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Stem Cell Reports
Published

Reproducibility of Molecular Phenotypes after Long-Term Differentiation to Human iPSC-Derived Neurons: A Multi-Site Omics Study.

Authors

Viola Volpato, James Smith, Cynthia Sandor, Janina S Ried, Anna Baud, Adam Handel, Sarah E Newey, Frank Wessely, Moustafa Attar, Emma Whiteley, Satyan Chintawar, An Verheyen, Thomas Barta, Majlinda Lako, Lyle Armstrong, Caroline Muschet, Anna Artati, Carlo Cusulin, Klaus Christensen, Christoph Patsch, Eshita Sharma, Jerome Nicod, Philip Brownjohn, Victoria Stubbs, Wendy E Heywood, Paul Gissen, Roberta De Filippis, Katharina Janssen, Peter Reinhardt, Jerzy Adamski, Ines Royaux, Pieter J Peeters, Georg C Terstappen, Martin Graf, Frederick J Livesey, Colin J Akerman, Kevin Mills, Rory Bowden, George Nicholson, Caleb Webber, M Zameel Cader, Viktor Lakics

Abstract

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.

PMID:30245212 | DOI:S2213-6711(18)30357-6

UK DRI Authors

Cynthia Sandor

Dr Cynthia Sandor

Group Leader

Developing new ways to detect and monitor Parkinson’s

Dr Cynthia Sandor
Caleb Webber

Prof Caleb Webber

Director of Data Science & Group Leader

Combining state-of-the-art stem cell models with bioinformatics techniques to boost our understanding of the biological mechanisms underlying Parkinson’s disease

Prof Caleb Webber