Single-cell sequencing reveals lineage-specific dynamic genetic regulation of gene expression during human cardiomyocyte differentiation.
Single-cell sequencing reveals lineage-specific dynamic genetic regulation of gene expression during human cardiomyocyte differentiation.
复制标题
DOI：
10.1371/journal.pgen.1009666
复制
发表时间：
2022-01
期刊：
影响因子：
4.5
通讯作者：
Battle A
中科院分区：
文献类型：
--
作者：
Elorbany R;Popp JM;Rhodes K;Strober BJ;Barr K;Qi G;Gilad Y;Battle A
动态和暂时特异性的基因调控变化可能是复杂疾病无法解释的遗传关联的基础。在细胞分化等动态过程中，组织（或体外培养）的整体细胞类型组成和每个细胞的基因调控谱都可能随着时间的推移发生重大变化。为了高分辨率地识别这些动态效应，我们收集了从诱导多能干细胞到心肌细胞分化过程中的单细胞rna测序数据，在19个人类细胞系的7个独特时间点取样。我们采用了一种灵活的方法来绘制动态eqtl，这些eqtl的影响在分岔分化轨迹的过程中显著变化，包括许多特定于这两个谱系之一的影响。我们的研究设计使我们能够区分影响特定细胞系的真正动态eqtl和由细胞系之间潜在的非遗传差异（如细胞组成）驱动的表达变化。此外，我们使用从单细胞数据中了解到的细胞类型概况对匹配的大量RNA-seq样本进行反卷积和重新分析。使用这种方法，我们能够在单细胞数据中识别出大量新的动态eqtl，同时还将动态效应归因于特定谱系。总的来说，我们发现使用单细胞数据来揭示动态的eqtl可以为心肌细胞分化过程中异质细胞类型之间发生的基因调控变化提供新的见解。许多复杂的性状和疾病都与基因变异有关，这些变异被怀疑可以调节附近基因的表达水平。然而，我们仍然无法确定许多相关的变异基因关联。先前的研究表明，基因表达的调节通常是特定于生物环境的，这表明测量不同环境下的基因表达可能揭示重要的关联。在这项工作中，我们确定了在包含多种瞬时细胞状态的复杂环境中随时间“动态”的遗传调控效应。我们收集了从干细胞到心肌细胞分化或改变状态的细胞在几个时间点的单细胞基因表达数据。我们描述了细胞在体外分化时所经历的两种不同的轨迹，并将每个细胞分配到沿着特定轨迹的特定点。然后，我们确定了数百个调控变异和基因表达水平之间的动态关联，包括许多特定于单一轨迹的动态关联。这项工作证明了在随时间变化或仅存在于细胞分化的短暂阶段的细胞类型中寻找变异基因关联的重要性，并为在人类发育特征的分岔轨迹中识别这些关联提供了一个框架。
Dynamic and temporally specific gene regulatory changes may underlie unexplained genetic associations with complex disease. During a dynamic process such as cellular differentiation, the overall cell type composition of a tissue (or an in vitro culture) and the gene regulatory profile of each cell can both experience significant changes over time. To identify these dynamic effects in high resolution, we collected single-cell RNA-sequencing data over a differentiation time course from induced pluripotent stem cells to cardiomyocytes, sampled at 7 unique time points in 19 human cell lines. We employed a flexible approach to map dynamic eQTLs whose effects vary significantly over the course of bifurcating differentiation trajectories, including many whose effects are specific to one of these two lineages. Our study design allowed us to distinguish true dynamic eQTLs affecting a specific cell lineage from expression changes driven by potentially non-genetic differences between cell lines such as cell composition. Additionally, we used the cell type profiles learned from single-cell data to deconvolve and re-analyze data from matched bulk RNA-seq samples. Using this approach, we were able to identify a large number of novel dynamic eQTLs in single cell data while also attributing dynamic effects in bulk to a particular lineage. Overall, we found that using single cell data to uncover dynamic eQTLs can provide new insight into the gene regulatory changes that occur among heterogeneous cell types during cardiomyocyte differentiation. Many complex traits and diseases are associated with genetic variants which are suspected to regulate the expression levels of nearby genes. However, we are still unable to identify many of the relevant variant-gene associations. Previous work has shown that regulation of gene expression is often specific to a biological context, suggesting that measuring gene expression in diverse contexts may reveal important associations. In this work, we identified genetic regulatory effects that are “dynamic” over time in a complex environment containing diverse and transient cell states. We collected single-cell gene expression data at several time points from cells differentiating, or changing state, from stem cells to cardiomyocytes. We characterized two distinct trajectories that cells undertake as they differentiate in vitro, and assigned each cell to a particular point along a specific trajectory. We then identified hundreds of dynamic associations between regulatory variants and gene expression levels, including many specific to a single trajectory. This work demonstrates the importance of searching for variant-gene associations in cell types that change over time or exist only during fleeting stages of cellular differentiation, and provides a framework for identifying these associations in the presence of bifurcating trajectories that are characteristic of human development.
登录
查看更多内容
影响因子：
64.8
作者：
Frazer, Kelly A.;Ballinger, Dennis G.;Cox, David R.;Hinds, David A.;Stuve, Laura L.;Gibbs, Richard A.;Belmont, John W.;Boudreau, Andrew;Hardenbol, Paul;Leal, Suzanne M.;Pasternak, Shiran;Wheeler, David A.;Willis, Thomas D.;Yu, Fuli;Yang, Huanming;Zeng, Changqing;Gao, Yang;Hu, Haoran;Hu, Weitao;Li, Chaohua;Lin, Wei;Liu, Siqi;Pan, Hao;Tang, Xiaoli;Wang, Jian;Wang, Wei;Yu, Jun;Zhang, Bo;Zhang, Qingrun;Zhao, Hongbin;Zhao, Hui;Zhou, Jun;Gabriel, Stacey B.;Barry, Rachel;Blumenstiel, Brendan;Camargo, Amy;Defelice, Matthew;Faggart, Maura;Goyette, Mary;Gupta, Supriya;Moore, Jamie;Nguyen, Huy;Onofrio, Robert C.;Parkin, Melissa;Roy, Jessica;Stahl, Erich;Winchester, Ellen;Ziaugra, Liuda;Altshuler, David;Shen, Yan;Yao, Zhijian;Huang, Wei;Chu, Xun;He, Yungang;Jin, Li;Liu, Yangfan;Shen, Yayun;Sun, Weiwei;Wang, Haifeng;Wang, Yi;Wang, Ying;Xiong, Xiaoyan;Xu, Liang;Waye, Mary M. Y.;Tsui, Stephen K. W.;Wong, J. Tze-Fei;Galver, Luana M.;Fan, Jian-Bing;Gunderson, Kevin;Murray, Sarah S.;Oliphant, Arnold R.;Chee, Mark S.;Montpetit, Alexandre;Chagnon, Fanny;Ferretti, Vincent;Leboeuf, Martin;Olivier, Jean-Franccois;Phillips, Michael S.;Roumy, Stephanie;Sallee, Clementine;Verner, Andrei;Hudson, Thomas J.;Kwok, Pui-Yan;Cai, Dongmei;Koboldt, Daniel C.;Miller, Raymond D.;Pawlikowska, Ludmila;Taillon-Miller, Patricia;Xiao, Ming;Tsui, Lap-Chee;Mak, William;Song, You Qiang;Tam, Paul K. H.;Nakamura, Yusuke;Kawaguchi, Takahisa;Kitamoto, Takuya;Morizono, Takashi;Nagashima, Atsushi;Ohnishi, Yozo;Sekine, Akihiro;Tanaka, Toshihiro;Tsunoda, Tatsuhiko;Deloukas, Panos;Bird, Christine P.;Delgado, Marcos;Dermitzakis, Emmanouil T.;Gwilliam, Rhian;Hunt, Sarah;Morrison, Jonathan;Powell, Don;Stranger, Barbara E.;Whittaker, Pamela;Bentley, David R.;Daly, Mark J.;de Bakker, Paul I. W.;Barrett, Jeff;Chretien, Yves R.;Maller, Julian;McCarroll, Steve;Patterson, Nick;Pe'er, Itsik;Price, Alkes;Purcell, Shaun;Richter, Daniel J.;Sabeti, Pardis;Saxena, Richa;Schaffner, Stephen F.;Sham, Pak C.;Varilly, Patrick;Altshuler, David;Stein, Lincoln D.;Krishnan, Lalitha;Smith, Albert Vernon;Tello-Ruiz, Marcela K.;Thorisson, Gudmundur A.;Chakravarti, Aravinda;Chen, Peter E.;Cutler, David J.;Kashuk, Carl S.;Lin, Shin;Abecasis, Goncalo R.;Guan, Weihua;Li, Yun;Munro, Heather M.;Qin, Zhaohui Steve;Thomas, Daryl J.;McVean, Gilean;Auton, Adam;Bottolo, Leonardo;Cardin, Niall;Eyheramendy, Susana;Freeman, Colin;Marchini, Jonathan;Myers, Simon;Spencer, Chris;Stephens, Matthew;Donnelly, Peter;Cardon, Lon R.;Clarke, Geraldine;Evans, David M.;Morris, Andrew P.;Weir, Bruce S.;Tsunoda, Tatsuhiko;Johnson, Todd A.;Mullikin, James C.;Sherry, Stephen T.;Feolo, Michael;Skol, Andrew
通讯作者：
Skol, Andrew
影响因子：
14.9
作者：
Frankish A;Diekhans M;Ferreira AM;Johnson R;Jungreis I;Loveland J;Mudge JM;Sisu C;Wright J;Armstrong J;Barnes I;Berry A;Bignell A;Carbonell Sala S;Chrast J;Cunningham F;Di Domenico T;Donaldson S;Fiddes IT;García Girón C;Gonzalez JM;Grego T;Hardy M;Hourlier T;Hunt T;Izuogu OG;Lagarde J;Martin FJ;Martínez L;Mohanan S;Muir P;Navarro FCP;Parker A;Pei B;Pozo F;Ruffier M;Schmitt BM;Stapleton E;Suner MM;Sycheva I;Uszczynska-Ratajczak B;Xu J;Yates A;Zerbino D;Zhang Y;Aken B;Choudhary JS;Gerstein M;Guigó R;Hubbard TJP;Kellis M;Paten B;Reymond A;Tress ML;Flicek P
通讯作者：
Flicek P
影响因子：
3.7
作者：
Ahmad F;Banerjee SK;Lage ML;Huang XN;Smith SH;Saba S;Rager J;Conner DA;Janczewski AM;Tobita K;Tinney JP;Moskowitz IP;Perez-Atayde AR;Keller BB;Mathier MA;Shroff SG;Seidman CE;Seidman JG
通讯作者：
Seidman JG
影响因子：
7
作者：
Battle A;Mostafavi S;Zhu X;Potash JB;Weissman MM;McCormick C;Haudenschild CD;Beckman KB;Shi J;Mei R;Urban AE;Montgomery SB;Levinson DF;Koller D
通讯作者：
Koller D
影响因子：
30.8
作者：
Fairfax, Benjamin P.;Makino, Seiko;Radhakrishnan, Jayachandran;Plant, Katharine;Leslie, Stephen;Dilthey, Alexander;Ellis, Peter;Langford, Cordelia;Vannberg, Fredrik O.;Knight, Julian C.
通讯作者：
Knight, Julian C.