A Chromatin Basis for Cell Lineage and Disease Risk in the Human Pancreas

Abstract

Understanding the genomic logic that underlies cellular diversity and developmental potential in the human pancreas will accelerate the growth of cell replacement therapies and reveal genetic risk mechanisms in diabetes. Here, we identified and characterized thousands of chromatin regions governing cell-specific gene regulation in human pancreatic endocrine and exocrine lineages, including islet β cells, α cells, duct, and acinar cells. Our findings have captured cellular ontogenies at the chromatin level, identified lineage-specific regulators potentially acting on these sites, and uncovered hallmarks of regulatory plasticity between cell types that suggest mechanisms to regenerate β cells from pancreatic endocrine or exocrine cells. Our work shows that disease risk variants related to pancreas are significantly enriched in these regulatory regions and reveals previously unrecognized links between endocrine and exocrine pancreas in diabetes risk.

Keywords: ATAC-seq; cell lineage; chromatin; development; diabetes; enhancer; genomics; histone modification; islet; pancreas.

Figure 1.

(A) Experimental approach used in this study. Pancreatic islets and exocrine tissue were obtained from human donors. α-, β-, duct and acinar cells were purified using FACS and followed by high-throughput sequencing assays. * RNA-Seq data reported in (Arda et al., 2016). See also Figure S1, Tables S1-S2. (B) FACS plots display distinct populations of pancreatic cells using the indicated cell surface markers. UCSC genome browser display of the PAX6 (C) and NR5A2 (D) locus. Normalized signal profiles of ATAC-Seq and H3K4me3, H3K4me1, H3K27ac, H3K27me3 ChIP-Seq obtained from sorted pancreatic cells are shown. Regions corresponding to cell type-specific signals are highlighted in blue.

Figure 2.

(A) Heat map shows distinct clustering of differentially open regions (DORs) identified in ATAC-Seq assays. Each column is a unique genomic region corresponding to an ATAC-Seq peak. Individual samples are organized in rows; magenta-α-cells, cyan-β-cells, black-duct cells, and orange-acinar cells. DOR clusters and the number of genomic regions in each cluster are indicated at the top of the heatmap. See also Figure S2 and Tables S3-S4. (B) Distribution of pancreas DORs with reference to genomic features. TSS; transcription start site. (C) Bar graphs represent the enrichment of GO Biological Process terms for genes associated with each DOR cluster. Select genes linked within these clusters are listed on the right.

Figure 3.

(A) Box plots show the distribution of expression specificity scores (ESS) of genes associated with DORs for each cell type (acacinar cells). Dashed red lines indicate the 0.25 threshold of specificity. See also Figure S3, Table S5. (B) 95% confidence intervals of expression specificity scores of genes associated with DORs are plotted. See also Table S6. (C) Model suggesting regulation of cell type-specific expression in human pancreatic cells. The dendrogram on left represents pancreatic lineages. Black tick marks indicate putative regulatory regions in the genome. Combination of lineage and cell type-specific regulatory regions confers cell type-specific gene expression.

Figure 4.

(A) Position weight matrices of TF motifs enriched in each DOR cluster. See also Table S7. (B) Bubble plot showing the motif enrichment scores and expression levels (rpkm) of TFs in pancreas cells. (C) Association index analysis reveals TF modules, marked with black rectangles (See STAR Methods for details). Matrices show the similarity between TF pairs based on their motif cooccurrence in DORs. See also Figure S4, Table S8.

Figure 5.

(A) Correlogram of Spearman rank correlation coefficients for cell type-specific DORs is shown. Circle size reflects the absolute coefficient value. See also Table S6. (B) Scatter plot shows the ESS values of genes linked to endocrine DORs in α- and β-cells. The orange contour lines indicate data density. Select genes (dots) are highlighted with red on the graph. (C) Heat map shows the chromatin states identified in purified human pancreas cells based on ChromHMM analysis. 10-state model was built using the histone mark ChIP-Seq data. The white-blue color bar represents the emission probability. States are numbered, colored and annotated as suggested in (Ernst and Kellis, 2012). (D) Bar graphs show the cumulative fraction of DOR overlap with each of the annotated chromatin state.

Figure 6.

(A) Hierarchical clustering of the ATAC-Seq signal corresponding to DORs that overlap with SNPs reported in GWAS studies related to diabetes and associated traits or pancreatic cancer. (B) Genome view of the SLC2A2 locus highlighting ATAC-Seq peaks found in α-, β-, duct and acinar cells and the location of the risk SNP that is linked to fasting glucose traits. On the right, the bar graph shows normalized RNA-Seq read counts representing gene expression of SLC2A2 in pancreatic cells.