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

Andrew T Mckenzie,Mads E Hauberg,Stella Dracheva,John F Fullard,Patrizia Casaccia,Panos Roussos,Minghui Wang,Yasmin L Hurd,Alexandra Keenan,Alexey Kozlenkov,Bin Zhang

doi:10.1038/s41598-018-27293-5

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.

Highlights

To interrogate the interactions of genes within each of the human brain cell types, we applied MEGENA, a multiscale clustering algorithm, to RNA-seq data from the human Darmanis et al data set, identifying modules within each cell type and data set that may correspond to particular subcellular compartments, and/or signaling pathways (Supplemental File 4)
To further validate our novel marker genes, we examined the signatures of open chromatin at these genes using ATAC-seq data in 4 brain cell types: gabaergic neurons, glutaminergic neurons, oligodendrocytes, and glial cells of microglia/astrocyte type
In networks associated with the other cell types, we found modules enriched in genes associated with cell type marker enrichments and GO terms suggesting cell type-specific activity, including “glutamate secretion” in astrocytes[44] (Module #19, Odds Ratio (OR) = 24, p = 0.0003, Supplemental Fig. 14), “regulation of platelet-derived growth factor receptor signaling pathway” in endothelial cells[45] (Module #24, OR = 261, p = 1.8e5, Supplemental Fig. 15), “interleukin-6 receptor activity” in microglia[46] (Module #38, OR = Inf, p = 0.0003, Supplemental Fig. 16), “glucose import” in oligodendrocytes[47] (Module #59, OR = 46, p = 0.003, Supplemental Fig. 17), and “microtubule anchoring at microtubule organizing center” in OPCs48 (Module #74, OR = 332, p = 3.8e-5, Supplemental Fig. 18)

Summary

Introduction

To interrogate the interactions of genes within each of the human brain cell types, we applied MEGENA, a multiscale clustering algorithm, to RNA-seq data from the human Darmanis et al data set, identifying modules within each cell type and data set that may correspond to particular subcellular compartments, and/or signaling pathways (Supplemental File 4). In networks associated with the other cell types, we found modules enriched in genes associated with cell type marker enrichments and GO terms suggesting cell type-specific activity, including “glutamate secretion” in astrocytes[44] (Module #19, Odds Ratio (OR) = 24, p = 0.0003, Supplemental Fig. 14), “regulation of platelet-derived growth factor receptor signaling pathway” in endothelial cells[45] (Module #24, OR = 261, p = 1.8e5, Supplemental Fig. 15), “interleukin-6 receptor activity” in microglia[46] (Module #38, OR = Inf, p = 0.0003, Supplemental Fig. 16), “glucose import” in oligodendrocytes[47] (Module #59, OR = 46, p = 0.003, Supplemental Fig. 17), and “microtubule anchoring at microtubule organizing center” in OPCs48 (Module #74, OR = 332, p = 3.8e-5, Supplemental Fig. 18). We suggest that these networks capture aspects of cell type-specific pathways in each of the six major brain cell types

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Discussion

Conclusion