB01 (-7.49\(\mu\)V) and MGH1887 (-4.14\(\mu\)V), is comparable to
the magnitude of effect seen for 4\(\mu\)g/mL \(\alpha\)HL
(-5.74\(\mu\)V), after accounting for the amplitudes observed prior to
treatment. In contrast, assessment of changes in waveform amplitudes in
cultures treated with 0.125\(\mu\)M - 1\(\mu\)M recombinant
A\(\beta_{42}\) showed that there was no significant decrease relative
to treatment with media alone at any concentration (\emph{not shown}).

\begin{figure}[h]

{\centering \includegraphics[ ]{figures/brain_extract_cluster_analysis_delta_waveform_plot-1} 

}

\caption{\label{brainExtractuVslopeComp}{\small \textbf{Assessing change in waveform amplitude following treatment with human brain extract.} Estimated change in spike waveform amplitude ($\Delta$ \textit{log}$_{10}$ $\mu$V) of spike clusters within cortical cultures treated with human brain extract from AD individual ALB01 (\textit{dark green}) and LP-NCI individual MGH1887 (\textit{orange}), between untreated (day-0) and treated (day-4) recordings. Treatment of (} 104 {\small replicate wells, within } 9 {\small experiments) for media alone, (} 98 , 9 {\small) for ALB01, and (} 50 , 4 {\small) MGH1887. Data presented as mean $\pm$ SE Hz obtained from linear mixed-effect model fitting amplitude as function of treatment and recording. Differences in amplitude assessed by general-linear hypothesis test, significance indicated by brackets and associated p-values. Table indicates number of spike clusters, and spike events available for analysis in each condition.}}\label{fig:brain_extract_cluster_analysis_delta_waveform_plot}
\end{figure}

\nopagebreak

\begin{table}[!h]

\caption{\label{tab:unnamed-chunk-13}human brain extract treatment, waveform analysis}
\centering
\begin{tabular}[t]{lrr}
\toprule
treatment & \textit{n} Clusters & \textit{n} Spikes\\
\midrule
media alone & 192 & 223646\\
ALB01 & 34 & 50156\\
MGH1887 & 53 & 78620\\
\bottomrule
\end{tabular}
\end{table}

\pagebreak
\newpage

\subsection{Discussion}\label{discussion-2}

Through this work, a spike sorting analysis pipeline was developed for
the purposes of identifying the voltage waveforms (spike clusters) of
individual neurons from the aggregate data obtained from multi-electrode
arrays recordings of primary cortical cultures. This pipeline is
comprised of open source software utilizing the R statistical
programming language. This analysis offers advantages over the
commercially available software products available for this purpose,
specifically in that it provides flexibility to perform spike sorting
across data from multiple recordings, and that it can be executed within
a parallel computing environment, utilizing multiple CPUs within a high
performance computing cluster. The ability to perform spike sorting
across multiple recordings is an important distinction of the pipeline
developed here relative to other packages. This is important, given the
longitudinal nature of many MEA experiments, and the problems with
sorting separately for each time point (Figure
\ref{singleVsMultiRecSortPlot}). Further, the ability to leverage
parallel computing is advantageous given the large number of recording
channels to be assessed (768 within the instrument used for this study)
and the time and computationally intensive nature of the mean-shift
algorithm underlying the analysis.

Utilizing this analysis pipeline, the dynamics of neuronal firing were
examined at a basal level of activity and in an \emph{in vitro} model of
Alzheimer's disease. Recordings of untreated cultures over several days
revealed highly stochastic patterns spontaneous firing of individual
cells (Figure \ref{stochasticFiringPlot}). This observation provided
essential grounding for how to draw inferences about changes in firing
patterns across populations of spike clusters, as demonstrated with the
use of \(\Gamma\)-distributed GLMs.

Examining these changes in neuronal firing within the context of an AD
model, treatment of cultures with brain extract from an AD individual as
expected caused a decrease in the number of active clusters and firing
frequency across the population. Interestingly, the partial recovery of
spontaneous firing observed in the culture following removal of AD brain
extract was attributed to an increase in the number of active clusters,
suggesting that the initial loss in firing observed in the presence of
treatment was not simply due to a loss in viability (Figure
\ref{brainExtractHzPlot}).

In addition to assessing the firing frequency, changes in the amplitude
of waveforms originating from individual cells were examined as a means
of assessing changes in voltage potentials resulting from perforation of
the cellular membrane consistent with the `channel hypothesis' of
A\(\beta_{42}\) toxicity. In order to establish the validity of this
approach, changes in waveform amplitude were assessed in response to
treatment with a pore-forming toxin, \(\alpha\)HL, and non-pore forming,
channel blocking toxin, TTX. Treatment with low doses of 2\(\mu\)g/mL
and 4\(\mu\)g/mL \(\alpha\)HL produced significant, albeit modest
decreases in waveform amplitudes on the order of -5.6\(\mu\)V and
-5.74\(\mu\)V. Treatment with AD brain extract actually induced a
decrease in waveform amplitude comparable with that of \(\alpha\)HL,
-7.49\(\mu\)V. However, attributing this decrease to an effect of
A\(\beta_{42}\) is belied by the finding that this decrease is not
significantly greater than that induced by treated with brain extract
from an LP-NCI individual, -4.14\(\mu\)V, and that treatment with high
doses of recombinant A\(\beta_{42}\) had no effect on waveform
amplitude.

In summary, spike sorting analysis of MEA recordings allows for higher
resolution insights into the dynamics of individual cells within
populations of spontaneously firing neurons. This study describes the
development of an analysis pipeline that is capable of scaling to handle
the volume of data generated by multi-well MEA instruments. The
application of this analysis has allowed for further characterization of
the response of cultured neurons to exposure of AD human extract,
including decrease and restoration of firing frequencies as well as
alterations in the magnitude of voltage potentials recorded. Expanding
upon the high temporal resolution of the data
(\textasciitilde{}100\(\mu\)s) provided by MEA recordings with the means
for resolving the signal down to the cell-level will allow for
investigation other phenotypes not examined here, including
identification of bursts of firing and behavior across networks of
neurons.

\newpage

\subsection{Methods}\label{methods-3}

\paragraph{Reagents}\label{reagents}

Alpha hemolysin (\(\alpha\)HL) and tetrodotoxin (TTX) were purchased
from Sigma Aldrich (St.~Louis, MO). Both reagents were reconstituted in
sterile water, and diluted to the specified concentration in BrainPhys
culture media containing SM-1 neuronal supplement (StemCell
Technologies). Human brain extracts were prepared in BrainPhys culture
media as previously described (Chapter 2 - Methods).

\paragraph{Tissue Culture and MEA
Recordings}\label{tissue-culture-and-mea-recordings}

Cortical cultures were established on 96-well multi-electrode array
plates (Axion Bioscience, Atlanta GA) from E18 Sprague Dawley rats
(Charles River Laboratories, Wilmington MA), and maintained in BrainPhys
culture media; all MEA recordings were performed using a Maestro
multi-well MEA recorder (Axion Bioscience), as previously described
(Chapter 2 - Methods).

MEA recordings were performed daily during all experiments.
\emph{Untreated} recordings were taken prior to addition of treatment on
the day treatment was initiated. \emph{Treated} recordings represent
1-day post treatment for \(\alpha\)HL/TTX experiments, and 4-day post
treatment for brain extract experiments. \emph{Washout} recordings
represent 1-day following removal of treatment for \(\alpha\)HL/TTX
experiments, and 4-day post treatment for brain extract experiments. All
treatments were added as 1X in 200\(\mu\)L following a complete media
change, washout constituted a complete removal of treatment containing
media, 1 time wash with fresh culture media, and replacement with
200\(\mu\)L of fresh culture media without treatment.

\paragraph{Cell Viability Assay}\label{cell-viability-assay}

Cell viability was assessed using the Pierce LDH Cytotoxicity assay kit
(ThermoFisher Scientific, Waltham MA) according to manufacturer's
protocol. Briefly, 50 \(\mu\)L of treated culture media along with 50
\(\mu\)L of detection reagent were combined in a clear 96-well
microtitre plate, and incubated at room temperature for 30min. Following
incubation, the stop reagent was added, and optical density (OD) was
measure at 490nm and 680nm using a Synergy HT plate reader (BioTek,
Winooski VT). Percent viability was calculated by first subtracting the
\(OD_{680}\) from the \(OD_{490nm}\), then normalizing each observation
to the median values of the cell lysis positive control (PC) and the
untreated negative control (NC) using the equation: \[
N(x) = (1 - \frac{OD_x - \tilde{OD_{NC}}}{\tilde{OD_{PC}} - \tilde{OD_{NC}}}) \times 100
\]

\paragraph{Spike Sorting}\label{spike-sorting}

Raw voltage, timestamp, and value of crossing threshold for each spike
event were extracted from the \textsf{.spk} files of MEA recordings
produced AxIS acquisition software (Axion Bioscience), using custom
MatLab scripts (MathWorks, Natick MA) using extractor functions provided
AxIS version 2.4. Following extraction of the raw recording data, all
analyses were performed using the R statistical programming language (R
Core Team 2017). For each spike events the following metrics were
calculated based on the vector of 38 momentary voltage measurements
within each instance: maximum voltage (\emph{peak}), minimum voltage
(\emph{valley}), waveform amplitude (\emph{peak-valley range}), time
interval between \emph{peak} and \emph{valley}, area under the curve
(\emph{AUC}), and non-linear energy (\emph{NLE}). \emph{AUC} was
calculated using the \texttt{auc} function within the R \textsf{MESS}
library (Ekstrøm 2018), NLE was calculated using the equation provided
by Kim and Kim (2000). \[
\Psi (x_n) = x^2_n - (x_{n-1} \times x_{n+1}) 
\] The data were aggregated across all recordings in a given experiment,
and segmented based on individual electrode. Principal components
analysis was performed using the \texttt{prcomp} function within the
base R \textsf{stats} library, following log transformation of the
metrics as recommended by Venables and Ripley (2002). Mean-shift
clustering of spike events was performed based on the values of the
first two principal components using the \texttt{msClustering} function
within the R \textsf{MeanShift} library (Ciollaro and Wang 2016), using
a Gaussian kernel and bandwidth value of \(h = 1.5\). All spike sorting
analysis was performed on the O2 high performance computing cluster
(Research Computing Group, Harvard Medical School, Boston MA).

\paragraph{Statistical Analyses}\label{statistical-analyses}

\emph{Gamma distributed generalized linear models} for the distribution
of spike cluster frequencies were constructed using the \texttt{glm}
function within the base R \textsf{stats} library. Firing frequency, Hz,
was modeled as the dependent variable, while treatment and recording
were used as interacting categorical independent variables. The
\emph{inverse} link function was applied for translating the model
coefficients estimating \(\beta\), the rate term of the \(\Gamma\)
distribution, to the distribution average \(\mu\). \emph{Linear mixed
effect models} for estimating changes in waveform amplitudes were
constructed using the \texttt{lme} function within the R \textsf{nlme}
library (Pinheiro et al. 2018). Amplitude, \(log_{10}\mu V\) was modeled
as the dependent variable; treatment and recording were interacting
categorical fix-effect variables; and spike cluster, electrode, and
experiment were nested random-effect variables. \emph{General linear
hypothesis tests} for comparing the predictions of the GLM and LMEs were
performed using the \texttt{contrMat}, \texttt{glht}
\texttt{summary.glht} functions within the R \textsf{multcomp} library
(Hothorn, Bretz, and Westfall 2008). Adjustments to p.values for
multiple comparisons was done using the default ``single-step'' method
within \texttt{summary.glht}. All Figures were generated using the R
\textsf{ggplot2} and accompanying \textsf{ggpubr} libraries (Wickham
2016; Kassambara 2018).

\FloatBarrier
\newpage

\singlespacing

\section{Chapter 6: Discussion}\label{chapter-6-discussion}

\doublespacing

The intent of this study was to investigate the effects of Alzheimer's
disease (AD) pathology on the functional behavior of neurons, with a
focus on the effects of soluble amyloid-\(\beta\) (A\(\beta\)) species.
To do so, efforts were made toward developing an \emph{in vitro} assays
that: \emph{i} reflects the physiology of the Alzheimer's brain,
\emph{ii} could be used to identify species associated with AD pathology
influencing neuronal function, and \emph{iii} is amenable to testing
investigational therapeutic agents. The decision was made to develop
this system utilizing primary cortical cultures and multi-electrode
arrays (MEA), as this would provide a means to monitor the spontaneous
firing of neurons in vitro over extended periods of time.

As outlined in Chapter 2, in order to implement this strategy a review
of the literature was performed to ascertain best practices for MEA
methodology. Building upon the information in the reported protocols,
several aspects of experimental design and data analysis were considered
and validated. These included, though were not limited to: surface
preparation, growth media, culture maturity, recording duration, data
acquisition parameters, and assay reporting metrics. Included in this
work was an assessment of the variability in spontaneous firing
frequencies across MEAs and the development of software tools for
assisting in cohort assignment for MEA experiment design.

In order to assess the effects of A\(\beta\) in this system, cultures
were treated with aqueous-soluble brain extract from AD patients or not
cognitively impaired individuals who did or did not exhibit AD pathology
(HP-NCI, LP-NCI). Soluble brain extract was chosen as the perturbation
for these experiments as it harbors the most disease relevant species of
A\(\beta\) and these extracts have been used in other experimental
systems reporting the modulatory effects that A\(\beta\) has on neurons
(Wang et al. 2017; Yang et al. 2017; Jin et al. 2018).

As presented in Chapter 3, the effects on firing activity observed in
this system could not be attributed to A\(\beta\), nor did the effect
size of the extracts conform to the diagnosis of the individual. In this
sense the MEA system as reported could be considered a poor assay for
investigating the actions of A\(\beta\) or simply for detecting
differences between the effect of AD and non-AD brain. However, that
conclusion would be seem premature. These extracts are in reality
complex mixtures representing the protein microenvironment of the human
brain. So, while hypothesized that the A\(\beta\) species contained
within would be primarily responsible for effects on neuronal firing, it
does not preclude the possibility that other factors may be involved.

The results of additional experimentation indicated that the effects on
spontaneous firing were due to proteinaceous species within the brain
extracts. Thus, it was difficult to conclude from the collective data
whether the effects on firing activity detected were simply a poor
indicator of AD pathology, or if the observed effects are an accurate
representation of the variability in protein expression across
individuals not reflected in the categorical assignment of diagnosis.

Nevertheless, given the lessons learned from this study, any future
efforts utilizing MEAs to assess functional neuronal activity using
human brain extract should give consideration to the refinement of the
preparation prior to treatment. Having failed to do so here, compounded
with the inherent variability in primary patient samples ultimately made
meaningful interpretation of these data challenging.

In order to assess differences in the constitutive proteins across the
brain extracts for AD, LP-NCI, and HP-NCI individuals, unbiased
proteomic profiling was performed on these samples. Hierarchical
clustering blinded with regard to diagnosis and based solely on the
correlated expression of over 1800 proteins resulted in the
identification of two groups of individuals. Gene Ontology enrichment
analysis based on differentially expressed proteins helped reveal that
the two groups were characterized by disparities in major elements of
the ubiquitin-proteasome pathway, and proteins associated with vesicle
processing and apoptosis. Further analysis examined the correlation of
protein expression within the brain extracts with the observed effects
in the MEA spontaneous firing assays and a separate imaging-based
neurite integrity assay. This indicated that particular protein
chaperones and modulators of microtubule structure that may be
protective of these structural and functional phenotypes. Conversely,
modulators of lipid metabolism emerged that may be acting to precipitate
their decline. Collectively, the unbiased assessment of protein
expression in conjunction with the phenotypic assay results presented a
number of protein candidates worthy of further investigation for their
potential role in AD pathogenesis.

In Chapter 4, of the two in vitro assays used to assess brain extracts
the diverging effects observed in the neurite integrity assay better
aligned with the segregation of extracts designated by hierarchical
clustering of protein expression profiles. Given the intrinsic
relationship between neuron structure and function one would expect the
output of the neurite integrity and MEA-spontaneous firing assays to be
complementary if not equally predictive. Therefore, it worth considering
the differences between the two assays whether elements of one could be
used to refine the other. One distinction is that the cultures used in
the neurite integrity assay were human induced pluripotent stem cell
(iPS) derived neurons. Thus, the neurite integrity experiment consisted
of treatment of human cells with human brain extract. Yet, the
iPS-derived neurons in the neurite integrity assay are cultured under
different conditions due to the requirements of that particular assay
platform. In order to resolve the neuritic structures using bright-field
microscopy the neurons are grown in the absence of glia and much sparser
than the cultures in the MEA assay (\(1.5\times 10^{4}\) versus
\(2.4\times 10^{5}\) cells/cm\(^2\)). At the outset of this study
several years ago, the use of primary rodent cortical cultures was
chosen over human iPS-derived neurons in order to establish this
experimental system without the variability introduced by repeated
differentiations of iPS cells. The use of primary cortical cultures also
served as a ready source for a mixed population of neurons and glia,
better representing the cellular environment of the brain. Since that
time efforts by colleagues in the Young-Pearse laboratory implementing
the iN differentiation protocol described by Zhang et al (2013) have
been able to reliably generate human neurons that demonstrate levels of
spontaneous firing activity in MEA recordings comparable to that
observed in primary rodent cultures. Additionally, a number of methods
have been reported for generating populations of glial cells, including
astrocytes and microglia, by either transdifferentiation of primary cell
or differentiation of iPS cells (Ryan et al. 2017; Abud et al. 2017).
While not fully recapitulating the cellular landscape of the brain,
together these developments now allow for testing the effects of AD
brain extracts on dense cultures of human neurons and glia using MEA
recordings.

\paragraph{\texorpdfstring{Future directions for modeling AD with
multi-electrode arrays
\textit{in vitro}}{Future directions for modeling AD with multi-electrode arrays }}\label{future-directions-for-modeling-ad-with-multi-electrode-arrays}

Beyond assessing the effects of A\(\beta\) pathology using species from
primary samples, MEAs can be employed in modeling AD biology through
testing the functional effects of mutations or modifications of disease
related genes as has been done for studies of neurological disorders
such as epilepsy, amyloid-lateral sclerosis, neuropathic pain, and
others (Wainger, Kiskinis, et al. 2014; Wainger, Buttermore, et al.
2014; Nageshappa et al. 2016; DeRosa et al. 2018). Applying the methods
discussed in Chapter 2 regarding balancing activity levels of cohorts
and statistical power per sample size, it is possible to design
interpretable experiments while accounting for the modest effect sizes
that may be expected from these genetic perturbations. To this end
preliminary experiments have been performed with my colleague in the
Young-Pearse laboratory, Dr.~Marty Fernandez, to examine effects of
endogenously generated A\(\beta\) on spontaneous firing by introducing
APP variants harboring mutations associated with familial AD into mature
cultures via lentiviral vectors. Additional efforts have been made to
examine the effect of APP loss through through use of a
tamoxifen-inducible Cre knockout system, expanding on findings from
other colleagues in the Young-Pearse laboratory that deletion of APP
leads to abnormalities in the developing cortex (Callahan et al. 2017).
Similarly, initial attempts have been made in collaboration with the
laboratory of Prof.~Bruce Yankner to assess the effect on firing
activity following the elimination of neuron-restrictive silencer factor
(REST) a transcriptional repressor shown to be neuroprotective in the
aging brain and down-regulated in AD (Lu et al. 2014).

While many studies using MEAs, including this one, have relied on
mean-firing-rate or similar aggregated spike frequency metrics for
reporting activity, these instruments have typically been used as a tool
for studying neuronal networks (Taketani and Baudry 2006). In Chapter 5
a method for spike-sorting analysis was described that can be
implemented in a distributed, unsupervised fashion. This technique
facilitates resolving the timing of firing events from individual
neurons in dense cultures to allow for reconstruction of activity
networks down to the single cells level. While other studies have
examined network dynamics within cultures on \emph{in vitro} MEAs, these
have typically been carried without first identifying individual cells
through spike sorting and instead have examined the temporal correlation
of events at the electrode level (Vincent et al. 2013). This obfuscates
elements of the network, since activity of multiple cells is likely
being aggregated on a single electrode. Taking advantage of parallel
computing, the approach described can be readily scaled in order to
perform this analysis on data from multiple arrays in a timely fashion.
This allows for experiments assaying for changes in network dynamics
rather than simply aggregated spike frequency. This is relevant for
modeling AD biology, as the changes in neuronal activity are likely to
present as aberrant firing patterns rather than simply an increase or
decrease in the frequency of spiking events (Palop and Mucke 2010).
Building on this, a collaboration has been started with Prof Vilas Menon
of Columbia University to use the outputs out the spike sorting methods
described here to examine the emergence of network activity within human
iPS-derived neuronal cultures overtime.

Additional analyses in Chapter 5 demonstrated the ability to detect
changes in the amplitude of waveforms within a spike cluster following
treatment with the pore forming toxin \(\alpha\)-hemolysin. While
outside the scope of modeling AD biology, this type of analysis may have
broader application in the field of toxicology where MEA systems are
commonly used. The technique described here for monitoring changes in
action potential amplitude is analogous to the methods used for
detecting changes in QT intervals in cardiomyocytes recorded using MEAs
(Tertoolen et al. 2018).

\paragraph{Taking it to the next
dimension}\label{taking-it-to-the-next-dimension}

In a recent review of the field, De Strooper and Kagan critiqued the
\emph{in vitro} models commonly used to study AD as being ``too acute''
for modeling a disease so complex and protracted in its development
(2016). Regardless of the culture type, perturbation, or sophistication
of the assay metric, using cells that are days to weeks old and assaying
changes on the scale of hours to days to understand a disease that takes
decades to manifest is counter intuitive. This criticism rightly extends
to the type of models described in this study. However, modeling the
disease on its natural timeline for the purpose of experimentation is
also clearly unreasonable. This logically leads to more sophisticated
models such as 3-dimensional \emph{in vitro} culture techniques or in
vivo animal models where the pathology emerges endogenously. The use of
MEAs is advantageous for assessing the activity of neuronal populations
in 3D systems as the signal does not have to be resolved to a narrow
horizontal plane as required of live imaging-based detection methods. As
such, elements of the analyses performed in this study will be
transferable to these 3D models.

Numerous animal models of AD have been developed exhibiting elements of
AD pathology by expressing transgenes of human APP or MAPT (Newman et
al. 2017). While no AD mouse model fully mimics all symptoms and
pathology of the disease, they are considered to represent early
alterations occurring in the AD brain (Zahs and Ashe 2010; De Strooper
and Karran 2016). In vivo multi-electrode recordings taken in AD mice
have shown disrupted firing patterns during sleep states and spatial
memory tests (Cheng and Ji 2013; Menkes-Caspi et al. 2015; Miranda et
al. 2018).

While animal models are valuable tools, scaling rodent experiments is
difficult for practical and humane reasons. Also, performing recording
in live animals also presents the additional challenge of parsing the
changes in firing activity resulting from the effect of the perturbation
away from the complex variations in activity of the brain itself.
Moreover, extrapolating findings from rodent models to humans is
inexact, hence the efforts toward developing human iPS-derived neuronal
models of AD. In the near term, a more representative \emph{in vitro}
approach for modeling AD in human cells may come from a 3D human
iPS-derived culture models. A report by Choi et al using a human neural
progenitor cell line over-expressing numerous copies of APP and PSEN1
each harboring familial AD mutations, demonstrated a 3D in vitro culture
model that exhibited both A\(\beta\) deposits and hyper-phosphorylated
tau fibrils (2014). This indicated that it is possible to replicate
essential elements of AD pathology in an in vitro systems. Separately, a
reported by Lancaster and colleagues showed an approach for generating
complex 3D `cerebral organoids' derived from pluripotent stem cells
(2013). Subsequent studies using cerebral organoids derived from an AD
iPS line showed disease related phenotypes, including: increased
A\(\beta_{42}\) expression, tau phosphorylation, and enlarged Rab-5
positive endosomes, though these reports did not include finding of
amyloid deposits for tau fibrils (Raja et al. 2016; Lin et al. 2018).
The method also was employed by Quadrato et al to generated cerebral
organoids expressing retinal-like cells expressing photoreceptors.
Performing recordings using a close-packed silicone micro-electrode
arrays, this study was able to remarkably show that these retinal-like
cells within these organoids were able to generate action potentials in
response to light stimulus (Quadrato et al. 2017; Scholvin et al. 2016).
Combining the cerebral organoid and micro-electrode recording methods
could provide a human cell model where it is possible to monitor changes
in spontaneous firing of neurons during the emergence of endogenous AD
phenotypes. Further, this experimental paradigm would be conducive for
assaying the effects of genetic modifications and investigational
therapeutic agents.

\paragraph{Conclusion}\label{conclusion-1}

The statistician George Box famously said, ``All models are wrong, but
some are useful.'' The methods and findings generated in this study are
imperfect for modeling and explaining the complex biological problem
that is Alzheimer's disease. Yet, there is hope that they will inform
the design of more refined experiments and techniques for providing
insight into a condition that sadly affects many of whom we hold dear.

\FloatBarrier
\newpage
\singlespacing

\section{References}\label{references}

\setlength{\parskip}{11pt}

\hypertarget{refs}{}
\hypertarget{ref-Abreha:2018hq}{}
Abreha, Measho H, Eric B Dammer, Lingyan Ping, Tian Zhang, Duc M Duong,
Marla Gearing, James J Lah, Allan I Levey, and Nicholas T Seyfried.
2018. ``Quantitative Analysis of the Brain Ubiquitylome in Alzheimer's
Disease.'' \emph{Proteomics}, September, e1800108.

\hypertarget{ref-Abud:2017bd}{}
Abud, Edsel M, Ricardo N Ramirez, Eric S Martinez, Luke M Healy, Cecilia
H H Nguyen, Sean A Newman, Andriy V Yeromin, et al. 2017. ``iPSC-Derived
Human Microglia-like Cells to Study Neurological Diseases.''
\emph{NEURON} 94 (2): 278--293.e9.

\hypertarget{ref-topGO}{}
Alexa, Adrian, and Jorg Rahnenfuhrer. 2016. \emph{TopGO: Enrichment
Analysis for Gene Ontology}.

\hypertarget{ref-Alexa:2006hg}{}
Alexa, Adrian, Jörg Rahnenführer, and Thomas Lengauer. 2006. ``Improved
scoring of functional groups from gene expression data by decorrelating
GO graph structure.'' \emph{Bioinformatics (Oxford, England)} 22 (13):
1600--1607.

\hypertarget{ref-Allen:2013km}{}
Allen, Nicola J. 2013. ``Role of glia in developmental synapse
formation.'' \emph{Current Opinion in Neurobiology} 23 (6): 1027--33.

\hypertarget{ref-Association:2018eq}{}
Alzheimer's Association. 2018. ``2018 Alzheimer's disease facts and
figures.'' \emph{Alzheimer's \& Dementia} 14 (3): 367--429.

\hypertarget{ref-Amatniek:2006kk}{}
Amatniek, Joan C, W Allen Hauser, Carrie DelCastillo-Castaneda, Diane M
Jacobs, Karen Marder, Karen Bell, Marilyn Albert, Joseph Brandt, and
Yaakov Stern. 2006. ``Incidence and predictors of seizures in patients
with Alzheimer's disease.'' \emph{Epilepsia} 47 (5): 867--72.

\hypertarget{ref-Andreev:2012eb}{}
Andreev, Victor P, Vladislav A Petyuk, Heather M Brewer, Yuliya V
Karpievitch, Fang Xie, Jennifer Clarke, David Camp, et al. 2012.
``Label-free quantitative LC-MS proteomics of Alzheimer's disease and
normally aged human brains.'' \emph{Journal of Proteome Research} 11
(6): 3053--67.

\hypertarget{ref-Arispe:1993vo}{}
Arispe, N, H B Pollard, and E Rojas. 1993. ``Giant multilevel cation
channels formed by Alzheimer disease amyloid beta-protein {[}A beta
P-(1-40){]} in bilayer membranes.'' \emph{Proceedings of the National
Academy of Sciences of the United States of America} 90 (22): 10573--7.

\hypertarget{ref-Arispe:1993th}{}
Arispe, N, E Rojas, and H B Pollard. 1993. ``Alzheimer disease amyloid
beta protein forms calcium channels in bilayer membranes: blockade by
tromethamine and aluminum.'' \emph{Proceedings of the National Academy
of Sciences of the United States of America} 90 (2): 567--71.

\hypertarget{ref-Ballatore:2007ir}{}
Ballatore, Carlo, Virginia M Y Lee, and John Q Trojanowski. 2007.
``Tau-mediated neurodegeneration in Alzheimer's disease and related
disorders.'' \emph{Nature Reviews. Neuroscience} 8 (9): 663--72.

\hypertarget{ref-Barry:2011jz}{}
Barry, Andrew E, Igor Klyubin, Jessica M Mc Donald, Alexandra J Mably,
Michael A Farrell, Michael Scott, Dominic M Walsh, and Michael J Rowan.
2011. ``Alzheimer's disease brain-derived amyloid-\(\beta\)-mediated
inhibition of LTP in vivo is prevented by immunotargeting cellular prion
protein.'' \emph{The Journal of Neuroscience : The Official Journal of
the Society for Neuroscience} 31 (20): 7259--63.

\hypertarget{ref-Basak:2012hv}{}
Basak, Jacob M, Philip B Verghese, Hyejin Yoon, Jungsu Kim, and David M
Holtzman. 2012. ``Low-density lipoprotein receptor represents an
apolipoprotein E-independent pathway of A\(\beta\) uptake and
degradation by astrocytes.'' \emph{Journal of Biological Chemistry} 287
(17): 13959--71.

\hypertarget{ref-Bateman:2012ca}{}
Bateman, Randall J, Chengjie Xiong, Tammie L S Benzinger, Anne M Fagan,
Alison Goate, Nick C Fox, Daniel S Marcus, et al. 2012. ``Clinical and
biomarker changes in dominantly inherited Alzheimer's disease.''
\emph{The New England Journal of Medicine} 367 (9): 795--804.

\hypertarget{ref-Bateup:2013bi}{}
Bateup, Helen S, Cassandra L Denefrio, Caroline A Johnson, Jessica L
Saulnier, and Bernardo L Sabatini. 2013. ``Temporal dynamics of a
homeostatic pathway controlling neural network activity.''
\emph{Frontiers in Molecular Neuroscience} 6: 28.

\hypertarget{ref-Bateup:2013dh}{}
Bateup, Helen S, Caroline A Johnson, Cassandra L Denefrio, Jessica L
Saulnier, Karl Kornacker, and Bernardo L Sabatini. 2013.
``Excitatory/inhibitory synaptic imbalance leads to hippocampal
hyperexcitability in mouse models of tuberous sclerosis.'' \emph{NEURON}
78 (3): 510--22.

\hypertarget{ref-Bean:2007gx}{}
Bean, Bruce P. 2007. ``The action potential in mammalian central
neurons.'' \emph{Nature Reviews. Neuroscience} 8 (6): 451--65.

\hypertarget{ref-Benilova:2012gg}{}
Benilova, Iryna, Eric Karran, and Bart De Strooper. 2012. ``The toxic
A\(\beta\) oligomer and Alzheimer's disease: an emperor in need of
clothes.'' \emph{Nature Neuroscience} 15 (3): 349--57.

\hypertarget{ref-Biffi:2013dr}{}
Biffi, Emilia, Giulia Regalia, Andrea Menegon, Giancarlo Ferrigno, and
Alessandra Pedrocchi. 2013. ``The influence of neuronal density and
maturation on network activity of hippocampal cell cultures: a
methodological study.'' \emph{PloS One} 8 (12): e83899.

\hypertarget{ref-Bifsha:2014ht}{}
Bifsha, Panojot, Jianqi Yang, Rory A Fisher, and Jacques Drouin. 2014.
``Rgs6 is required for adult maintenance of dopaminergic neurons in the
ventral substantia nigra.'' \emph{PLoS Genetics} 10 (12): e1004863.

\hypertarget{ref-Black:2018hl}{}
Black, Bryan J, Rahul Atmaramani, Rajeshwari Kumaraju, Sarah Plagens,
Mario Romero-Ortega, Gregory Dussor, Theodore J Price, Zachary T
Campbell, and Joseph J Pancrazio. 2018. ``Adult mouse sensory neurons on
microelectrode arrays exhibit increased spontaneous and stimulus-evoked
activity in the presence of interleukin-6.'' \emph{Journal of
Neurophysiology} 120 (3): 1374--85.

\hypertarget{ref-Brehme:2014hd}{}
Brehme, Marc, Cindy Voisine, Thomas Rolland, Shinichiro Wachi, James
Soper, Yitan Zhu, Kai Orton, et al. 2014. ``A Chaperome Subnetwork
Safeguards Proteostasis in Aging and Neurodegenerative Disease.''
\emph{Cell Reports} 9 (3): 1135--50.

\hypertarget{ref-Brewer:2009ko}{}
Brewer, G J, M D Boehler, R A Pearson, A A DeMaris, A N Ide, and B C
Wheeler. 2009. ``Neuron network activity scales exponentially with
synapse density.'' \emph{Journal of Neural Engineering} 6 (1): 014001.

\hypertarget{ref-Buzsaki:2012db}{}
Buzsáki, György, Costas A Anastassiou, and Christof Koch. 2012. ``The
origin of extracellular fields and currents--EEG, ECoG, LFP and
spikes.'' \emph{Nature Reviews. Neuroscience} 13 (6): 407--20.

\hypertarget{ref-Buzsaki:2015ka}{}
Buzsáki, György, Eran Stark, Antal Berényi, Dion Khodagholy, Daryl R
Kipke, Euisik Yoon, and Kensall D Wise. 2015. ``Tools for probing local
circuits: high-density silicon probes combined with optogenetics.''
\emph{NEURON} 86 (1): 92--105.

\hypertarget{ref-Callahan:2017ex}{}
Callahan, D G, W M Taylor, M Tilearcio, T Cavanaugh, D J Selkoe, and T L
Young-Pearse. 2017. ``Embryonic mosaic deletion of APP results in
displaced Reelin-expressing cells in the cerebral cortex.''
\emph{Developmental Biology} 424 (2): 138--46.

\hypertarget{ref-Catterall:2005iw}{}
Catterall, William A, Alan L Goldin, and Stephen G Waxman. 2005.
``International Union of Pharmacology. XLVII. Nomenclature and
structure-function relationships of voltage-gated sodium channels.''
\emph{Pharmacological Reviews} 57 (4): 397--409.

\hypertarget{ref-Charkhkar:2015ck}{}
Charkhkar, Hamid, Susheela Meyyappan, Evgenia Matveeva, Jonathan R Moll,
Daniel G McHail, Nathalia Peixoto, Richard O Cliff, and Joseph J
Pancrazio. 2015. ``Amyloid beta modulation of neuronal network activity
in vitro.'' \emph{Brain Research} 1629 (December): 1--9.

\hypertarget{ref-Chen:2018fi}{}
Chen, Ming-Kai, Adam P Mecca, Mika Naganawa, Sjoerd J Finnema, Takuya
Toyonaga, Shu-fei Lin, Soheila Najafzadeh, et al. 2018. ``Assessing
Synaptic Density in Alzheimer Disease With Synaptic Vesicle Glycoprotein
2A Positron Emission Tomographic Imaging.'' \emph{JAMA Neurology} 75
(10): 1215--24.

\hypertarget{ref-Cheng:2013ia}{}
Cheng, Jingheng, and Daoyun Ji. 2013. ``Rigid firing sequences undermine
spatial memory codes in a neurodegenerative mouse model.'' \emph{ELife}
2 (June): e00647.

\hypertarget{ref-Choi:2014gu}{}
Choi, Se Hoon, Young Hye Kim, Matthias Hebisch, Christopher Sliwinski,
Seungkyu Lee, Carla D'Avanzo, Hechao Chen, et al. 2014. ``A
three-dimensional human neural cell culture model of Alzheimer's
disease.'' \emph{Nature}, October.

\hypertarget{ref-Chung:2015jg}{}
Chung, Won-Suk, Christina A Welsh, Ben A Barres, and Beth Stevens. 2015.
``Do glia drive synaptic and cognitive impairment in disease?''
\emph{Nature Neuroscience} 18 (11): 1539--45.

\hypertarget{ref-MeanShift}{}
Ciollaro, Mattia, and Daren Wang. 2016. \emph{MeanShift: Clustering via
the Mean Shift Algorithm}.
\url{https://CRAN.R-project.org/package=MeanShift}.

\hypertarget{ref-Clarke:2013gg}{}
Clarke, Laura E, and Ben A Barres. 2013. ``Emerging roles of astrocytes
in neural circuit development.'' \emph{Nature Reviews. Neuroscience} 14
(5): 311--21.

\hypertarget{ref-Clemons:2009fk}{}
Clemons, Paul A, Nicola J Tolliday, and Bridget K Wagner, eds. 2009.
\emph{Cell-Based Assays for High-Throughput Screening}. Vol. 486.
Methods in Molecular Biology. Totowa, NJ: Humana Press.

\hypertarget{ref-Coleman:2003wp}{}
Coleman, Paul D, and Pamela J Yao. 2003. ``Synaptic slaughter in
Alzheimer's disease.'' \emph{Neurobiology of Aging} 24 (8): 1023--7.

\hypertarget{ref-Colonna:2016kf}{}
Colonna, Marco, and Yaming Wang. 2016. ``TREM2 variants: new keys to
decipher Alzheimer disease pathogenesis.'' \emph{Nature Reviews.
Neuroscience} 17 (4): 201--7.

\hypertarget{ref-Comaniciu:2002cr}{}
Comaniciu, D, and P Meer. 2002. ``Mean shift: a robust approach toward
feature space analysis.'' \emph{IEEE Transactions on Pattern Analysis
and Machine Intelligence} 24 (5): 603--19.

\hypertarget{ref-Cox:2014eo}{}
Cox, Jürgen, Marco Y Hein, Christian A Luber, Igor Paron, Nagarjuna
Nagaraj, and Matthias Mann. 2014. ``Accurate proteome-wide label-free
quantification by delayed normalization and maximal peptide ratio
extraction, termed MaxLFQ.'' \emph{Molecular \& Cellular Proteomics :
MCP} 13 (9): 2513--26.

\hypertarget{ref-Cullen:2010fz}{}
Cullen, D Kacy, Meghan E Gilroy, Hillary R Irons, and Michelle C
Laplaca. 2010. ``Synapse-to-neuron ratio is inversely related to
neuronal density in mature neuronal cultures.'' \emph{Brain Research}
1359 (November): 44--55.

\hypertarget{ref-Cummings:2014gn}{}
Cummings, Jeffrey L, Travis Morstorf, and Kate Zhong. 2014.
``Alzheimer's disease drug-development pipeline: few candidates,
frequent failures.'' \emph{Alzheimer's Research \& Therapy} 6 (4): 37.

\hypertarget{ref-DeStrooper:2016ca}{}
De Strooper, Bart, and Eric Karran. 2016. ``The Cellular Phase of
Alzheimer's Disease.'' \emph{Cell} 164 (4): 603--15.

\hypertarget{ref-DeRosa:2018go}{}
DeRosa, Brooke A, Jimmy El Hokayem, Elena Artimovich, Catherine
Garcia-Serje, Andre W Phillips, Derek Van Booven, Jonathan E Nestor, et
al. 2018. ``Convergent Pathways in Idiopathic Autism Revealed by Time
Course Transcriptomic Analysis of Patient-Derived Neurons.''
\emph{Scientific Reports} 8 (1): 8423.

\hypertarget{ref-Deshpande:2006hi}{}
Deshpande, Atul, Erene Mina, Charles Glabe, and Jorge Busciglio. 2006.
``Different conformations of amyloid beta induce neurotoxicity by
distinct mechanisms in human cortical neurons.'' \emph{The Journal of
Neuroscience : The Official Journal of the Society for Neuroscience} 26
(22): 6011--8.

\hypertarget{ref-DiScala:2016ho}{}
Di Scala, Coralie, Nouara Yahi, Sonia Boutemeur, Alessandra Flores, Léa
Rodriguez, Henri Chahinian, and Jacques Fantini. 2016. ``Common
molecular mechanism of amyloid pore formation by Alzheimer's
\(\beta\)-amyloid peptide and \(\alpha\)-synuclein.'' \emph{Scientific
Reports} 6 (June): 28781.

\hypertarget{ref-diggle2002analysis}{}
Diggle, P, D M S P J Diggle, P J Diggle, P Heagerty, P J Heagerty, K Y
Liang, and S Zeger. 2002. \emph{Analysis of Longitudinal Data}. Oxford
Statistical Science Series. OUP Oxford.

\hypertarget{ref-MESS}{}
Ekstrøm, Claus Thorn. 2018. \emph{MESS: Miscellaneous Esoteric
Statistical Scripts}. \url{https://CRAN.R-project.org/package=MESS}.

\hypertarget{ref-Faissner:2010jd}{}
Faissner, Andreas, Martin Pyka, Maren Geissler, Thomas Sobik, Renato
Frischknecht, Eckart D Gundelfinger, and Constanze Seidenbecher. 2010.
``Contributions of astrocytes to synapse formation and maturation -
Potential functions of the perisynaptic extracellular matrix.''
\emph{Brain Research Reviews} 63 (1-2): 26--38.

\hypertarget{ref-Fa:2016ku}{}
Fá, M, D Puzzo, R Piacentini, A Staniszewski, H Zhang, M A Baltrons, D D
Li Puma, et al. 2016. ``Extracellular Tau Oligomers Produce An Immediate
Impairment of LTP and Memory.'' \emph{Scientific Reports} 6 (January):
19393.

\hypertarget{ref-Feng:2018do}{}
Feng, Xiao, Benjamin M Bader, Fan Yang, Monica Segura, Luise Schultz,
Olaf H-U Schröder, Arndt Rolfs, and Jiankai Luo. 2018. ``Improvement of
impaired electrical activity in NPC1 mutant cortical neurons upon DHPG
stimulation detected by micro-electrode array.'' \emph{Brain Research}
1694 (September): 87--93.

\hypertarget{ref-Fejtl:2006}{}
Fetjl, Michael, Alfred Stett, Wilfried Nisch, Karl Heinz Boven, and
Andreas Moller. 2006. ``On Micro-Electrode Array Revival: Its
Development, Sophistication of Recording, and Stimulation.'' In
\emph{Network Electrophysiology}, edited by Makoto Taketani and Michel
Baudry, 26. Springer-Verlag New York.

\hypertarget{ref-Freir:2011gx}{}
Freir, Darragh B, Andrew J Nicoll, Igor Klyubin, Silvia Panico, Jessica
M Mc Donald, Emmanuel Risse, Emmanuel A Asante, et al. 2011.
``Interaction between prion protein and toxic amyloid \(\beta\)
assemblies can be therapeutically targeted at multiple sites.''
\emph{Nature Communications} 2 (June): 336.

\hypertarget{ref-Frost:2010eo}{}
Frost, Bess, and Marc I Diamond. 2010. ``Prion-like mechanisms in
neurodegenerative diseases.'' \emph{Nature Reviews. Neuroscience} 11
(3): 155--59.

\hypertarget{ref-Frost:2009ee}{}
Frost, Bess, Rachel L Jacks, and Marc I Diamond. 2009. ``Propagation of
tau misfolding from the outside to the inside of a cell.'' \emph{The
Journal of Biological Chemistry} 284 (19): 12845--52.

\hypertarget{ref-Fukunaga:1975dk}{}
Fukunaga, K, and L Hostetler. 1975. ``The estimation of the gradient of
a density function, with applications in pattern recognition.''
\emph{IEEE Transactions on Information Theory} 21 (1): 32--40.

\hypertarget{ref-GarciaLeon:2018hx}{}
García-León, Juan Antonio, Alfredo Cabrera-Socorro, Kristel Eggermont,
Ann Swijsen, Joke Terryn, Raheem Fazal, FatemehArefeh Nami, et al. 2018.
``Generation of a human induced pluripotent stem cell-based model for
tauopathies combining three microtubule-associated protein tau mutations
which displays several phenotypes linked to neurodegeneration.''
\emph{Alzheimer's \& Dementia: The Journal of the Alzheimer's
Association}, July.

\hypertarget{ref-Garwood:2011kd}{}
Garwood, C J, A M Pooler, J Atherton, D P Hanger, and W Noble. 2011.
``Astrocytes are important mediators of A\(\beta\)-induced neurotoxicity
and tau phosphorylation in primary culture.'' \emph{Cell Death \&
Disease} 2 (June): e167.

\hypertarget{ref-Gibson:2012ua}{}
Gibson, Sarah Paige. 2012. ``Neural Spike Sorting in Hardware: From
Theory to Practice.''

\hypertarget{ref-Goedert:1992vl}{}
Goedert, M, M G Spillantini, N J Cairns, and R A Crowther. 1992. ``Tau
proteins of Alzheimer paired helical filaments: abnormal phosphorylation
of all six brain isoforms.'' \emph{NEURON} 8 (1): 159--68.

\hypertarget{ref-Gold:2007dy}{}
Gold, Carl, Darrell A Henze, and Christof Koch. 2007. ``Using
extracellular action potential recordings to constrain compartmental
models.'' \emph{Journal of Computational Neuroscience} 23 (1): 39--58.

\hypertarget{ref-Gold:2006ho}{}
Gold, Carl, Darrell A Henze, Christof Koch, and György Buzsáki. 2006.
``On the origin of the extracellular action potential waveform: A
modeling study.'' \emph{Journal of Neurophysiology} 95 (5): 3113--28.

\hypertarget{ref-Guerreiro:2010cu}{}
Guerreiro, Rita Joao, Miquel Baquero, Rafael Blesa, Mercè Boada, Jose
Miguel Brás, Maria J Bullido, Ana Calado, et al. 2010. ``Genetic
screening of Alzheimer's disease genes in Iberian and African samples
yields novel mutations in presenilins and APP.'' \emph{Neurobiology of
Aging} 31 (5): 725--31.

\hypertarget{ref-Guix:2018kq}{}
Guix, Francesc X, Grant T Corbett, Diana J Cha, Maja Mustapic, Wen Liu,
David Mengel, Zhicheng Chen, et al. 2018. ``Detection of
Aggregation-Competent Tau in Neuron-Derived Extracellular Vesicles.''
\emph{International Journal of Molecular Sciences} 19 (3): 663.

\hypertarget{ref-Haass:2007db}{}
Haass, Christian, and Dennis J Selkoe. 2007. ``Soluble protein oligomers
in neurodegeneration: lessons from the Alzheimer's amyloid
beta-peptide.'' \emph{Nature Reviews. Molecular Cell Biology} 8 (2):
101--12.

\hypertarget{ref-Han:2002ti}{}
Han, Xianlin, David M Holtzman, Daniel W McKeel, John Kelley, and John C
Morris. 2002. ``Substantial sulfatide deficiency and ceramide elevation
in very early Alzheimer's disease: potential role in disease
pathogenesis.'' \emph{Journal of Neurochemistry} 82 (4): 809--18.

\hypertarget{ref-Hardy:1992ub}{}
Hardy, J A, and G A Higgins. 1992. ``Alzheimer's disease: the amyloid
cascade hypothesis.'' \emph{Science} 256 (5054): 184--85.

\hypertarget{ref-Hardy:2002dh}{}
Hardy, John, and Dennis J Selkoe. 2002. ``The amyloid hypothesis of
Alzheimer's disease: progress and problems on the road to
therapeutics.'' \emph{Science} 297 (5580): 353--56.

\hypertarget{ref-He:2010ce}{}
He, Xingxuan, Yu Huang, Bin Li, Cheng-Xin Gong, and Edward H Schuchman.
2010. ``Deregulation of sphingolipid metabolism in Alzheimer's
disease.'' \emph{Neurobiology of Aging} 31 (3): 398--408.

\hypertarget{ref-Heneka:2015bp}{}
Heneka, Michael T, Monica J Carson, Joseph El Khoury, Gary E Landreth,
Frederic Brosseron, Douglas L Feinstein, Andreas H Jacobs, et al. 2015.
``Neuroinflammation in Alzheimer's disease.'' \emph{The Lancet
Neurology} 14 (4): 388--405.

\hypertarget{ref-Heywood:2015gr}{}
Heywood, Wendy E, Daniela Galimberti, Emily Bliss, Ernestas Sirka, Ross
W Paterson, Nadia K Magdalinou, Miryam Carecchio, et al. 2015.
``Identification of novel CSF biomarkers for neurodegeneration and their
validation by a high-throughput multiplexed targeted proteomic assay.''
\emph{Molecular Neurodegeneration} 10 (1): 64.

\hypertarget{ref-Holm:1979hl}{}
Holm, Sture. 1979. ``A Simple Sequentially Rejective Multiple Test
Procedure.'' \emph{Scandinavian Journal of Statistics} 6 (2): 65--70.

\hypertarget{ref-Holt:1999wi}{}
Holt, G R, and C Koch. 1999. ``Electrical interactions via the
extracellular potential near cell bodies.'' \emph{Journal of
Computational Neuroscience} 6 (2): 169--84.

\hypertarget{ref-multcomp}{}
Hothorn, Torsten, Frank Bretz, and Peter Westfall. 2008. ``Simultaneous
Inference in General Parametric Models.'' \emph{Biometrical Journal} 50
(3): 346--63.

\hypertarget{ref-Hu:2010jy}{}
Hu, Xiangyou, Xiangdong Zhou, Wanxia He, Jun Yang, Wencheng Xiong,
Philip Wong, Christopher G Wilson, and Riqiang Yan. 2010. ``BACE1
deficiency causes altered neuronal activity and neurodegeneration.''
\emph{The Journal of Neuroscience : The Official Journal of the Society
for Neuroscience} 30 (26): 8819--29.

\hypertarget{ref-Huber:2002ud}{}
Huber, Wolfgang, Anja von Heydebreck, Holger Sültmann, Annemarie
Poustka, and Martin Vingron. 2002. ``Variance stabilization applied to
microarray data calibration and to the quantification of differential
expression.'' \emph{Bioinformatics (Oxford, England)} 18 Suppl 1:
S96--104.

\hypertarget{ref-Illes:2014bi}{}
Illes, Sebastian, Martin Jakab, Felix Beyer, Renate Gelfert, Sebastien
Couillard-Després, Alfons Schnitzler, Markus Ritter, and Ludwig Aigner.
2014. ``Intrinsically active and pacemaker neurons in pluripotent stem
cell-derived neuronal populations.'' \emph{Stem Cell Reports} 2 (3):
323--36.

\hypertarget{ref-Ivenshitz:2010kp}{}
Ivenshitz, Miriam, and Menahem Segal. 2010. ``Neuronal density
determines network connectivity and spontaneous activity in cultured
hippocampus.'' \emph{Journal of Neurophysiology} 104 (2): 1052--60.

\hypertarget{ref-Jack:2013eb}{}
Jack, Clifford R Jr, David S Knopman, William J Jagust, Ronald Carl
Petersen, Michael W Weiner, Paul S Aisen, Leslie M Shaw, et al. 2013.
``Tracking pathophysiological processes in Alzheimer's disease.''
\emph{The Lancet Neurology} 12 (2): 207--16.

\hypertarget{ref-Jain:1988vm}{}
Jain, Anil K, and Richard C Dubes. 1988. \emph{Algorithms for clustering
data}. Prentice Hall.

\hypertarget{ref-Jin:2018ju}{}
Jin, Ming, Brian O'Nuallain, Wei Hong, Justin Boyd, Valentina N
Lagomarsino, Tiernan T O'Malley, Wen Liu, et al. 2018. ``An in vitro
paradigm to assess potential anti-A\(\beta\) antibodies for Alzheimer's
disease.'' \emph{Nature Communications} 9 (1): 2676.

\hypertarget{ref-Jin:2011gm}{}
Jin, Ming, Nina Shepardson, Ting Yang, Gang Chen, Dominic Walsh, and
Dennis J Selkoe. 2011. ``Soluble amyloid beta-protein dimers isolated
from Alzheimer cortex directly induce Tau hyperphosphorylation and
neuritic degeneration.'' \emph{Proceedings of the National Academy of
Sciences} 108 (14): 5819--24.

\hypertarget{ref-Jones:2013hx}{}
Jones, Raasay S, Aedín M Minogue, Thomas J Connor, and Marina A Lynch.
2013. ``Amyloid-\(\beta\)-induced astrocytic phagocytosis is mediated by
CD36, CD47 and RAGE.'' \emph{Journal of Neuroimmune Pharmacology : The
Official Journal of the Society on NeuroImmune Pharmacology} 8 (1):
301--11.

\hypertarget{ref-Jun:2017dg}{}
Jun, Gyungah R, Jaeyoon Chung, Jesse Mez, Robert Barber, Gary W Beecham,
David A Bennett, Joseph D Buxbaum, et al. 2017. ``Transethnic
genome-wide scan identifies novel Alzheimer's disease loci.''
\emph{Alzheimer's \& Dementia: The Journal of the Alzheimer's
Association} 13 (7): 727--38.

\hypertarget{ref-Kagan:2002vt}{}
Kagan, Bruce L, Yutaka Hirakura, Rustam Azimov, Rushana Azimova, and
Meng-Chin Lin. 2002. ``The channel hypothesis of Alzheimer's disease:
current status.'' \emph{Peptides} 23 (7): 1311--5.

\hypertarget{ref-Kandel:2017cu}{}
Kandel, Nabin, Tianyu Zheng, Qun Huo, and Suren A Tatulian. 2017.
``Membrane Binding and Pore Formation by a Cytotoxic Fragment of Amyloid
\(\beta\) Peptide.'' \emph{The Journal of Physical Chemistry B} 121
(45): 10293--10305.

\hypertarget{ref-Kaplan:2017is}{}
Kaplan, Andrew, Barbara Morquette, Antje Kroner, SooYuen Leong, Carolin
Madwar, Ricardo Sanz, Sara L Banerjee, et al. 2017. ``Small-Molecule
Stabilization of 14-3-3 Protein-Protein Interactions Stimulates Axon
Regeneration.'' \emph{NEURON} 93 (5): 1082--1093.e5.

\hypertarget{ref-Karpievitch:2012ig}{}
Karpievitch, Yuliya V, Alan R Dabney, and Richard D Smith. 2012.
``Normalization and missing value imputation for label-free LC-MS
analysis.'' \emph{BMC Bioinformatics} 13 Suppl 16 (16): S5.

\hypertarget{ref-Karran:2011co}{}
Karran, Eric, Marc Mercken, and Bart De Strooper. 2011. ``The amyloid
cascade hypothesis for Alzheimer's disease: an appraisal for the
development of therapeutics.'' \emph{Nature Reviews. Drug Discovery} 10
(9): 698--712.

\hypertarget{ref-ggpubr}{}
Kassambara, Alboukadel. 2018. \emph{Ggpubr: 'Ggplot2' Based Publication
Ready Plots}. \url{https://CRAN.R-project.org/package=ggpubr}.

\hypertarget{ref-Kayed:2009cf}{}
Kayed, Rakez, Anna Pensalfini, Larry Margol, Yuri Sokolov, Floyd
Sarsoza, Elizabeth Head, James Hall, and Charles Glabe. 2009. ``Annular
protofibrils are a structurally and functionally distinct type of
amyloid oligomer.'' \emph{The Journal of Biological Chemistry} 284 (7):
4230--7.

\hypertarget{ref-Keller:2000tk}{}
Keller, J N, K B Hanni, and W R Markesbery. 2000. ``Impaired proteasome
function in Alzheimer's disease.'' \emph{Journal of Neurochemistry} 75
(1): 436--39.

\hypertarget{ref-Kim:2000jl}{}
Kim, Kyung Hwan, and Sung June Kim. 2000. ``Neural spike sorting under
nearly 0-dB signal-to-noise ratio using nonlinear energy operator and
artificial neural-network classifier.'' \emph{IEEE Transactions on
Biomedical Engineering} 47 (10): 1406--11.

\hypertarget{ref-Kiral:2018dk}{}
Kiral, Ferdi Ridvan, Friederike Elisabeth Kohrs, Eugene Jennifer Jin,
and Peter Robin Hiesinger. 2018. ``Rab GTPases and Membrane Trafficking
in~Neurodegeneration.'' \emph{Current Biology : CB} 28 (8): R471--R486.

\hypertarget{ref-Kohli:2016jk}{}
Kohli, Martin A, Holly N Cukier, Kara L Hamilton-Nelson, Sophie Rolati,
Brian W Kunkle, Patrice L Whitehead, Stephan L Züchner, et al. 2016.
``Segregation of a rare TTC3 variant in an extended family with
late-onset Alzheimer disease.'' \emph{Neurology. Genetics} 2 (1): e41.

\hypertarget{ref-Kopke:1993vw}{}
Köpke, E, Y C Tung, S Shaikh, A C Alonso, K Iqbal, and I Grundke-Iqbal.
1993. ``Microtubule-associated protein tau. Abnormal phosphorylation of
a non-paired helical filament pool in Alzheimer disease.'' \emph{The
Journal of Biological Chemistry} 268 (32): 24374--84.

\hypertarget{ref-Kuperstein:2010fw}{}
Kuperstein, Inna, Kerensa Broersen, Iryna Benilova, Jef Rozenski, Wim
Jonckheere, Maja Debulpaep, Annelies Vandersteen, et al. 2010.
``Neurotoxicity of Alzheimer's disease A\(\beta\) peptides is induced by
small changes in the A\(\beta\)42 to A\(\beta\)40 ratio.'' \emph{The
EMBO Journal} 29 (19): 3408--20.

\hypertarget{ref-Kurbatskaya:2016fs}{}
Kurbatskaya, Ksenia, Emma C Phillips, Cara L Croft, Giacomo Dentoni,
Martina M Hughes, Matthew A Wade, Safa Al-Sarraj, et al. 2016.
``Upregulation of calpain activity precedes tau phosphorylation and loss
of synaptic proteins in Alzheimer's disease brain.'' \emph{Acta
Neuropathologica Communications} 4 (1): 34.

\hypertarget{ref-Lacor:2007gn}{}
Lacor, Pascale N, Maria C Buniel, Paul W Furlow, Antonio Sanz Clemente,
Pauline T Velasco, Margaret Wood, Kirsten L Viola, and William L Klein.
2007. ``Abeta oligomer-induced aberrations in synapse composition,
shape, and density provide a molecular basis for loss of connectivity in
Alzheimer's disease.'' \emph{The Journal of Neuroscience : The Official
Journal of the Society for Neuroscience} 27 (4): 796--807.

\hypertarget{ref-Lancaster:2013gu}{}
Lancaster, Madeline A, Magdalena Renner, Carol-Anne Martin, Daniel
Wenzel, Louise S Bicknell, Matthew E Hurles, Tessa Homfray, Josef M
Penninger, Andrew P Jackson, and Juergen A Knoblich. 2013. ``Cerebral
organoids model human brain development and microcephaly.''
\emph{Nature} 501 (7467): 373--79.

\hypertarget{ref-Lashuel:2002eg}{}
Lashuel, Hilal A, Dean Hartley, Benjamin M Petre, Thomas Walz, and Peter
T Lansbury. 2002. ``Neurodegenerative disease: amyloid pores from
pathogenic mutations.'' \emph{Nature} 418 (6895): 291.

\hypertarget{ref-Laske:2015dz}{}
Laske, Christoph, Konstantinos Stellos, Ingrid Kempter, Elke Stransky,
Walter Maetzler, Ingrid Fleming, and Voahanginirina Randriamboavonjy.
2015. ``Increased cerebrospinal fluid calpain activity and microparticle
levels in Alzheimer's disease.'' \emph{Alzheimer's \& Dementia: The
Journal of the Alzheimer's Association} 11 (5): 465--74.

\hypertarget{ref-Lavalley:2016dl}{}
Lavalley, Nicholas J, Sunny R Slone, Huiping Ding, Andrew B West, and
Talene A Yacoubian. 2016. ``14-3-3 Proteins regulate mutant LRRK2 kinase
activity and neurite shortening.'' \emph{Human Molecular Genetics} 25
(1): 109--22.

\hypertarget{ref-Lemere:1996eh}{}
Lemere, C A, J K Blusztajn, H Yamaguchi, T Wisniewski, T C Saido, and D
J Selkoe. 1996. ``Sequence of deposition of heterogeneous amyloid
beta-peptides and APO E in Down syndrome: implications for initial
events in amyloid plaque formation.'' \emph{Neurobiology of Disease} 3
(1): 16--32.

\hypertarget{ref-Lewis:2001ej}{}
Lewis, J, D W Dickson, W L Lin, L Chisholm, A Corral, G Jones, S H Yen,
et al. 2001. ``Enhanced neurofibrillary degeneration in transgenic mice
expressing mutant tau and APP.'' \emph{Science} 293 (5534): 1487--91.

\hypertarget{ref-Li:2011hs}{}
Li, S, M Jin, T Koeglsperger, N E Shepardson, G M Shankar, and D J
Selkoe. 2011. ``Soluble A~ Oligomers Inhibit Long-Term Potentiation
through a Mechanism Involving Excessive Activation of Extrasynaptic
NR2B-Containing NMDA Receptors.'' \emph{The Journal of Neuroscience :
The Official Journal of the Society for Neuroscience} 31 (18): 6627--38.

\hypertarget{ref-Li:2013jv}{}
Li, Yun R, Oliver D King, James Shorter, and Aaron D Gitler. 2013.
``Stress granules as crucibles of ALS pathogenesis.'' \emph{The Journal
of Cell Biology} 201 (3): 361--72.

\hypertarget{ref-Lin:2018he}{}
Lin, Yuan-Ta, Jinsoo Seo, Fan Gao, Heather M Feldman, Hsin-Lan Wen, Jay
Penney, Hugh P Cam, et al. 2018. ``APOE4 Causes Widespread Molecular and
Cellular Alterations Associated with Alzheimer's Disease Phenotypes in
Human iPSC-Derived Brain Cell Types.'' \emph{NEURON} 98 (6): 1294.

\hypertarget{ref-Lingaraju:2014iq}{}
Lingaraju, Gondichatnahalli M, Richard D Bunker, Simone Cavadini, Daniel
Hess, Ulrich Hassiepen, Martin Renatus, Eric S Fischer, and Nicolas H
Thomä. 2014. ``Crystal structure of the human COP9 signalosome.''
\emph{Nature} 512 (7513): 161--65.

\hypertarget{ref-Lu:2014fj}{}
Lu, Tao, Liviu Aron, Joseph Zullo, Ying Pan, Haeyoung Kim, Yiwen Chen,
Tun-Hsiang Yang, et al. 2014. ``REST and stress resistance in ageing and
Alzheimer's disease.'' \emph{Nature} 507 (7493): 448--54.

\hypertarget{ref-Makino:2001dg}{}
Makino, Teruhiko, Mikiro Takaishi, Masaaki Morohashi, and Nam-ho Huh.
2001. ``Hornerin, a Novel Profilaggrin-like Protein and
Differentiation-specific Marker Isolated from Mouse Skin.'' \emph{The
Journal of Biological Chemistry} 276 (50): 47445--52.

\hypertarget{ref-Masliah:1994ui}{}
Masliah, E, M Mallory, L Hansen, R DeTeresa, M Alford, and R Terry.
1994. ``Synaptic and neuritic alterations during the progression of
Alzheimer's disease.'' \emph{Neuroscience Letters} 174 (1): 67--72.

\hypertarget{ref-Maziuk:2017bt}{}
Maziuk, Brandon, Heather I Ballance, and Benjamin Wolozin. 2017.
``Dysregulation of RNA Binding Protein Aggregation in Neurodegenerative
Disorders.'' \emph{Frontiers in Molecular Neuroscience} 10: 89.

\hypertarget{ref-McConnell:2012jp}{}
McConnell, Emma R, Maxine A McClain, James Ross, William R Lefew, and
Timothy J Shafer. 2012. ``Evaluation of multi-well microelectrode arrays
for neurotoxicity screening using a chemical training set.''
\emph{Neurotoxicology} 33 (5): 1048--57.

\hypertarget{ref-McCullagh1989glm}{}
McCullagh, P., and J.A. Nelder. 1989. \emph{Generalized Linear Models,
Second Edition}. Chapman and Hall/Crc Monographs on Statistics and
Applied Probability Series. Chapman \& Hall.
\url{http://books.google.com/books?id=h9kFH2/_FfBkC}.

\hypertarget{ref-McGeer:2016kk}{}
McGeer, Patrick L, Joseph Rogers, and Edith G McGeer. 2016.
``Inflammation, Antiinflammatory Agents, and Alzheimer's Disease: The
Last 22 Years.'' \emph{Journal of Alzheimer's Disease : JAD} 54 (3):
853--57.

\hypertarget{ref-McMillin:2013kn}{}
McMillin, Douglas W, Joseph M Negri, and Constantine S Mitsiades. 2013.
``The role of tumour-stromal interactions in modifying drug response:
challenges and opportunities.'' \emph{Nature Reviews. Drug Discovery} 12
(3): 217--28.

\hypertarget{ref-Meister:2016bp}{}
Meister, Cindy, Miriam Kolog Gulko, Anna M Köhler, and Gerhard H Braus.
2016. ``The devil is in the details: comparison between COP9 signalosome
(CSN) and the LID of the 26S proteasome.'' \emph{Current Genetics} 62
(1): 129--36.

\hypertarget{ref-MenkesCaspi:2015gn}{}
Menkes-Caspi, Noa, Hagar G Yamin, Vered Kellner, Tara L Spires-Jones,
Dana Cohen, and Edward A Stern. 2015. ``Pathological tau disrupts
ongoing network activity.'' \emph{NEURON} 85 (5): 959--66.

\hypertarget{ref-Micelli:2004hj}{}
Micelli, Silvia, Daniela Meleleo, Vittorio Picciarelli, and Enrico
Gallucci. 2004. ``Effect of sterols on beta-amyloid peptide (AbetaP
1-40) channel formation and their properties in planar lipid
membranes.'' \emph{Biophysical Journal} 86 (4): 2231--7.

\hypertarget{ref-Minkeviciene:2009id}{}
Minkeviciene, Rimante, Sylvain Rheims, Marton B Dobszay, Misha
Zilberter, Jarmo Hartikainen, Lívia Fülöp, Botond Penke, et al. 2009.
``Amyloid beta-induced neuronal hyperexcitability triggers progressive
epilepsy.'' \emph{The Journal of Neuroscience : The Official Journal of
the Society for Neuroscience} 29 (11): 3453--62.

\hypertarget{ref-Miranda:2018js}{}
Miranda, Andre M, Mathieu Herman, Rong Cheng, Eden Nahmani, Geoffrey
Barrett, Elizabeta Micevska, Gaelle Fontaine, et al. 2018. ``Excess
Synaptojanin 1 Contributes to Place Cell Dysfunction and Memory Deficits
in the Aging Hippocampus in Three Types of Alzheimer's Disease.''
\emph{Cell Reports} 23 (10): 2967--75.

\hypertarget{ref-Morris:2010gq}{}
Morris, John C, Catherine M Roe, Chengjie Xiong, Anne M Fagan, Alison M
Goate, David M Holtzman, and Mark A Mintun. 2010. ``APOE predicts
amyloid-beta but not tau Alzheimer pathology in cognitively normal
aging.'' \emph{Annals of Neurology} 67 (1): 122--31.

\hypertarget{ref-Morris:2011hu}{}
Morris, Meaghan, Sumihiro Maeda, Keith Vossel, and Lennart Mucke. 2011.
``The many faces of tau.'' \emph{NEURON} 70 (3): 410--26.

\hypertarget{ref-Mucke:2000uq}{}
Mucke, L, E Masliah, G Q Yu, M Mallory, E M Rockenstein, G Tatsuno, K
Hu, D Kholodenko, K Johnson-Wood, and L McConlogue. 2000. ``High-level
neuronal expression of abeta 1-42 in wild-type human amyloid protein
precursor transgenic mice: synaptotoxicity without plaque formation.''
\emph{The Journal of Neuroscience : The Official Journal of the Society
for Neuroscience} 20 (11): 4050--8.

\hypertarget{ref-Mucke:2012hp}{}
Mucke, Lennart, and Dennis J Selkoe. 2012. ``Neurotoxicity of amyloid
\(\beta\)-protein: synaptic and network dysfunction.'' \emph{Cold Spring
Harbor Perspectives in Medicine} 2 (7): a006338.

\hypertarget{ref-Mullan:1992dz}{}
Mullan, M, F Crawford, K Axelman, H Houlden, L Lilius, B Winblad, and L
Lannfelt. 1992. ``A pathogenic mutation for probable Alzheimer's disease
in the APP gene at the N-terminus of beta-amyloid.'' \emph{Nature
Genetics} 1 (5): 345--47.

\hypertarget{ref-Muratore:2014dn}{}
Muratore, Christina R, Heather C Rice, Priya Srikanth, Dana G Callahan,
Taehwan Shin, Lawrence N P Benjamin, Dominic M Walsh, Dennis J Selkoe,
and Tracy L Young-Pearse. 2014. ``The familial Alzheimer's disease
APPV717I mutation alters APP processing and Tau expression in
iPSC-derived neurons.'' \emph{Human Molecular Genetics} 23 (13):
3523--36.

\hypertarget{ref-Murrell:2000tk}{}
Murrell, J R, A M Hake, K A Quaid, M R Farlow, and B Ghetti. 2000.
``Early-onset Alzheimer disease caused by a new mutation (V717L) in the
amyloid precursor protein gene.'' \emph{Archives of Neurology} 57 (6):
885--87.

\hypertarget{ref-Murrell:1991vi}{}
Murrell, J, M Farlow, B Ghetti, and M D Benson. 1991. ``A mutation in
the amyloid precursor protein associated with hereditary Alzheimer's
disease.'' \emph{Science} 254 (5028): 97--99.

\hypertarget{ref-Nageshappa:2016dn}{}
Nageshappa, S, C Carromeu, C A Trujillo, P Mesci, I Espuny-Camacho, E
Pasciuto, P Vanderhaeghen, et al. 2016. ``Altered neuronal network and
rescue in a human MECP2 duplication model.'' \emph{Molecular Psychiatry}
21 (2): 178--88.

\hypertarget{ref-Narahasi:1964wx}{}
Narahasi, T, J W Moore, and W R Scott. 1964. ``TETRODOTOXIN BLOCKAGE OF
SODIUM CONDUCTANCE INCREASE IN LOBSTER GIANT AXONS.'' \emph{The Journal
of General Physiology} 47 (5): 965--74.

\hypertarget{ref-Naslund:2000wf}{}
Näslund, J, V Haroutunian, R Mohs, K L Davis, P Davies, P Greengard, and
J D Buxbaum. 2000. ``Correlation between elevated levels of amyloid
beta-peptide in the brain and cognitive decline.'' \emph{JAMA} 283 (12):
1571--7.

\hypertarget{ref-Nelson:2012bc}{}
Nelson, Peter T, Irina Alafuzoff, Eileen H Bigio, Constantin Bouras,
Heiko Braak, Nigel J Cairns, Rudolph J Castellani, et al. 2012.
``Correlation of Alzheimer disease neuropathologic changes with
cognitive status: a review of the literature.'' \emph{Journal of
Neuropathology and Experimental Neurology} 71 (5): 362--81.

\hypertarget{ref-Neumann:2011hm}{}
Neumann, Manuela, Eva Bentmann, Dorothee Dormann, Ali Jawaid, Mariely
DeJesus-Hernandez, Olaf Ansorge, Sigrun Roeber, et al. 2011. ``FET
proteins TAF15 and EWS are selective markers that distinguish FTLD with
FUS pathology from amyotrophic lateral sclerosis with FUS mutations.''
\emph{Brain : A Journal of Neurology} 134 (Pt 9): 2595--2609.

\hypertarget{ref-Newman:2017bj}{}
Newman, Morgan, Doris Kretzschmar, Imran Khan, Menqi Chen, Giuseppe
Verdile, and Michael Lardelli. 2017. ``Animal Models of Alzheimers
Disease.'' In \emph{Animal Models for the Study of Human Disease},
1031--85. Academic Press.

\hypertarget{ref-Nimmrich:2008ju}{}
Nimmrich, Volker, Christiane Grimm, Andreas Draguhn, Stefan Barghorn,
Alexander Lehmann, Hans Schoemaker, Heinz Hillen, Gerhard Gross, Ulrich
Ebert, and Claus Bruehl. 2008. ``Amyloid beta oligomers (A beta(1-42)
globulomer) suppress spontaneous synaptic activity by inhibition of
P/Q-type calcium currents.'' \emph{The Journal of Neuroscience : The
Official Journal of the Society for Neuroscience} 28 (4): 788--97.

\hypertarget{ref-Nomura:2003ir}{}
Nomura, Masaya, Shigeomi Shimizu, Tomoyasu Sugiyama, Masashi Narita,
Toshinori Ito, Hikaru Matsuda, and Yoshihide Tsujimoto. 2003. ``14-3-3
Interacts directly with and negatively regulates pro-apoptotic Bax.''
\emph{The Journal of Biological Chemistry} 278 (3): 2058--65.

\hypertarget{ref-Novellino:2011jz}{}
Novellino, A, Bibiana Scelfo, T Palosaari, A Price, Tomasz Sobanski, T J
Shafer, A F M Johnstone, et al. 2011. ``Development of micro-electrode
array based tests for neurotoxicity: assessment of interlaboratory
reproducibility with neuroactive chemicals.'' \emph{Frontiers in
Neuroengineering} 4: 4.

\hypertarget{ref-Oberg:2008fo}{}
Oberg, Ann L, Douglas W Mahoney, Jeanette E Eckel-Passow, Christopher J
Malone, Russell D Wolfinger, Elizabeth G Hill, Leslie T Cooper, et al.
2008. ``Statistical analysis of relative labeled mass spectrometry data
from complex samples using ANOVA.'' \emph{Journal of Proteome Research}
7 (1): 225--33.

\hypertarget{ref-Obien:2014kt}{}
Obien, Marie Engelene J, Kosmas Deligkaris, Torsten Bullmann, Douglas J
Bakkum, and Urs Frey. 2014. ``Revealing neuronal function through
microelectrode array recordings.'' \emph{Frontiers in Neuroscience} 8:
423.

\hypertarget{ref-Ogawa:2003dc}{}
Ogawa, Osamu, Hyoung-gon Lee, Xiongwei Zhu, Arun Raina, Peggy L R
Harris, Rudolph J Castellani, George Perry, and Mark A Smith. 2003.
``Increased p27, an essential component of cell cycle control, in
Alzheimer's disease.'' \emph{Aging Cell} 2 (2): 105--10.

\hypertarget{ref-Ondrejcak:2018wb}{}
Ondrejcak, Tomas, Igor Klyubin, Grant T Corbett, Graham Fraser, Wei
Hong, Alexandra Mably, Matthew Gardner, et al. 2018. ``Cellular Prion
Protein Mediates the Disruption of Hippocampal Synaptic Plasticity by
Soluble Tau \emph{In Vivo \textbf{}}.'' \emph{The Journal of
Neuroscience : The Official Journal of the Society for Neuroscience}
Accepted (December).

\hypertarget{ref-OBrien:2011is}{}
O'Brien, Richard J, and Philip C Wong. 2011. ``Amyloid precursor protein
processing and Alzheimer's disease.'' \emph{Annual Review of
Neuroscience} 34: 185--204.

\hypertarget{ref-OMalley:2014ei}{}
O'Malley, Tiernan T, Nur Alia Oktaviani, Dainan Zhang, Aleksey Lomakin,
Brian O'Nuallain, Sara Linse, George B Benedek, Michael J Rowan, Frans A
A Mulder, and Dominic M Walsh. 2014. ``A\(\beta\) dimers differ from
monomers in structural propensity, aggregation paths and population of
synaptotoxic assemblies.'' \emph{The Biochemical Journal} 461 (3):
413--26.

\hypertarget{ref-Palop:2010bu}{}
Palop, Jorge J, and Lennart Mucke. 2010. ``Amyloid-{[}beta{]}-induced
neuronal dysfunction in Alzheimer's disease: from synapses toward neural
networks.'' \emph{Nature Neuroscience} 13 (7): 812--18.

\hypertarget{ref-Papanikolopoulou:2018fd}{}
Papanikolopoulou, Katerina, Sofia Grammenoudi, Martina Samiotaki, and
Efthimios M C Skoulakis. 2018. ``Differential effects of 14-3-3 dimers
on Tau phosphorylation, stability and toxicity in vivo.'' \emph{Human
Molecular Genetics} 27 (13): 2244--61.

\hypertarget{ref-Park:2012et}{}
Park, Esther, Susan Chi, and Dongeun Park. 2012. ``Activity-dependent
modulation of the interaction between CaMKII\(\alpha\) and Abi1 and its
involvement in spine maturation.'' \emph{The Journal of Neuroscience :
The Official Journal of the Society for Neuroscience} 32 (38):
13177--88.

\hypertarget{ref-Parker:2005gy}{}
Parker, Michael W, and Susanne C Feil. 2005. ``Pore-forming protein
toxins: from structure to function.'' \emph{Progress in Biophysics and
Molecular Biology} 88 (1): 91--142.

\hypertarget{ref-Perez:2016bb}{}
Perez, Carlos, Jokubas Ziburkus, and Ghanim Ullah. 2016. ``Analyzing and
Modeling the Dysfunction of Inhibitory Neurons in Alzheimer's Disease.''
\emph{PloS One} 11 (12): e0168800.

\hypertarget{ref-nlme}{}
Pinheiro, Jose, Douglas Bates, Saikat DebRoy, Deepayan Sarkar, and R
Core Team. 2018. \emph{nlme: Linear and Nonlinear Mixed Effects Models}.
\url{https://CRAN.R-project.org/package=nlme}.

\hypertarget{ref-Pinheiro:2000cp}{}
Pinheiro, José C, and Douglas M Bates. 2000. \emph{Mixed-Effects Models
in Sand S-PLUS}. Statistics and Computing. New York, NY: Springer New
York.

\hypertarget{ref-Prasher:1998bz}{}
Prasher, V P, M J Farrer, A M Kessling, E M Fisher, R J West, P C
Barber, and A C Butler. 1998. ``Molecular mapping of Alzheimer-type
dementia in Down's syndrome.'' \emph{Annals of Neurology} 43 (3):
380--83.

\hypertarget{ref-Proepper:2007hq}{}
Proepper, Christian, Svenja Johannsen, Stefan Liebau, Janine Dahl,
Bianca Vaida, Juergen Bockmann, Michael R Kreutz, Eckart D Gundelfinger,
and Tobias M Boeckers. 2007. ``Abelson interacting protein 1 (Abi-1) is
essential for dendrite morphogenesis and synapse formation.'' \emph{The
EMBO Journal} 26 (5): 1397--1409.

\hypertarget{ref-Proepper:2011jf}{}
Proepper, Christian, Konrad Steinestel, Michael J Schmeisser, Jutta
Heinrich, Julie Steinestel, Juergen Bockmann, Stefan Liebau, and Tobias
M Boeckers. 2011. ``Heterogeneous nuclear ribonucleoprotein k interacts
with Abi-1 at postsynaptic sites and modulates dendritic spine
morphology.'' \emph{PloS One} 6 (11): e27045.

\hypertarget{ref-Prusiner:1982tu}{}
Prusiner, S B. 1982. ``Novel proteinaceous infectious particles cause
scrapie.'' \emph{Science} 216 (4542): 136--44.

\hypertarget{ref-PurvesNeuro}{}
Purves, Dale, George Augustine, David Fitzpatrick, William Hall,
Anthony-Samuel LaMantia, Richard Mooney, Michael Platt, and Leonard
White. 2017. \emph{Neuroscience}. 6th ed. Sinauer Associates.

\hypertarget{ref-Quadrato:2017by}{}
Quadrato, Giorgia, Tuan Nguyen, Evan Z Macosko, John L Sherwood, Sung
Min Yang, Daniel R Berger, Natalie Maria, et al. 2017. ``Cell diversity
and network dynamics in photosensitive human brain organoids.''
\emph{Nature} 545 (7652): 48--53.

\hypertarget{ref-Querfurth:2010iv}{}
Querfurth, Henry W, and Frank M LaFerla. 2010. ``Alzheimer's disease.''
\emph{The New England Journal of Medicine} 362 (4): 329--44.

\hypertarget{ref-Quist:2005dg}{}
Quist, Arjan, Ivo Doudevski, Hai Lin, Rushana Azimova, Douglas Ng, Blas
Frangione, Bruce Kagan, Jorge Ghiso, and Ratnesh Lal. 2005. ``Amyloid
ion channels: a common structural link for protein-misfolding disease.''
\emph{Proceedings of the National Academy of Sciences of the United
States of America} 102 (30): 10427--32.

\hypertarget{ref-R}{}
R Core Team. 2017. \emph{R: A Language and Environment for Statistical
Computing}. Vienna, Austria: R Foundation for Statistical Computing.
\url{https://www.R-project.org/}.

\hypertarget{ref-Raja:2016ii}{}
Raja, Waseem K, Alison E Mungenast, Yuan-Ta Lin, Tak Ko, Fatema
Abdurrob, Jinsoo Seo, and Li-Huei Tsai. 2016. ``Self-Organizing 3D Human
Neural Tissue Derived from Induced Pluripotent Stem Cells Recapitulate
Alzheimer's Disease Phenotypes.'' \emph{PloS One} 11 (9): e0161969.

\hypertarget{ref-Rami:2003tn}{}
Rami, A. 2003. ``Ischemic neuronal death in the rat hippocampus: the
calpain-calpastatin-caspase hypothesis.'' \emph{Neurobiology of Disease}
13 (2): 75--88.

\hypertarget{ref-Rangaraju:2018kc}{}
Rangaraju, Srikant, Eric B Dammer, Syed Ali Raza, Tianwen Gao, Hailian
Xiao, Ranjita Betarbet, Duc M Duong, et al. 2018. ``Quantitative
proteomics of acutely-isolated mouse microglia identifies novel immune
Alzheimer's disease-related proteins.'' \emph{Molecular
Neurodegeneration} 13 (1): 34.

\hypertarget{ref-Rao:2014kk}{}
Rao, Mala V, Mary Kate McBrayer, Jabbar Campbell, Asok Kumar, Audrey
Hashim, Henry Sershen, Philip H Stavrides, Masuo Ohno, Michael Hutton,
and Ralph A Nixon. 2014. ``Specific calpain inhibition by calpastatin
prevents tauopathy and neurodegeneration and restores normal lifespan in
tau P301L mice.'' \emph{The Journal of Neuroscience : The Official
Journal of the Society for Neuroscience} 34 (28): 9222--34.

\hypertarget{ref-Roberson:2011ku}{}
Roberson, Erik D, Brian Halabisky, Jong W Yoo, Jinghua Yao, Jeannie
Chin, Fengrong Yan, Tiffany Wu, et al. 2011.
``Amyloid-\(\beta\)/Fyn-induced synaptic, network, and cognitive
impairments depend on tau levels in multiple mouse models of Alzheimer's
disease.'' \emph{The Journal of Neuroscience : The Official Journal of
the Society for Neuroscience} 31 (2): 700--711.

\hypertarget{ref-RoveletLecrux:2006hu}{}
Rovelet-Lecrux, Anne, Didier Hannequin, Gregory Raux, Nathalie Le Meur,
Annie Laquerrière, Anne Vital, Cécile Dumanchin, et al. 2006. ``APP
locus duplication causes autosomal dominant early-onset Alzheimer
disease with cerebral amyloid angiopathy.'' \emph{Nature Genetics} 38
(1): 24--26.

\hypertarget{ref-Ronicke:2011js}{}
Rönicke, Raik, Marina Mikhaylova, Sabine Rönicke, Jessica Meinhardt,
Ulrich H Schröder, Marcus Fändrich, Georg Reiser, Michael R Kreutz, and
Klaus G Reymann. 2011. ``Early neuronal dysfunction by amyloid \(\beta\)
oligomers depends on activation of NR2B-containing NMDA receptors.''
\emph{Neurobiology of Aging} 32 (12): 2219--28.

\hypertarget{ref-Russo:2018im}{}
Russo, Fabiele Baldino, Beatriz Camille Freitas, Graciela Conceição
Pignatari, Isabella Rodrigues Fernandes, Jonathan Sebat, Alysson Renato
Muotri, and Patricia Cristina Baleeiro Beltrão-Braga. 2018. ``Modeling
the Interplay Between Neurons and Astrocytes in Autism Using Human
Induced Pluripotent Stem Cells.'' \emph{Biological Psychiatry} 83 (7):
569--78.

\hypertarget{ref-Ryan:2017kr}{}
Ryan, Katie J, Charles C White, Kruti Patel, Jishu Xu, Marta Olah,
Joseph M Replogle, Michael Frangieh, et al. 2017. ``A human
microglia-like cellular model for assessing the effects of
neurodegenerative disease gene variants.'' \emph{Science Translational
Medicine} 9 (421): eaai7635.

\hypertarget{ref-Sainath:2015jl}{}
Sainath, Rajiv, and Gianluca Gallo. 2015. ``Cytoskeletal and signaling
mechanisms of neurite formation.'' \emph{Cell and Tissue Research} 359
(1): 267--78.

\hypertarget{ref-Saito:2014gc}{}
Saito, Takashi, Yukio Matsuba, Naomi Mihira, Jiro Takano, Per Nilsson,
Shigeyoshi Itohara, Nobuhisa Iwata, and Takaomi C Saido. 2014. ``Single
App knock-in mouse models of Alzheimer's disease.'' \emph{Nature
Neuroscience} 17 (5): 661--63.

\hypertarget{ref-Sanders:2014cv}{}
Sanders, David W, Sarah K Kaufman, Sarah L DeVos, Apurwa M Sharma, Hilda
Mirbaha, Aimin Li, Scarlett J Barker, et al. 2014. ``Distinct tau prion
strains propagate in cells and mice and define different tauopathies.''
\emph{NEURON} 82 (6): 1271--88.

\hypertarget{ref-Sarkar:2018ki}{}
Sarkar, Anindita, Arianna Mei, Apua C M Paquola, Shani Stern, Cedric
Bardy, Jason R Klug, Stacy Kim, et al. 2018. ``Efficient Generation of
CA3 Neurons from Human Pluripotent Stem Cells Enables Modeling of
Hippocampal Connectivity In~Vitro.'' \emph{Cell Stem Cell} 22 (5):
684--697.e9.

\hypertarget{ref-vonSchaper:2018ji}{}
Schaper, Eva von. 2018. ``Everything but amyloid: new thinking prompts
FDA revamp.'' \emph{Nature Biotechnology} 36 (6): 483--84.

\hypertarget{ref-Scheff:2006fw}{}
Scheff, Stephen W, and Douglas A Price. 2006. ``Alzheimer's
disease-related alterations in synaptic density: Neocortex and
hippocampus.'' \emph{Journal of Alzheimer's Disease} 9 (s3): 101--15.

\hypertarget{ref-Scheff:2015er}{}
Scheff, Stephen W, Douglas A Price, Mubeen A Ansari, Kelly N Roberts,
Frederick A Schmitt, Milos D Ikonomovic, and Elliott J Mufson. 2015.
``Synaptic change in the posterior cingulate gyrus in the progression of
Alzheimer's disease.'' \emph{Journal of Alzheimer's Disease : JAD} 43
(3): 1073--90.

\hypertarget{ref-Scheff:2006ho}{}
Scheff, Stephen W, Douglas A Price, Frederick A Schmitt, and Elliott J
Mufson. 2006. ``Hippocampal synaptic loss in early Alzheimer's disease
and mild cognitive impairment.'' \emph{Neurobiology of Aging} 27 (10):
1372--84.

\hypertarget{ref-Scheuner:1996tr}{}
Scheuner, D, C Eckman, M Jensen, X Song, M Citron, N Suzuki, T D Bird,
et al. 1996. ``Secreted amyloid beta-protein similar to that in the
senile plaques of Alzheimer's disease is increased in vivo by the
presenilin 1 and 2 and APP mutations linked to familial Alzheimer's
disease.'' \emph{Nature Medicine} 2 (8): 864--70.

\hypertarget{ref-Scholvin:2016km}{}
Scholvin, Jorg, Justin P Kinney, Jacob G Bernstein, Caroline
Moore-Kochlacs, Nancy Kopell, Clifton G Fonstad, and Edward S Boyden.
2016. ``Close-Packed Silicon Microelectrodes for Scalable Spatially
Oversampled Neural Recording.'' \emph{IEEE Transactions on Biomedical
Engineering} 63 (1): 120--30.

\hypertarget{ref-Selkoe:2016bp}{}
Selkoe, Dennis J, and John Hardy. 2016. ``The amyloid hypothesis of
Alzheimer's disease at 25~years.'' \emph{EMBO Molecular Medicine} 8 (6):
595--608.

\hypertarget{ref-SerraBatiste:2016dg}{}
Serra-Batiste, Montserrat, Martí Ninot-Pedrosa, Mariam Bayoumi,
Margarida Gairí, Giovanni Maglia, and Natàlia Carulla. 2016.
``A\(\beta\)42 assembles into specific \(\beta\)-barrel pore-forming
oligomers in membrane-mimicking environments.'' \emph{Proceedings of the
National Academy of Sciences} 113 (39): 10866--71.

\hypertarget{ref-Seyfried:2017bx}{}
Seyfried, Nicholas T, Eric B Dammer, Vivek Swarup, Divya Nandakumar, Duc
M Duong, Luming Yin, Qiudong Deng, et al. 2017. ``A Multi-network
Approach Identifies Protein-Specific Co-expression in Asymptomatic and
Symptomatic Alzheimer's Disease.'' \emph{Cell Systems} 4 (1): 60--72.e4.

\hypertarget{ref-Shaffer:1995uw}{}
Shaffer, L M, M D Dority, R Gupta-Bansal, R C Frederickson, S G Younkin,
and K R Brunden. 1995. ``Amyloid beta protein (A beta) removal by
neuroglial cells in culture.'' \emph{Neurobiology of Aging} 16 (5):
737--45.

\hypertarget{ref-Shafrir:2010fd}{}
Shafrir, Yinon, Stewart Durell, Nelson Arispe, and H Robert Guy. 2010.
``Models of membrane-bound Alzheimer's Abeta peptide assemblies.''
\emph{Proteins} 78 (16): 3473--87.

\hypertarget{ref-Shankar:2007ep}{}
Shankar, Ganesh M, Brenda L Bloodgood, Matthew Townsend, Dominic M
Walsh, Dennis J Selkoe, and Bernardo L Sabatini. 2007. ``Natural
oligomers of the Alzheimer amyloid-beta protein induce reversible
synapse loss by modulating an NMDA-type glutamate receptor-dependent
signaling pathway.'' \emph{The Journal of Neuroscience : The Official
Journal of the Society for Neuroscience} 27 (11): 2866--75.

\hypertarget{ref-Shankar:2008bg}{}
Shankar, Ganesh M, Shaomin Li, Tapan H Mehta, Amaya Garcia-Munoz, Nina E
Shepardson, Imelda Smith, Francesca M Brett, et al. 2008. ``Amyloid-beta
protein dimers isolated directly from Alzheimer's brains impair synaptic
plasticity and memory.'' \emph{Nature Medicine} 14 (8): 837--42.

\hypertarget{ref-Sheng:2012cc}{}
Sheng, Morgan, Bernardo L Sabatini, and Thomas C Südhof. 2012.
``Synapses and Alzheimer's disease.'' \emph{Cold Spring Harbor
Perspectives in Biology} 4 (5).

\hypertarget{ref-Shrestha:2012iv}{}
Shrestha, Smeeta, Shenglan Cao, and Valerie C L Lin. 2012. ``The local
microenvironment instigates the regulation of mammary tetratricopeptide
repeat domain 9A during lactation and involution through local
regulation of the activity of estrogen receptor \(\alpha\).''
\emph{Biochemical and Biophysical Research Communications} 426 (1):
65--70.

\hypertarget{ref-singer2003applied}{}
Singer, J D, and J B Willett. 2003. \emph{Applied Longitudinal Data
Analysis: Modeling Change and Event Occurrence}. Oxford University
Press.

\hypertarget{ref-Slomowitz:2015fi}{}
Slomowitz, Edden, Boaz Styr, Irena Vertkin, Hila Milshtein-Parush,
Israel Nelken, Michael Slutsky, and Inna Slutsky. 2015. ``Interplay
between population firing stability and single neuron dynamics in
hippocampal networks.'' \emph{ELife} 4.

\hypertarget{ref-Slone:2015cc}{}
Slone, Sunny Rae, Nicholas Lavalley, Michael McFerrin, Bing Wang, and
Talene Alene Yacoubian. 2015. ``Increased 14-3-3 phosphorylation
observed in Parkinson's disease reduces neuroprotective potential of
14-3-3 proteins.'' \emph{Neurobiology of Disease} 79 (July): 1--13.

\hypertarget{ref-Sluchanko:2017iw}{}
Sluchanko, Nikolai N, and Nikolai B Gusev. 2017. ``Moonlighting
chaperone-like activity of the universal regulatory 14-3-3 proteins.''
\emph{The FEBS Journal} 284 (9): 1279--95.

\hypertarget{ref-Smith:2004bc}{}
Smith, David F. 2004. ``Tetratricopeptide repeat cochaperones in steroid
receptor complexes.'' \emph{Cell Stress \& Chaperones} 9 (2): 109--21.

\hypertarget{ref-Sontag:2013im}{}
Sontag, Emily M, Lukasz A Joachimiak, Zhiqun Tan, Anthony Tomlinson,
David E Housman, Charles G Glabe, Steven G Potkin, Judith Frydman, and
Leslie M Thompson. 2013. ``Exogenous delivery of chaperonin subunit
fragment ApiCCT1 modulates mutant Huntingtin cellular phenotypes.''
\emph{Proceedings of the National Academy of Sciences} 110 (8):
3077--82.

\hypertarget{ref-Sorgjerd:2013jq}{}
Sörgjerd, Karin Margareta, Tamotsu Zako, Masafumi Sakono, Peter C
Stirling, Michel R Leroux, Takashi Saito, Per Nilsson, Misaki Sekimoto,
Takaomi C Saido, and Mizuo Maeda. 2013. ``Human prefoldin inhibits
amyloid-\(\beta\) (A\(\beta\)) fibrillation and contributes to formation
of nontoxic A\(\beta\) aggregates.'' \emph{Biochemistry} 52 (20):
3532--42.

\hypertarget{ref-Spira:2013jh}{}
Spira, Micha E, and Aviad Hai. 2013. ``Multi-electrode array
technologies for neuroscience and cardiology.'' \emph{Nature
Nanotechnology} 8 (2): 83--94.

\hypertarget{ref-Stefano:2009kt}{}
Stefano, Luisa, Gabriella Racchetti, Fabio Bianco, Nadia Passini, Radhey
S Gupta, Paola Panina Bordignon, and Jacopo Meldolesi. 2009. ``The
surface-exposed chaperone, Hsp60, is an agonist of the microglial TREM2
receptor.'' \emph{Journal of Neurochemistry} 110 (1): 284--94.

\hypertarget{ref-Tai:2008kp}{}
Tai, Hwan-Ching, and Erin M Schuman. 2008. ``Ubiquitin, the proteasome
and protein degradation in neuronal function and dysfunction.''
\emph{Nature Reviews. Neuroscience} 9 (11): 826--38.

\hypertarget{ref-Takano:2014hp}{}
Takano, Mariko, Erika Tashiro, Akira Kitamura, Hiroshi Maita, Sanae M M
Iguchi-Ariga, Masataka Kinjo, and Hiroyoshi Ariga. 2014. ``Prefoldin
prevents aggregation of \(\alpha\)-synuclein.'' \emph{Brain Research}
1542 (January): 186--94.

\hypertarget{ref-Taketani:2006wx}{}
Taketani, Makoto, and Michel Baudry, eds. 2006. \emph{Advances in
Network Electrophysiology}.

\hypertarget{ref-Tamagnini:2015dr}{}
Tamagnini, Francesco, Sarah Scullion, Jon T Brown, and Andrew D Randall.
2015. ``Intrinsic excitability changes induced by acute treatment of
hippocampal CA1 pyramidal neurons with exogenous amyloid \(\beta\)
peptide.'' \emph{Hippocampus} 25 (7): 786--97.

\hypertarget{ref-Tashiro:2013cg}{}
Tashiro, Erika, Tamotsu Zako, Hideki Muto, Yoshinori Itoo, Karin
Sörgjerd, Naofumi Terada, Akira Abe, et al. 2013. ``Prefoldin protects
neuronal cells from polyglutamine toxicity by preventing aggregation
formation.'' \emph{Journal of Biological Chemistry} 288 (27): 19958--72.

\hypertarget{ref-Terry:1991hb}{}
Terry, R D, E Masliah, D P Salmon, N Butters, R DeTeresa, R Hill, L A
Hansen, and R Katzman. 1991. ``Physical basis of cognitive alterations
in Alzheimer's disease: synapse loss is the major correlate of cognitive
impairment.'' \emph{Annals of Neurology} 30 (4): 572--80.

\hypertarget{ref-Tertoolen:2018hu}{}
Tertoolen, L G J, S R Braam, B J van Meer, R Passier, and C L Mummery.
2018. ``Interpretation of field potentials measured on a multi electrode
array in pharmacological toxicity screening on primary and human
pluripotent stem cell-derived cardiomyocytes.'' \emph{Biochemical and
Biophysical Research Communications} 497 (4): 1135--41.

\hypertarget{ref-Thomas:1972uo}{}
Thomas, C A, P A Springer, G E Loeb, Y Berwald-Netter, and L M Okun.
1972. ``A miniature microelectrode array to monitor the bioelectric
activity of cultured cells.'' \emph{Experimental Cell Research} 74 (1):
61--66.

\hypertarget{ref-Varghese:2010ba}{}
Varghese, Kucku, Peter Molnar, Mainak Das, Neelima Bhargava, Stephen
Lambert, Mark S Kindy, and James J Hickman. 2010. ``A new target for
amyloid beta toxicity validated by standard and high-throughput
electrophysiology.'' \emph{PloS One} 5 (1): e8643.

\hypertarget{ref-Valikangas:2018jh}{}
Välikangas, Tommi, Tomi Suomi, and Laura L Elo. 2018. ``A systematic
evaluation of normalization methods in quantitative label-free
proteomics.'' \emph{Briefings in Bioinformatics} 19 (1): 1--11.

\hypertarget{ref-venables2002modern}{}
Venables, W N, and B D Ripley. 2002. \emph{Modern Applied Statistics
with S}. Statistics and Computing. Springer.

\hypertarget{ref-Vertkin:2015de}{}
Vertkin, Irena, Boaz Styr, Edden Slomowitz, Nir Ofir, Ilana Shapira,
David Berner, Tatiana Fedorova, et al. 2015. ``GABAB receptor deficiency
causes failure of neuronal homeostasis in hippocampal networks.''
\emph{Proceedings of the National Academy of Sciences} 112 (25):
E3291--9.

\hypertarget{ref-Vickers:2001cv}{}
Vickers, A J. 2001. ``The use of percentage change from baseline as an
outcome in a controlled trial is statistically inefficient: a simulation
study.'' \emph{BMC Medical Research Methodology} 1 (1): 6.

\hypertarget{ref-Villemagne:2013dk}{}
Villemagne, Victor L, Samantha Burnham, Pierrick Bourgeat, Belinda
Brown, Kathryn A Ellis, Olivier Salvado, Cassandra Szoeke, et al. 2013.
``Amyloid \(\beta\) deposition, neurodegeneration, and cognitive decline
in sporadic Alzheimer's disease: a prospective cohort study.'' \emph{The
Lancet Neurology} 12 (4): 357--67.

\hypertarget{ref-Vincent:2015ck}{}
Vincent, Fabien, Paula Loria, Marko Pregel, Robert Stanton, Linda
Kitching, Karl Nocka, Regis Doyonnas, et al. 2015. ``Developing
predictive assays: The phenotypic screening `rule of 3'.'' \emph{Science
Translational Medicine} 7 (293): 293ps15.

\hypertarget{ref-Vincent:2013el}{}
Vincent, Kathleen, Joseph S Tauskela, Geoffrey A Mealing, and
Jean-Philippe Thivierge. 2013. ``Altered network communication following
a neuroprotective drug treatment.'' \emph{PloS One} 8 (1): e54478.

\hypertarget{ref-Wainger:2014fv}{}
Wainger, Brian J, Elizabeth D Buttermore, Julia T Oliveira, Cassidy
Mellin, Seungkyu Lee, Wardiya Afshar Saber, Amy J Wang, et al. 2014.
``Modeling pain in vitro using nociceptor neurons reprogrammed from
fibroblasts.'' \emph{Nature Neuroscience}, November.

\hypertarget{ref-Wainger:2014gb}{}
Wainger, Brian J, Evangelos Kiskinis, Cassidy Mellin, Ole Wiskow, Steve
S W Han, Jackson Sandoe, Numa P Perez, et al. 2014. ``Intrinsic membrane
hyperexcitability of amyotrophic lateral sclerosis patient-derived motor
neurons.'' \emph{Cell Reports} 7 (1): 1--11.

\hypertarget{ref-Walsh:2002ia}{}
Walsh, Dominic M, Igor Klyubin, Julia V Fadeeva, William K Cullen, Roger
Anwyl, Michael S Wolfe, Michael J Rowan, and Dennis J Selkoe. 2002.
``Naturally secreted oligomers of amyloid beta protein potently inhibit
hippocampal long-term potentiation in vivo.'' \emph{Nature} 416 (6880):
535--39.

\hypertarget{ref-Walsh:2009kg}{}
Walsh, Dominic M, Eva Thulin, Aedín M Minogue, Niklas Gustavsson, Eric
Pang, David B Teplow, and Sara Linse. 2009. ``A facile method for
expression and purification of the Alzheimer's disease-associated
amyloid beta-peptide.'' \emph{The FEBS Journal} 276 (5): 1266--81.

\hypertarget{ref-Walsh:2005hw}{}
Walsh, Dominic M, Matthew Townsend, Marcia B Podlisny, Ganesh M Shankar,
Julia V Fadeeva, Omar El Agnaf, Dean M Hartley, and Dennis J Selkoe.
2005. ``Certain Inhibitors of Synthetic Amyloid \(\beta\)-Peptide
(A\(\beta\)) Fibrillogenesis Block Oligomerization of Natural A\(\beta\)
and Thereby Rescue Long-Term Potentiation.'' \emph{The Journal of
Neuroscience : The Official Journal of the Society for Neuroscience} 25
(10): 2455--62.

\hypertarget{ref-Wang:2017kq}{}
Wang, Zemin, Rosemary J Jackson, Wei Hong, Walter M Taylor, Grant T
Corbett, Arturo Moreno, Wen Liu, et al. 2017. ``Human Brain-Derived
A\(\beta\) Oligomers Bind to Synapses and Disrupt Synaptic Activity in a
Manner That Requires APP.'' \emph{The Journal of Neuroscience : The
Official Journal of the Society for Neuroscience} 37 (49): 11947--66.

\hypertarget{ref-Whyte:2017gj}{}
Whyte, Lauren S, Adeline A Lau, Kim M Hemsley, John J Hopwood, and
Timothy J Sargeant. 2017. ``Endo-lysosomal and autophagic dysfunction: a
driving factor in Alzheimer's disease?'' \emph{Journal of
Neurochemistry} 140 (5): 703--17.

\hypertarget{ref-ggplot2}{}
Wickham, Hadley. 2016. \emph{Ggplot2: Elegant Graphics for Data
Analysis}. Springer-Verlag New York. \url{http://ggplot2.org}.

\hypertarget{ref-Wong:2017ci}{}
Wong, Matthew W, Nady Braidy, Anne Poljak, Russell Pickford, Madhav
Thambisetty, and Perminder S Sachdev. 2017. ``Dysregulation of lipids in
Alzheimer's disease and their role as potential biomarkers.''
\emph{Alzheimer's \& Dementia: The Journal of the Alzheimer's
Association} 13 (7): 810--27.

\hypertarget{ref-Xu:2018hl}{}
Xu, Wei, Fang Fang, Jianqing Ding, and Chengbiao Wu. 2018.
``Dysregulation of Rab5-mediated endocytic pathways in Alzheimer's
disease.'' \emph{Traffic} 19 (4): 253--62.

\hypertarget{ref-Yaffe:1997vf}{}
Yaffe, M B, K Rittinger, S Volinia, P R Caron, A Aitken, H Leffers, S J
Gamblin, S J Smerdon, and L C Cantley. 1997. ``The structural basis for
14-3-3:phosphopeptide binding specificity.'' \emph{Cell} 91 (7):
961--71.

\hypertarget{ref-Yamashima:2013fp}{}
Yamashima, Tetsumori. 2013. ``Reconsider Alzheimer's disease by the
'calpain-cathepsin hypothesis'--a perspective review.'' \emph{Progress
in Neurobiology} 105 (June): 1--23.

\hypertarget{ref-Yang:2017hh}{}
Yang, Ting, Shaomin Li, Huixin Xu, Dominic M Walsh, and Dennis J Selkoe.
2017. ``Large Soluble Oligomers of Amyloid \(\beta\)-Protein from
Alzheimer Brain Are Far Less Neuroactive Than the Smaller Oligomers to
Which They Dissociate.'' \emph{The Journal of Neuroscience : The
Official Journal of the Society for Neuroscience} 37 (1): 152--63.

\hypertarget{ref-Zahs:2010if}{}
Zahs, Kathleen R, and Karen H Ashe. 2010. ``'Too much good news' - are
Alzheimer mouse models trying to tell us how to prevent, not cure,
Alzheimer's disease?'' \emph{Trends in Neurosciences} 33 (8): 381--89.

\hypertarget{ref-Zhang:1999hf}{}
Zhang, Lixin, Jing Chen, and Haian Fu. 1999. ``Suppression of apoptosis
signal-regulating kinase 1-induced cell death by 14-3-3 proteins.''
\emph{Proceedings of the National Academy of Sciences of the United
States of America} 96 (15): 8511--5.

\hypertarget{ref-Zhang:2018iu}{}
Zhang, Qi, Cheng Ma, Marla Gearing, Peng George Wang, Lih-Shen Chin, and
Lian Li. 2018. ``Integrated proteomics and network analysis identifies
protein hubs and network alterations in Alzheimer's disease.''
\emph{Acta Neuropathologica Communications} 6 (1): 19.

\hypertarget{ref-Zhang:2017gk}{}
Zhang, Shu-Xin, Li-Hui Duan, Shun-Ji He, Gui-Feng Zhuang, and Xiang Yu.
2017. ``Phosphatidylinositol 3,4-bisphosphate regulates neurite
initiation and dendrite morphogenesis via actin aggregation.''
\emph{Cell Research} 27 (2): 253--73.

\hypertarget{ref-Zhang:2018fu}{}
Zhang, Xian, Timothy Y Huang, Joel Yancey, Hong Luo, and Yunwu Zhang.
2018. ``The Role of Rab GTPases in Alzheimer's Disease.'' \emph{ACS
Chemical Neuroscience}, September.

\hypertarget{ref-Zhang:2013kj}{}
Zhang, Yingsha, ChangHui Pak, Yan Han, Henrik Ahlenius, Zhenjie Zhang,
Soham Chanda, Samuele Marro, et al. 2013. ``Rapid single-step induction
of functional neurons from human pluripotent stem cells.'' \emph{NEURON}
78 (5): 785--98.

\hypertarget{ref-Zhao:2016ey}{}
Zhao, Xiaobei, Xu-Qiao Chen, Eugene Han, Yue Hu, Paul Paik, Zhiyong
Ding, Julia Overman, et al. 2016. ``TRiC subunits enhance BDNF axonal
transport and rescue striatal atrophy in Huntington's disease.''
\emph{Proceedings of the National Academy of Sciences} 113 (38):
E5655--64.

\hypertarget{ref-kableExtra}{}
Zhu, Hao. 2018. \emph{KableExtra: Construct Complex Table with 'Kable'
and Pipe Syntax}. \url{https://CRAN.R-project.org/package=kableExtra}.

\FloatBarrier
\newpage
 \setlength{\parskip}{0pt}

\singlespacing

\section{Appendix}\label{appendix}

\textbf{Software for data extraction and analysis from multi-electrode array recordings}

\doublespacing

\subsection{Introduction}\label{introduction-4}

The following sections contain the source code developed to facilitate
data extraction and analysis of multi-electrode array (MEA) recordings.
These software tools were written to support the analysis methods
described previously in this thesis. The output of these tools represent
descriptors and parameters of multi-electrode array recordings that were
used as part of the hypothesis testing and inferential statistics
performed throughout this work and described within the respective
\emph{Methods} sections of each chapter. This material is included here
for the purposes of documentation and distribution to others who wish to
implement the methods presented in this thesis.

A note to those wishing to utilize this software, the source code below
in part utilizes functions and libraries developed by third parties that
extend beyond the built-in utilities of the base programming language in
which the particular scripts are written. The use and authors of these
code are cited throughout this document and the use of third party
software within this work was performed under licensing granted by the
authors. The authors of these functions and libraries retain the
copyright of their work, and as such the source code for these routines
is not provided as part of this document. The software included here is
not fully executable without access to these functions and libraries.
Those seeking to use this source code for their own purposes will need
obtain access to these third party extensions through appropriate means.

A workflow for the utilization of this software is presented in Figure
\ref{meaAnalysisFlowChart}. This workflow reflects the utilization of
these tools for the analysis reported here, however, this code may
require reconfiguration and or extension to fulfill other analysis
applications. The input for all analyses were \textsf{.spk} files of MEA
recordings generated by the AxIS acquisition software (Axion
Biosystems).

\begin{figure}

{\centering \includegraphics[width=.9\linewidth]{~/Documents/YoungPearseLab/Analysis/appendixGraphics/meaAnalysisFlowChart} 

}

\caption{\label{meaAnalysisFlowChart}{\small \textbf{Example workflow for application of MEA data extraction and analysis software.}}}\label{fig:meaAnalysisFlowChartDiagram}
\end{figure}

\newpage

\subsection{Appendix A: Voltage waveform data
extraction}\label{appendix-a-voltage-waveform-data-extraction}

\paragraph{spkParse96well.m}\label{spkparse96well.m}

The following is a MatLab (MathWorks) script used to obtain the voltage
recordings and associated meta-data: timestamp, crossing threshold value
(SD), and electrode position; for each firing event. This script
utilizes the MatLab \texttt{AxisFile} and dependent functions provided
with AxIS acquisition software. This script is designed to parse this
data from 96-well format multi-well MEAs. This script should be executed
within a directory containing one or more \textsf{.spk} files.

\singlespacing

\begin{Shaded}
\begin{Highlighting}[]

\KeywordTok{%} \NormalTok{spkParse96well.m}
\KeywordTok{path}\NormalTok{(}\StringTok{'~/matlab/axion/'}\NormalTok{,path);}
\KeywordTok{spk_files} \NormalTok{= dir(}\StringTok{'*.spk'} \NormalTok{);}
\KeywordTok{for} \KeywordTok{f} \NormalTok{= 1:length(spk_files)}
    \KeywordTok{SpkRec} \NormalTok{= spk_files(f)}\KeywordTok{.name;}
    \KeywordTok{SpkRecString} \NormalTok{= split(spk_files(f)}\KeywordTok{.name}\NormalTok{,[string(}\StringTok{'_'}\NormalTok{),}\KeywordTok{string}\NormalTok{(}\StringTok{'Rec-'}\NormalTok{)]);}
    \KeywordTok{SpkRecData} \NormalTok{= AxisFile(SpkRec)}\KeywordTok{.DataSets.LoadData;}
    \KeywordTok{for} \KeywordTok{r} \NormalTok{= 1:8}
        \KeywordTok{for} \KeywordTok{c} \NormalTok{= 1:12}
           \KeywordTok{for} \KeywordTok{x} \NormalTok{= 1:3}
             \KeywordTok{for} \KeywordTok{y} \NormalTok{= 1:3}
              \KeywordTok{try}
         \KeywordTok{arrayVolt} \NormalTok{= []}\KeywordTok{;} \KeywordTok{%clear} \NormalTok{arrayVolt to free mem in suscessive loops}
         \KeywordTok{arrayTime} \NormalTok{= []}\KeywordTok{;} \KeywordTok{%clear} \NormalTok{arrayTime}
         \KeywordTok{cellSD} \NormalTok{= []}\KeywordTok{;} \KeywordTok{%clear} \NormalTok{cellSD}
         \KeywordTok{vectorSD} \NormalTok{= []}\KeywordTok{;%clear} \NormalTok{vectorSD}
                 \KeywordTok{arrayVolt} \NormalTok{= SpkRecData}\DataTypeTok{\{r,c,x,y\}}\NormalTok{.GetVoltageVector}\KeywordTok{;}
         \KeywordTok{arrayTime} \NormalTok{= SpkRecData}\DataTypeTok{\{r,c,x,y\}}\NormalTok{.GetTimeVector}\KeywordTok{;}
                 \KeywordTok{cellSD} \NormalTok{= }\DataTypeTok{\{SpkRecData\{r,c,x,y\}.StandardDeviation\}}\KeywordTok{;}
                 \KeywordTok{vectorSD} \NormalTok{= cell2mat(cellSD);}
                 \KeywordTok{catch} \NormalTok{ME}
                    \KeywordTok{if} \KeywordTok{(strcmp}\NormalTok{(ME.identifier,}\StringTok{'MATLAB:structRefFromNonStruct'}\KeywordTok{)}\NormalTok{)}
                            \KeywordTok{continue}
                    \KeywordTok{end}
              \KeywordTok{end}
              \KeywordTok{plate} \NormalTok{= SpkRecString(3);}
              \KeywordTok{rec} \NormalTok{= SpkRecString(9);}
              \KeywordTok{row} \NormalTok{= num2str(r);}
              \KeywordTok{column} \NormalTok{= num2str(c);}
              \KeywordTok{array_x} \NormalTok{= num2str(x);}
              \KeywordTok{array_y} \NormalTok{= num2str(y);}
              \KeywordTok{filenameVolt} \NormalTok{= strcat(plate,}\StringTok{'_'}\NormalTok{,rec,}\StringTok{'_'}\NormalTok{,row,}\StringTok{'_'}\NormalTok{,column,...}
                  \StringTok{'_'}\NormalTok{,}\KeywordTok{array_x}\NormalTok{,}\StringTok{'_'}\NormalTok{,array_y,}\StringTok{'_'}\NormalTok{,}\StringTok{'volt'}\NormalTok{,}\StringTok{'.csv'}\NormalTok{);}
          \KeywordTok{csvwrite}\NormalTok{(char(filenameVolt),}\KeywordTok{arrayVolt}\NormalTok{);}
          \KeywordTok{filenameTime} \NormalTok{= strcat(plate,}\StringTok{'_'}\NormalTok{,rec,}\StringTok{'_'}\NormalTok{,row,}\StringTok{'_'}\NormalTok{,column,...}
          \StringTok{'_'}\NormalTok{,}\KeywordTok{array_x}\NormalTok{,}\StringTok{'_'}\NormalTok{,array_y,}\StringTok{'_'}\NormalTok{,}\StringTok{'time'}\NormalTok{,}\StringTok{'.csv'}\NormalTok{);}
          \KeywordTok{csvwrite}\NormalTok{(char(filenameTime),}\KeywordTok{arrayTime}\NormalTok{(1,:));}
              \KeywordTok{filenameSD} \NormalTok{= strcat(plate,}\StringTok{'_'}\NormalTok{,rec,}\StringTok{'_'}\NormalTok{,row,}\StringTok{'_'}\NormalTok{,column,...}
                  \StringTok{'_'}\NormalTok{,}\KeywordTok{array_x}\NormalTok{,}\StringTok{'_'}\NormalTok{,array_y}\StringTok{','}\NormalTok{_}\StringTok{','}\NormalTok{SD}\StringTok{','}\NormalTok{.csv}\StringTok{');}
\StringTok{              csvwrite(char(filenameSD),vectorSD);}
\StringTok{             end}
\StringTok{           end}
\StringTok{        end}
\StringTok{    end}
\StringTok{end   }
\StringTok{exit   }
\end{Highlighting}
\end{Shaded}

\doublespacing

\paragraph{wfMetics.R}\label{wfmetics.r}

The following is a R script used to calculate the waveform parameters:
\textit{peak, valley, amplitude, area-under-the-curve, peak-valley-amplitude, and nonlinear energy}
for each firing event as well as append timestamp, crossing threshold,
and electrode positional information. The following script requires
three arguments:

\begin{enumerate}
\item \textsf{volt.csv} a file of spike event voltage data.
\item \textsf{time.csv} a file of spike event timestamp data.
\item \textsf{SD.csv} a file of spike event cross threshold data.
\end{enumerate}

Each of these files representing the recording data per a given
electrode within a given recording. This script requires the
\textsf{data.table}, \textsf{MESS}, \textsf{geepack}, \textsf{geeM}
libraries available on the Comprehensive R Archive Network (CRAN)
\texttt{https://CRAN.r-project.org}.

\singlespacing

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{#!/usr/local/bin/Rscript}

\CommentTok{#  ****************************************************************************}
\CommentTok{#  wfMetrics.R}
\CommentTok{#  ****************************************************************************}

\CommentTok{#  load libraries}
\KeywordTok{library}\NormalTok{(data.table)}
\KeywordTok{library}\NormalTok{(MESS)}
\KeywordTok{library}\NormalTok{(geepack)}
\KeywordTok{library}\NormalTok{(geeM)}

\CommentTok{#  define variables}
\NormalTok{args <-}\StringTok{ }\KeywordTok{commandArgs}\NormalTok{(}\DataTypeTok{trailingOnly =} \OtherTok{TRUE}\NormalTok{)}

\CommentTok{# set seed}
\KeywordTok{set.seed}\NormalTok{(}\DecValTok{1}\NormalTok{)}

\CommentTok{# generate dt of voltage array files, and parse filenames for }
\CommentTok{# recording, plate, well, and node identifiers}
\NormalTok{files <-}\StringTok{ }\KeywordTok{data.table}\NormalTok{(}
  \DataTypeTok{volt.file=}\NormalTok{args[}\DecValTok{1}\NormalTok{], }\CommentTok{# volt.csv file }
  \DataTypeTok{SD.file=}\NormalTok{args[}\DecValTok{2}\NormalTok{],}\CommentTok{# SD.csv file }
  \DataTypeTok{time.file=}\NormalTok{args[}\DecValTok{3}\NormalTok{]}\CommentTok{# time.csv file }
\NormalTok{)}

\NormalTok{files[,}
      \StringTok{`}\DataTypeTok{:=}\StringTok{`}\NormalTok{(}
          \DataTypeTok{plate.num=}\KeywordTok{strsplit}\NormalTok{(volt.file,}\StringTok{"_"}\NormalTok{)[[}\DecValTok{1}\NormalTok{]][}\DecValTok{1}\NormalTok{],}
          \DataTypeTok{rec=}\KeywordTok{strsplit}\NormalTok{(volt.file,}\StringTok{"_"}\NormalTok{)[[}\DecValTok{1}\NormalTok{]][}\DecValTok{2}\NormalTok{],}
          \DataTypeTok{well_row=}\NormalTok{LETTERS[}\KeywordTok{as.numeric}\NormalTok{(}\KeywordTok{strsplit}\NormalTok{(volt.file[}\DecValTok{1}\NormalTok{],}\StringTok{"_"}\NormalTok{)[[}\DecValTok{1}\NormalTok{]][}\DecValTok{3}\NormalTok{])],}
          \DataTypeTok{well_column=}\KeywordTok{as.numeric}\NormalTok{(}\KeywordTok{strsplit}\NormalTok{(volt.file,}\StringTok{"_"}\NormalTok{)[[}\DecValTok{1}\NormalTok{]][}\DecValTok{4}\NormalTok{],}
          \DataTypeTok{array_x=}\KeywordTok{as.numeric}\NormalTok{(}\KeywordTok{strsplit}\NormalTok{(volt.file,}\StringTok{"_"}\NormalTok{)[[}\DecValTok{1}\NormalTok{]][}\DecValTok{5}\NormalTok{]),}
          \DataTypeTok{array_y=}\KeywordTok{as.numeric}\NormalTok{(}\KeywordTok{strsplit}\NormalTok{(volt.file,}\StringTok{"_"}\NormalTok{)[[}\DecValTok{1}\NormalTok{]][}\DecValTok{6}\NormalTok{]),}
          \DataTypeTok{rec.node=}\KeywordTok{paste0}\NormalTok{(}
                    \NormalTok{rec,}\StringTok{"_"}\NormalTok{,plate.num,}\StringTok{"_"}\NormalTok{,well_row,}
                    \NormalTok{well_column,}\StringTok{"_"}\NormalTok{,array_x,array_y)}
          \NormalTok{),}
          \DataTypeTok{by=}\NormalTok{volt.file}
          \NormalTok{]}

\CommentTok{# name output file}
\NormalTok{outputFile <-}\StringTok{ }\KeywordTok{paste}\NormalTok{(}
  \NormalTok{files$plate.num,}
  \NormalTok{files$rec,}
  \NormalTok{files$well_row,}
  \NormalTok{files$well_column,}
  \NormalTok{files$array_x,}
  \NormalTok{files$array_y,}
  \StringTok{"wfMetrics.csv"}\NormalTok{,}
  \DataTypeTok{sep =} \StringTok{"_"}
  \NormalTok{)}

\CommentTok{# capture plate and rec meta-data}
\NormalTok{plate.num <-}\StringTok{ }\NormalTok{files$plate.num}
\NormalTok{rec <-}\StringTok{ }\NormalTok{files$rec}
\NormalTok{well_row <-}\StringTok{ }\NormalTok{files$well_row}
\NormalTok{well_column <-}\StringTok{ }\NormalTok{files$well_column}
\NormalTok{array_x <-}\StringTok{ }\NormalTok{files$array_x}
\NormalTok{array_y <-}\StringTok{ }\NormalTok{files$array_y}

\CommentTok{# import volt data}
\NormalTok{volt.array <-}\StringTok{ }\KeywordTok{as.matrix}\NormalTok{(}\KeywordTok{read.csv}\NormalTok{(files$volt.file, }\DataTypeTok{header =} \OtherTok{FALSE}\NormalTok{))}

\CommentTok{# multiply this matrix by 10^6 to convert values from V to uV}
\NormalTok{volt.array.uV <-}\StringTok{ }\NormalTok{volt.array*}\DecValTok{1000000}

\CommentTok{# save the number of rows in this large matrix as a variable}
\CommentTok{# this should be equal to 38 for default Axis recording settings}
\NormalTok{nrow.volt <-}\StringTok{ }\KeywordTok{nrow}\NormalTok{(volt.array.uV)}

\CommentTok{# remove the original voltage matrix to conserve memory}
\KeywordTok{rm}\NormalTok{(volt.array)}

\CommentTok{# calculate waveform parameters for each spike}
\NormalTok{peak <-}\StringTok{ }\KeywordTok{apply}\NormalTok{(volt.array.uV,}\DecValTok{2}\NormalTok{,max)}
\NormalTok{valley <-}\StringTok{ }\KeywordTok{abs}\NormalTok{(}\KeywordTok{apply}\NormalTok{(volt.array.uV,}\DecValTok{2}\NormalTok{,min))}
\NormalTok{peak.valley <-}\StringTok{ }\NormalTok{peak+valley}
\NormalTok{auc <-}\StringTok{ }
\StringTok{  }\KeywordTok{apply}\NormalTok{(}
    \NormalTok{volt.array.uV,}
    \DecValTok{2}\NormalTok{, }\CommentTok{#apply function across columns}
    \NormalTok{function(x)\{}
               \KeywordTok{auc}\NormalTok{(}\DecValTok{1}\NormalTok{:}\DecValTok{38}\NormalTok{,x, }\DataTypeTok{type =} \StringTok{'linear'}\NormalTok{, }\DataTypeTok{absolutearea =} \OtherTok{TRUE}\NormalTok{)}
              \NormalTok{\}}
         \NormalTok{)}
\NormalTok{pvi <-}\StringTok{ }
\StringTok{  }\KeywordTok{apply}\NormalTok{(}
        \NormalTok{volt.array.uV,}
        \DecValTok{2}\NormalTok{, }\CommentTok{#apply function across columns}
        \NormalTok{function(x)\{}
               \KeywordTok{abs}\NormalTok{(}\KeywordTok{which.max}\NormalTok{(x)-}\KeywordTok{which.min}\NormalTok{(x))}
              \NormalTok{\}}
         \NormalTok{)}
\NormalTok{NLE.max <-}\StringTok{ }
\StringTok{  }\KeywordTok{apply}\NormalTok{(}
      \NormalTok{volt.array.uV,}
      \DecValTok{2}\NormalTok{, }\CommentTok{#apply function across columns}
      \NormalTok{function(x)\{}
        \KeywordTok{max}\NormalTok{(x[}\DecValTok{2}\NormalTok{:(nrow.volt}\DecValTok{-1}\NormalTok{)]^}\DecValTok{2}\NormalTok{-(x[}\DecValTok{1}\NormalTok{:(nrow.volt}\DecValTok{-2}\NormalTok{)]*x[}\DecValTok{3}\NormalTok{:nrow.volt]))}
                 \NormalTok{\}}
      \NormalTok{)}
\NormalTok{SD.array <-}\StringTok{ }\KeywordTok{as.matrix}\NormalTok{(}\KeywordTok{read.csv}\NormalTok{(files$SD.file, }\DataTypeTok{header =} \OtherTok{FALSE}\NormalTok{))}
\NormalTok{time.array <-}\StringTok{ }\KeywordTok{as.matrix}\NormalTok{(}\KeywordTok{read.csv}\NormalTok{(files$time.file, }\DataTypeTok{header =} \OtherTok{FALSE}\NormalTok{))}

\CommentTok{# Transpose the volt.array.uV matrix, so that voltage recordings from}
\CommentTok{# each spike event are placed in adjacent columns}
\NormalTok{volt.array.uV <-}\StringTok{ }\KeywordTok{t}\NormalTok{(volt.array.uV)}

\CommentTok{# Transpose SD.array and time.array so that each spike is represented }
\CommentTok{# as a row rather than column}
\NormalTok{SD.array <-}\StringTok{ }\KeywordTok{t}\NormalTok{(SD.array)}
\NormalTok{time.array <-}\StringTok{ }\KeywordTok{t}\NormalTok{(time.array)}

\CommentTok{# combine calculated metrics, SD, and time into data.table}
\NormalTok{array.metrics.dt <-}\StringTok{ }
\StringTok{  }\KeywordTok{data.table}\NormalTok{(}
    \KeywordTok{cbind}\NormalTok{(peak,valley,peak.valley,pvi,auc,NLE.max,SD.array,time.array)}
    \NormalTok{)}
\KeywordTok{colnames}\NormalTok{(array.metrics.dt) <-}\StringTok{ }
\StringTok{  }\KeywordTok{c}\NormalTok{(}\StringTok{"peak"}\NormalTok{,}\StringTok{"valley"}\NormalTok{,}\StringTok{"peak.valley"}\NormalTok{,}\StringTok{"pvi"}\NormalTok{,}\StringTok{"auc"}\NormalTok{,}\StringTok{"NLE.max"}\NormalTok{,}\StringTok{"SD"}\NormalTok{,}\StringTok{"time"}\NormalTok{)}
\NormalTok{array.metrics.dt[,spike:}\ErrorTok{=}\DecValTok{1}\NormalTok{:}\KeywordTok{nrow}\NormalTok{(array.metrics.dt)]}
\KeywordTok{setkey}\NormalTok{(array.metrics.dt,spike)}

\CommentTok{# convert volt.array.uV into data.table}
\NormalTok{volt.array.uV <-}\StringTok{ }\KeywordTok{data.table}\NormalTok{(volt.array.uV)}
\NormalTok{volt.array.uV[,spike:}\ErrorTok{=}\DecValTok{1}\NormalTok{:}\KeywordTok{nrow}\NormalTok{(volt.array.uV)]}
\KeywordTok{setkey}\NormalTok{(volt.array.uV,spike)}

\CommentTok{# combine waveform metrics and voltage data into single dt}
\NormalTok{array.metrics.volt.dt <-}\StringTok{ }\NormalTok{array.metrics.dt[volt.array.uV]}

\CommentTok{# add plate, rec meta-data}
\NormalTok{array.metrics.volt.dt[,}
                      \StringTok{`}\DataTypeTok{:=}\StringTok{`}\NormalTok{(}
                        \DataTypeTok{plate.num=}\NormalTok{plate.num,}
                        \DataTypeTok{well_row=}\NormalTok{well_row,}
                        \DataTypeTok{well_column=}\NormalTok{well_column,}
                        \DataTypeTok{array_x=}\NormalTok{array_x,}
                        \DataTypeTok{array_y=}\NormalTok{array_y,}
                        \DataTypeTok{rec=}\NormalTok{rec}
                      \NormalTok{)]}

\CommentTok{# write data to out to csv}
\KeywordTok{fwrite}\NormalTok{(}
  \NormalTok{array.metrics.volt.dt,}
  \DataTypeTok{file =} \NormalTok{outputFile,}
  \DataTypeTok{sep =} \StringTok{","}\NormalTok{,}
  \DataTypeTok{row.names =} \OtherTok{FALSE}\NormalTok{,}
  \DataTypeTok{col.names =} \OtherTok{TRUE}
\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\doublespacing

\paragraph{wfMeticsAgg.sh}\label{wfmeticsagg.sh}

The following is a bash script used to aggregate the
\textsf{wfMetrics.csv} files output from \textsf{wfMetics.R} into single
file. This script is handy since the prior script (\textsf{wfMetics.R})
is typically executed in parallel fashion for multiple electrodes
monitored across multiple recordings.

\singlespacing

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{#!/bin/bash}

\CommentTok{#  ****************************************************************************}
\CommentTok{#  wfMetricsAgg.sh}
\CommentTok{#  ****************************************************************************}

\OtherTok{Date=$(}\KeywordTok{date} \StringTok{'+%Y-%m-%d'}\OtherTok{)}
\OtherTok{currentPath=$(}\KeywordTok{pwd}\OtherTok{)}
\OtherTok{currentDir=$(}\KeywordTok{basename} \OtherTok{$currentPath)}
\OtherTok{Experiment=$(}\KeywordTok{echo} \OtherTok{$\{currentDir#}\NormalTok{*_}\OtherTok{\})}
\OtherTok{wfMetricsFiles=($(}\KeywordTok{find} \NormalTok{./ -type f -name }\StringTok{'*wfMetrics.csv'}\OtherTok{))}
\KeywordTok{srun} \NormalTok{sed 1q }\OtherTok{$\{wfMetricsFiles[0]\}} \KeywordTok{>>} \OtherTok{$\{Date\}}\NormalTok{_}\OtherTok{$\{Experiment\}}\NormalTok{_wf.metrics.csv}
\KeywordTok{for} \KeywordTok{f} \NormalTok{in }\OtherTok{$\{wfMetricsFiles[@]\}}\KeywordTok{;}
        \KeywordTok{do} \KeywordTok{srun} \NormalTok{sed 1d }\OtherTok{$f} \KeywordTok{>>} \OtherTok{$\{Date\}}\NormalTok{_}\OtherTok{$\{Experiment\}}\NormalTok{_wf.metrics.csv}\KeywordTok{;}
\KeywordTok{done}\NormalTok{;}
\end{Highlighting}
\end{Shaded}

\doublespacing
\newpage

\subsection{Appendix B: Calculation of array-level firing
frequency}\label{appendix-b-calculation-of-array-level-firing-frequency}

\paragraph{wfToWellLogHz.R}\label{wftowellloghz.r}

The following is a R script used to calculate the log transformed
frequency (log\(_{10}\)Hz) of spontaneous neuronal firing events across
all electrodes of a single array (or well) of a multi-well MEA per
recording. The following script requires two arguments:

\begin{enumerate}
\item A \textsf{.csv} of annotated waveform data, such as that output by the \textsf{wfMetricsAgg.sh} script described above.
\item Recording duration in seconds provided as an integer value (e.g. 1800 for a 30 minute recording).
\end{enumerate}

\singlespacing

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{#!/usr/local/bin/Rscript}

\CommentTok{#  ****************************************************************************}
\CommentTok{#  wfToWellLogHz.R}
\CommentTok{#  ****************************************************************************}

\CommentTok{#  load libraries}
\KeywordTok{library}\NormalTok{(data.table)}

\CommentTok{#  define variables}
\NormalTok{args <-}\StringTok{ }\KeywordTok{commandArgs}\NormalTok{(}\DataTypeTok{trailingOnly =} \OtherTok{TRUE}\NormalTok{)}

\NormalTok{durationSec <-}\StringTok{ }\KeywordTok{as.numeric}\NormalTok{(args[}\DecValTok{2}\NormalTok{])}

\CommentTok{#  define functions}

\CommentTok{#  fuction to convert NA values to min MFR}
\NormalTok{dt.na.to.min <-}\StringTok{ }\NormalTok{function(DT)\{}
  \CommentTok{#by name :}
  \NormalTok{for (j in }\KeywordTok{names}\NormalTok{(DT))}
    \KeywordTok{set}\NormalTok{(DT,}\KeywordTok{which}\NormalTok{(}\KeywordTok{is.na}\NormalTok{(DT[[j]])),j,}\KeywordTok{log10}\NormalTok{(}\DecValTok{1}\NormalTok{/durationSec))}
\NormalTok{\}}

\CommentTok{#  import waveform data}
\NormalTok{wfMetrics <-}\StringTok{ }\KeywordTok{fread}\NormalTok{(}
  \NormalTok{args[}\DecValTok{1}\NormalTok{],}
  \DataTypeTok{stringsAsFactors =} \OtherTok{FALSE}
  \NormalTok{)}
  
\CommentTok{#  remove original spike column (inaccurate)}
\NormalTok{wfMetrics[,spike:}\ErrorTok{=}\OtherTok{NULL}\NormalTok{]}

\CommentTok{#  filter for spikes recorded by low-background electrodes----}
\NormalTok{upperLimit <-}\StringTok{ }\NormalTok{wfMetrics[rec==}\StringTok{"00"}\NormalTok{][,}\KeywordTok{mean}\NormalTok{(SD)]+(}\DecValTok{3}\NormalTok{*wfMetrics[,}\KeywordTok{sd}\NormalTok{(SD)])}

\CommentTok{#  if statement catch for if rec 00 is encoded as character or numeric}
\NormalTok{if(}\KeywordTok{is.na}\NormalTok{(upperLimit))\{}
  \NormalTok{(upperLimit <-}\StringTok{ }\NormalTok{wfMetrics[rec==}\DecValTok{0}\NormalTok{][,}\KeywordTok{mean}\NormalTok{(SD)]+(}\DecValTok{3}\NormalTok{*wfMetrics[,}\KeywordTok{sd}\NormalTok{(SD)]))}
  \NormalTok{\}}

\CommentTok{#  if dataset does not include rec 00, calculate upperLimit}
\CommentTok{#  from entire recording set}
\NormalTok{if(}\KeywordTok{is.na}\NormalTok{(upperLimit))\{}
  \NormalTok{(upperLimit <-}\StringTok{ }\NormalTok{wfMetrics[,}\KeywordTok{mean}\NormalTok{(SD)]+(}\DecValTok{3}\NormalTok{*wfMetrics[,}\KeywordTok{sd}\NormalTok{(SD)]))}
\NormalTok{\}}

\CommentTok{#  calculate logHz for each well and recording}
\NormalTok{wellLevelData <-}\StringTok{ }
\StringTok{  }\NormalTok{wfMetrics[}
    \NormalTok{SD<=upperLimit,}
    \NormalTok{.(}\DataTypeTok{totalSpikes=}\KeywordTok{length}\NormalTok{(V1)), }
    \NormalTok{by=.(plate.num,well_row,well_column,rec)}
    \NormalTok{]}

\NormalTok{wellLevelData[,}\StringTok{`}\DataTypeTok{:=}\StringTok{`}\NormalTok{(}\DataTypeTok{logHz=}\KeywordTok{log10}\NormalTok{((totalSpikes}\DecValTok{+1}\NormalTok{)/durationSec))]}

\NormalTok{wellLevelData[,wellID:}\ErrorTok{=}\KeywordTok{paste0}\NormalTok{(plate.num,}\StringTok{"_"}\NormalTok{,well_row,well_column)]}

\CommentTok{#  generate wide-form of logHz data}
\CommentTok{#  exposes 'gaps' in well-level data, for wells included in treatment}
\CommentTok{#  but for whom there is no activity (no spikes) in a subsequent recording}
\NormalTok{wellLogHzWide <-}\StringTok{ }\KeywordTok{dcast.data.table}\NormalTok{(}
  \NormalTok{wellLevelData,}
  \NormalTok{plate.num +}\StringTok{ }\NormalTok{well_row +}\StringTok{ }\NormalTok{well_column +}\StringTok{ }\NormalTok{wellID ~}\StringTok{ }\NormalTok{rec,}
  \DataTypeTok{value.var =} \StringTok{"logHz"}\NormalTok{)}

\CommentTok{# apply dt.na.to.min to apply nominal activity (equivalent of 1 spike)}
\CommentTok{# to those wells}
\KeywordTok{dt.na.to.min}\NormalTok{(wellLogHzWide)}

\CommentTok{#  write wellLevelData.logHz.wide to csv}
\KeywordTok{fwrite}\NormalTok{(}
  \NormalTok{wellLogHzWide,}
  \KeywordTok{paste}\NormalTok{(}\KeywordTok{Sys.Date}\NormalTok{(),}\StringTok{"well_logHz_wide.csv"}\NormalTok{, }\DataTypeTok{sep =} \StringTok{"_"}\NormalTok{)}
\NormalTok{)}

\CommentTok{#  covert wide-form back to long-form}
\CommentTok{#  now with any 'gaps' filled}
\NormalTok{wellLogHzLong <-}\StringTok{ }\KeywordTok{melt.data.table}\NormalTok{(}
  \NormalTok{wellLogHzWide,}
  \DataTypeTok{id.vars =} \KeywordTok{c}\NormalTok{(}\StringTok{"plate.num"}\NormalTok{, }\StringTok{"well_row"}\NormalTok{, }\StringTok{"well_column"}\NormalTok{, }\StringTok{"wellID"}\NormalTok{),}
  \DataTypeTok{variable.name =} \StringTok{"rec"}\NormalTok{,}
  \DataTypeTok{variable.factor =} \OtherTok{FALSE}\NormalTok{,}
  \DataTypeTok{value.name =} \StringTok{"logHz"}
\NormalTok{)}

\CommentTok{#  write wellLogHzLong to csv}
\KeywordTok{fwrite}\NormalTok{(}
  \NormalTok{wellLogHzLong,}
  \DataTypeTok{file =} \KeywordTok{paste}\NormalTok{(}\KeywordTok{Sys.Date}\NormalTok{(),}\StringTok{"well_logHz_long.csv"}\NormalTok{,}\DataTypeTok{sep =} \StringTok{"_"}\NormalTok{),}
  \DataTypeTok{row.names =} \OtherTok{FALSE}
\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\doublespacing
\newpage

\subsection{Appendix C: Bootstrapping simulation for cohort assignment
of
arrays}\label{appendix-c-bootstrapping-simulation-for-cohort-assignment-of-arrays}

\paragraph{bootstrapCohortAssing.R}\label{bootstrapcohortassing.r}

The following is a R script used to generate experiment treatment groups
or cohorts for multi-well MEA based experiments based on baseline firing
activity. The script performs bootstrapping simulation of thousands of
possible assignments to identify the configuration the minimizes the
difference in mean firing frequency between groups. This script requires
a two arguments:

\begin{enumerate}
\item A \textsf{treatment-samples.csv} file containing containing two columns: \textit{treatment} and textit{replicates} detailing the treatment conditions and desired number of replicates per condition.
\item The second argument is a \textsf{well\_logHz\_long.csv} file, containing the baseline activity. This file is produced by the \textsf{wfToWellLogHz.R} script.
\end{enumerate}

This script will return two outputs:

\begin{enumerate}
\item A file \textsf{treatment-map.csv} containing the resulting treatment assignments.
\item A \textsf{.pdf} file depicting the spatial layout of the treatment groups across the MEA plate(s).
\end{enumerate}

This script requires the \textsf{data.table}, \textsf{ggplot2}, and
\textsf{broom} libraries available on CRAN
(\texttt{https://CRAN.r-project.org}).

\singlespace

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{#!/usr/local/bin/Rscript}

\CommentTok{#  ****************************************************************************}
\CommentTok{#  bootstrapCohortAssign.R}
\CommentTok{#  ****************************************************************************}

\CommentTok{# load libraries}
\KeywordTok{library}\NormalTok{(data.table)}
\KeywordTok{library}\NormalTok{(ggplot2)}
\KeywordTok{library}\NormalTok{(broom)}

\CommentTok{# set seed, this sets the origin of the random number generator, so that random }
\CommentTok{# samples are reproducible over successive executions of the script}
\KeywordTok{set.seed}\NormalTok{(}\DecValTok{1}\NormalTok{)}

\CommentTok{# define variables}

\CommentTok{# write arguments to args vector}
\NormalTok{args <-}\StringTok{ }\KeywordTok{commandArgs}\NormalTok{(}\DataTypeTok{trailingOnly =} \OtherTok{TRUE}\NormalTok{)}

\CommentTok{# number of iterations}
\NormalTok{bootstrapIterations <-}\StringTok{ }\DecValTok{10000}

\CommentTok{# exclusion threshold (SD)}
\NormalTok{exclusionThres <-}\StringTok{ }\DecValTok{2}

\CommentTok{# define functions}

\CommentTok{# population mode}
\NormalTok{pop.mode <-}\StringTok{ }\NormalTok{function(x) \{}
  \NormalTok{ux <-}\StringTok{ }\KeywordTok{unique}\NormalTok{(x)}
  \NormalTok{ux[}\KeywordTok{which.max}\NormalTok{(}\KeywordTok{tabulate}\NormalTok{(}\KeywordTok{match}\NormalTok{(x, ux)))]}
\NormalTok{\}}

\CommentTok{# per plate treatment map}
\NormalTok{per.plate.treat.map <-}\StringTok{ }\NormalTok{function(p)\{}
  \CommentTok{#Generate figure of treatment_map, called treatment_map_layout, using ggplot}
  \NormalTok{treatment_map_layout <-}\StringTok{ }\KeywordTok{ggplot}\NormalTok{(}\DataTypeTok{data=}\NormalTok{treatment_map[plate.num==p],}
                                 \KeywordTok{aes}\NormalTok{(}\DataTypeTok{x=}\NormalTok{well_column,}\DataTypeTok{y=}\NormalTok{well_row,}
                                     \DataTypeTok{fill=}\NormalTok{treatment_group)) +}
\StringTok{    }\KeywordTok{geom_tile}\NormalTok{()+}
\StringTok{    }\KeywordTok{ggtitle}\NormalTok{(p)+}
\StringTok{    }\CommentTok{#Define colours to be used in heatmap, ranging from low to high}
\StringTok{    }\KeywordTok{scale_fill_grey}\NormalTok{()+}
\StringTok{    }\CommentTok{#Reverse the order of the y axis, so that A1 appears top-left}
\StringTok{    }\KeywordTok{scale_y_discrete}\NormalTok{(}\DataTypeTok{limits=}\KeywordTok{c}\NormalTok{(}\StringTok{"H"}\NormalTok{,}\StringTok{"G"}\NormalTok{,}\StringTok{"F"}\NormalTok{,}\StringTok{"E"}\NormalTok{,}
                              \StringTok{"D"}\NormalTok{,}\StringTok{"C"}\NormalTok{,}\StringTok{"B"}\NormalTok{,}\StringTok{"A"}\NormalTok{))+}
\StringTok{    }\KeywordTok{scale_x_discrete}\NormalTok{(}\DataTypeTok{limits=}\KeywordTok{c}\NormalTok{(}\DecValTok{1}\NormalTok{:}\DecValTok{12}\NormalTok{))+}
\StringTok{    }\KeywordTok{theme_bw}\NormalTok{()}
  
  \CommentTok{#Call the value 'treatment_map_layout' so that the figure is rendered}
  \NormalTok{treatment_map_layout}
  
  \CommentTok{#Save a copy of 'treatment_map_layout' as a pdf in the source directory}
  \KeywordTok{ggsave}\NormalTok{(}
    \DataTypeTok{filename=}\KeywordTok{paste0}\NormalTok{(}\KeywordTok{Sys.Date}\NormalTok{(),}\StringTok{"_"}\NormalTok{,p,}\StringTok{"_treatment-map.pdf"}\NormalTok{),}
    \DataTypeTok{width =} \DecValTok{8}\NormalTok{,}
    \DataTypeTok{height =} \DecValTok{5}\NormalTok{,}
    \DataTypeTok{units =} \StringTok{"in"}
    \NormalTok{)}
    
  \KeywordTok{return}\NormalTok{(}\OtherTok{NULL}\NormalTok{)}
\NormalTok{\}}

\CommentTok{# load well level data}
\NormalTok{well.level.all <-}\StringTok{ }\NormalTok{(}
  \KeywordTok{fread}\NormalTok{(}
    \NormalTok{args[}\DecValTok{2}\NormalTok{],}
    \DataTypeTok{stringsAsFactors =} \OtherTok{FALSE}\NormalTok{)}
\NormalTok{)}

\NormalTok{treatment.samples <-}\StringTok{ }
\StringTok{  }\KeywordTok{fread}\NormalTok{(}
    \NormalTok{args[}\DecValTok{1}\NormalTok{],}
    \DataTypeTok{stringsAsFactors =} \OtherTok{FALSE}
  \NormalTok{)}

\CommentTok{# determine mode of well logHz distribution}
\NormalTok{mode.logHz <-}\StringTok{ }\KeywordTok{pop.mode}\NormalTok{(}\KeywordTok{round}\NormalTok{(well.level.all$logHz,}\DecValTok{1}\NormalTok{)) }\CommentTok{# calc mode}
\NormalTok{sd.logHz <-}\StringTok{ }\KeywordTok{sd}\NormalTok{(well.level.all$logHz) }\CommentTok{# calc SD}

\CommentTok{# subset active well by SD threshold}
\NormalTok{well.level.active.all <-}\StringTok{ }
\StringTok{  }\NormalTok{well.level.all[logHz>=(mode.logHz-(exclusionThres*sd.logHz))]}

\CommentTok{# determine treatment groups}
\NormalTok{treatments <-}\StringTok{ }\NormalTok{treatment.samples$treatment}

\CommentTok{#  define treament groups}
\NormalTok{num.groups <-}\StringTok{ }\KeywordTok{uniqueN}\NormalTok{(treatments)}

\NormalTok{treatment_group <-}\StringTok{ }\DecValTok{1}\NormalTok{:num.groups}

\CommentTok{#  append treatment groups to samples}
\NormalTok{treatment.samples <-}\StringTok{ }\KeywordTok{cbind}\NormalTok{(treatment_group,treatment.samples)}

\CommentTok{#  define number of replicates per treatment group}
\NormalTok{num.reps <-}\StringTok{ }\NormalTok{treatment.samples$replicates}

\CommentTok{#  determine number of wells}
\NormalTok{num.wells <-}\StringTok{ }\KeywordTok{sum}\NormalTok{(num.reps)}

\CommentTok{# Sort well.level dt by descending MFR.well.log.trans}
\KeywordTok{setorder}\NormalTok{(well.level.active.all,-logHz)}

\CommentTok{# Select most active num.wells based on MFR.well.log.trans }
\NormalTok{exp.wells <-}\StringTok{ }\NormalTok{well.level.active.all[,}\KeywordTok{head}\NormalTok{(.SD,num.wells)]}

\CommentTok{# generate a vector containing the number of treatment groups and number of}
\CommentTok{# replicates for each treatment group}
\NormalTok{groups <-}\StringTok{ }\KeywordTok{Reduce}\NormalTok{(}
  \NormalTok{c,}\KeywordTok{lapply}\NormalTok{(}\DecValTok{1}\NormalTok{:}\KeywordTok{length}\NormalTok{(treatment_group),}
           \NormalTok{function(x)\{}\KeywordTok{rep}\NormalTok{(treatment_group[x],num.reps[x])\})}
\NormalTok{)}

\CommentTok{# generate an empty martrix, exp_assignments, to be populated with random}
\CommentTok{# assortments of the treatment groups and replicates}
\CommentTok{# excute a for loop to generate 10,000 random assignments of the treatment}
\CommentTok{# groups, and use these samples to populate the exp_assignments matrix}
\NormalTok{exp_assignments <-}\StringTok{ }
\StringTok{  }\KeywordTok{replicate}\NormalTok{(bootstrapIterations,}\KeywordTok{sample}\NormalTok{(groups),}\DataTypeTok{simplify =} \StringTok{"array"}\NormalTok{)}

\CommentTok{# generate an empty vector, assignment_names, to be populated with assignment}
\CommentTok{# column names }
\CommentTok{# execute a for loop to generate assignment names for each of the}
\CommentTok{# random assortments of the treatment groups }
\CommentTok{# assign these assignment names with the formate assignment_XX, as the column}
\CommentTok{# names of the matrix exp_assignments}
\NormalTok{assignment_names <-}\StringTok{ }\OtherTok{NULL}
\NormalTok{for(i in }\DecValTok{1}\NormalTok{:}\KeywordTok{ncol}\NormalTok{(exp_assignments)) assignment_names[i] <-}
\StringTok{  }\KeywordTok{paste}\NormalTok{(}\StringTok{"assignment"}\NormalTok{, i, }\DataTypeTok{sep=}\StringTok{'_'}\NormalTok{)}
\KeywordTok{colnames}\NormalTok{(exp_assignments) <-}\StringTok{ }\NormalTok{assignment_names}

\CommentTok{#Convert the matrix exp_assignments to a data.table}
\NormalTok{exp_assignments <-}\StringTok{ }\KeywordTok{as.data.table}\NormalTok{(exp_assignments)}

\NormalTok{exp_assignments <-}\StringTok{ }\KeywordTok{cbind}\NormalTok{(exp.wells,exp_assignments)}

\CommentTok{#  generate an empty matrix, assignment_Fvalue, to be populated with }
\CommentTok{#  F-values generated from one-way ANOVA assessing logHz as a function of }
\CommentTok{#  treatment group }
\CommentTok{#  Execute a for loop to perform one-way ANOVA for each of the random}
\CommentTok{#  assignments.}

\NormalTok{assignment_Fvalue <-}\StringTok{ }\KeywordTok{c}\NormalTok{()}
\NormalTok{for (i in }\DecValTok{1}\NormalTok{:}\KeywordTok{length}\NormalTok{(assignment_names))\{}
  \NormalTok{assignment_Fvalue[i] <-}
\StringTok{    }\KeywordTok{tidy}\NormalTok{(}
      \KeywordTok{aov}\NormalTok{(}
        \NormalTok{logHz ~}\StringTok{ }\KeywordTok{factor}\NormalTok{(exp_assignments[[assignment_names[i]]]),}
        \NormalTok{exp_assignments}
      \NormalTok{)}
    \NormalTok{)$statistic[}\DecValTok{1}\NormalTok{]}
\NormalTok{\}}

\CommentTok{# determine the index of the minimum F-value, }
\CommentTok{# this number will correspond to the}
\CommentTok{# the number of the random assignment}
\NormalTok{min_variance <-}\StringTok{ }\KeywordTok{which.min}\NormalTok{(assignment_Fvalue)}

\CommentTok{# generation of treatment map}

\CommentTok{# compile well data along with assignment (treatment groups) }
\CommentTok{# determined to have minimal variance}
\NormalTok{treatment_map <-}\StringTok{ }\NormalTok{exp_assignments[}
  \NormalTok{,.(}
    \NormalTok{well_row,}
    \NormalTok{well_column,}
    \NormalTok{plate.num,}
    \NormalTok{logHz,}
    \DataTypeTok{treatment_group=}\NormalTok{exp_assignments[[assignment_names[min_variance]]]}
  \NormalTok{)}
  \NormalTok{]}

\CommentTok{# reclassify treatment_group as factor}
\NormalTok{treatment_map[,treatment_group:}\ErrorTok{=}\KeywordTok{as.factor}\NormalTok{(treatment_group)]}

\NormalTok{treatment_map <-}\StringTok{ }\KeywordTok{merge}\NormalTok{(treatment_map,}
                       \NormalTok{treatment.samples[,.(treatment,treatment_group)],}
                       \DataTypeTok{by=}\StringTok{"treatment_group"}\NormalTok{)}

\CommentTok{# treatment map plots}
\CommentTok{# generate plate map for each plate}
\KeywordTok{lapply}\NormalTok{(treatment_map[,}\KeywordTok{unique}\NormalTok{(plate.num)], per.plate.treat.map)}

\CommentTok{# generate scatter plot of treatment groups}
\NormalTok{treatmentMapScatterPlot <-}
\StringTok{  }\KeywordTok{ggplot}\NormalTok{(}
         \NormalTok{treatment_map,}
         \KeywordTok{aes}\NormalTok{(}
             \DataTypeTok{x=}\NormalTok{treatment_group,}
             \DataTypeTok{y=}\NormalTok{logHz}
           \NormalTok{)}
         \NormalTok{)+}
\StringTok{       }\KeywordTok{geom_point}\NormalTok{(}
             \DataTypeTok{shape=}\DecValTok{1}
         \NormalTok{)+}
\StringTok{       }\KeywordTok{stat_summary}\NormalTok{(}
             \DataTypeTok{fun.data =} \NormalTok{mean_se,}
             \DataTypeTok{shape=}\StringTok{""}
         \NormalTok{)+}
\StringTok{       }\KeywordTok{stat_summary}\NormalTok{(}
             \DataTypeTok{geom=}\StringTok{"point"}\NormalTok{,}
             \DataTypeTok{fun.y =} \NormalTok{mean,}
             \DataTypeTok{shape =} \StringTok{"-"}\NormalTok{,}
             \DataTypeTok{size =} \DecValTok{11}
         \NormalTok{)+}
\StringTok{  }\KeywordTok{theme_bw}\NormalTok{()}

\KeywordTok{ggsave}\NormalTok{(}
  \DataTypeTok{filename=}\KeywordTok{paste0}\NormalTok{(}\KeywordTok{Sys.Date}\NormalTok{(),}\StringTok{"_"}\NormalTok{,p,}\StringTok{"_treatment-map-scatter.pdf"}\NormalTok{),}
  \DataTypeTok{plot =} \NormalTok{treatmentMapScatterPlot,}
  \DataTypeTok{width =} \DecValTok{8}\NormalTok{,}
  \DataTypeTok{height =} \DecValTok{5}\NormalTok{,}
  \DataTypeTok{units =} \StringTok{"in"}
\NormalTok{)}

\CommentTok{# Export treatment_map as .csv file}
\KeywordTok{fwrite}\NormalTok{(}
  \NormalTok{treatment_map,}
  \DataTypeTok{file=}\KeywordTok{paste0}\NormalTok{(}\KeywordTok{Sys.Date}\NormalTok{(),}\StringTok{"_treatment-map.csv"}\NormalTok{),}
  \DataTypeTok{row.names =} \OtherTok{FALSE}
  \NormalTok{)}
\end{Highlighting}
\end{Shaded}

\doublespacing
\newpage

\subsection{Appendix D: PCA and mean-shift clustering of voltage
waveforms}\label{appendix-d-pca-and-mean-shift-clustering-of-voltage-waveforms}

\paragraph{wfNodeSubset.R}\label{wfnodesubset.r}

The following is a R script used to subsetting waveform data from MEA
recording by individual electrode (node) across recordings from a single
experiment. This script requires a two arguments:

\begin{enumerate}
\item A \textsf{.csv} file containing all of the waveform data from a given experiment with each line representing a single spike event, such as that returned by the \textsf{wfMetricsAgg.sh} script.
\item The number of logic cores available for parallel processing provided as an integer value.
\end{enumerate}

This script will return \emph{n} \textsf{.csv} files into a
\textsf{/node} subdirectory with each file containing waveform data from
a unique node (by plate and well). This script requires the
\textsf{data.table} and \textsf{parallel} libraries available on CRAN
(\texttt{https://CRAN.r-project.org}).

\singlespacing

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{#!/usr/local/bin/Rscript}

\CommentTok{#  ****************************************************************************}
\CommentTok{#  wfNodeSubset.R}
\CommentTok{#  ****************************************************************************}

\CommentTok{# load libraries}
\KeywordTok{library}\NormalTok{(data.table)}
\KeywordTok{library}\NormalTok{(parallel)}

\CommentTok{# define variables}
\NormalTok{args <-}\StringTok{ }\KeywordTok{commandArgs}\NormalTok{(}\DataTypeTok{trailingOnly =} \OtherTok{TRUE}\NormalTok{)}

\CommentTok{# number of logic cores available}
\NormalTok{n.cores <-}\StringTok{ }\NormalTok{args[}\DecValTok{2}\NormalTok{]}

\CommentTok{#  create subdirectory}
\NormalTok{mainDir <-}\StringTok{ }\KeywordTok{getwd}\NormalTok{()}
\NormalTok{subDir <-}\StringTok{ "node"}
\KeywordTok{dir.create}\NormalTok{(}\KeywordTok{file.path}\NormalTok{(mainDir, subDir))}

\CommentTok{#  import waveform data}
\NormalTok{wf.metrics <-}\StringTok{ }\KeywordTok{fread}\NormalTok{(args[}\DecValTok{1}\NormalTok{])}
\CommentTok{#  Remove original spike column (inaccurate),}
\CommentTok{#  name redundant "V1" column to spike}
\NormalTok{wf.metrics[,spike:}\ErrorTok{=}\OtherTok{NULL}\NormalTok{]}
\CommentTok{# setnames(wf.metrics,1,"spike")}

\NormalTok{upper.lim <-}\StringTok{ }\NormalTok{wf.metrics[,}\KeywordTok{mean}\NormalTok{(SD)]+(}\DecValTok{3}\NormalTok{*wf.metrics[,}\KeywordTok{sd}\NormalTok{(SD)])}
\NormalTok{wf.metrics <-}\StringTok{ }\NormalTok{wf.metrics[SD<=upper.lim]}

\CommentTok{#  add node id}
\NormalTok{wf.metrics[}
  \NormalTok{,node_ID:}\ErrorTok{=}\KeywordTok{paste0}\NormalTok{(plate.num,}\StringTok{"_"}\NormalTok{,well_row,well_column,}\StringTok{"_"}\NormalTok{,array_x,array_y)}
  \NormalTok{]}

\NormalTok{unique.nodes <-}\StringTok{ }\KeywordTok{unique}\NormalTok{(wf.metrics$node_ID)}

\CommentTok{#  subset wf.metrics based on unique node}
\KeywordTok{mclapply}\NormalTok{(unique.nodes,}
       \NormalTok{function(x)\{}
         \NormalTok{subset.wf <-}\StringTok{ }\NormalTok{wf.metrics[node_ID==x]}
         \KeywordTok{fwrite}\NormalTok{(subset.wf,}\DataTypeTok{file =} \KeywordTok{paste0}\NormalTok{(}\StringTok{"node/"}\NormalTok{,x,}\StringTok{".csv"}\NormalTok{),}\DataTypeTok{row.names =} \OtherTok{FALSE}\NormalTok{)}
         \KeywordTok{return}\NormalTok{(}\OtherTok{NULL}\NormalTok{)}
       \NormalTok{\},}
       \DataTypeTok{mc.cores =} \NormalTok{n.cores)}
\end{Highlighting}
\end{Shaded}

\doublespacing

\paragraph{msCluster.R}\label{mscluster.r}

The following is a R script used to perform principal components
analysis followed by mean-shift clustering on all spike events detected
by an by individual electrode (node) from across all MEA recordings of a
single experiment. This script requires two arguments:

\begin{enumerate}
\item A \textsf{.csv} file containing all of the waveform data from a given node, such as that produced by \textsf{wfNodeSubset.R}
\item The number of logic cores available for parallel processing provided as an integer value.
\end{enumerate}

This script will return a single \textsf{MSclstr.csv} file with the
original data plus the calculated principle component values and the
corresponding cluster value for each spike event. In order to process
all electrodes from a given experiment, this script should be
incorporated as a sub-routine within a script capable of initiating
multiple parallel analyses, such as on a high performance computing
cluster. This script requires the \textsf{data.table},
\textsf{MeanShift}, \textsf{wavethresh}, and \textsf{parallel} libraries
available on CRAN (\texttt{https://CRAN.r-project.org}).

\singlespacing

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{#!/usr/local/bin/Rscript}

\CommentTok{#  ****************************************************************************}
\CommentTok{#  msCluster.R}
\CommentTok{#  ****************************************************************************}

\CommentTok{# load libraries}
\KeywordTok{library}\NormalTok{(data.table)}
\KeywordTok{library}\NormalTok{(parallel)}
\KeywordTok{library}\NormalTok{(wavethresh)}
\KeywordTok{library}\NormalTok{(MeanShift)}

\KeywordTok{set.seed}\NormalTok{(}\DecValTok{1}\NormalTok{)}

\CommentTok{# define variables}

\CommentTok{# write arguments to args vector}
\NormalTok{args <-}\StringTok{ }\KeywordTok{commandArgs}\NormalTok{(}\DataTypeTok{trailingOnly =} \OtherTok{TRUE}\NormalTok{)}

\CommentTok{# save name of node being analyzed}
\NormalTok{node <-}\StringTok{ }\KeywordTok{strsplit}\NormalTok{(args[}\DecValTok{1}\NormalTok{],}\StringTok{".csv"}\NormalTok{)[[}\DecValTok{1}\NormalTok{]][}\DecValTok{1}\NormalTok{]}

\CommentTok{# number of cores to use for processing}
\NormalTok{n.cores <-}\StringTok{ }\NormalTok{args[}\DecValTok{2}\NormalTok{]}

\CommentTok{# waveform  metrics}
\NormalTok{wfMetricNames <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}\StringTok{"peak"}\NormalTok{,}\StringTok{"valley"}\NormalTok{,}\StringTok{"peak.valley"}\NormalTok{,}\StringTok{"pvi"}\NormalTok{,}\StringTok{"auc"}\NormalTok{,}\StringTok{"NLE.max"}\NormalTok{)}

\CommentTok{# read in wf data from individual node}
\NormalTok{node.wf.metrics <-}\StringTok{ }\KeywordTok{read.csv}\NormalTok{(args[}\DecValTok{1}\NormalTok{])}

\CommentTok{# identify position of wfMetric columns}
\NormalTok{wfMetricCols <-}\StringTok{ }\KeywordTok{match}\NormalTok{(wfMetricNames, }\KeywordTok{colnames}\NormalTok{(node.wf.metrics))}
\CommentTok{# isolate wf metrics from voltage and node meta-data}
\NormalTok{array.metrics <-}\StringTok{ }\KeywordTok{as.matrix}\NormalTok{(node.wf.metrics[,wfMetricCols])}

\CommentTok{# perform log transformtion on wf metrics}
\NormalTok{log.array.metrics <-}\StringTok{ }\KeywordTok{log10}\NormalTok{(array.metrics) }

\CommentTok{# calculate principle components across wf metrics}
\NormalTok{array.pc <-}\StringTok{ }\KeywordTok{prcomp}\NormalTok{(log.array.metrics,}
                   \DataTypeTok{center =} \OtherTok{TRUE}\NormalTok{,}
                   \DataTypeTok{scale. =} \OtherTok{TRUE}\NormalTok{)}

\CommentTok{# transform PCs from columns to rows}
\NormalTok{spike.pc.data <-}\StringTok{ }\KeywordTok{t}\NormalTok{(array.pc$x[,}\DecValTok{1}\NormalTok{:}\DecValTok{3}\NormalTok{])}

\CommentTok{# Perform Mean-Shift Clustering on PC1 & PC2}
\KeywordTok{options}\NormalTok{(}\DataTypeTok{mc.cores=}\NormalTok{n.cores) }\CommentTok{# pass n.cores to mc.cores}
\NormalTok{spk.clustering <-}\StringTok{ }\KeywordTok{msClustering}\NormalTok{(spike.pc.data[}\DecValTok{1}\NormalTok{:}\DecValTok{2}\NormalTok{,],}
                               \DataTypeTok{h=}\FloatTok{1.5}\NormalTok{,}
                               \DataTypeTok{kernel =} \StringTok{"gaussianKernel"}\NormalTok{,}
                               \DataTypeTok{multi.core =} \OtherTok{TRUE}\NormalTok{)}

\CommentTok{# Combine wf metric, PC, cluster, voltage, and metadata}
\NormalTok{node.wf.metrics.pca.clstr <-}\StringTok{ }\KeywordTok{cbind}\NormalTok{(}
  \NormalTok{node.wf.metrics,}
  \DataTypeTok{cluster=}\NormalTok{spk.clustering$labels,}
  \NormalTok{array.pc$x[,}\DecValTok{1}\NormalTok{:}\DecValTok{3}\NormalTok{]}
  \NormalTok{)}

\CommentTok{# write data to csv}
\KeywordTok{fwrite}\NormalTok{(}
  \NormalTok{node.wf.metrics.pca.clstr, }
  \KeywordTok{paste0}\NormalTok{(node,}\StringTok{"_"}\NormalTok{,}\StringTok{"MSclstr.csv"}\NormalTok{),}
  \DataTypeTok{row.names =} \OtherTok{FALSE}
  \NormalTok{)}
\end{Highlighting}
\end{Shaded}

\doublespacing

\paragraph{nodesToRedux.sh}\label{nodestoredux.sh}

The following is a convenient bash script to identify which
\textsf{.csv} files of electrode (node) level waveform data have
\textbf{not} been successfully processed by the \textsf{msCluster.R}
script. Running the mean-shift clustering algorithm can be time
intensive and if the \textsf{msCluster.R} script is being executed on a
computing cluster utilizing a job scheduler software, it is not uncommon
for individual executions of the algorithm to exceed allotted time
limits. This script should be executed from a parent directory
containing a subdirectory \textsf{/node} housing the files to be
reviewed. The script will generate copies of the unprocessed files in a
second subdirectory \textsf{/nodeRedux} to facilitate further
processing.

\singlespacing

\begin{Shaded}
\begin{Highlighting}[]

\CommentTok{#!/bin/bash}

\CommentTok{#  ****************************************************************************}
\CommentTok{#  nodesToRedux.sh}
\CommentTok{#  ****************************************************************************}

\CommentTok{# define line count thresholds}
\CommentTok{# files containing this number lines or fewer will be ignored}
\OtherTok{minimum=}\NormalTok{10}

\CommentTok{# generate array containing all nodes}
\CommentTok{# excluding those successfully processed by msCluster.R ('MS')}
\CommentTok{# or subset and denoted with the modified file name 'sample}
\OtherTok{allNodes=($(}\KeywordTok{ls} \NormalTok{node }\KeywordTok{|} \KeywordTok{grep} \NormalTok{-v }\StringTok{'MS'} \KeywordTok{|} \KeywordTok{grep} \NormalTok{-v }\StringTok{'sample'} \KeywordTok{|} \KeywordTok{grep} \StringTok{'csv'}\OtherTok{))}

\CommentTok{# modify array to eliminate extraneous text from each entry}
\OtherTok{allNodes=($(}\KeywordTok{echo} \OtherTok{$\{allNodes[@]%}\NormalTok{.csv}\OtherTok{\}))}

\CommentTok{# create array with nodes successfully clustered}
\OtherTok{clusteredNodes=($(}\KeywordTok{for} \KeywordTok{i} \NormalTok{in node/*MS*}\KeywordTok{;} \KeywordTok{do} \KeywordTok{echo} \OtherTok{$\{i\}}\KeywordTok{;} \KeywordTok{done}\OtherTok{))}

\CommentTok{# strip extraneous text from clustered nodes}
\OtherTok{clusteredNodes=($(}\KeywordTok{echo} \OtherTok{$\{clusteredNodes[@]\}} \NormalTok{\textbackslash{}}
  \KeywordTok{|} \KeywordTok{sed} \StringTok{'s/node\textbackslash{}///g;s/sampled_//g;s/_MSclstr.csv//g'}\OtherTok{))}

\CommentTok{# identify lines (i.e. nodes) present within allNodes but not within}
\CommentTok{# clusteredNodes save to an array missedNodes # comm requires sorted lists as}
\CommentTok{# input, hence printing arrays as lists and sorting output option -1 suppresses}
\CommentTok{#unique members of first list option -3 suppresses members common to both lists}
\OtherTok{missedNodes=($(}\KeywordTok{comm} \NormalTok{-13 }\KeywordTok{<(printf} \StringTok{'%s\textbackslash{}n'} \StringTok{"}\OtherTok{$\{clusteredNodes[@]\}}\StringTok{"} \KeywordTok{|} \KeywordTok{sort)} \DataTypeTok{\textbackslash{} }
  \KeywordTok{<(printf} \StringTok{'%s\textbackslash{}n'} \StringTok{"}\OtherTok{$\{allNodes[@]\}}\StringTok{"} \KeywordTok{|} \KeywordTok{sort)}\OtherTok{))}

\CommentTok{# make directory nodesRedux}
\KeywordTok{mkdir} \NormalTok{nodeRedux}

\CommentTok{# copy data from each missing node to nodesRedux directory}
\KeywordTok{for} \KeywordTok{i} \NormalTok{in }\OtherTok{$\{missedNodes[@]\}}
        \KeywordTok{do} 
        \OtherTok{line_count=$(}\KeywordTok{wc} \NormalTok{-l node/}\OtherTok{$i}\NormalTok{.csv }\KeywordTok{|} \KeywordTok{cut} \NormalTok{-f1 -d}\StringTok{' '}\OtherTok{)}
                \KeywordTok{if [} \StringTok{"}\OtherTok{$line_count}\StringTok{"} \OtherTok{-gt} \StringTok{"}\OtherTok{$minimum}\StringTok{"}\KeywordTok{ ]}
                \KeywordTok{then} 
                        \KeywordTok{cp} \NormalTok{node/}\OtherTok{$i}\NormalTok{.csv nodeRedux }
                \KeywordTok{fi}
        \KeywordTok{done}\NormalTok{;}
\end{Highlighting}
\end{Shaded}

\doublespacing

\paragraph{spkClstrdAgg.sh}\label{spkclstrdagg.sh}

The following is a bash script is analogous to the
\textsf{wfMetricsAgg.sh} script described above. This script will
aggregate the \textsf{MSclstr.csv} files output from multiple
invocations of msCluster.R into single file.

\singlespacing

\begin{Shaded}
\begin{Highlighting}[]

\CommentTok{#!/bin/bash}

\CommentTok{#  ****************************************************************************}
\CommentTok{#  spkClstrdAgg.sh}
\CommentTok{#  ****************************************************************************}

\OtherTok{Date=$(}\KeywordTok{date} \StringTok{'+%Y-%m-%d'}\OtherTok{)}

\OtherTok{currentPath=$(}\KeywordTok{pwd}\OtherTok{)}

\OtherTok{currentDir=$(}\KeywordTok{basename} \OtherTok{$currentPath)}

\OtherTok{Experiment=$(}\KeywordTok{echo} \OtherTok{$\{currentDir#}\NormalTok{*_}\OtherTok{\})}

\OtherTok{clusteredNodeFiles=($(}\KeywordTok{find} \NormalTok{./node/ -type f -name }\StringTok{'*MS*'}\OtherTok{))}

\KeywordTok{sed} \NormalTok{1q }\OtherTok{$\{clusteredNodeFiles[0]\}} \KeywordTok{>} \OtherTok{$\{Date\}}\NormalTok{_}\OtherTok{$\{Experiment\}}\NormalTok{_wf.metrics.clustered.csv}

\KeywordTok{for} \KeywordTok{f} \NormalTok{in }\OtherTok{$\{clusteredNodeFiles[@]\}}\KeywordTok{;} 
  \KeywordTok{do} \KeywordTok{sed} \NormalTok{1d }\OtherTok{$f} \KeywordTok{>>} \OtherTok{$\{Date\}}\NormalTok{_}\OtherTok{$\{Experiment\}}\NormalTok{_wf.metrics.clustered.csv}\KeywordTok{;}
  \KeywordTok{done}\NormalTok{;}
\end{Highlighting}
\end{Shaded}

\doublespacing

\paragraph{spkClusterMetrics.R}\label{spkclustermetrics.r}

The following is a R script used to for compiling cluster level metrics
of voltage waveforms from MEA recording data following cluster analysis
of waveforms by mean-shift, k-means, or similar method.

This script requires five arguments, including:

\begin{enumerate}
  \item \textsf{wf.metrics.clustered.csv} a file contains waveform level data consisting of 
meta-data (plate serial number, well, node, recording); waveform metrics; cluster annotation; and voltage data. This file is the type generated by the \textsf{spkClstrdAgg.sh} described above.
  \item \textsf{superactive\_nodes.txt} a delimited file with two columns \textit{node} and \textit{line\_count}. This file contains a list of nodes (electrodes) which which had record a large number of spike events (e.g. >10000) and thus a random sample was used during the clustering analysis. Inclusion of this file is important for back calculating the spike frequency of the clusters (i.e. individual neurons) detected by this node.
  \item Recording duration in seconds provided as an integer value (e.g. 1800 for a 30 minute recording).
  \item The line count threshold used for denoting \textit{superactive} electrodes provided as an integer value (e.g. 10000).
  \item the line count threshold used for denoting the minimum number of events considered to constitute a valid cluster provided as an integer value (e.g. 10).
\end{enumerate}

This script will return four outputs, including:

\begin{enumerate}
  \item \textsf{clusterWfData.csv} a file of waveform-level data for only those spike events within assigned clusters, containing associated meta-data, including assigned cluster, and calculated waveform metrics and principle components, but not momentary voltage recordings.
  \item \textsf{clusterCentroids.csv} a file containing the coordinates of the calculated center of each identified cluster as principal components (PC1, PC2).
  \item \textsf{clusterData.csv} a file of waveform-level data for the spike determined to be closest to the center of each cluster by euclidean distance containing associated meta-data, including assigned cluster, and calculated waveform metrics and principle components, but not momentary voltage recordings.
  \item \textsf{clusterVoltageLong.csv} a file of waveform-level data for the spike determined to be closest to the center of each cluster by euclidean distance containing voltage recordings in long form, meaning a single voltage recording per line. This output is useful for generating graphic depictions of a representative waveform for the cluster.
\end{enumerate}

This script requires the \textsf{data.table} and \textsf{parallel}
libraries available on CRAN.

\singlespacing

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{#!/usr/local/bin/Rscript}

\CommentTok{#  ****************************************************************************}
\CommentTok{#  spkClusterMetrics.R}
\CommentTok{#  ****************************************************************************}

\CommentTok{# load libraries}
\KeywordTok{library}\NormalTok{(data.table)}
\KeywordTok{library}\NormalTok{(parallel)}

\CommentTok{# define variables}

\CommentTok{# capture command line arguments}
\NormalTok{args <-}\StringTok{ }\KeywordTok{commandArgs}\NormalTok{(}\DataTypeTok{trailingOnly =} \OtherTok{TRUE}\NormalTok{)}

\CommentTok{# cluster waveforms file}
\NormalTok{clusterWaveFile <-}\StringTok{ }\NormalTok{args[}\DecValTok{1}\NormalTok{]}

\CommentTok{# superactive node file}
\NormalTok{superactiveNodeFile <-}\StringTok{ }\NormalTok{args[}\DecValTok{2}\NormalTok{]}

\CommentTok{# recording duration in sec}
\NormalTok{durationSec <-}\StringTok{ }\KeywordTok{as.integer}\NormalTok{(args[}\DecValTok{3}\NormalTok{])}

\CommentTok{# set threshold for num of spikes determining a superactive node}
\NormalTok{superactiveThres <-}\StringTok{ }\KeywordTok{as.integer}\NormalTok{(args[}\DecValTok{4}\NormalTok{])}

\CommentTok{# set threshold for num of spikes determining a small cluster}
\NormalTok{smallClusterThres <-}\StringTok{ }\KeywordTok{as.integer}\NormalTok{(args[}\DecValTok{5}\NormalTok{])}

\CommentTok{# load waveform data with clustering annotation}
\NormalTok{wfClusterDataDT <-}\StringTok{ }
\StringTok{  }\KeywordTok{fread}\NormalTok{(}
    \NormalTok{clusterWaveFile,}
    \DataTypeTok{stringsAsFactors =} \OtherTok{FALSE}
  \NormalTok{)}

\CommentTok{#ensure row 'F' is not encoded as 'FALSE'}
\NormalTok{wfClusterDataDT[well_row==}\StringTok{"FALSE"}\NormalTok{,well_row:}\ErrorTok{=}\StringTok{"F"}\NormalTok{] }
\CommentTok{#rename node_ID for brevity}
\KeywordTok{setnames}\NormalTok{(wfClusterDataDT,}\StringTok{"node_ID"}\NormalTok{,}\StringTok{"node"}\NormalTok{)}
\NormalTok{wfClusterDataDT[,}\StringTok{`}\DataTypeTok{:=}\StringTok{`}\NormalTok{(}
  \CommentTok{#single identifier for well}
  \DataTypeTok{well=}\KeywordTok{paste}\NormalTok{(well_row,well_column,}\DataTypeTok{sep =} \StringTok{""}\NormalTok{),}
   \CommentTok{#identifier for cluster within node}
  \DataTypeTok{nodeCluster =} \KeywordTok{paste}\NormalTok{(node,cluster,}\DataTypeTok{sep =} \StringTok{"_"}\NormalTok{),}
  \CommentTok{#identifier for cluster within node within recording}
  \DataTypeTok{nodeClusterRec =} \KeywordTok{paste}\NormalTok{(node,cluster,rec,}\DataTypeTok{sep =} \StringTok{"_"}\NormalTok{)}
\NormalTok{)]}

\CommentTok{# load superactive node data----}
\NormalTok{superactiveNodes <-}\StringTok{ }\KeywordTok{data.table}\NormalTok{(}
  \KeywordTok{read.delim}\NormalTok{(}
    \NormalTok{superactiveNodeFile,}
    \DataTypeTok{header =} \OtherTok{TRUE}\NormalTok{,}
    \DataTypeTok{sep =} \StringTok{" "}\NormalTok{,}
    \DataTypeTok{stringsAsFactors =} \OtherTok{FALSE}
  \NormalTok{)}
\NormalTok{)}

\CommentTok{# identify cluster centers and determine spike frequency per cluster}

\CommentTok{# calculate coefficient for each node}
\NormalTok{superactiveNodes[,spk_coeffiecient:}\ErrorTok{=}\NormalTok{(line_count/superactiveThres),by=}\StringTok{"node"}\NormalTok{]}

\CommentTok{# determine to coordinates in PC space for the centroid of each cluster}
\NormalTok{clusterCentroidsQ <-}\StringTok{ }
\StringTok{  }\NormalTok{wfClusterDataDT[,}
    \NormalTok{.(}\DataTypeTok{PC1=}\KeywordTok{quantile}\NormalTok{(PC1,}\KeywordTok{c}\NormalTok{(.}\DecValTok{5}\NormalTok{), }\DataTypeTok{na.rm =} \OtherTok{TRUE}\NormalTok{, }\OtherTok{TRUE}\NormalTok{, }\DecValTok{3}\NormalTok{),}
      \DataTypeTok{PC2=}\KeywordTok{quantile}\NormalTok{(PC2,}\KeywordTok{c}\NormalTok{(.}\DecValTok{5}\NormalTok{), }\DataTypeTok{na.rm =} \OtherTok{TRUE}\NormalTok{, }\OtherTok{TRUE}\NormalTok{, }\DecValTok{3}\NormalTok{)),}
      \NormalTok{by=.(nodeCluster)}
      \NormalTok{]}

\CommentTok{# calculate the number of spikes in each cluster}
\NormalTok{spikesPerClusterPerRecDT <-}\StringTok{ }
\StringTok{  }\NormalTok{wfClusterDataDT[}
    \NormalTok{,.(}\DataTypeTok{spikesPerClusterRec=}\KeywordTok{length}\NormalTok{(PC1)),by=.(nodeCluster,nodeClusterRec)}
    \NormalTok{]}
\NormalTok{spikesPerClusterDT <-}\StringTok{ }
\StringTok{  }\NormalTok{wfClusterDataDT[,.(}\DataTypeTok{spikesPerCluster=}\KeywordTok{length}\NormalTok{(PC1)),by=.(nodeCluster)]}

\CommentTok{# determine which clusters are 'small clusters'}
\NormalTok{smallClusters <-}\StringTok{ }
\StringTok{  }\NormalTok{spikesPerClusterDT[spikesPerCluster<smallClusterThres,nodeCluster]}

\NormalTok{eachCluster <-}\StringTok{ }
\StringTok{  }\KeywordTok{unique}\NormalTok{(wfClusterDataDT[!nodeCluster %in%}\StringTok{ }\NormalTok{smallClusters,nodeCluster])}

\CommentTok{# exclude waveforms from small clusters}
\NormalTok{wfClusterDataDT <-}\StringTok{ }\NormalTok{wfClusterDataDT[!nodeCluster %in%}\StringTok{ }\NormalTok{smallClusters]}

\CommentTok{# add unique identifier for each spike}
\NormalTok{spkNum <-}\StringTok{ }\KeywordTok{nrow}\NormalTok{(wfClusterDataDT)}

\NormalTok{wfClusterDataDT[,spikeID:}\ErrorTok{=}\KeywordTok{seq}\NormalTok{(}\DecValTok{1}\NormalTok{:spkNum)]}

\CommentTok{# exclude centroids of small clusters}
\NormalTok{clusterCentroidsQ <-}\StringTok{ }\NormalTok{clusterCentroidsQ[!nodeCluster %in%}\StringTok{ }\NormalTok{smallClusters]}

\CommentTok{# identify waveform within each cluster that is the closest euclidean distance}
\CommentTok{# to calculated center}
\NormalTok{clusterDataDT <-}\StringTok{ }\KeywordTok{rbindlist}\NormalTok{(}
  \KeywordTok{lapply}\NormalTok{(}
    \NormalTok{eachCluster,}
    \NormalTok{function(x)\{}
      \NormalTok{clusterCenter <-}\KeywordTok{as.matrix}\NormalTok{(clusterCentroidsQ[nodeCluster==x][}\DecValTok{1}\NormalTok{,}\KeywordTok{c}\NormalTok{(}\DecValTok{2}\NormalTok{,}\DecValTok{3}\NormalTok{)])}
      \NormalTok{clusterWfs <-}\StringTok{ }\KeywordTok{as.matrix}\NormalTok{(wfClusterDataDT[nodeCluster==x,.(PC1,PC2)])}
      \NormalTok{clusterMatrix <-}\StringTok{ }\KeywordTok{rbind}\NormalTok{(}
        \NormalTok{clusterCenter,}
        \NormalTok{clusterWfs}
      \NormalTok{)}
      \NormalTok{clusterEucDisMtrx <-}\StringTok{ }
\StringTok{        }\KeywordTok{as.matrix}\NormalTok{(stats::}\KeywordTok{dist}\NormalTok{(clusterMatrix,}\DataTypeTok{method =} \StringTok{"euclidean"}\NormalTok{))}
      \NormalTok{minEucDisIndex <-}\StringTok{ }\KeywordTok{which.min}\NormalTok{(clusterEucDisMtrx[-}\DecValTok{1}\NormalTok{,}\DecValTok{1}\NormalTok{])[[}\DecValTok{1}\NormalTok{]]}
      \NormalTok{centerWF <-}\StringTok{ }\NormalTok{wfClusterDataDT[nodeCluster==x][minEucDisIndex]}
      \KeywordTok{return}\NormalTok{(centerWF)}
    \NormalTok{\}}
  \NormalTok{)}
\NormalTok{)}

\CommentTok{# remove columns which pertain to the 'center' waveform occurred but does not}
\CommentTok{# reflect the cluster as a whole}
\NormalTok{clusterDataDT[,}\KeywordTok{c}\NormalTok{(}\StringTok{"SD"}\NormalTok{,}\StringTok{"time"}\NormalTok{,}\StringTok{"rec"}\NormalTok{,}\StringTok{"nodeClusterRec"}\NormalTok{,}\StringTok{"spikeID"}\NormalTok{):}\ErrorTok{=}\OtherTok{NULL}\NormalTok{] }

\NormalTok{clusterDataDT <-}\StringTok{ }\KeywordTok{merge}\NormalTok{(clusterDataDT,spikesPerClusterPerRecDT,}\DataTypeTok{by=}\StringTok{"nodeCluster"}\NormalTok{)}

\CommentTok{# determine spike frequency of each cluster per recording}

\CommentTok{# for 'superactive' nodes, multiply the number of spikes by the corressponding}
\CommentTok{# coefficient for that node}
\KeywordTok{sapply}\NormalTok{(clusterDataDT$node,}
       \NormalTok{function(x)\{}
         \NormalTok{if (x %in%}\StringTok{ }\NormalTok{superactiveNodes$node) \{}
           \NormalTok{spk_coefficient =}\StringTok{ }\NormalTok{superactiveNodes[node==x,spk_coeffiecient]}
           \NormalTok{clusterDataDT[}
             \NormalTok{node==x,}
             \NormalTok{spikesAdjusted :}\ErrorTok{=}\StringTok{ }\KeywordTok{as.integer}\NormalTok{(}
              \KeywordTok{floor}\NormalTok{(spikesPerClusterRec*spk_coefficient)}
              \NormalTok{)}
             \NormalTok{]}
         \NormalTok{\} else \{}
           \NormalTok{clusterDataDT[}
             \NormalTok{node==x,}
             \NormalTok{spikesAdjusted :}\ErrorTok{=}\StringTok{ }\NormalTok{spikesPerClusterRec}
             \NormalTok{]}
         \NormalTok{\}}
         \KeywordTok{return}\NormalTok{(}\OtherTok{NULL}\NormalTok{)}
       \NormalTok{\}}
\NormalTok{)}

\NormalTok{clusterDataDT[,logHz:}\ErrorTok{=}\KeywordTok{log10}\NormalTok{(spikesAdjusted/durationSec)]}

\CommentTok{# reassign 'rec' to allow subsetting cluster metrics by recording}
\NormalTok{clusterDataDT[}
  \NormalTok{,rec:}\ErrorTok{=}\KeywordTok{strsplit}\NormalTok{(nodeClusterRec, }\DataTypeTok{split =} \StringTok{"_"}\NormalTok{)[[}\DecValTok{1}\NormalTok{]][}\DecValTok{5}\NormalTok{],}
  \NormalTok{by=.(nodeClusterRec)}
  \NormalTok{]}

\CommentTok{# remove spikesPerClusterRec, redundant after calculating spikesAdjusted}
\NormalTok{clusterDataDT[,spikesPerClusterRec:}\ErrorTok{=}\OtherTok{NULL}\NormalTok{]}

\CommentTok{# reorder columns}
\NormalTok{metadataCols <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}
  \StringTok{"nodeCluster"}\NormalTok{,}
  \StringTok{"nodeClusterRec"}\NormalTok{,}
  \StringTok{"cluster"}\NormalTok{,}
  \StringTok{"plate.num"}\NormalTok{,}
  \StringTok{"well"}\NormalTok{,}
  \StringTok{"well_row"}\NormalTok{,}
  \StringTok{"well_column"}\NormalTok{,}
  \StringTok{"node"}\NormalTok{,}
  \StringTok{"array_x"}\NormalTok{,}
  \StringTok{"array_y"}\NormalTok{,}
  \StringTok{"rec"}
\NormalTok{)}

\NormalTok{dataCols <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}
  \StringTok{"peak"}\NormalTok{,}
  \StringTok{"valley"}\NormalTok{,}
  \StringTok{"peak.valley"}\NormalTok{,}
  \StringTok{"pvi"}\NormalTok{,}
  \StringTok{"auc"}\NormalTok{,}
  \StringTok{"NLE.max"}\NormalTok{,}
  \StringTok{"PC1"}\NormalTok{,}
  \StringTok{"PC2"}\NormalTok{,}
  \StringTok{"PC3"}\NormalTok{,}
  \StringTok{"spikesAdjusted"}\NormalTok{,}
  \StringTok{"logHz"}
\NormalTok{)}

\NormalTok{voltCols <-}\StringTok{ }\KeywordTok{paste0}\NormalTok{(}\StringTok{"V"}\NormalTok{,}\KeywordTok{c}\NormalTok{(}\DecValTok{1}\NormalTok{:}\DecValTok{38}\NormalTok{))}

\KeywordTok{setcolorder}\NormalTok{(clusterDataDT,}\KeywordTok{c}\NormalTok{(metadataCols,dataCols,voltCols))}

\CommentTok{# reformat tables: separate cluster voltages from cluster metrics}
\NormalTok{wfDataCols <-}\StringTok{ }\NormalTok{dataCols[}\DecValTok{1}\NormalTok{:}\DecValTok{9}\NormalTok{]}
\NormalTok{wfMetadataCols <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}\StringTok{"spikeID"}\NormalTok{,metadataCols)}

\NormalTok{wfData <-}\StringTok{ }\KeywordTok{cbind}\NormalTok{(}
  \NormalTok{wfClusterDataDT[,..wfMetadataCols], }\CommentTok{#dt of metadata columns}
  \NormalTok{wfClusterDataDT[,..wfDataCols] }\CommentTok{#dt of wavform data columns}
\NormalTok{)}

\NormalTok{clusterVoltageWide <-}\StringTok{ }\KeywordTok{unique}\NormalTok{(clusterDataDT,}\DataTypeTok{by=}\StringTok{"nodeCluster"}\NormalTok{)}
\NormalTok{clusterVoltageWide[,(dataCols):}\ErrorTok{=}\OtherTok{NULL}\NormalTok{]}
\NormalTok{clusterVoltageWide[,}\KeywordTok{c}\NormalTok{(}\StringTok{"nodeClusterRec"}\NormalTok{,}\StringTok{"rec"}\NormalTok{):}\ErrorTok{=}\OtherTok{NULL}\NormalTok{]}

\NormalTok{clusterVoltageLong <-}\StringTok{ }\KeywordTok{melt.data.table}\NormalTok{(}
  \NormalTok{clusterVoltageWide,}
  \DataTypeTok{measure.vars =} \NormalTok{voltCols,}
  \DataTypeTok{variable.name =} \StringTok{"Interval"}\NormalTok{,}
  \DataTypeTok{variable.factor =} \OtherTok{FALSE}\NormalTok{,}
  \DataTypeTok{value.name =} \StringTok{"uV"}\NormalTok{)}

\CommentTok{# add time in milliseconds}
\NormalTok{clusterVoltageLong[,}
                   \NormalTok{timeInterval:}\ErrorTok{=}\KeywordTok{as.integer}\NormalTok{(}
                     \KeywordTok{strsplit}\NormalTok{(Interval,}\DataTypeTok{split =} \StringTok{"V"}\NormalTok{)[[}\DecValTok{1}\NormalTok{]][}\DecValTok{2}\NormalTok{]}
                   \NormalTok{),}
                   \NormalTok{by=.(Interval)]}

\NormalTok{clusterVoltageLong[,milliSec:}\ErrorTok{=}\DecValTok{3}\NormalTok{*(timeInterval/}\KeywordTok{max}\NormalTok{(timeInterval))]}

\CommentTok{# remove voltages from cluster}
\NormalTok{clusterDataDT[,(voltCols):}\ErrorTok{=}\OtherTok{NULL}\NormalTok{]}

\CommentTok{# write outputs}
\NormalTok{experiment <-}\StringTok{ }\KeywordTok{strsplit}\NormalTok{(}\KeywordTok{basename}\NormalTok{(}\KeywordTok{getwd}\NormalTok{()),}\DataTypeTok{split =} \StringTok{"_"}\NormalTok{)[[}\DecValTok{1}\NormalTok{]][-}\DecValTok{1}\NormalTok{]}
\NormalTok{experiment <-}\StringTok{ }\KeywordTok{paste}\NormalTok{(experiment, }\DataTypeTok{collapse =} \StringTok{"_"}\NormalTok{)}

\CommentTok{# clustered waveforms metrics}
\KeywordTok{fwrite}\NormalTok{(}
  \NormalTok{wfData,}
  \DataTypeTok{file =} \KeywordTok{paste}\NormalTok{(}
    \KeywordTok{Sys.Date}\NormalTok{(),}
    \NormalTok{experiment,}
    \StringTok{"clusterWfData.csv"}\NormalTok{,}
    \DataTypeTok{sep=}\StringTok{"_"}
  \NormalTok{),}
  \DataTypeTok{row.names =} \OtherTok{FALSE}
\NormalTok{)}

\CommentTok{# cluster centroids}
\KeywordTok{fwrite}\NormalTok{(}
  \NormalTok{clusterCentroidsQ,}
  \DataTypeTok{file =} \KeywordTok{paste}\NormalTok{(}
    \KeywordTok{Sys.Date}\NormalTok{(),}
    \NormalTok{experiment,}
    \StringTok{"clusterCentroids.csv"}\NormalTok{,}
    \DataTypeTok{sep=}\StringTok{"_"}
  \NormalTok{),}
  \DataTypeTok{row.names =} \OtherTok{FALSE}
\NormalTok{)}

\CommentTok{# cluster voltages}
\KeywordTok{fwrite}\NormalTok{(}
  \NormalTok{clusterVoltageLong,}
  \DataTypeTok{file =} \KeywordTok{paste}\NormalTok{(}
    \KeywordTok{Sys.Date}\NormalTok{(),}
    \NormalTok{experiment,}
    \StringTok{"clusterVoltageLong.csv"}\NormalTok{,}
    \DataTypeTok{sep=}\StringTok{"_"}
  \NormalTok{),}
  \DataTypeTok{row.names =} \OtherTok{FALSE}
\NormalTok{)}

\CommentTok{# cluster data}
\KeywordTok{fwrite}\NormalTok{(}
  \NormalTok{clusterDataDT,}
  \DataTypeTok{file =} \KeywordTok{paste}\NormalTok{(}
    \KeywordTok{Sys.Date}\NormalTok{(),}
    \NormalTok{experiment,}
    \StringTok{"clusterData.csv"}\NormalTok{,}
    \DataTypeTok{sep=}\StringTok{"_"}
  \NormalTok{),}
  \DataTypeTok{row.names =} \OtherTok{FALSE}
\NormalTok{)}
\end{Highlighting}
\end{Shaded}


\end{document}
