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Integrating genetics with single-cell multiomic measurements across disease states identifies mechanisms of beta cell dysfunction in type 2 diabetes

Nature Genetics volume 55, pages 984–994 (2023)Cite this article

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Abstract

Dysfunctional pancreatic islet beta cells are a hallmark of type 2 diabetes (T2D), but a comprehensive understanding of the underlying mechanisms, including gene dysregulation, is lacking. Here we integrate information from measurements of chromatin accessibility, gene expression and function in single beta cells with genetic association data to nominate disease-causal gene regulatory changes in T2D. Using machine learning on chromatin accessibility data from 34 nondiabetic, pre-T2D and T2D donors, we identify two transcriptionally and functionally distinct beta cell subtypes that undergo an abundance shift during T2D progression. Subtype-defining accessible chromatin is enriched for T2D risk variants, suggesting a causal contribution of subtype identity to T2D. Both beta cell subtypes exhibit activation of a stress-response transcriptional program and functional impairment in T2D, which is probably induced by the T2D-associated metabolic environment. Our findings demonstrate the power of multimodal single-cell measurements combined with machine learning for characterizing mechanisms of complex diseases.

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Fig. 1: Beta cells exhibit changes in chromatin activity in T2D.
Fig. 2: Machine learning identifies two beta cell subtypes with differential abundance in T2D.
Fig. 3: The two beta cell subtypes are distinguished by chromatin accessibility, gene expression and function.
Fig. 4: GRNs defining the two beta cell subtypes.
Fig. 5: Beta cell functional and gene regulatory changes in T2D.
Fig. 6: T2D risk variants affect beta cell subtype chromatin accessibility.

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Data availability

snATAC-seq data and processed data are available through the Gene Expression Omnibus (GEO) under accession number GSE169453, single-nucleus multiome data are available under accession number GSE200044, and genotyping data are available under accession number GSE170763. UCSC Genome Browser Sessions of aggregated snATAC-seq data are available at https://genome.ucsc.edu/s/gaowei/hg19_cell_type and https://genome.ucsc.edu/s/gaowei/hg19_beta_cell. Previously published16,17 Patch-seq data are available as raw sequencing reads in GEO under accession numbers GSE124742 and GSE164875. Additional Patch-seq data are accessible at the HPAP database (https://hpap.pmacs.upenn.edu). Blacklisted regions from ENCODE are found at https://www.encodeproject.org/annotations/ENCSR636HFF. Source data are provided with this paper.

Code availability

Custom codes for the main analysis used in this study have been deposited on GitHub at https://github.com/gaoweiwang/Islet_snATACseq.

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Acknowledgements

This publication includes data generated at the UCSD IGM Genomics Center using an Illumina NovaSeq 6000 that was purchased with funding from a National Institutes of Health (NIH) Shared Instrument Grant (#S10 OD026929). This work was supported by NIH U01DK105541 and R01DK122607 to M.S. and K.J.G.; R01DK114650 to K.J.G.; R01DK068471 to M.S.; U01DK120447 to P.E.M.; U01DK123716 to S.K.K. and P.E.M.; and UC4-DK112217, UC4-DK112232 and P30 DK116074 to S.K.K. Work at the UCSD Center for Epigenomics was supported by the UCSD School of Medicine. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank H. Gao, Y. Shi, K. A. Frazer, B. Ren, and members of Sander lab for scientific discussions and input on the project. We also thank the organ donors and their families for their contributions to make this study possible.

Author information

Author notes

  1. These authors contributed equally: Gaowei Wang, Joshua Chiou, Chun Zeng.

  2. These authors jointly supervised this work: Sebastian Preissl, Kyle J. Gaulton, Maike Sander.

Authors and Affiliations

  1. Department of Pediatrics, University of California San Diego, La Jolla, CA, USA

    Gaowei Wang, Joshua Chiou, Chun Zeng, Ileana Matta, Nikita Kadakia, Mei-Lin Okino, Elisha Beebe, Medhavi Mallick, Kyle J. Gaulton & Maike Sander

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

    Gaowei Wang, Joshua Chiou, Chun Zeng, Ileana Matta, Nikita Kadakia, Mei-Lin Okino, Elisha Beebe, Medhavi Mallick, Kyle J. Gaulton & Maike Sander

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

    Joshua Chiou

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

    Michael Miller, Jee Yun Han & Sebastian Preissl

  5. Department of Bioengineering, Stanford University, Stanford, CA, USA

    Joan Camunas-Soler

  6. Department of Pharmacology, University of Alberta, Edmonton, Alberta, Canada

    Theodore dos Santos, Xiao-Qing Dai, Cara Ellis & Patrick E. MacDonald

  7. Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada

    Theodore dos Santos, Xiao-Qing Dai, Cara Ellis & Patrick E. MacDonald

  8. Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA

    Yan Hang & Seung K. Kim

  9. Departments of Medicine and of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA

    Yan Hang & Seung K. Kim

  10. Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA, USA

    Seung K. Kim

  11. Department of Clinical Diabetes, Endocrinology & Metabolism, City of Hope, Duarte, CA, USA

    Fouad R. Kandeel

  12. Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, Freiburg, Germany

    Sebastian Preissl

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

    Kyle J. Gaulton & Maike Sander

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

    Maike Sander

  15. Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany

    Maike Sander

Authors
  1. Gaowei Wang
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  2. Joshua Chiou
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  3. Chun Zeng
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  4. Michael Miller
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  5. Ileana Matta
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  7. Nikita Kadakia
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  15. Yan Hang
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  18. Fouad R. Kandeel
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  19. Sebastian Preissl
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  20. Kyle J. Gaulton
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Contributions

M.S., K.J.G. and S.P. conceived and supervised the research in the study; M.S., K.J.G., G.W. and J.C. wrote the manuscript; G.W. and J.C. performed analyses of single-cell and genetic data; C.Z., I.M., N.K., J.Y.H. and M.-L.O. performed experiments; M. Miller performed 10x single-cell assays; E.B. and M. Mallick contributed to data analyses; F.R.K. provided human islets; J.C.-S., T.d.S., X.-Q.D., C.E., Y.H., S.K.K. and P.E.M. provided Patch-seq data.

Corresponding authors

Correspondence to Sebastian Preissl, Kyle J. Gaulton or Maike Sander.

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Competing interests

K.J.G. holds stock in Vertex Pharmaceuticals and Neurocrine Biosciences. J.C. is now employed by and holds stock in Pfizer Inc. The other authors declare no competing interests.

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Nature Genetics thanks Vivek Swarup, Judith Zaugg, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Quality control of snATAC-seq data.

(a) Steps for snATAC-seq data processing and quality control. (b) Representative quality control metrics for each donor. Log10 total reads, fraction of reads overlapping promoters, fraction of reads overlapping peaks, and fraction of reads overlapping mitochondria DNA distribution of cells from library JYH809 as example. Blue vertical lines denote thresholds of 1000 minimal fragment number, 15% fragments overlapping promoters, 30% fragments overlapping peaks, and 10% fraction of reads overlapping mitochondria DNA, respectively. Red vertical lines denote thresholds to identify top 1% barcodes with extremely high total fragment number and fraction of reads overlapping promoters and peaks, respectively. (c) Representative cell clustering from library JYH809. (d) Promoter chromatin accessibility in a 5 kb window around TSS for endocrine marker genes in individual nuclei library JYH809. Total counts normalization and log-transformation were applied. (e) Cell clustering of chromatin accessibility profiles from all donors. (f) Representative low-quality cluster and subcluster. Cells in cluster 14 (top, highlighted in red) have significantly lower unique fragment than cells in other clusters (p = 2.3e-9, n = 255,598 cells). Cells in subcluster 1 (bottom, highlighted in red) have significantly lower fraction of reads overlapping peaks than cells in other clusters (p = 4.8e-5, n = 16,296 cells). Data are shown as mean ± S.E.M., ANOVA test with sex, age, BMI, disease status as covariates. (g) Log10 total reads, fraction of reads overlapping peaks and fraction of reads in promoters of cells from each cluster in Fig. 1b. Data are shown as mean ± S.E.M. (h) Promoter chromatin accessibility in a 5 kb window around TSS for selected endocrine and non-endocrine marker genes for each profiled cell (alpha: GCG, beta: INS-IGF2, delta: SST, gamma: PPY, acinar: REG1A, ductal: CFTR, stellate: PDGFRB, endothelial: CLEC14A, immune: CCL3). The UMAP projection is the same as in the main Fig. 1b. (i) Genome browser tracks showing aggregate read density (scaled to uniform 1 × 106 read depth) for cells within each cell type cluster at hormone gene loci for endocrine islet cell types. The gene body of each gene is highlighted.

Source data

Extended Data Fig. 2 Identification of factors explaining donor variability in snATAC-seq data.

(a,d,g,j) Absolute Spearman correlation coefficient between the first 6 principle components (PCs) and each biological or technical variable in beta (a), alpha (d), delta (g), and gamma (j) cells. An absolute Spearman correlation threshold of 0.4 was used to identify factors having a high correlation with each PC. (b,e,h,k) Principal component analysis (PCA) based on cCREs in beta (b), alpha (e), delta (h), and gamma (k) cells from individual non-diabetic (n = 11), pre-T2D (n = 8), and T2D (n = 15) donors which are color-coded by disease status. Each donor in the space is defined by the first two principal components (left) and the two principal components (right) that show highest correlation with disease status. (c,f,i,l) Pairwise Spearman correlation coefficients between biological or technical variables in beta (c), alpha (f), delta (i), and gamma (l) cells.

Source data

Extended Data Fig. 3 Validation of beta cell T2D-differential cCREs in snATAC-seq data from an independent cohort of donor islets.

(a) Clustering of chromatin accessibility profiles from HPAP human islet snATAC-seq data (non-diabetic, n = 13; pre-T2D, n = 2; T2D, n = 5). Nuclei are plotted using the first two UMAP components. Clusters are assigned cell type identities based on promoter accessibility of known marker genes (see Extended Data Fig. 3b). The number of nuclei for each cell type cluster is shown in parentheses. (b) Promoter chromatin accessibility in a 5 kb window around TSS for selected endocrine and non-endocrine marker genes for each profiled nucleus (alpha: GCG, beta: INS-IGF2, delta: SST, acinar: REG1A). (c) Heatmap showing chromatin accessibility at differential cCREs identified in Fig. 1e in HPAP snATAC-seq data. Columns represent beta cells from each donor (non-diabetic, n = 13; T2D, n = 5) and all non-diabetic and T2D donors with accessibility of peaks normalized by CPM (counts per million).

Source data

Extended Data Fig. 4 T2D affects chromatin accessibility more profoundly in beta cells than in other endocrine cell types.

(a) Volcano plot showing differential cCREs in beta cells between type 2 diabetic (T2D) and non-diabetic donors. Panels show all beta cells (left), beta cells down-sampled to 15,000 (middle), and 5,000 cells (right). Each dot represents a cCRE. cCREs with FDR < .1 after Benjamini-Hochberg correction (red dots) were considered differentially accessible. (b) Volcano plot showing differential cCREs in alpha cells between T2D and non-diabetic donors. Panels show all alpha cells (left), alpha cells down-sampled to 15,000 (middle), and 5,000 cells (right). Each dot represents a chromatin accessible cCRE. cCREs with FDR < .1 after Benjamini-Hochberg correction (red dots) were considered differentially accessible. (c) Volcano plot showing differential cCREs in delta cells between T2D and non-diabetic donors. Panels show all delta cells (left) and delta cells down-sampled to 5,000 cells (right). Each dot represents a chromatin accessible cCRE. cCREs with FDR < .1 after Benjamini-Hochberg correction (red dots) were considered differentially accessible.

Source data

Extended Data Fig. 5 Machine learning identifies two beta cell subtypes.

(a) Clustering of chromatin accessibility profiles from 92,780 beta cells from non-diabetic, pre-T2D and T2D donor islets using Scanpy (resolution=0.5). Nuclei are plotted using the first two UMAP components. (b) Position of beta cells from representative non-diabetic (MM80), pre-T2D (MM55), and T2D (MM54) donors on the UMPA. (c) Illustration of the strategy for distinguishing gradual from subtype changes in beta cells using machine learning. Possible scenarios for cell changes during T2D progression and expected disease state prediction accuracies for each scenario. In the case of no T2D-associated changes, the prediction accuracy for each disease state would be random (scenario 1), gradual cell state changes would be reflected by high prediction accuracy in each disease state (scenario 2), and subtype changes would be reflected by median prediction accuracies (scenario 3, here shown for two cell subtypes). (d, f, h) Relative abundance of predicted disease state among beta (d), alpha (f), and delta (h) cells from each donor using XGBOOST. Each column represents nuclei from one donor. (e, g, i) Relative abundance of predicted disease state among beta (e), alpha (g), and delta (i) cells in non-diabetic, pre-T2D and T2D donor islets. Data are shown as mean ± S.E.M. (n = 11 non-diabetic, n = 8 pre-T2D, n = 15 T2D donors), dots denote data points from individual donors. (j) Illustration of the strategy for identifying a classifier capable of distinguishing the two beta cell subtypes.

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Extended Data Fig. 6 Validation of beta cell subtypes using independent data and computational methods.

(a) Relative abundance of beta-1 and beta-2 cells in male and female donor islets. Data are shown as mean ± S.E.M. (n = 9 females, n = 25 males), dots denote data points from individual donors. ANOVA test with age, disease, BMI, and islet index as covariates. (b-d) Pearson correlation between relative abundance of beta-2 cells and BMI (b), islet index (c), age (d) across donors (n = 34 donors). The bands around the linear regression line represent the range of the 95% confidence interval, two-sided Pearson test. (e) Relative abundance of beta-1 and beta-2 cells in islet snATAC-seq data from an independent cohort (n = 13 non-diabetic, n = 5 T2D donors). Each column represents cells from one donor. (f) Relative abundance of each beta cell subtype in non-diabetic and T2D donor islets. Data are shown as mean ± S.E.M (n = 13 non-diabetic, n = 5 T2D donors). **P < .01; ANOVA test with age, sex, and BMI as covariates. (g) Clustering of chromatin accessibility profiles from 92,780 beta cells from non-diabetic, pre-T2D and T2D donors using beta cell differential cCREs between non-diabetic and T2D donors from Fig. 1e. Cells are plotted using the first two UMAP components. (h) Relative abundance of each beta cell cluster based on UMAP annotation. Each column represents cells from one donor. (i) Position of beta cells from non-diabetic, pre-T2D and T2D donors on the UMAP. (j) Position of beta cells from representative non-diabetic (MM80), pre-T2D (MM55) and T2D (MM54) donors on the UMPA. (k) Relative abundance of each beta cell cluster in non-diabetic, pre-T2D and T2D donor islets. Data are shown as mean ± S.E.M. (n = 11 non-diabetic, n = 8 pre-T2D, n = 15 T2D donors). **P < .01, *P < .05; ANOVA test with age, sex, BMI, and islet index as covariates. (l) Overlap between beta cell subtypes identified using machine learning and beta cell clusters from UMPA. The overlap is 76.6% between cluster 1 and beta-1 and 74.3% between cluster 2 and beta-2. P = 2.2e-16 (Two-sided Binominal test).

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Extended Data Fig. 7 Validation and characterization of beta cell subtypes using multiome data.

(a, b) Clustering of chromatin accessibility profiles of nuclei from multiome data (a). (b) Clustering of gene expression profiles of cells from multiome data (b). Nuclei are plotted using the first two UMAP components. Clusters are assigned cell type identities based on expression levels or promoter chromatin accessibility of known marker genes (alpha: GCG, beta: INS, delta: SST, gamma: PPY). The number of nuclei for each cell type cluster is shown in parentheses. n = 20 donors (c) Clustering of gene expression profiles of beta cells from multiome data using genes linked to differential proximal (within ± 5 kb of a TSS in GENCODE V19) and distal (based on potential distal cCRE-promoter connections inferred from Cicero, see Methods) cCREs between non-diabetic and T2D beta cells from Fig. 1e. Nuclei are plotted using the first two UMAP components. (d) Plots of beta cell subtypes predicted from chromatin accessibility profiles of beta cells from multiome data by machine learning. (e-f) Correlation between changes in proximal cCRE (within ± 5 kb of a TSS in GENCODE V19) accessibility and gene expression differences between beta-1 and beta-2 cells for differentially expressed genes from Fig. 3b. There are 544 proximal cCREs and target gene pairs in total, of which 511 have consistent changes between proximal cCRE accessibility and gene expression (e). Correlation between changes in distal cCRE (potential distal cCRE-promoter connections inferred from Cicero, see Methods) accessibility and gene expression differences between beta-1 and beta-2 cells for differentially expressed genes from Fig. 3b. There are 85 distal cCREs and target gene pairs in total, of which 72 have consistent changes between distal cCRE accessibility and gene expression (f). Two-sided Pearson test. (g) Enriched gene ontology terms among genes (see Fig. 3b) with higher (proximal or distal) cCRE accessibility and expression in beta-1 compared to beta-2 cells (left) and higher (proximal or distal) cCRE accessibility and expression in beta-2 compared to beta-1 cells (right).

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Extended Data Fig. 8 Beta-1 and beta-2 cell classification in scRNA-seq data from independent cohorts.

(a, d, g, j) Clustering of gene expression profiles of beta cells from cohort 15, cohort 212, cohort 322, and Patch-seq cohort using differentially expressed genes between beta-1 and beta-2 from Fig. 3b. Cells are plotted using the first two UMAP components. The number of donors for each cohort and cells for each cell cluster is shown in parentheses. (b, e, h, k) Heatmap showing pseudo-bulk expression levels of differentially expressed genes between beta-1 and beta-2 (see Fig. 3b) in beta cells from cluster 1 and cluster 2 of cohort 15, cohort 212, cohort 322, and Patch-seq cohort. Expression levels of genes are normalized by TPM (transcripts per million). (c, f, i, l) Relative abundance of each beta cell subtype in non-diabetic and T2D donor islets in cohort 15 (n = 5 non-diabetic, n = 4 T2D) cohort 212 (n = 12 non-diabetic, n = 6 T2D), cohort 322 (n = 10 non-diabetic), and Patch-seq cohort (n = 15 non-diabetic, n = 16 pre-T2D, n = 14 T2D). Data are shown as mean ± S.E.M., dots denote data points from individual donors. **P < .01, ***P < .001; ANOVA test with age, sex, and BMI as covariates.

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Extended Data Fig. 9 Transcriptional programs distinguishing the two beta cell subtypes.

(a) Genome browser tracks showing aggregate RNA and ATAC read density at representative genes positively regulated by HNF1A, HNF4A or HNF4G. Beta cell cCREs with binding sites for HNF1A, HNF4A and HNF4G are shown. (b) Genome browser tracks showing aggregate RNA and ATAC read density at representative genes positively regulated by NEUROD1, NFIA or TCF4. Beta cell cCREs with binding sites for NEUROD1, NFIA and TCF4 are shown.(a, b) All tracks are scaled to uniform 1 × 106 read depth, differential expressed genes between beta-1 and beta-2 are indicated by grey shaded boxes. (c) Box plots showing accessibility at HNF1A, HNF4A and HNF4G proximal cCREs in beta-1 and beta-2 cells. Genomic coordinates of promotor regions are shown in parentheses. (d) Bar plots showing expression of HNF1A, HNF4A and HNF4G in beta-1 and beta-2 cells. (e) Bar plots showing accessibility at NEUROD1, NFIA and TCF4 proximal cCREs in beta-1 and beta-2 cells. Proximal region of genes were shown in parentheses. (f) Bar plots showing expression of NEUROD1, NFIA, and TCF4 in beta-1 and beta-2. (c-f) Accessibility of peaks is normalized by CPM, gene expression is normalized by TPM. Data are shown as mean ± S.E.M., n = 20 donors, Two-sided paired t-test. (g) Genome browser tracks showing aggregate RNA and ATAC read density at HNF1A, HNF4A and HNF4G in beta-1 and beta-2 cells. Beta cell cCREs with binding sites for HNF1A, HNF4A and HNF4G are shown. (h) Genome browser tracks showing aggregate RNA and ATAC read density at NEUROD1, NFIA and TCF4 in beta-1 and beta-2 cells. Beta cell cCREs with binding sites for NEUROD1, NFIA and TCF4 are shown. (i) Genome browser tracks showing aggregate RNA and ATAC read density at HNF1A and TCF4 in beta-1 and beta-2 cells. Beta cell cCREs with binding sites for HNF1A and TCF4 are shown. All tracks are scaled to uniform 1 × 106 read depth.

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Extended Data Fig. 10 Transcriptional programs changed in both beta cell subtypes in T2D.

(a) Heatmap showing chromatin accessibility at cCREs with differential accessibility in beta-1 cells from non-diabetic and T2D donors. Columns represent beta cells from each donor (non-diabetic, n = 11; pre-T2D, n = 8; T2D, n = 15) with accessibility of peaks normalized by CPM (counts per million). (b) Heatmap showing chromatin accessibility at cCREs with differential accessibility in beta-2 cells from non-diabetic and T2D donors. Columns represent beta cells from each donor (non-diabetic, n = 11; pre-T2D, n = 8; T2D, n = 15) with accessibility of peaks normalized by CPM. (c) Heatmap showing expression of genes negatively regulated by TFs (green) with higher activity in non-diabetic compared to T2D beta-1 cells (see Methods) and TFs (red) with lower activity in non-diabetic compared to T2D beta-1 cells (n = 6 non-diabetic, n = 8 pre-T2D, n = 6 T2D donors). Representative target genes of individual TFs are highlighted and classified by biological processes. Gene expression is normalized by TPM (transcripts per million). # denotes TFs with decreased or increased expression in T2D in both beta-1 and beta-2 cells. (d) Heatmap showing expression of genes negatively regulated by TFs (green) with higher activity in non-diabetic compared to T2D beta-2 cells (see Methods) and TFs (red) with lower activity in non-diabetic compared to T2D beta-2 cells (n = 6 non-diabetic, n = 8 pre-T2D, n = 6 T2D donors). Representative target genes of individual TFs are highlighted and classified by biological processes. Gene expression is normalized by TPM (transcripts per million). # denotes TFs with decreased or increased expression in T2D in both beta-1 and beta-2 cells. (e-g) Pearson correlation of expression levels between indicated TFs across pseudo-bulk RNA profiles from each beta cell subtype (40 dots in total: 20 donors including n = 6 non-diabetic, n = 8 pre-T2D, n = 6 T2D). The bands around the linear regression line represent the range of the 95% confidence interval. Two-sided Pearson test.

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Wang, G., Chiou, J., Zeng, C. et al. Integrating genetics with single-cell multiomic measurements across disease states identifies mechanisms of beta cell dysfunction in type 2 diabetes. Nat Genet 55, 984–994 (2023). https://doi.org/10.1038/s41588-023-01397-9

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