RT Journal Article
SR Electronic
T1 Single cell ATAC-seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 749283
DO 10.1101/749283
A1 Vivek Rai
A1 Daniel X. Quang
A1 Michael R. Erdos
A1 Darren A. Cusanovich
A1 Riza M. Daza
A1 Narisu Narisu
A1 Luli S. Zou
A1 John P. Didion
A1 Yuanfang Guan
A1 Jay Shendure
A1 Stephen C.J. Parker
A1 Francis S. Collins
YR 2019
UL http://biorxiv.org/content/early/2019/09/07/749283.abstract
AB 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 the associated single nucleotide polymorphisms (SNPs) localize to non-coding regions and are enriched in chromatin-defined islet enhancer elements, indicating a strong regulatory component to disease susceptibility. Pancreatic islets are a mixture of cell types expressing distinct hormonal programs, and 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 the 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, we find that T2D GWAS SNPs are significantly enriched in beta cell-specific and cross 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.(ATAC-seq)Assay for Transposase Accessible Chromatin Sequencing(GWAS)Genome wide association study(eQTL)Expression quantitative trait loci(GCG)Glucagon(INS)Insulin(SST)Somatostatin.