Transcriptomic correlates of neuron electrophysiological diversity
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Released: Mon Sep 11 14:49:06 PDT 2017 – Archived: Thu Feb 22 16:04:13 PST 2018
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TitleTranscriptomic correlates of neuron electrophysiological diversity
Study Global IDhdl:11272/10485
AuthorsTripathy, Shreejoy (University of British Columbia); Toker, Lilah (University of British Columbia); Li, Brenna (University of British Columbia); Crichlow, Cindy-Lee (University of British Columbia); Tebaykin, Dmitry (University of British Columbia); Mancarci, Ogan (University of British Columbia); Shreejoy Tripathy (University of British Columbia)
Production PlaceUniversity of British Columbia
Funding AgencyNeuroDevNet, CIHR, NSERC, NIH
Deposit DateMay 04, 2017
Original Dataverse
Description and Scope

How neuronal diversity emerges from complex patterns of gene expression remains poorly understood. Here we present an approach to understand electrophysiological diversity through gene expression by integrating pooled- and single-cell transcriptomics with intracellular electrophysiology. Using neuroinformatics methods, we compiled a brain-wide dataset of 34 neuron types with paired gene expression and intrinsic electrophysiological features from publically accessible sources, the largest such collection to date. We identified 420 genes whose expression levels significantly correlated with variability in one or more of 11 physiological parameters. We next trained statistical models to infer cellular features from multivariate gene expression. Such models were predictive of gene-electrophysiological relationships in an independent collection of 12 visual cortex cell types from the Allen Institute, suggesting that these correlations might reflect general principles relating expression patterns to phenotypic diversity across very different cell types. Many associations reported here have the potential to provide new insights into how neurons generate functional diversity, and correlations of ion channel genes like Gabrd and Scn1a (Nav1.1) with resting potential and spiking frequency are consistent with known causal mechanisms. Our work highlights the promise and inherent challenges in using cell type-specific transcriptomics to understand the mechanistic origins of neuronal diversity.

Topic ClassificationOpen (Open Access Tag)
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Number of Files 9
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This work is licensed under a Creative Commons Attribution 4.0 International License.
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"Transcriptomic correlates of neuron electrophysiological diversity", hdl:11272/10485