Single-cell ATAC-Seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures
Quang, Daniel X
Erdos, Michael R
Cusanovich, Darren A
Daza, Riza M
Zou, Luli S
Didion, John P
Parker, Stephen C J
Collins, Francis S
AffiliationUniv Arizona, Dept Cellular & Mol Med
MetadataShow full item record
CitationRai, V., Quang, D. X., Erdos, M. R., Cusanovich, D. A., Daza, R. M., Narisu, N., ... & Parker, S. C. (2020). Single-cell ATAC-Seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures. Molecular Metabolism, 32, 109-121. doi:10.1016/j.molmet.2019.12.006
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AbstractObjective: Type 2 diabetes (T2D) is a complex disease characterized by pancreatic islet dysfunction, insulin resistance, and disruption of blood glucose levels. Genome-wide association studies (GWAS) have identified > 400 independent signals that encode genetic predisposition. More than 90% of associated single-nucleotide polymorphisms (SNPs) localize to non-coding regions and are enriched in chromatin-defined islet enhancer elements, indicating a strong transcriptional regulatory component to disease susceptibility. Pancreatic islets are a mixture of cell types that express distinct hormonal programs, so each cell type may contribute differentially to the underlying regulatory processes that modulate T2D-associated transcriptional circuits. Existing chromatin profiling methods such as ATAC-seq and DNase-seq, applied to islets in bulk, produce aggregate profiles that mask important cellular and regulatory heterogeneity. Methods: We present genome-wide single-cell chromatin accessibility profiles in >1,600 cells derived from a human pancreatic islet sample using single-cell combinatorial indexing ATAC-seq (sci-ATAC-seq). We also developed a deep learning model based on U-Net architecture to accurately predict open chromatin peak calls in rare cell populations. Results: We show that sci-ATAC-seq profiles allow us to deconvolve alpha, beta, and delta cell populations and identify cell-type-specific regulatory signatures underlying T2D. Particularly, T2D GWAS SNPs are significantly enriched in beta cell-specific and across cell-type shared islet open chromatin, but not in alpha or delta cell-specific open chromatin. We also demonstrate, using less abundant delta cells, that deep learning models can improve signal recovery and feature reconstruction of rarer cell populations. Finally, we use co-accessibility measures to nominate the cell-specific target genes at 104 non-coding T2D GWAS signals. Conclusions: Collectively, we identify the islet cell type of action across genetic signals of T2D predisposition and provide higher-resolution mechanistic insights into genetically encoded risk pathways. Published by Elsevier GmbH.
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VersionFinal published version
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