The Alzheimer’s Cell Atlas (TACA): A single‐cell type transcriptomics and network pathobiology map for target identification and drug repurposing in Alzheimer’s disease and related dementia

Abstract

AbstractBackgroundRecent advances in massive single‐cell/nucleus (sc/sn) transcriptomic data have great potential for identification of cell‐type specific Alzheimer’s disease (AD) pathobiology, as well as target discovery for drug repurposing.MethodWe designed and implemented a multimodal omics analytic framework for the processing of sc/sn transcriptomic datasets, analyses of gene expression, pathobiology, cell‐cell communications with results presentation, interpretation, and translation. We performed an exhaustive search of cell type‐specific gene/protein network modules and cell‐cell interaction networks using our recently compiled ligand‐receptor and the human protein‐protein interactome networks. Furthermore, we developed a web portal with comprehensive visualization tools, such as cell and gene expression viewers, volcano plot and protein‐protein interaction network for differentially expressed genes (DEGs), ligand‐receptor interaction network for cell‐cell interactions, and heatmap for drug perturbation profiles cell type‐specific DEGs.ResultWe created The Alzheimer’s Cell Atlas (TACA, available at https://taca.lerner.ccf.org). In this endeavor, we compiled an AD brain cell atlas consisting of more than one million cells/nuclei from 20 datasets, covering major brain regions (cortex, hippocampus, cerebellum, etc.) and cell types (astrocytes, microglia, neurons, oligodendrocytes, etc.). We also conducted over 1,000 DE comparisons of these datasets to reveal cell‐type specific gene expression alterations. Major comparison types are (i) case vs healthy control; (ii) sex‐specific differential expression, (iii) genotype‐driven DE (i.e., APOE4/4 vs. APOE3/3; TREM2 R47H vs. common variants) analysis; and (iv) others. In addition, each comparison was followed by human protein‐protein interactome network module analysis, pathway enrichment analysis, and gene‐set enrichment analysis. For drug screening, we conducted gene set enrichment analysis for all comparisons with over 700,000 drug perturbation profiles connecting more than 12,000 human genes and 13,000 drugs/compounds. A total of over 300 analyses of cell‐cell interactions against 6,000 experimentally validated ligand‐receptor interactions were also conducted. We then generated summaries for the genes (for target identification) and drugs (for drug repurposing) from all analyses in sex‐specific and cell type‐specific manners.ConclusionWe envision that TACA will be a highly valuable resource for both basic and translational research in AD, as it provides abundant information for AD pathobiology and actionable systems biology tools for therapeutic discovery.