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. 2020 Mar 2;12(5):4124-4162.
doi: 10.18632/aging.102840. Epub 2020 Mar 2.

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

Affiliations

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

Ignazio S Piras et al. Aging (Albany NY). .

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.

Keywords: RNA sequencing; brain homogenates; endothelial cells; laser capture microdissection; oligodendrocytes.

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Conflict of interest statement

CONFLICTS OF INTEREST: The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Prevalence of gene classes expressed in different cells across the brain regions analyzed.
Figure 2
Figure 2
Heatmap representing the proportion of up-regulated AD genes for cell type and brain region in cell-specific and “mixed” genes. The stars represent a significant enrichment of a particular gene type among DEGs.
Figure 3
Figure 3
Top 15 significant GO classes identified in the different cell types combining the results for the seven brain regions analyzed. The color scale indicates the significance (blue to red as the significance increases), whereas the size shows the number of genes in that specific enriched class in AD.

References

    1. Mastroeni D, Sekar S, Nolz J, Delvaux E, Lunnon K, Mill J, Liang WS, Coleman PD. ANK1 is up-regulated in laser captured microglia in Alzheimer’s brain; the importance of addressing cellular heterogeneity. PLoS One. 2017; 12:e0177814. 10.1371/journal.pone.0177814 - DOI - PMC - PubMed
    1. Mastroeni D, Nolz J, Sekar S, Delvaux E, Serrano G, Cuyugan L, Liang WS, Beach TG, Rogers J, Coleman PD. Laser-captured microglia in the Alzheimer’s and Parkinson’s brain reveal unique regional expression profiles and suggest a potential role for hepatitis B in the Alzheimer’s brain. Neurobiol Aging. 2018; 63:12–21. 10.1016/j.neurobiolaging.2017.10.019 - DOI - PMC - PubMed
    1. Sekar S, McDonald J, Cuyugan L, Aldrich J, Kurdoglu A, Adkins J, Serrano G, Beach TG, Craig DW, Valla J, Reiman EM, Liang WS. Alzheimer’s disease is associated with altered expression of genes involved in immune response and mitochondrial processes in astrocytes. Neurobiol Aging. 2015; 36:583–91. 10.1016/j.neurobiolaging.2014.09.027 - DOI - PMC - PubMed
    1. Liang WS, Dunckley T, Beach TG, Grover A, Mastroeni D, Ramsey K, Caselli RJ, Kukull WA, McKeel D, Morris JC, Hulette CM, Schmechel D, Reiman EM, et al.. Altered neuronal gene expression in brain regions differentially affected by Alzheimer’s disease: a reference data set. Physiol Genomics. 2008; 33:240–56. 10.1152/physiolgenomics.00242.2007 - DOI - PMC - PubMed
    1. Mathys H, Davila-Velderrain J, Peng Z, Gao F, Mohammadi S, Young JZ, Menon M, He L, Abdurrob F, Jiang X, Martorell AJ, Ransohoff RM, Hafler BP, et al.. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature. 2019; 570:332–37. 10.1038/s41586-019-1195-2 - DOI - PMC - PubMed

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