Single-cell allele-specific expression analysis reveals dynamic and cell-type-specific regulatory effects.

Autor: Qi G; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.; Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA., Strober BJ; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA., Popp JM; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA., Keener R; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA., Ji H; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA., Battle A; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA. ajbattle@jhu.edu.; Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA. ajbattle@jhu.edu.; Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA. ajbattle@jhu.edu.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2023 Oct 09; Vol. 14 (1), pp. 6317. Date of Electronic Publication: 2023 Oct 09.
DOI: 10.1038/s41467-023-42016-9
Abstrakt: Differential allele-specific expression (ASE) is a powerful tool to study context-specific cis-regulation of gene expression. Such effects can reflect the interaction between genetic or epigenetic factors and a measured context or condition. Single-cell RNA sequencing (scRNA-seq) allows the measurement of ASE at individual-cell resolution, but there is a lack of statistical methods to analyze such data. We present Differential Allelic Expression using Single-Cell data (DAESC), a powerful method for differential ASE analysis using scRNA-seq from multiple individuals, with statistical behavior confirmed through simulation. DAESC accounts for non-independence between cells from the same individual and incorporates implicit haplotype phasing. Application to data from 105 induced pluripotent stem cell (iPSC) lines identifies 657 genes dynamically regulated during endoderm differentiation, with enrichment for changes in chromatin state. Application to a type-2 diabetes dataset identifies several differentially regulated genes between patients and controls in pancreatic endocrine cells. DAESC is a powerful method for single-cell ASE analysis and can uncover novel insights on gene regulation.
(© 2023. Springer Nature Limited.)
Databáze: MEDLINE
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