Single-cell allele-specific expression analysis reveals dynamic and cell-type-specific regulatory effects.
Single-cell allele-specific expression analysis reveals dynamic and cell-type-specific regulatory effects.
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DOI：
10.1038/s41467-023-42016-9
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发表时间：
2023-10-09
影响因子：
16.6
通讯作者：
Battle, Alexis
中科院分区：
文献类型：
--
作者：
Qi, Guanghao;Strober, Benjamin J.;Popp, Joshua M.;Keener, Rebecca;Ji, Hongkai;Battle, Alexis
差异等位基因特异性表达（ASE）是研究基因表达上下文特异性顺式调控的有力工具。这种影响可以反映遗传或表观遗传因素与测量的环境或条件之间的相互作用。单细胞RNA测序（scRNA-seq）允许在单个细胞分辨率下测量ASE，但缺乏统计方法来分析这些数据。我们提出了使用单细胞数据（DAESC）的差异等位基因表达，这是一种使用来自多个个体的scRNA-seq进行差异ASE分析的强大方法，通过模拟证实了统计行为。DAESC解释了来自同一个体的细胞之间的非独立性，并结合了隐性单倍型相位。利用105个诱导多能干细胞（iPSC）细胞系的数据，鉴定出657个基因在内胚层分化过程中受到动态调控，富集了染色质状态的变化。应用于2型糖尿病数据集确定了胰腺内分泌细胞中患者和对照者之间的几个差异调节基因。DAESC是一种强大的单细胞ASE分析方法，可以揭示基因调控的新见解。在这里，作者开发了DAESC，这是一种使用单细胞RNA-seq数据进行差异等位基因特异性表达分析的统计方法。应用DAESC确定内胚层分化的动态调节作用以及2型糖尿病与健康对照之间的差异效应。
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. Here the authors develop DAESC, a statistical method for differential allele-specific expression analysis using single-cell RNA-seq data. Application of DAESC identifies dynamic regulatory effects along endoderm differentiation and differential effects between type 2 diabetes and healthy controls.
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遗传对人体组织基因表达的影响。
影响因子：
64.8
作者：
GTEx Consortium;Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group;Statistical Methods groups—Analysis Working Group;Enhancing GTEx (eGTEx) groups;NIH Common Fund;NIH/NCI;NIH/NHGRI;NIH/NIMH;NIH/NIDA;Biospecimen Collection Source Site—NDRI;Biospecimen Collection Source Site—RPCI;Biospecimen Core Resource—VARI;Brain Bank Repository—University of Miami Brain Endowment Bank;Leidos Biomedical—Project Management;ELSI Study;Genome Browser Data Integration &Visualization—EBI;Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz;Lead analysts:;Laboratory, Data Analysis &Coordinating Center (LDACC):;NIH program management:;Biospecimen collection:;Pathology:;eQTL manuscript working group:;Battle A;Brown CD;Engelhardt BE;Montgomery SB
通讯作者：
Montgomery SB
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
14.9
作者：
The Gene Ontology Consortium
通讯作者：
The Gene Ontology Consortium
影响因子：
30.8
作者：
Gutierrez-Arcelus, Maria;Baglaenko, Yuriy;Raychaudhuri, Soumya
通讯作者：
Raychaudhuri, Soumya
影响因子：
16.6
作者：
Cuomo, Anna S. E.;Seaton, Daniel D.;Stegle, Oliver
通讯作者：
Stegle, Oliver