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Single-cell chromatin accessibility identifies pancreatic islet cell type– and state-specific regulatory programs of diabetes risk

Nature Genetics volume 53, pages 455–466 (2021)Cite this article

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Abstract

Single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq) creates new opportunities to dissect cell type–specific mechanisms of complex diseases. Since pancreatic islets are central to type 2 diabetes (T2D), we profiled 15,298 islet cells by using combinatorial barcoding snATAC-seq and identified 12 clusters, including multiple alpha, beta and delta cell states. We cataloged 228,873 accessible chromatin sites and identified transcription factors underlying lineage- and state-specific regulation. We observed state-specific enrichment of fasting glucose and T2D genome-wide association studies for beta cells and enrichment for other endocrine cell types. At T2D signals localized to islet-accessible chromatin, we prioritized variants with predicted regulatory function and co-accessibility with target genes. A causal T2D variant rs231361 at the KCNQ1 locus had predicted effects on a beta cell enhancer co-accessible with INS and genome editing in embryonic stem cell–derived beta cells affected INS levels. Together our findings demonstrate the power of single-cell epigenomics for interpreting complex disease genetics.

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Fig. 1: Pancreatic islet cell type–accessible chromatin defined using snATAC-seq.
Fig. 2: Heterogeneity in endocrine cell–accessible chromatin and regulatory programs.
Fig. 3: Enrichment of islet-accessible chromatin for diabetes and fasting glycemia.
Fig. 4: Genetic variants with islet cell type– and state-specific effects on chromatin accessibility.
Fig. 5: Chromatin co-accessibility links diabetes risk variants to target genes.

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Data availability

Raw sequencing data have been deposited into the National Center for Biotechnology Information Gene Expression Omnibus with accession numbers GSE160472, GSE160473 and GSE163610. Processed data files and annotations for snATAC-seq are available through the Diabetes Epigenome Atlas (https://www.diabetesepigenome.org/). All other data are either found in the article or available upon request to the corresponding author. Source data are provided with this paper.

Code availability

The code for processing and clustering the snATAC-seq datasets is available at https://github.com/kjgaulton/pipelines/tree/master/islet_snATAC_pipeline.

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Acknowledgements

This work was supported by NIH grant nos. R01DK114650 and U01DK105554 (sub-award) to K.G., grant nos. R01DK068471 and U01DK105541 to M.S. and grant no. U01DK120429 to K.G. and M.S. and by the University of California, San Diego School of Medicine to the Center for Epigenomics. We thank the QB3 Macrolab at University of California, Berkeley for the purification of the Tn5 transposase. We thank K. Jepsen, the University of California, San Diego Institute for Genomic Medicine Genomics Center and S. Kuan for sequencing and B. Li for bioinformatics support. Data from the UK Biobank was accessed under application no. 24058. We thank I. Matta for the preparation of the RNA-seq libraries.

Author information

Author notes

  1. David U. Gorkin

    Present address: Department of Biology, Emory University, Atlanta, GA, USA

  2. These authors contributed equally: Joshua Chiou, Chun Zeng.

  3. These authors jointly supervised this work: Maike Sander, David U. Gorkin, Kyle J. Gaulton.

Authors and Affiliations

  1. Biomedical Graduate Studies Program, University of California, San Diego, La Jolla, CA, USA

    Joshua Chiou

  2. Department of Pediatrics, Pediatric Diabetes Research Center, University of California, San Diego, La Jolla, CA, USA

    Chun Zeng, Michael Schlichting, Serina Huang, Jinzhao Wang, Yinghui Sui, Allison Deogaygay, Mei-Lin Okino, Ying Sun, Parul Kudtarkar, Maike Sander & Kyle J. Gaulton

  3. Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA

    Chun Zeng, Michael Schlichting, Jinzhao Wang, Yinghui Sui, Yunjiang Qiu, Rongxin Fang, Maike Sander & David U. Gorkin

  4. Center for Epigenomics, University of California, San Diego, La Jolla, CA, USA

    Zhang Cheng, Jee Yun Han, Michael Miller, Robert Mendez, Sebastian Preissl & David U. Gorkin

  5. Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA

    Maike Sander & Kyle J. Gaulton

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K.J.G., D.U.G. and M. Sander conceived and supervised the research. J.C. performed the analyses of the single-cell and genetic data. C.Z., M. Schlichting and J.W. performed the hESC experiments. Z.C. performed the analyses of the single-cell and Hi-C data. J.Y.H. performed the combinatorial barcoding single-cell assays and genotyping. M.M. performed the 10x single-cell assays. R.M. performed the Hi-C experiments. S.H., A.D. and M.-L.O. performed the reporter experiments. Y.Q. performed the analyses of the 4C data. Y. Sui performed the analyses of the hESC data. Y. Sun and P.K. developed and processed the data for the epigenome database. R.F. contributed to the analyses of the single-cell data. S.P. contributed to the development of the single-cell assays. K.J.G., D.U.G., M. Sander, J.C., C.Z. and Z.C. wrote the manuscript.

Corresponding authors

Correspondence to Maike Sander, David U. Gorkin or Kyle J. Gaulton.

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Competing interests

K.J.G. does consulting work for Genentech and holds stock in Vertex Pharmaceuticals; neither is related to the work in this study. The other authors declare no competing interests.

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Peer review information Nature Genetics thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Quality control metrics and aggregate comparison to bulk islet ATAC.

a, Insert size distribution for aggregate reads from each snATAC-seq experiment. b, Aggregated read coverage from each snATAC-seq experiment in a ± 2 kb window around individual promoters (top) and averaged across all promoters (bottom). c, Spearman correlation between normalized read coverage within a merged set of peaks from 3 aggregated islet snATAC-seq, 42 bulk islet ATAC-seq, and 4 bulk pancreas ATAC-seq datasets. Names of samples are from the original sources of the data. d, Binned log10 read depth distribution for each experiment.

Extended Data Fig. 2 Flowchart of the snATAC-seq data processing pipeline.

a, Flowchart summarizing key steps of the snATAC-seq processing pipeline, including the various steps where cells were filtered out. Samples were first processed individually. All samples were then combined using a batch correction method. Clusters corresponding to cells from low quality cells, including those with low read depth in highly variable windows and low fraction of reads in peaks were then removed. After re-clustering, iterative subclustering of the main clusters at high resolution was used to identify and remove doublet subclusters. The final clusters are not driven by potential confounders such as donor of origin. Boxplot center lines, limits, and whiskers represent median, quartiles, and 1.5 IQR respectively.

Extended Data Fig. 3 Analysis of islet single cell gene expression data.

a, log10 transformed read depth or (b) total number of genes expressed compared with number of marker genes expressed per cell from scRNA-seq data. Boxplot center lines, limits, and whiskers represent median, quartiles, and 1.5 IQR respectively. Cells expressing more than one marker gene (defined by mixture models) were marked as doublets and filtered out. c, Clusters of islet cells from single cell RNA-seq data plotted on UMAP coordinates. quies. stellate, quiescent stellate. activ. stellate, activated stellate. d, Selected marker gene log2(expression) for each cluster plotted on UMAP coordinates. e, Row-normalized t-statistics of marker gene specificity showing the most specific genes (t-statistic>20) for each cluster.

Extended Data Fig. 4 Comparison of motif enrichment between alpha and gamma cells.

Differential enrichment of motifs between alpha cell open chromatin regions and gamma cell open chromatin regions as measured by a 2-sided T-test, with FDR calculated by the Benjamini-Hochberg procedure. Examples are highlighted of motifs enriched in alpha cells and gamma cells, respectively (MAFG, HOXA9). UMAP plots show enrichment z-scores for the indicated motifs in alpha and gamma cells. Violin plots below show the distribution of enrichment z-scores across alpha or gamma cells, where the lines represent median and quartiles.

Extended Data Fig. 5 Differentially accessible promoters across pseudo-states.

a, Pseudo-state (trajectory) values for alpha cells plotted on UMAP coordinates (left) and percentage of cells with GCG promoter accessibility decreases across 10 bins along the alpha (α) cell trajectory (right). b, Pseudo-state (trajectory) values for beta (β) cells plotted on UMAP coordinates (left) and percentage of cells with INS promoter accessibility decreases across 10 bins along the beta cell trajectory (right). c, Pseudo-state (trajectory) values for delta (δ) cells plotted on UMAP coordinates (left) and percentage of cells with SST promoter accessibility decreases across 10 bins along the beta cell trajectory (right). d, Heatmaps showing promoters with dynamic accessibility across trajectories for alpha (top), beta (middle) and delta (bottom) cell trajectories. Gene promoters are clustered into 4 groups for each trajectory with k-medoids clustering. Enriched gene ontology for each k-medoid cluster (left) and selected genes present in at least one enriched gene ontology.

Extended Data Fig. 6 Single cell GWAS enrichment and correlation with TF motifs.

a, Single cell GWAS enrichment z-scores for Major depressive disorder and Systemic lupus erythematosus projected onto UMAP coordinates (left panels), z-score enrichment distribution per cell type and state (middle panels) and z-score enrichment distribution split into 10 bins based on beta cell trajectory values (right panels). Boxplot center lines, limits, and whiskers represent median, quartiles, and 1.5 IQR respectively. b, Correlation between single cell GWAS enrichment z-scores for Type 2 Diabetes and chromVAR TF motif enrichment z-scores across either all cells (left) or beta cells (right). Inset scatterplots highlight the top correlated motifs in either direction. c, Variants mapping directly in sequence motifs positively correlated with T2D risk in beta cells are enriched for T2D association, whereas variants mapping in motifs negatively correlated with T2D risk in beta cells show no such enrichment. Values represent effect size and SE.

Extended Data Fig. 7 Single cell co-accessibility analyses in islet cell types.

a, Distance-matched odds that delta cell co-accessibility links overlap islet pcHi-C chromatin loops at different co-accessibility threshold bins in 0.05 intervals demonstrate that co-accessible links are enriched for chromatin interactions. b, Same analysis as in (a) but with alpha cell co-accessibility. c, Same analysis as in (a) but with beta cell co-accessibility and Hi-C loops. d, Same analysis as in (a) but with delta cell co-accessibility and Hi-C loops. e, Same analysis as in (a) but with alpha cell co-accessibility and Hi-C loops. f, Number of distal sites linked to each promoter peak for alpha, beta, and delta cells. g, Number of promoters linked to each distal site for alpha, beta, and delta cells.

Extended Data Fig. 8 Cell type-specific and shared co-accessible sites.

a, An example of co-accessibility anchored at the promoter for the delta cell identity TF HHEX. Co-accessibility for beta, delta, and alpha cells are shown compared to high-confidence pcHi-C loops from ensemble islets. Genome browser plots scale: 0-10. b, An example of co-accessibility anchored at the promoter for the alpha cell identity TF ARX. c, An example of shared co-accessibility anchored at the promoter for the shared islet identity TF NEUROD1.

Extended Data Fig. 9 3D chromatin interactions at the T2D-associated KCNQ1 locus.

Top panels show Hi-C contact matrices from hESC-derived beta cells, visualized at 25 kb resolution. Region shown is chr11:500,00-4,500,000, hg19. Black arrows indicate putative interaction point of INS TSS and KCNQ1 enhancer. Genome browser plot below shows a zoomed view of chr11:1,750,000-3,250,000. Data from 4C-seq anchored on the INS promoter in EndoC-βH1 cells (Jian & Felsenfeld72) is shown, as analyzed with the 4C-ker package. Normalized read counts are shown in black from 3 biological replicates. Significant interactions from INS promoter are shown as arcs below read counts tracks. Interactions calls from data pooled across 3 replicates are shown here. The region containing the KCNQ1 enhancer was called as a significant interaction region with INS promotor independently in each 4C replicate. Virtual 4C plots in green show log(normalized Hi-C interaction frequency) from INS promoter.

Extended Data Fig. 10 Genome editing of the KCNQ1 locus in hESCs.

a, Schematic of the workflow and (b) Sanger sequencing for KCNQ1 enhancer deletion in three independent hESC clones. c, Representative figures of flow cytometry analysis for NKX6-1 and INS comparing control and KCNQ1ΔEnh cells (left). Quantification of the percentage of NKX6-1+/INS+ cells in beta cell stage cultures from control (n = 6; 2 clones × 3 differentiations) and KCNQ1∆Enh (n = 9; 3 clones × 3 differentiations) cells (right). Values represent mean and SEM. ns, not significant by two-sided Student’s T-test without adjustment for multiple comparisons. d, Schematic of the workflow and (e) Sanger sequencing for two independent KCNQ1G/G clones and three KCNQ1A/A clones. f, Representative figures of flow cytometry analysis for NKX6-1 and INS comparing KCNQ1G/G and KCNQ1A/A clones (left). of the percentage of NKX6-1+/INS+ cells in beta cell stage cultures from KCNQ1G/G (n = 6; 2 clones × 3 differentiations) and KCNQ1A/A (n = 9; 3 clones × 3 differentiations) cells (right). ns, not significant by two-sided Student’s T-test without adjustment for multiple comparisons. Values represent mean and SEM.

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Chiou, J., Zeng, C., Cheng, Z. et al. Single-cell chromatin accessibility identifies pancreatic islet cell type– and state-specific regulatory programs of diabetes risk. Nat Genet 53, 455–466 (2021). https://doi.org/10.1038/s41588-021-00823-0

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