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PyMINEr Finds Gene and Autocrine/Paracrine Networks from Human Islet scRNAseq

ABSTRACT: Toolsets available for in-depth analysis of scRNAseq datasets by biologists with little informatics experience is limited. Here we describe an informatics tool (PyMINEr) that fully automates cell type identification, cell type-specific pathway analyses, graph theory-based analysis of gene regulation, and detection of autocrine/paracrine signaling networks in silico. We applied PyMINEr to interrogate human pancreatic islet scRNAseq datasets and discovered several features of co-expression graphs including: concordance of scRNAseq-graph structure with both protein-protein interactions and 3D-genomic architecture; association of high connectivity and low expression genes with cell type-enrichment; and potential for graph-structure to clarify potential etiologies of enigmatic disease-associated variants. We further created a consensus co-expression network and autocrine/paracrine signaling networks within and across islet cell types from 7-datasets. PyMINEr correctly identified changes in BMP/WNT signaling associated with cystic fibrosis pancreatic acinar-cell loss. This proof-of-principle study demonstrates that the PyMINEr framework will be a valuable resource for scRNAseq analyses.

ORGANISM(S): Homo sapiens

PROVIDER: GSE116753 | GEO | 2019/01/11

REPOSITORIES: GEO

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Project description:Pancreatic islet transplantation as a cure for type 1 diabetes (T1D) cannot be scaled up due to a scarcity of human pancreas donors. In vitro expansion of beta cells from mature human pancreatic islets provides an alternative source of insulin-producing cells. The exact nature of the expanded cells produced by diverse expansion protocols, and their potential for differentiation into functional beta cells, remain elusive. We performed a large-scale meta-analysis of gene expression in human pancreatic islet cells, which were processed using three different previously described protocols for expansion and attempted re-differentiation. All three expansion protocols induced dramatic changes in the expression profiles of pancreatic islets; many of these changes are shared among the three protocols. Attempts at re-differentiation of expanded cells induce a limited number of gene expression changes. Nevertheless, these fail to restore a pancreatic islet-like gene expression pattern. Comparison with a collection of public microarray datasets confirmed that expanded cells are highly comparable to mesenchymal stem cells. Genes induced in expanded cells are also enriched for targets of transcription factors important for pluripotency induction. The present data increases our understanding of the active pathways in expanded and re-differentiated islets. Knowledge of the mesenchymal stem cell potential may help development of drug therapeutics to restore beta cell mass in T1D patients. Experiment Overall Design: In this study, we have tested three different protocols to expand human pancreatic islets in monolayer and after attempted maneuvers to re-differentiate the expanded cells back to islets. We have characterized the resulting cells in detail by performing microarray analyses with fresh pancreatic islets, expanded islet cells and re-differentiated cells. Genes modified by either of three protocols have 70 to 80% overlap with the genes changed by the other two protocols. Although there are promising changes in the right direction, none of the three protocols could achieve a return to a functional islet state. The expanded cells highly resemble Mesenchymal Stem Cells (MSC), and similar gene regulatory networks seem to be active in both cell types. On the other hand, the expanded islet cells are different from MSC in that they seem to retain activity of some islet gene modules. The current results highlight the importance of designing new strategies that take into account the MSC potential of expanded cells.

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PyMINEr Finds Gene and Autocrine/Paracrine Networks from Human Islet scRNAseq

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