Reproducibility of Molecular Phenotypes after Long-Term Differentiation to Human iPSC-Derived Neurons: A Multi-Site Omics Study.
Reproducibility of Molecular Phenotypes after Long-Term Differentiation to Human iPSC-Derived Neurons: A Multi-Site Omics Study.
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DOI：
10.1016/j.stemcr.2018.08.013
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发表时间：
2018-10-09
影响因子：
5.9
通讯作者：
Lakics V
中科院分区：
文献类型：
--
作者：
Volpato V;Smith J;Sandor C;Ried JS;Baud A;Handel A;Newey SE;Wessely F;Attar M;Whiteley E;Chintawar S;Verheyen A;Barta T;Lako M;Armstrong L;Muschet C;Artati A;Cusulin C;Christensen K;Patsch C;Sharma E;Nicod J;Brownjohn P;Stubbs V;Heywood WE;Gissen P;De Filippis R;Janssen K;Reinhardt P;Adamski J;Royaux I;Peeters PJ;Terstappen GC;Graf M;Livesey FJ;Akerman CJ;Mills K;Bowden R;Nicholson G;Webber C;Cader MZ;Lakics V
分子和细胞研究中的复制是科学发现的基础。为了建立明确定义的长期神经元分化方案的可重复性，我们在五个不同的实验室重复了相同的两个iPSC系的细胞和分子比较。尽管发现在个别实验室可接受的变异性，我们检测到这两个线之间的差异基因表达签名的跨站点再现性差。因子分析确定实验室是变异的最大来源，沿着几个变异膨胀混杂因素，如传代效应和祖细胞储存。单细胞转录组学显示了实验室间变异性的实质性细胞异质性，并导致差异基因表达推断的偏倚。基于因子分析的组合数据集的归一化可以消除讨厌的技术影响，从而能够执行稳健的假设生成研究。我们的研究表明，多中心合作可以暴露系统性偏倚，并确定在发布新方案时需要标准化的关键因素，从而有助于提高跨中心的重现性。基于iPSC的分子实验中的跨位点再现性差基于因子分析的归一化可用于分析滋扰变异iPSC实验分子数据的外部验证对于再现性至关重要需要合作研究来揭示系统偏倚以提高再现性在这篇文章中，Lakics及其同事表明，虽然各个实验室能够鉴定iPSC神经元模型之间一致的分子和看似统计学上稳健的差异，但交叉位点再现性差。他们的研究结果支持多中心合作，以暴露系统性偏倚，并确定需要标准化的关键因素，以提高基于iPSC的分子实验的重现性。
Reproducibility in molecular and cellular studies is fundamental to scientific discovery. To establish the reproducibility of a well-defined long-term neuronal differentiation protocol, we repeated the cellular and molecular comparison of the same two iPSC lines across five distinct laboratories. Despite uncovering acceptable variability within individual laboratories, we detect poor cross-site reproducibility of the differential gene expression signature between these two lines. Factor analysis identifies the laboratory as the largest source of variation along with several variation-inflating confounders such as passaging effects and progenitor storage. Single-cell transcriptomics shows substantial cellular heterogeneity underlying inter-laboratory variability and being responsible for biases in differential gene expression inference. Factor analysis-based normalization of the combined dataset can remove the nuisance technical effects, enabling the execution of robust hypothesis-generating studies. Our study shows that multi-center collaborations can expose systematic biases and identify critical factors to be standardized when publishing novel protocols, contributing to increased cross-site reproducibility. Cross-site reproducibility in iPSC-based molecular experiments is poor Factor analysis-based normalization can be used to analyze nuisance variation External validation of iPSC experimental molecular data is critical for reproducibility Collaborative studies are needed to reveal systematic biases to improve reproducibility In this article, Lakics and colleagues show that, while individual laboratories are able to identify consistent molecular and seemingly statistically robust differences between iPSC neuronal models, cross-site reproducibility is poor. Their findings support multi-center collaborations to expose systematic biases and identify critical factors to be standardized to improve reproducibility in iPSC-based molecular experiments.
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影响因子：
14.9
作者：
Sunkin SM;Ng L;Lau C;Dolbeare T;Gilbert TL;Thompson CL;Hawrylycz M;Dang C
通讯作者：
Dang C
影响因子：
3
作者：
Freytag S;Gagnon-Bartsch J;Speed TP;Bahlo M
通讯作者：
Bahlo M
影响因子：
46.9
作者：
Risso, Davide;Ngai, John;Speed, Terence P.;Dudoit, Sandrine
通讯作者：
Dudoit, Sandrine
影响因子：
48
作者：
Picelli, Simone;Bjorklund, Asa K.;Sandberg, Rickard
通讯作者：
Sandberg, Rickard
影响因子：
15.1
作者：
Heywood WE;Galimberti D;Bliss E;Sirka E;Paterson RW;Magdalinou NK;Carecchio M;Reid E;Heslegrave A;Fenoglio C;Scarpini E;Schott JM;Fox NC;Hardy J;Bhatia K;Heales S;Sebire NJ;Zetterberg H;Mills K
通讯作者：
Mills K