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Volpato, V.* ; Smith, J.* ; Sandor, C.* ; Ried, J.S.* ; Baud, A.* ; Handel, A.* ; Newey, S.E.* ; Wessely, F.* ; Attar, M.* ; Whiteley, E.* ; Chintawar, S.* ; Verheyen, A.* ; Barta, T.* ; Lako, M.* ; Armstrong, L.L.* ; Muschet, C. ; Artati, A. ; Cusulin, C.* ; Christensen, K.* ; Patsch, C.* ; Sharma, E.* ; Nicod, J.* ; Brownjohn, P.* ; Stubbs, V.* ; Heywood, W.E.* ; Gissen, P.* ; De Filippis, R.* ; Janssen, K.* ; Reinhardt, P.* ; Adamski, J. ; Royaux, I.* ; Peeters, P.J.* ; Terstappen, G.C.* ; Graf, M.* ; Livesey, F.J.* ; Akerman, C.J.* ; Mills, K.* ; Bowden, R.* ; Nicholson, G.* ; Webber, C.* ; Cader, M.Z.* ; Lakics, V.*

Reproducibility of molecular phenotypes after long-term differentiation to human iPSC-derived neurons: A multi-site omics study.

Stem Cell Rep. 11, 897-911 (2018)
Publ. Version/Full Text DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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. 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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Publication type Article: Journal article
Document type Scientific Article
Keywords Cortical Neurons ; Cross-site Experimental Variation ; Gene Expression Profile ; Induced Pluripotent Stem Cell ; Molecular Profiling ; Proteomic Profiles ; Public-private Partnership ; Reproducibility ; Single-cell Sequencing ; Stembancc; Pluripotent Stem-cells; Cerebral-cortex Development; Disease
Language english
Publication Year 2018
HGF-reported in Year 2018
ISSN (print) / ISBN 2213-6711
Journal Stem Cell Reports
Quellenangaben Volume: 11, Issue: 4, Pages: 897-911 Article Number: , Supplement: ,
Publisher Cell Press
Publishing Place Maryland Heights, MO
Reviewing status Peer reviewed
Institute(s) Institute of Experimental Genetics (IEG)
POF-Topic(s) 30201 - Metabolic Health
Research field(s) Genetics and Epidemiology
PSP Element(s) G-500600-001
DOI 10.1016/j.stemcr.2018.08.013
WOS ID WOS:000446691800007
Scopus ID 85054753995
PubMed ID 30245212
Erfassungsdatum 2018-09-26
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