Deciphering cellular transcriptional alterations in Alzheimer’s disease brains
Xue Wang, Mariet Allen, Shaoyu Li, Zachary Quicksall, Tulsi Patel, Troy Carnwath, Joseph S. Reddy, Minerva M. Carrasquillo, Sarah Lincoln, Thuy Nguyen, Kimberly G. Malphrus, Dennis W. Dickson, Julia E. Crook, Yan W. Asmann, Nilüfer Ertekin‐Taner
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.