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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-12-14

AUTHORS

Shubham Kumar, P. K. Vinod

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 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. More... »

PAGES

1-14

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12572-018-0239-4

DOI

http://dx.doi.org/10.1007/s12572-018-0239-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1110639013


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