Benchmarking CRISPR Whole genome Drop out Screen B S
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ABSTRACT:
PROVIDER: EGAS00001002931 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:This study is a benchmarking exercise to explore potential source of variation between different CRISPR drop out libraries. .
This dataset contains all the data available for this study on 2019-08-07.
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Project description: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.
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