Integrative single‐nucleus multi‐omics analysis prioritizes candidate cis and trans regulatory networks and their target genes in Alzheimer’s disease brains

Abstract

AbstractBackgroundThe 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.MethodsIn this study we performed a parallel single‐nucleus (sn) multi‐omics analysis to simultaneously profile gene expression (snRNA‐seq) and chromatin accessibility (snATAC‐seq) using nuclei from 12 normal and 12 LOAD brains. We developed a pipeline based on integrative analysis of multimodal datasets that utilizes several bioinformatics packages to catalogue cis‐ and trans‐ regulatory network and their target genes.ResultsWe identified cell subtype specific LOAD‐associated differentially expressed genes (DEGs), differentially accessible peaks (DAPs) and cis co‐accessibility networks (CCANs). Integrative analysis defined disease‐relevant CCANs in multiple cell subtypes and discovered LOAD‐associated cell subtype specific candidate cis regulatory elements (cCRE), their candidate target genes, and trans‐interacting transcription factors (TF), some of which were LOAD‐DEG, for example, ELK1 in a specific sub type of excitatory neurons and ATF7 and JUN, found in multiple cell subtypes. Overall, our analysis discovered 53 new candidate LOAD genes and validated 16 gene previously implicated in LOAD. Finally, we focused on a subset of cell subtype‐specific CCANs that overlap known LOAD‐GWAS regions and catalogued putative functional SNPs disrupting TF motifs within LOAD‐cCREs linked to LOAD‐DEGs including, APOE and MYO1E in a specific subtype of microglia.ConclusionsIn this study we identified candidate cis‐ and trans‐ regulatory factors and their target genes that contribute to the pathogenesis of LOAD. To our knowledge, this study represents the most comprehensive systematic interrogation of regulatory networks underlying gene dysregulation in LOAD at an unprecedented cell‐subtype resolution. Collectively, our findings provide a rich dataset for future mechanistic experiments, confirm known LOAD‐GWAS loci while also identifying novel loci, and suggest the disease‐relevant cell types and subtypes for follow‐up validation studies using in vitro and in vivo disease model systems. This information is important for the development of new therapeutic targets and interventions for LOAD.