Single-cell transcriptomic analysis of pancreatic islets in health and type 2 diabetes

  • Shubham Kumar
  • P. K. VinodEmail author


Studies on how pancreatic islets respond under physiological and pathological conditions are obtained mostly based on the analysis of whole-islet transcriptome. However, the measurement from the whole islets quantifies the average behaviour of dominant cell types, thereby making it difficult to understand the cell-type-specific changes. Recently, the advent of single-cell RNA sequencing (scRNA-seq) technique has generated valuable resource on islet biology and type 2 diabetes (T2D). This provides an opportunity to understand the different cell types/states at both the network and individual gene expression levels. Here, we inferred the gene regulatory networks (GRNs) of pancreatic cells from publicly available scRNA-seq data in healthy and T2D using single-cell regulatory network inference and clustering workflow. Clustering of cells based on GRNs identifies endocrine and exocrine cells and multiple stable cell states in each alpha, beta and ductal cells. The phenotypic variations in cell states due to obesity and T2D are indistinguishable. Therefore, the trajectory of cells in pseudotime was constructed based on the cell-type-specific gene expression using Monocle2. The analysis shows that continuous spectrum of cell states exists with phenotypic-dependent branching and donor cell–cell variability in endocrine and exocrine cell types. We characterized the genes that give rise to bifurcation in the trajectory. Our study demonstrates that the network and trajectory inference approaches can be used to better understand the behaviour of pancreatic cells in health and disease.


Single-cell Heterogeneity Network biology Pseudotime Trajectory Bifurcation Pancreas 



PKV acknowledges financial support from the Early Career Research Award Scheme (ECR/2016/000488), Science and Engineering Research Board, DST, India.

Supplementary material

12572_2018_239_MOESM1_ESM.docx (18 kb)
Table S1: Number of high quality cells isolated from donors (DOCX 18 kb)
12572_2018_239_MOESM2_ESM.pdf (6.9 mb)
Figure S1: t-SNE plots showing individual regulon activity in pancreatic cells. Cells are coloured based on the AUC score calculated by AUCell for each regulon. Colours represent the scaled AUC values. A binary regulon activity (active/inactive) was calculated from the distribution of these AUC values. The number inside the bracket indicates the total number of genes in each regulon. The label ‘extended’ indicates regulon including both direct and indirect targets (PDF 7063 kb)
12572_2018_239_MOESM3_ESM.pdf (143 kb)
Figure S2: t-SNE plots with cells coloured based on a gender and b age of the donor (PDF 143 kb)
12572_2018_239_MOESM4_ESM.pdf (236 kb)
Figure S3: Heatmap of genes showing significant branch-dependent expression in beta cells (PDF 236 kb)
12572_2018_239_MOESM5_ESM.pdf (242 kb)
Figure S4: Heatmap of genes showing significant branch-dependent expression at the first branch point in alpha cells (PDF 242 kb)
12572_2018_239_MOESM6_ESM.pdf (236 kb)
Figure S5: Heatmap of genes showing significant branch-dependent expression at the second branch point in alpha cells (PDF 236 kb)
12572_2018_239_MOESM7_ESM.pdf (241 kb)
Figure S6: Heatmap of genes showing significant branch-dependent expression in gamma cells (PDF 240 kb)
12572_2018_239_MOESM8_ESM.pdf (188 kb)
Figure S7: Heatmap of genes showing significant branch-dependent expression in acinar cells (PDF 188 kb)
12572_2018_239_MOESM9_ESM.pdf (231 kb)
Figure S8: Heatmap of genes showing significant branch-dependent expression in ductal cells (PDF 231 kb)


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© Indian Institute of Technology Madras 2018

Authors and Affiliations

  1. 1.Centre for Computational Natural Sciences and BioinformaticsInternational Institute of Information Technology (IIIT)HyderabadIndia

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