Person:
Masland, Richard

Profile Picture

Email Address

AA Acceptance Date

Birth Date

Research Projects

Organizational Units

Job Title

Last Name

Masland

First Name

Richard

Name

Masland, Richard
Author's Publications: search.filters.isAuthorOfPublication.e36f52a4-5ef6-4a99-a2e7-4d49918f9811

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Publication
    Automated computation of arbor densities: a step toward identifying neuronal cell types
    (Frontiers Media S.A., 2014) Sümbül, Uygar; Zlateski, Aleksandar; Vishwanathan, Ashwin; Masland, Richard; Seung, H. Sebastian
    The shape and position of a neuron convey information regarding its molecular and functional identity. The identification of cell types from structure, a classic method, relies on the time-consuming step of arbor tracing. However, as genetic tools and imaging methods make data-driven approaches to neuronal circuit analysis feasible, the need for automated processing increases. Here, we first establish that mouse retinal ganglion cell types can be as precise about distributing their arbor volumes across the inner plexiform layer as they are about distributing the skeletons of the arbors. Then, we describe an automated approach to computing the spatial distribution of the dendritic arbors, or arbor density, with respect to a global depth coordinate based on this observation. Our method involves three-dimensional reconstruction of neuronal arbors by a supervised machine learning algorithm, post-processing of the enhanced stacks to remove somata and isolate the neuron of interest, and registration of neurons to each other using automatically detected arbors of the starburst amacrine interneurons as fiducial markers. In principle, this method could be generalizable to other structures of the CNS, provided that they allow sparse labeling of the cells and contain a reliable axis of spatial reference.
  • Thumbnail Image
    Publication
    A genetic and computational approach to structurally classify neuronal types
    (2014) Sümbül, Uygar; Song, Sen; McCulloch, Kyle; Becker, Michael; Lin, Bin; Sanes, Joshua; Masland, Richard; Seung, H. Sebastian
    The importance of cell types in understanding brain function is widely appreciated but only a tiny fraction of neuronal diversity has been catalogued. Here, we exploit recent progress in genetic definition of cell types in an objective structural approach to neuronal classification. The approach is based on highly accurate quantification of dendritic arbor position relative to neurites of other cells. We test the method on a population of 363 mouse retinal ganglion cells. For each cell, we determine the spatial distribution of the dendritic arbors, or “arbor density” with reference to arbors of an abundant, well-defined interneuronal type. The arbor densities are sorted into a number of clusters that is set by comparison with several molecularly defined cell types. The algorithm reproduces the genetic classes that are pure types, and detects six newly clustered cell types that await genetic definition.
  • Thumbnail Image
    Publication
    Organotypic Culture of Physiologically Functional Adult Mammalian Retinas
    (Public Library of Science, 2007) Koizumi, Amane; Zeck, Günther; Ben, Yixin; Masland, Richard; Jakobs, Tatjana
    Background: The adult mammalian retina is an important model in research on the central nervous system. Many experiments require the combined use of genetic manipulation, imaging, and electrophysiological recording, which make it desirable to use an in vitro preparation. Unfortunately, the tissue culture of the adult mammalian retina is difficult, mainly because of the high energy consumption of photoreceptors. Methods and Findings: We describe an interphase culture system for adult mammalian retina that allows for the expression of genes delivered to retinal neurons by particle-mediated transfer. The retinas retain their morphology and function for up to six days— long enough for the expression of many genes of interest—so that effects upon responses to light and receptive fields could be measured by patch recording or multielectrode array recording. We show that a variety of genes encoding pre- and post-synaptic marker proteins are localized correctly in ganglion and amacrine cells. Conclusions: In this system the effects on neuronal function of one or several introduced exogenous genes can be studied within intact neural circuitry of adult mammalian retina. This system is flexible enough to be compatible with genetic manipulation, imaging, cell transfection, pharmacological assay, and electrophysiological recordings.