Person: Weinstein, Eli
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Publication Genetically Targeted All-Optical Electrophysiology with a Transgenic Cre-Dependent Optopatch Mouse
(Society for Neuroscience, 2016) Lou, Shan; Adam, Yoav; Weinstein, Eli; Williams, E.; Williams, K.; Parot, Vicente; Kavokine, N.; Liberles, Stephen; Madisen, L.; Zeng, H.; Cohen, AdamRecent advances in optogenetics have enabled simultaneous optical perturbation and optical readout of membrane potential in diverse cell types. Here, we develop and characterize a Cre-dependent transgenic Optopatch2 mouse line that we call Floxopatch. The animals expressed a blue-shifted channelrhodopsin, CheRiff, and a near infrared Archaerhodopsin-derived voltage indicator, QuasAr2, via targeted knock-in at the rosa26 locus. In Optopatch-expressing animals, we tested for overall health, genetically targeted expression, and function of the optogenetic components. In offspring of Floxopatch mice crossed with a variety of Cre driver lines, we observed spontaneous and optically evoked activity in vitro in acute brain slices and in vivo in somatosensory ganglia. Cell-type-specific expression allowed classification and characterization of neuronal subtypes based ontheir firing patterns. The Floxopatch mouse line is a usefultool for fast and sensitive characterization of neural activity in genetically specified cell types in intact tissue.
Publication All-Optical Electrophysiology for High-Throughput Functional Characterization of a Human iPSC-Derived Motor Neuron Model of ALS
(Elsevier, 2018) Kiskinis, Evangelos; Kralj, Joel M.; Zou, Peng; Weinstein, Eli; Zhang, Hongkang; Tsioras, Konstantinos; Wiskow, Ole; Ortega, J. Alberto; Eggan, Kevin; Cohen, AdamSummary Human induced pluripotent stem cell (iPSC)-derived neurons are an attractive substrate for modeling disease, yet the heterogeneity of these cultures presents a challenge for functional characterization by manual patch-clamp electrophysiology. Here, we describe an optimized all-optical electrophysiology, “Optopatch,” pipeline for high-throughput functional characterization of human iPSC-derived neuronal cultures. We demonstrate the method in a human iPSC-derived motor neuron (iPSC-MN) model of amyotrophic lateral sclerosis (ALS). In a comparison of iPSC-MNs with an ALS-causing mutation (SOD1 A4V) with their genome-corrected controls, the mutants showed elevated spike rates under weak or no stimulus and greater likelihood of entering depolarization block under strong optogenetic stimulus. We compared these results with numerical simulations of simple conductance-based neuronal models and with literature results in this and other iPSC-based models of ALS. Our data and simulations suggest that deficits in slowly activating potassium channels may underlie the changes in electrophysiology in the SOD1 A4V mutation.
Publication Coevolution of interacting proteins through non-contacting and non-specific mutations
(Cold Spring Harbor Laboratory, 2021-10-08) Ding, Fengning; Green, Anna; Wang, Boyuan; Lite, Thuy-Lan; Weinstein, Eli; Marks, Debora; Laub, Michael T.Proteins often accumulate neutral mutations that do not affect current functions1 but can profoundly influence future mutational possibilities and functions2–4. Understanding such hidden potential has major implications for protein design and evolutionary forecasting5–7, but has been limited by a lack of systematic efforts to identify potentiating mutations8,9. Here, through the comprehensive analysis of a bacterial toxin-antitoxin system, we identified all possible single substitutions in the toxin that enable it to tolerate otherwise interface-disrupting mutations in its antitoxin. Strikingly, the majority of enabling mutations in the toxin do not contact, and promote tolerance non-specifically to, many different antitoxin mutations, despite covariation in homologs occurring primarily between specific pairs of contacting residues across the interface. In addition, the enabling mutations we identified expand future mutational paths that both maintain old toxin-antitoxin interactions and form new ones. These non-specific mutations are missed by widely used covariation and machine learning methods10,11. Identifying such enabling mutations will be critical for ensuring continued binding of therapeutically relevant proteins, such as antibodies, aimed at evolving targets12–14.