1. Publications

Single-cell ATAC-Seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell- specific type 2 diabetes regulatory signatures

Vivek Rai, Daniel X Quang, Michael R Erdos, Darren A Cusanovich, Riza M Daza, Narisu Narisu, Luli S Zou, John P Didion, Yuanfang Guan, Jay Shendure, Stephen C J Parker, Francis S Collins.
Mol Metab. 2019-12-20;32:109-121.
Abstract
Objective: 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. Keywords: Chromatin; Deep learning; Epigenomics; Islet; Single cell; Type 2 diabetes.
Consortium data used in this publication
The data reported in this paper were deposited in the database of Genotypes and Phenotypes (accession no. phs001188.v2.p1; FUSION Tissue Biopsy Study-Islet Expression and Regulation by RNAseq and ATACseq). The code for deep learning analysis is available on GitHub at https://github.com/ParkerLab/PillowNet. Custom scripts are available at https://github.com/ParkerLab/islet_sci-ATAC-seq_2019. Additional URLs are provided in Table S7 of the Supplementary Data.