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Cell Rep. 2019 Mar 12;26(11):3132-3144.e7. doi: 10.1016/j.celrep.2019.02.043.

Single-Cell Heterogeneity Analysis and CRISPR Screen Identify Key β-Cell-Specific Disease Genes.

Author information

1
Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA.
2
Center for Cancer and Immunology Research, Brain Tumor Institute, Children's National Medical Center, Washington, D.C. 20010, USA.
3
Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA; Department of Neurology, the Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province 519000, China.
4
Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA.
5
Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA; The Biomedical Sciences Training Program (BSTP), School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA.
6
Division of Metabolism, Endocrinology, and Diabetes, University of Michigan Medical Center, Ann Arbor, MI 48109, USA.
7
Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA; Department of Population and Quantitative Health Sciences, Department of Electrical Engineering and Computer Science, Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH 44106, USA. Electronic address: fxj45@case.edu.
8
Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA. Electronic address: yxl1379@case.edu.

Abstract

Identification of human disease signature genes typically requires samples from many donors to achieve statistical significance. Here, we show that single-cell heterogeneity analysis may overcome this hurdle by significantly improving the test sensitivity. We analyzed the transcriptome of 39,905 single islets cells from 9 donors and observed distinct β cell heterogeneity trajectories associated with obesity or type 2 diabetes (T2D). We therefore developed RePACT, a sensitive single-cell analysis algorithm to identify both common and specific signature genes for obesity and T2D. We mapped both β-cell-specific genes and disease signature genes to the insulin regulatory network identified from a genome-wide CRISPR screen. Our integrative analysis discovered the previously unrecognized roles of the cohesin loading complex and the NuA4/Tip60 histone acetyltransferase complex in regulating insulin transcription and release. Our study demonstrated the power of combining single-cell heterogeneity analysis and functional genomics to dissect the etiology of complex diseases.

KEYWORDS:

CRISPR screen; Cellular heterogeneity; Drop-seq; bioinformatics; diabetes; functional genomics; obesity; pancreatic islet; single cell; β cell

PMID:
30865899
DOI:
10.1016/j.celrep.2019.02.043
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