Transcriptomic correlates of neuron electrophysiological diversity
Version: 3 – Released: Thu Feb 22 16:04:13 PST 2018
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Tripathy SJ, Toker L, Li B, et al. "Transcriptomic correlates of neuron electrophysiological diversity." Ayers J, ed. PLoS Computational Biology. 2017;13(10):e1005814. doi:10.1371/journal.pcbi.1005814.
Data Citation Details
TitleTranscriptomic correlates of neuron electrophysiological diversity
Study Global IDhdl:11272/10485
AuthorsTripathy, Shreejoy (MSL and Department of Psychiatry, UBC); Toker, Lilah (MSL and Department of Psychiatry, UBC); Li, Brenna (MSL and Department of Psychiatry, UBC); Crichlow, Cindy-Lee (MSL and Department of Psychiatry, UBC); Tebaykin, Dmitry (MSL and Department of Psychiatry, UBC); Mancarci, Ogan (MSL and Department of Psychiatry, UBC); Shreejoy Tripathy (MSL and Department of Psychiatry, UBC)
ProducerMichael Smith Laboratories (MSL); Department of Psychiatry (Dept of Psych)
Production Date2017
Production PlaceVancouver, BC
Funding AgencyNeuroDevNet, CIHR, NSERC, NIH
DistributorUniversity of British Columbia (UBC)
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)
Related MaterialPavlidis Lab supplementary information page:
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"Transcriptomic correlates of neuron electrophysiological diversity", hdl:11272/10485