GSE101207: High-throughput single cell transcriptome analysis and CRISPR screen identify key β cell-specific disease genes

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GROUP CONDITION SAMPLES
pancreatic islets cells
GSM2700338 GSM2700339 GSM2700340 GSM2700341 GSM2700342 GSM2863188
GSM2700343 GSM2700344 GSM2863189
Description

Submission Date: Jul 11, 2017

Summary: Pancreatic endocrine cells orchestrate the precise control of blood glucose levels, but the contribution of each cell type to diabetes or obesity remains elusive. Here we used a massively parallel single-cell RNA-seq technology (Drop-Seq) to analyze the transcriptome of 26,677 pancreatic islets cells from both healthy and type II diabetic (T2D) donors. We have analyzed cell type-specific gene signatures, and detected several rare α or β cell subpopulations with high sensitivity. We also developed RePACT, a sensitive single cell analysis algorithm to identify genes associated with rare disease causing cells, or to capture the subtle disease-relevant cellular variation. We successfully identified both common and specific signature genes of obesity and T2D with only a small number of islet samples. We also performed an unbiased genome-wide CRISPR screen and mapped these Drop-Seq signature genes to the core insulin regulatory network in β cells. Notably, our integrative analysis discovered a β cell-specific function of the cohesin loading complex in regulating insulin gene transcription, and a previously unrecognized role of the NuA4/Tip60 histone acetyltransferase complex in regulating insulin release. These data demonstrated that single-cell trancriptomics is necessary to dissect the heterogeneity, disease state, and functionality of islet β cells and other cell types.

GEO Accession ID: GSE101207

PMID: 30865899

Description

Submission Date: Jul 11, 2017

Summary: Pancreatic endocrine cells orchestrate the precise control of blood glucose levels, but the contribution of each cell type to diabetes or obesity remains elusive. Here we used a massively parallel single-cell RNA-seq technology (Drop-Seq) to analyze the transcriptome of 26,677 pancreatic islets cells from both healthy and type II diabetic (T2D) donors. We have analyzed cell type-specific gene signatures, and detected several rare α or β cell subpopulations with high sensitivity. We also developed RePACT, a sensitive single cell analysis algorithm to identify genes associated with rare disease causing cells, or to capture the subtle disease-relevant cellular variation. We successfully identified both common and specific signature genes of obesity and T2D with only a small number of islet samples. We also performed an unbiased genome-wide CRISPR screen and mapped these Drop-Seq signature genes to the core insulin regulatory network in β cells. Notably, our integrative analysis discovered a β cell-specific function of the cohesin loading complex in regulating insulin gene transcription, and a previously unrecognized role of the NuA4/Tip60 histone acetyltransferase complex in regulating insulin release. These data demonstrated that single-cell trancriptomics is necessary to dissect the heterogeneity, disease state, and functionality of islet β cells and other cell types.

GEO Accession ID: GSE101207

PMID: 30865899

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Bulk RNA-seq Appyter

This pipeline enables you to analyze and visualize your bulk RNA sequencing datasets with an array of downstream analysis and visualization tools. The pipeline includes: PCA analysis, Clustergrammer interactive heatmap, library size analysis, differential gene expression analysis, enrichment analysis, and L1000 small molecule search.