Single-Cell Heterogeneity Analysis and CRISPR Screen Identify Key ß-Cell-Specific Disease Genes.
Cell Rep
; 26(11): 3132-3144.e7, 2019 03 12.
Article
em En
| MEDLINE
| ID: mdl-30865899
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 ß 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 ß-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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Heterogeneidade Genética
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Diabetes Mellitus Tipo 2
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Células Secretoras de Insulina
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Transcriptoma
Limite:
Animals
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Humans
Idioma:
En
Revista:
Cell Rep
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Estados Unidos
País de publicação:
EEUU
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ESTADOS UNIDOS
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ESTADOS UNIDOS DA AMERICA
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EUA
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UNITED STATES
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UNITED STATES OF AMERICA
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US
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USA