单细胞精度的表达数量性状位点研究进展

doi:10.16288/j.yczz.24-162

遗传 ›› 2024, Vol. 46 ›› Issue (10): 795-806.doi: 10.16288/j.yczz.24-162

单细胞精度的表达数量性状位点研究进展

徐晓鹏¹(), 范小英¹^,²()

1.广州国家实验室基础研究部，广州 510005
2.广州医科大学-中国科学院广州生物医药与健康研究院联合生命科学学院，广州 511436

收稿日期:2024-06-05 修回日期:2024-08-30 出版日期:2024-09-13 发布日期:2024-09-13
通讯作者: 范小英，博士，研究员，研究方向：单细胞多组学。E-mail: fan_xiaoying@gzlab.ac.cn
作者简介:徐晓鹏，博士，助理研究员，研究方向：医学遗传学。E-mail: xu_xiaopeng@gzlab.ac.cn
基金资助:
国家自然科学基金项目(82202047);珠江青年人才计划(2021QN02Y747);广州国家实验室专项项目(GZNL2024A03001)

Research progress on single-cell expression quantitative trait loci

Xiaopeng Xu¹(), Xiaoying Fan¹^,²()

1. Department of Basic Research, Guangzhou Laboratory, Guangzhou 510005, China
2. Joint School of Life Sciences, Guangzhou Medical University and Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 511436, China

Received:2024-06-05 Revised:2024-08-30 Published:2024-09-13 Online:2024-09-13
Supported by:
National Natural Science Foundation of China(82202047);Pearl River Talent Program(2021QN02Y747);Guangzhou National Laboratory Specialized Program(GZNL2024A03001)

摘要/Abstract

摘要：

表达数量性状位点(expression quantitative trait loci，eQTL)表示控制基因表达量的遗传变异位点。eQTL分析是后全基因组关联研究时代鉴定疾病相关遗传位点功能的重要方法，且取得了诸多重要发现。传统的eQTL分析基于全基因组测序结合整体RNA测序技术，细胞间差异的基因表达水平会被掩盖，从而无法鉴定细胞类型或状态依赖的eQTL，因此也难以解析特定环境下疾病相关遗传变异位点。近年来，随着单细胞转录组测序技术的发展和应用普及，基于单细胞转录组测序的eQTL (single-cell RNA sequencing-based eQTL，sc-eQTL)研究技术逐渐成为热点，其优势在于可以充分利用单细胞测序的分辨率和颗粒度挖掘细胞类型、细胞状态以及细胞动态依赖的表达变异位点，显著提升解析基因表达关联的遗传变异位点的能力，对人们探究复杂器官的形成以及疾病的发生、发展、干预和治疗具有重要意义。本文主要从sc-eQTL研究的发展、设计方案、建模策略以及面临的挑战等诸多方面综述近年来的研究进展，以期为科研工作者挖掘致病位点，解析基因调控提供全新的视角。

关键词: 表达数量性状位点, 全基因组关联研究, 单细胞转录组测序, 调控元件, 细胞类型特异性

Abstract:

Expression quantitative trait loci (eQTL) represent genetic variants that regulate gene expression levels. eQTL analysis has become a crucial method for identifying the functional roles of disease-associated genetic variants in the post-genome-wide association study (GWAS) era, yielding numerous significant discoveries. Traditional eQTL analysis relies on whole-genome sequencing combined with bulk RNA-seq, which obscures gene expression differences between cells and thus fails to identify cell type- or state-dependent eQTL. This limitation makes it challenging to elucidate the roles of disease-associated genetic variants under specific conditions. In recent years, with the development and widespread application of single-cell RNA sequencing (scRNA-seq) technology, scRNA-seq-based eQTL (sc-eQTL) research has emerged as a focal point. The advantage of this approach lies in its ability to leverage the resolution and granularity of single-cell sequencing to uncover eQTL that are dependent on cell type, cell state, and cellular dynamics. This significantly enhances our ability to analyze genetic variants associated with gene expression. Consequently, it holds substantial significance for advancing our understanding of the formation of complex organs and the mechanisms underlying disease onset, progression, intervention, and treatment. This review comprehensively examines the recent advancements in sc-eQTL studies, focusing on their development, experimental design strategies, modeling approaches, and current challenges. The aim is to offer researchers novel perspectives for identifying disease-associated loci and elucidating gene regulatory mechanisms

Key words: expression quantitative trait loci, genome-wide association study, single cell RNA-seq, genetic regulatory element, cell-type specificity

徐晓鹏, 范小英. 单细胞精度的表达数量性状位点研究进展[J]. 遗传, 2024, 46(10): 795-806.

Xiaopeng Xu, Xiaoying Fan. Research progress on single-cell expression quantitative trait loci[J]. Hereditas(Beijing), 2024, 46(10): 795-806.

图/表 4

图1

图2

表1

2018年至今sc-eQTL研究发展概况"

年份	细胞或组织类型	群体大小	细胞数目	科学发现	参考文献
2018	PBMC	45	25,000	第1篇sc-eQTL研究论文，表明基于单细胞数据的eQTL定位可以发现先前许多被批量转录组数据掩盖的eQTL位点	[16]
2019	iPSC	53	5447	定义了variance eQTL	[18]
2020	iPSC-内胚层	125	36,044	鉴定控制内胚层动态分化过程中基因变化的遗传位点	[19]
2021	iPSC-神经元	215	1,027,401	鉴定控制不同阶段神经元分化以及鱼藤酮诱导的氧化应激反应的遗传变异位点	[20]
2021	成纤维细胞	79	64,018	第1篇分析成纤维细胞的sc-eQTL文章，展示了个体间的遗传差异如何影响细胞类型特异的eQTL位点变化	[21]
2021	PBMC	90	235,161	鉴定控制不同种群外周血单核细胞感染流感之后的差异eQTL位点	[22]
2022	大脑组织	192	-	第1篇以组织进行单细胞测序，进而鉴定组织内部细胞特异性的eQTL位点	[23]
2022	iPSC-心肌细胞	19	230,822	鉴定了dynamic eQTL位点	[24]
2022	记忆T细胞	259	500,089	利用基于单细胞分辨率的PME模型鉴定了记忆T细胞状态依赖的eQTL位点	[25]
2022	PBMC	120	1,300,000	鉴定调控不同个体对病原体响应的差异eQTL位点，并鉴定1个IFN调控基因	[26]
2022	PBMC	261	1,200,000	利用多种族群体样本，鉴定了与系统性红斑狼疮相关的遗传变异位点	[27]
2022	PBMC	89	735,000	通过激活CD4⁺ T细胞，分析了19种不同T细胞亚群的eQTL位点	[28]
2022	CD4⁺ T细胞	119	655,349	鉴定了38个CD4⁺ T细胞亚群在3个激活时间节点的特定eQTL位点	[29]
2022	PBMC	982	1,270,000	第1篇群体规模接近1000的sc-eQTL文章，系统分析了14种免疫细胞的eQTL位点	[30]
2023	PBMC	222	1,047,824	系统分析了不同种群受SARS-CoV-2和IAV刺激之后的eQTL差异变化	[31]
2023	PBMC	1,037	1,131,414	基于单细胞分辨率eQTL模型，鉴定了细胞动态依赖的控制HLA表达的位点	[32]
2023	成纤维细胞	68	22,188	开发了GASPACHO定位模型鉴定细胞轨迹变化的eQTL位点	[33]
2024	大脑组织	388	2,800,000	第1篇结合多个种族，并结合scATAC-seq大规模的大脑细胞亚型eQTL分析，构建了精准的疾病预测模型	[34]
2024	大脑组织	424	1,500,000	系统鉴定了大脑64种细胞亚型的eQTL位点，并鉴定到多个与阿尔兹海默相关的新的遗传变异位点	[35]
2024	肺部组织	114	475,047	系统鉴定了肺组织中38种细胞亚型的eQTL位点，且鉴定与肺纤维化相关的细胞类型特异变异位点	[36]

表1

图3

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单细胞精度的表达数量性状位点研究进展

Research progress on single-cell expression quantitative trait loci

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