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Transcriptomic correlates of electrophysiological and morphological diversity within and across excitatory and inhibitory neuron classes

In order to further our understanding of how gene expression contributes to key functional properties of neurons, we combined publicly accessible gene expression, electrophysiology, and morphology measurements to identify cross-cell type correlations between these data modalities. Building on our pr...

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Autores principales: Bomkamp, Claire, Tripathy, Shreejoy J., Bengtsson Gonzales, Carolina, Hjerling-Leffler, Jens, Craig, Ann Marie, Pavlidis, Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6599125/
https://www.ncbi.nlm.nih.gov/pubmed/31211786
http://dx.doi.org/10.1371/journal.pcbi.1007113
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author Bomkamp, Claire
Tripathy, Shreejoy J.
Bengtsson Gonzales, Carolina
Hjerling-Leffler, Jens
Craig, Ann Marie
Pavlidis, Paul
author_facet Bomkamp, Claire
Tripathy, Shreejoy J.
Bengtsson Gonzales, Carolina
Hjerling-Leffler, Jens
Craig, Ann Marie
Pavlidis, Paul
author_sort Bomkamp, Claire
collection PubMed
description In order to further our understanding of how gene expression contributes to key functional properties of neurons, we combined publicly accessible gene expression, electrophysiology, and morphology measurements to identify cross-cell type correlations between these data modalities. Building on our previous work using a similar approach, we distinguished between correlations which were “class-driven,” meaning those that could be explained by differences between excitatory and inhibitory cell classes, and those that reflected graded phenotypic differences within classes. Taking cell class identity into account increased the degree to which our results replicated in an independent dataset as well as their correspondence with known modes of ion channel function based on the literature. We also found a smaller set of genes whose relationships to electrophysiological or morphological properties appear to be specific to either excitatory or inhibitory cell types. Next, using data from PatchSeq experiments, allowing simultaneous single-cell characterization of gene expression and electrophysiology, we found that some of the gene-property correlations observed across cell types were further predictive of within-cell type heterogeneity. In summary, we have identified a number of relationships between gene expression, electrophysiology, and morphology that provide testable hypotheses for future studies.
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spelling pubmed-65991252019-07-12 Transcriptomic correlates of electrophysiological and morphological diversity within and across excitatory and inhibitory neuron classes Bomkamp, Claire Tripathy, Shreejoy J. Bengtsson Gonzales, Carolina Hjerling-Leffler, Jens Craig, Ann Marie Pavlidis, Paul PLoS Comput Biol Research Article In order to further our understanding of how gene expression contributes to key functional properties of neurons, we combined publicly accessible gene expression, electrophysiology, and morphology measurements to identify cross-cell type correlations between these data modalities. Building on our previous work using a similar approach, we distinguished between correlations which were “class-driven,” meaning those that could be explained by differences between excitatory and inhibitory cell classes, and those that reflected graded phenotypic differences within classes. Taking cell class identity into account increased the degree to which our results replicated in an independent dataset as well as their correspondence with known modes of ion channel function based on the literature. We also found a smaller set of genes whose relationships to electrophysiological or morphological properties appear to be specific to either excitatory or inhibitory cell types. Next, using data from PatchSeq experiments, allowing simultaneous single-cell characterization of gene expression and electrophysiology, we found that some of the gene-property correlations observed across cell types were further predictive of within-cell type heterogeneity. In summary, we have identified a number of relationships between gene expression, electrophysiology, and morphology that provide testable hypotheses for future studies. Public Library of Science 2019-06-18 /pmc/articles/PMC6599125/ /pubmed/31211786 http://dx.doi.org/10.1371/journal.pcbi.1007113 Text en © 2019 Bomkamp et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Bomkamp, Claire
Tripathy, Shreejoy J.
Bengtsson Gonzales, Carolina
Hjerling-Leffler, Jens
Craig, Ann Marie
Pavlidis, Paul
Transcriptomic correlates of electrophysiological and morphological diversity within and across excitatory and inhibitory neuron classes
title Transcriptomic correlates of electrophysiological and morphological diversity within and across excitatory and inhibitory neuron classes
title_full Transcriptomic correlates of electrophysiological and morphological diversity within and across excitatory and inhibitory neuron classes
title_fullStr Transcriptomic correlates of electrophysiological and morphological diversity within and across excitatory and inhibitory neuron classes
title_full_unstemmed Transcriptomic correlates of electrophysiological and morphological diversity within and across excitatory and inhibitory neuron classes
title_short Transcriptomic correlates of electrophysiological and morphological diversity within and across excitatory and inhibitory neuron classes
title_sort transcriptomic correlates of electrophysiological and morphological diversity within and across excitatory and inhibitory neuron classes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6599125/
https://www.ncbi.nlm.nih.gov/pubmed/31211786
http://dx.doi.org/10.1371/journal.pcbi.1007113
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