Molecular Neurodegeneration (Jul 2020)

Deciphering cellular transcriptional alterations in Alzheimer’s disease brains

  • Xue Wang,
  • Mariet Allen,
  • Shaoyu Li,
  • Zachary S. Quicksall,
  • Tulsi A. Patel,
  • Troy P. Carnwath,
  • Joseph S. Reddy,
  • Minerva M. Carrasquillo,
  • Sarah J. Lincoln,
  • Thuy T. Nguyen,
  • Kimberly G. Malphrus,
  • Dennis W. Dickson,
  • Julia E. Crook,
  • Yan W. Asmann,
  • Nilüfer Ertekin-Taner

DOI
https://doi.org/10.1186/s13024-020-00392-6
Journal volume & issue
Vol. 15, no. 1
pp. 1 – 15

Abstract

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Abstract Large-scale brain bulk-RNAseq studies identified molecular pathways implicated in Alzheimer’s disease (AD), however these findings can be confounded by cellular composition changes in bulk-tissue. To identify cell intrinsic gene expression alterations of individual cell types, we designed a bioinformatics pipeline and analyzed three AD and control bulk-RNAseq datasets of temporal and dorsolateral prefrontal cortex from 685 brain samples. We detected cell-proportion changes in AD brains that are robustly replicable across the three independently assessed cohorts. We applied three different algorithms including our in-house algorithm to identify cell intrinsic differentially expressed genes in individual cell types (CI-DEGs). We assessed the performance of all algorithms by comparison to single nucleus RNAseq data. We identified consensus CI-DEGs that are common to multiple brain regions. Despite significant overlap between consensus CI-DEGs and bulk-DEGs, many CI-DEGs were absent from bulk-DEGs. Consensus CI-DEGs and their enriched GO terms include genes and pathways previously implicated in AD or neurodegeneration, as well as novel ones. We demonstrated that the detection of CI-DEGs through computational deconvolution methods is promising and highlight remaining challenges. These findings provide novel insights into cell-intrinsic transcriptional changes of individual cell types in AD and may refine discovery and modeling of molecular targets that drive this complex disease.

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