bioRxivGenomicsPreprint
Single-cell gene expression and chromatin accessibility profiling of human pancreatic islets at basal and stimulatory conditions nominates mechanisms of type 1 diabetes genetic risk
Ricardo D’Oliveira AlbanusiD, Xuming TangiD, Henry J. TayloriD, Nandini ManickamiD, Michael R. ErdosiD, Narisu NarisuiD, Yuling HaniD, Peter OrchardiD, Arushi VarshneyiD, Chengyang LiuiD, Ali NajiiD, Francis S. CollinsiD, Shuibing CheniD, Stephen C.J. ParkeriD
Abstract
Type 1 diabetes (T1D) is a complex autoimmune disease characterized by the loss of pancreatic islet beta cells. The mechanisms of T1D genetic risk remain poorly understood. Here, we present a multi-omic integrative study of single-cell/nucleus molecular profiles of gene expression and chromatin accessibility in the same biological samples from healthy and beta cell autoantibody+ (AAB+) human pancreatic islets to characterize mechanisms of islet-mediated T1D genetic risk. We additionally performed single-cell/nucleus multi-omic profiling of healthy islets under two stimulatory conditions used as in vitro models of T1D (cytokine cocktail and CVB4 infection) to evaluate how environmental exposures recapitulate multi-omic signatures of T1D. In total, we analyzed 121,272 cells/nuclei across 34 libraries, identifying 10 distinct cell types. We identified cell-type-specific and disease-associated cis-regulatory elements and nominated likely target genes. We provide evidence that T1D genetic risk is mediated through multiple pancreatic cell populations, including islet endocrine cells (beta, alpha, gamma, and delta), exocrine acinar and ductal cells, and immune cells. Finally, we identified three independent T1D risk variants acting through pancreatic islet endocrine cells at the TOX, RASGRP1, and DLK1/MEG3 loci. Together, this work improves our understanding of how non-coding genetic variants encode T1D risk through a complex interplay of different cell types in the pancreas.
- organism: human
- assay: scRNA-seq
- assay: scATAC-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
- Citations
- 3
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