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, ,

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

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

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.


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.


A: astrocyte; AD: Alzheimer’s Disease; Aβ: Amyloid-β; CBE: cerebellum; CERAD: Consortium to establish a registry for Alzheimer’s Disease; DE: differentially expressed; DEGs: differentially expressed genes; DLPFC: dorsolateral prefrontal cortex; EC: endothelial cell; ESHRD: enrichment score homogenate RNA deconvolution; FC: fold change; FDR: false discovery rate; FP: frontal pole; fpkm: fragments per kilobase million; GO: gene ontology; GSEA: Gene Set Enrichment Analysis; IFG: inferior frontal gyrus; LCM: laser capture Microdissection; M: microglia; MSA: multiple system atrophy; N: neuron; ND: non-demented; O: oligodendrocyte; PCA: principal component analysis; PHG: parahippocampal gyrus; ROSMAP: Religious Order Study and Memory and Aging Project; scRNA: single-cell RNA sequencing; STG: superior temporal gyrus; TCX: temporal cortex.