Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data.
Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data.
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通过整体和单细胞RNA测序数据的综合分析检测细胞类型特异性等位基因表达失衡。
DOI：
10.1371/journal.pgen.1009080
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发表时间：
2021-03
期刊：
影响因子：
4.5
通讯作者：
Li M
中科院分区：
文献类型：
--
作者：
Fan J;Wang X;Xiao R;Li M
等位基因表达不平衡（AEI），通过二倍体生物体中基因的两个等位基因的相对表达来量化，可以帮助解释个体之间的表型变异。传统方法使用批量RNA测序（RNA-seq）数据检测AEI，这是一种在细胞类型之间的基因表达中平均细胞与细胞异质性的数据类型。由于AEI的模式可以在不同的细胞类型中变化，因此希望以细胞类型特异性的方式研究AEI。虽然这可以通过单细胞RNA测序（scRNA-seq）来实现，但它需要在大量个体的单细胞中对全长转录物进行测序，这仍然是成本高昂的。为了克服这一限制并利用大量现有的疾病相关的批量组织RNA-seq数据，我们开发了BSCET，其通过整合从一小组scRNA-seq样品推断的细胞类型组成信息（可能从外部数据集获得）来表征批量RNA-seq数据中的细胞类型特异性AEI。通过建立协变量效应模型，BSCET还可以检测细胞类型特异性AEI与临床因素相关的基因。通过广泛的基准评估，我们表明BSCET使用批量RNA-seq数据正确检测了健康和患病样本之间具有细胞类型特异性AEI和差异AEI的基因。BSCET还发现了当AEI的方向在不同细胞类型中相反时在批量数据分析中遗漏的细胞类型特异性AEI。我们进一步将BSCET应用于两个胰岛批量RNA-seq数据集，并检测显示与2型糖尿病进展相关的细胞类型特异性AEI的基因。由于批量RNA-seq数据很容易获得，BSCET提供了一个方便的工具来整合来自scRNA-seq数据的信息，以了解细胞类型分辨率的AEI。这种分析的结果将促进我们对人类疾病中细胞类型贡献的理解。等位基因表达不平衡（AEI）是一种基因的两个等位基因在其表达量上不同的现象，其检测是理解个体间表型变异的关键步骤。现有方法使用批量RNA测序（RNA-seq）数据检测AEI，并且忽略不同细胞类型之间的AEI变化。虽然单细胞RNA测序（scRNA-seq）已经能够表征基因表达中的细胞间异质性，但高成本限制了其在AEI分析中的应用。为了克服这一限制，我们开发了BSCET，通过整合从scRNA-seq样品推断的细胞类型组成信息，使用广泛可用的批量RNA-seq数据来表征细胞类型特异性AEI。由于AEI的程度可能随疾病表型而变化，我们进一步扩展了BSCET以检测细胞类型特异性AEI与临床因素相关的基因。通过对两个胰岛批量RNA-seq数据集进行广泛的基准评估和分析，我们证明了BSCET能够将批量水平的AEI细化为细胞类型分辨率，并鉴定其细胞类型特异性AEI与2型糖尿病进展相关的基因。凭借大量易于获得的批量RNA-seq数据，我们相信BSCET将成为阐明人类疾病中细胞类型贡献的宝贵工具。
Allelic expression imbalance (AEI), quantified by the relative expression of two alleles of a gene in a diploid organism, can help explain phenotypic variations among individuals. Traditional methods detect AEI using bulk RNA sequencing (RNA-seq) data, a data type that averages out cell-to-cell heterogeneity in gene expression across cell types. Since the patterns of AEI may vary across different cell types, it is desirable to study AEI in a cell-type-specific manner. Although this can be achieved by single-cell RNA sequencing (scRNA-seq), it requires full-length transcript to be sequenced in single cells of a large number of individuals, which are still cost prohibitive to generate. To overcome this limitation and utilize the vast amount of existing disease relevant bulk tissue RNA-seq data, we developed BSCET, which enables the characterization of cell-type-specific AEI in bulk RNA-seq data by integrating cell type composition information inferred from a small set of scRNA-seq samples, possibly obtained from an external dataset. By modeling covariate effect, BSCET can also detect genes whose cell-type-specific AEI are associated with clinical factors. Through extensive benchmark evaluations, we show that BSCET correctly detected genes with cell-type-specific AEI and differential AEI between healthy and diseased samples using bulk RNA-seq data. BSCET also uncovered cell-type-specific AEIs that were missed in bulk data analysis when the directions of AEI are opposite in different cell types. We further applied BSCET to two pancreatic islet bulk RNA-seq datasets, and detected genes showing cell-type-specific AEI that are related to the progression of type 2 diabetes. Since bulk RNA-seq data are easily accessible, BSCET provides a convenient tool to integrate information from scRNA-seq data to gain insight on AEI with cell type resolution. Results from such analysis will advance our understanding of cell type contributions in human diseases. Detection of allelic expression imbalance (AEI), a phenomenon where the two alleles of a gene differ in their expression magnitude, is a key step towards the understanding of phenotypic variations among individuals. Existing methods detect AEI using bulk RNA sequencing (RNA-seq) data and ignore AEI variations among different cell types. Although single-cell RNA sequencing (scRNA-seq) has enabled the characterization of cell-to-cell heterogeneity in gene expression, the high costs have limited its application in AEI analysis. To overcome this limitation, we developed BSCET to characterize cell-type-specific AEI using the widely available bulk RNA-seq data by integrating cell-type composition information inferred from scRNA-seq samples. Since the degree of AEI may vary with disease phenotypes, we further extended BSCET to detect genes whose cell-type-specific AEIs are associated with clinical factors. Through extensive benchmark evaluations and analyses of two pancreatic islet bulk RNA-seq datasets, we demonstrated BSCET’s ability to refine bulk-level AEI to cell-type resolution, and to identify genes whose cell-type-specific AEIs are associated with the progression of type 2 diabetes. With the vast amount of easily accessible bulk RNA-seq data, we believe BSCET will be a valuable tool for elucidating cell type contributions in human diseases.
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影响因子：
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