Brain Cell Type Specific Gene Expression and Co-expression Network Architectures

Abstract Elucidating brain cell type specific gene expression patterns is critical towards a better understanding of how cell-cell communications may influence brain functions and dysfunctions. We set out to compare and contrast five human and murine cell type-specific transcriptome-wide RNA express...

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Autores principales:	Andrew T. McKenzie, Minghui Wang, Mads E. Hauberg, John F. Fullard, Alexey Kozlenkov, Alexandra Keenan, Yasmin L. Hurd, Stella Dracheva, Patrizia Casaccia, Panos Roussos, Bin Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4f166a71fd834b69ad514d8d5441276a
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spelling	oai:doaj.org-article:4f166a71fd834b69ad514d8d5441276a2021-12-02T15:08:55ZBrain Cell Type Specific Gene Expression and Co-expression Network Architectures10.1038/s41598-018-27293-52045-2322https://doaj.org/article/4f166a71fd834b69ad514d8d5441276a2018-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-27293-5https://doaj.org/toc/2045-2322Abstract Elucidating brain cell type specific gene expression patterns is critical towards a better understanding of how cell-cell communications may influence brain functions and dysfunctions. We set out to compare and contrast five human and murine cell type-specific transcriptome-wide RNA expression data sets that were generated within the past several years. We defined three measures of brain cell type-relative expression including specificity, enrichment, and absolute expression and identified corresponding consensus brain cell “signatures,” which were well conserved across data sets. We validated that the relative expression of top cell type markers are associated with proxies for cell type proportions in bulk RNA expression data from postmortem human brain samples. We further validated novel marker genes using an orthogonal ATAC-seq dataset. We performed multiscale coexpression network analysis of the single cell data sets and identified robust cell-specific gene modules. To facilitate the use of the cell type-specific genes for cell type proportion estimation and deconvolution from bulk brain gene expression data, we developed an R package, BRETIGEA. In summary, we identified a set of novel brain cell consensus signatures and robust networks from the integration of multiple datasets and therefore transcend limitations related to technical issues characteristic of each individual study.Andrew T. McKenzieMinghui WangMads E. HaubergJohn F. FullardAlexey KozlenkovAlexandra KeenanYasmin L. HurdStella DrachevaPatrizia CasacciaPanos RoussosBin ZhangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-19 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Andrew T. McKenzie Minghui Wang Mads E. Hauberg John F. Fullard Alexey Kozlenkov Alexandra Keenan Yasmin L. Hurd Stella Dracheva Patrizia Casaccia Panos Roussos Bin Zhang Brain Cell Type Specific Gene Expression and Co-expression Network Architectures
description	Abstract Elucidating brain cell type specific gene expression patterns is critical towards a better understanding of how cell-cell communications may influence brain functions and dysfunctions. We set out to compare and contrast five human and murine cell type-specific transcriptome-wide RNA expression data sets that were generated within the past several years. We defined three measures of brain cell type-relative expression including specificity, enrichment, and absolute expression and identified corresponding consensus brain cell “signatures,” which were well conserved across data sets. We validated that the relative expression of top cell type markers are associated with proxies for cell type proportions in bulk RNA expression data from postmortem human brain samples. We further validated novel marker genes using an orthogonal ATAC-seq dataset. We performed multiscale coexpression network analysis of the single cell data sets and identified robust cell-specific gene modules. To facilitate the use of the cell type-specific genes for cell type proportion estimation and deconvolution from bulk brain gene expression data, we developed an R package, BRETIGEA. In summary, we identified a set of novel brain cell consensus signatures and robust networks from the integration of multiple datasets and therefore transcend limitations related to technical issues characteristic of each individual study.
format	article
author	Andrew T. McKenzie Minghui Wang Mads E. Hauberg John F. Fullard Alexey Kozlenkov Alexandra Keenan Yasmin L. Hurd Stella Dracheva Patrizia Casaccia Panos Roussos Bin Zhang
author_facet	Andrew T. McKenzie Minghui Wang Mads E. Hauberg John F. Fullard Alexey Kozlenkov Alexandra Keenan Yasmin L. Hurd Stella Dracheva Patrizia Casaccia Panos Roussos Bin Zhang
author_sort	Andrew T. McKenzie
title	Brain Cell Type Specific Gene Expression and Co-expression Network Architectures
title_short	Brain Cell Type Specific Gene Expression and Co-expression Network Architectures
title_full	Brain Cell Type Specific Gene Expression and Co-expression Network Architectures
title_fullStr	Brain Cell Type Specific Gene Expression and Co-expression Network Architectures
title_full_unstemmed	Brain Cell Type Specific Gene Expression and Co-expression Network Architectures
title_sort	brain cell type specific gene expression and co-expression network architectures
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/4f166a71fd834b69ad514d8d5441276a
work_keys_str_mv	AT andrewtmckenzie braincelltypespecificgeneexpressionandcoexpressionnetworkarchitectures AT minghuiwang braincelltypespecificgeneexpressionandcoexpressionnetworkarchitectures AT madsehauberg braincelltypespecificgeneexpressionandcoexpressionnetworkarchitectures AT johnffullard braincelltypespecificgeneexpressionandcoexpressionnetworkarchitectures AT alexeykozlenkov braincelltypespecificgeneexpressionandcoexpressionnetworkarchitectures AT alexandrakeenan braincelltypespecificgeneexpressionandcoexpressionnetworkarchitectures AT yasminlhurd braincelltypespecificgeneexpressionandcoexpressionnetworkarchitectures AT stelladracheva braincelltypespecificgeneexpressionandcoexpressionnetworkarchitectures AT patriziacasaccia braincelltypespecificgeneexpressionandcoexpressionnetworkarchitectures AT panosroussos braincelltypespecificgeneexpressionandcoexpressionnetworkarchitectures AT binzhang braincelltypespecificgeneexpressionandcoexpressionnetworkarchitectures
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Brain Cell Type Specific Gene Expression and Co-expression Network Architectures

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