Research Paper|Volume 12, Issue 5|pp 4124—4162

ESHRD: deconvolution of brain homogenate RNA expression data to identify cell-type-specific alterations in Alzheimer’s disease

Ignazio S. Piras¹, Christiane Bleul¹, Joshua S. Talboom¹, Matthew D. De Both¹, Isabelle Schrauwen², Glenda Halliday³, Amanda J. Myers⁴, Geidy E. Serrano⁵, Thomas G. Beach⁵, Matthew J. Huentelman¹

¹Neurogenomics Division, The Translational Genomics Research Institute, Phoenix, AZ 85004, USA
²Center for Statistical Genetics, Department of Neurology, Gertrude H. Sergievsky Center, Columbia University Medical Center, New York, NY 10032, USA
³The University of Sydney School of Medicine, Sydney, Camperdown NSW 2050, Australia
⁴University of Miami, Miami, FL 33124, USA
⁵Banner Sun Health Research Institute, Sun City, AZ 85351, USA

Received: August 16, 2019Accepted: February 4, 2020Published: March 2, 2020

https://doi.org/10.18632/aging.102840

Copyright © 2020 Piras et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Objective: We describe herein a bioinformatics approach that leverages gene expression data from brain homogenates to derive cell-type specific differential expression results.

Results: We found that differentially expressed (DE) cell-specific genes were mostly identified as neuronal, microglial, or endothelial in origin. However, a large proportion (75.7%) was not attributable to specific cells due to the heterogeneity in expression among brain cell types. Neuronal DE genes were consistently downregulated and associated with synaptic and neuronal processes as described previously in the field thereby validating this approach. We detected several DE genes related to angiogenesis (endothelial cells) and proteoglycans (oligodendrocytes).

Conclusions: We present a cost- and time-effective method exploiting brain homogenate DE data to obtain insights about cell-specific expression. Using this approach we identify novel findings in AD in endothelial cells and oligodendrocytes that were previously not reported.

Methods: We derived an enrichment score for each gene using a publicly available RNA profiling database generated from seven different cell types isolated from mouse cerebral cortex. We then classified the differential expression results from 3 publicly accessible Late-Onset Alzheimer’s disease (AD) studies including seven different brain regions.