Carcamo-Orive Ivan, Hoffman Gabriel E, Cundiff Paige, Beckmann Noam D, D'Souza Sunita L, Knowles Joshua W, Patel Achchhe, Papatsenko Dimitri, Abbasi Fahim, Reaven Gerald M, Whalen Sean, Lee Philip, Shahbazi Mohammad, Henrion Marc Y R, Zhu Kuixi, Wang Sven, Roussos Panos, Schadt Eric E, Pandey Gaurav, Chang Rui, Quertermous Thomas, Lemischka Ihor
Department of Medicine and Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA 94305, USA.
Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Cell Stem Cell. 2017 Apr 6;20(4):518-532.e9. doi: 10.1016/j.stem.2016.11.005. Epub 2016 Dec 22.
Variability in induced pluripotent stem cell (iPSC) lines remains a concern for disease modeling and regenerative medicine. We have used RNA-sequencing analysis and linear mixed models to examine the sources of gene expression variability in 317 human iPSC lines from 101 individuals. We found that ∼50% of genome-wide expression variability is explained by variation across individuals and identified a set of expression quantitative trait loci that contribute to this variation. These analyses coupled with allele-specific expression show that iPSCs retain a donor-specific gene expression pattern. Network, pathway, and key driver analyses showed that Polycomb targets contribute significantly to the non-genetic variability seen within and across individuals, highlighting this chromatin regulator as a likely source of reprogramming-based variability. Our findings therefore shed light on variation between iPSC lines and illustrate the potential for our dataset and other similar large-scale analyses to identify underlying drivers relevant to iPSC applications.
诱导多能干细胞（iPSC）系的变异性仍是疾病建模和再生医学领域所关注的问题。我们运用RNA测序分析和线性混合模型，研究了来自101名个体的317个人类iPSC系中基因表达变异性的来源。我们发现，全基因组表达变异性中约50%可由个体间差异来解释，并鉴定出一组对这种变异有贡献的表达数量性状位点。这些分析结合等位基因特异性表达表明，iPSC保留了供体特异性基因表达模式。网络、通路和关键驱动因素分析显示，多梳蛋白靶点对个体内部和个体之间存在的非遗传变异性有显著贡献，突出了这种染色质调节因子作为基于重编程的变异性的一个可能来源。因此，我们的研究结果揭示了iPSC系之间的差异，并说明了我们的数据集及其他类似大规模分析在识别与iPSC应用相关的潜在驱动因素方面的潜力。
