PLoS Computational Biology (2020-08-01)

Deconvolving the contributions of cell-type heterogeneity on cortical gene expression.

  • Ellis Patrick,
  • Mariko Taga,
  • Ayla Ergun,
  • Bernard Ng,
  • William Casazza,
  • Maria Cimpean,
  • Christina Yung,
  • Julie A Schneider,
  • David A Bennett,
  • Chris Gaiteri,
  • Philip L De Jager,
  • Elizabeth M Bradshaw,
  • Sara Mostafavi

DOI
https://doi.org/10.1371/journal.pcbi.1008120
Journal volume & issue
Vol. 16, no. 8
p. e1008120

Abstract

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Complexity of cell-type composition has created much skepticism surrounding the interpretation of bulk tissue transcriptomic studies. Recent studies have shown that deconvolution algorithms can be applied to computationally estimate cell-type proportions from gene expression data of bulk blood samples, but their performance when applied to brain tissue is unclear. Here, we have generated an immunohistochemistry (IHC) dataset for five major cell-types from brain tissue of 70 individuals, who also have bulk cortical gene expression data. With the IHC data as the benchmark, this resource enables quantitative assessment of deconvolution algorithms for brain tissue. We apply existing deconvolution algorithms to brain tissue by using marker sets derived from human brain single cell and cell-sorted RNA-seq data. We show that these algorithms can indeed produce informative estimates of constituent cell-type proportions. In fact, neuronal subpopulations can also be estimated from bulk brain tissue samples. Further, we show that including the cell-type proportion estimates as confounding factors is important for reducing false associations between Alzheimer's disease phenotypes and gene expression. Lastly, we demonstrate that using more accurate marker sets can substantially improve statistical power in detecting cell-type specific expression quantitative trait loci (eQTLs).