Dataset Information

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

ABSTRACT:

Background

The 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.

Methods

Here, we present the largest parallel single-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) and cis co-accessibility networks (CCANs).

Results

Integrative analysis defined disease-relevant CCANs in multiple cell subtypes and discovered LOAD-associated cell subtype-specific candidate cis regulatory elements (cCREs), their candidate target genes, and trans-interacting transcription factors (TFs), some of which, including ELK1, JUN, and SMAD4 in excitatory neurons, were also LOAD-DEGs. 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 APOE and MYO1E in a specific subtype of microglia and BIN1 in a subpopulation of oligodendrocytes.

Conclusions

To 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 reveal crosstalk between epigenetic, genomic, and transcriptomic determinants 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-specific cis-trans interactions between regulatory elements and TFs, and the genes dysregulated by these networks contribute to the development of LOAD.

SUBMITTER: Gamache J

PROVIDER: S-EPMC10546724 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Publications

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

Gamache Julia J Gingerich Daniel D Shwab E Keats EK Barrera Julio J Garrett Melanie E ME Hume Cordelia C Crawford Gregory E GE Ashley-Koch Allison E AE Chiba-Falek Ornit O

Cell & bioscience 20231003 1

<h4>Background</h4>The 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.<h4>Methods</h4>Here, we present the largest parallel single-nucleus (sn) ...[more]

PMID: 37789374

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Project description:Biomedical research studies have generated large multi-omic datasets to study complex diseases like Alzheimer's disease (AD). An important aim of these studies is the identification of candidate genes that demonstrate congruent disease-related alterations across the different data types measured by the study. We developed a new method to detect such candidate genes in large multi-omic case-control studies that measure multiple data types in the same set of samples. The method is based on a gene-centric integrative coefficient quantifying to what degree consistent differences are observed in the different data types. For statistical inference, a Bayesian hierarchical model is used to study the distribution of the integrative coefficient. The model employs a conditional autoregressive prior to integrate a functional gene network and to share information between genes known to be functionally related. We applied the method to an AD dataset consisting of histone acetylation, DNA methylation, and RNA transcription data from human cortical tissue samples of 233 subjects, and we detected 816 genes with consistent differences between persons with AD and controls. The findings were validated in protein data and in RNA transcription data from two independent AD studies. Finally, we found three subnetworks of jointly dysregulated genes within the functional gene network which capture three distinct biological processes: myeloid cell differentiation, protein phosphorylation and synaptic signaling. Further investigation of the myeloid network indicated an upregulation of this network in early stages of AD prior to accumulation of hyperphosphorylated tau and suggested that increased CSF1 transcription in astrocytes may contribute to microglial activation in AD. Thus, we developed a method that integrates multiple data types and external knowledge of gene function to detect candidate genes, applied the method to an AD dataset, and identified several disease-related genes and processes demonstrating the usefulness of the integrative approach.

Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

Similar Datasets

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