Inference of cell type content from human brain transcriptomic datasets illuminates the effects of age, manner of death, dissection, and psychiatric diagnosis

PloS One
Megan Hastings HagenauerHuda Akil

Abstract

Psychiatric illness is unlikely to arise from pathology occurring uniformly across all cell types in affected brain regions. Despite this, transcriptomic analyses of the human brain have typically been conducted using macro-dissected tissue due to the difficulty of performing single-cell type analyses with donated post-mortem brains. To address this issue statistically, we compiled a database of several thousand transcripts that were specifically-enriched in one of 10 primary cortical cell types in previous publications. Using this database, we predicted the relative cell type content for 833 human cortical samples using microarray or RNA-Seq data from the Pritzker Consortium (GSE92538) or publicly-available databases (GSE53987, GSE21935, GSE21138, CommonMind Consortium). These predictions were generated by averaging normalized expression levels across transcripts specific to each cell type using our R-package BrainInABlender (validated and publicly-released on github). Using this method, we found that the principal components of variation in the datasets strongly correlated with the predicted neuronal/glial content of the samples. This variability was not simply due to dissection-the relative balance of brain cell types appear...Continue Reading

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Citations

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Datasets Mentioned

BETA
GSE52564
GSE6783
GSE19380
GSE21138

Methods Mentioned

BETA
laser capture microscopy
RNA-Seq
dissection
chip
dissections

Software Mentioned

fGSEA
DAVID : Functional Annotation Tool
BrainInABlender
Gene Set Enrichment Analysis
GSEA
R package AffyRNADegradation
DAVID
Gene Set Enrichment Analysis ( GSEA
excel
R

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