Single-cell transcriptomics analysis of pancreatic islets in health and type 2 diabetes
Shubham Kumar and P K Vinod*
Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology (IIIT), Hyderabad-500032, India
Corresponding Author: vinod.pk@iiit.ac.in
Abstract
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 both at the network and individual gene expression levels. Here, we inferred the gene regulatory network (GRN) of pancreatic cells from scRNA-seq data in health and T2D. Clustering of cells based on GRN activity identifies endocrine and exocrine cells, and multiple cell-states. 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. The analysis shows that continuous spectrum of cell states exist with phenotypic-dependent branching and donor cell-cell variability in beta, alpha, acinar and ductal cells. 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.
Data: https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-5061/
This repository contains the code for analysing single cell rna-seq data for the manuscript "Single-cell transcriptomics analysis of pancreatic islets in health and type 2 diabetes".
load.R : Data pre-processing and filtering
monocle.R : Single cell trajectory analysis of pancreas, BEAM analysis for all the celltypes and there arguments
scenic.R : Normalization of raw count data, scenic analysis as described in the manuscript.
Additional result are included in the respective folders.