Integrative single-nucleus multi-omics analysis prioritizes candidatecisandtransregulatory networks and their target genes in Alzheimer’s disease brains

Abstract

BackgroundThe genetic underpinnings of late-onset Alzheimer’s disease (LOAD) are yet to be fully elucidated. Although numerous LOAD-associated loci have been discovered, the causal variants and their target genes remain largely unknown. Since the brain is composed of heterogenous cell subtypes, it is imperative to study the brain on a cell subtype specific level to explore the biological processes underlying LOAD.MethodsHere, we present the largestparallelsingle-nucleus (sn) multi-omics study to simultaneously profile gene expression (snRNA-seq) and chromatin accessibility (snATAC-seq) to date, using nuclei from 12 normal and 12 LOAD brains. We identified cell subtype clusters based on gene expression and chromatin accessibility profiles and characterized cell subtype-specific LOAD-associated differentially expressed genes (DEGs), differentially accessible peaks (DAPs) andcisco-accessibility networks (CCANs).ResultsIntegrative analysis defined disease-relevant CCANs in multiple cell subtypes and discovered LOAD-associated cell subtype specific candidatecisregulatory elements (cCREs), their candidate target genes, andtrans-interacting transcription factors (TFs), some of which were LOAD-DEG, for example,ELK1in excitatory neurons (Exc1) andKLF13andJUN, found in multiple cell subtypes. Finally, we focused on a subset of cell subtype-specific CCANs that overlap known LOAD-GWAS regions and catalogued putative functional SNPs changing the affinities of TF motifs within LOAD-cCREs linked to LOAD-DEGs including,APOEandMYO1Ein a specific subtype of microglia andBIN1in a subpopulation of oligodendrocytes.ConclusionsTo our knowledge, this study represents the most comprehensive systematic interrogation to date of regulatory networks and the impact of genetic variants on gene dysregulation in LOAD at a cell subtype resolution. Our findings revealed crosstalk between epigenetic, genomic, and transcriptomic determinates of LOAD pathogenesis and define catalogues of candidate genes, cCREs, and variants involved in LOAD genetic etiology and the cell subtypes in which they act to exert their pathogenic effects. Overall, these results suggest that cell subtype-specificcis-transinteractions between regulatory elements and TFs, and the genes dysregulated by these networks contribute to the development of LOAD.