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

Authors :
George Nicholson
Lyle Armstrong
Caroline Muschet
Adam E. Handel
Wendy E. Heywood
Eshita Sharma
Pieter J. Peeters
Philip W. Brownjohn
Frank Wessely
Georg C. Terstappen
Carlo Cusulin
Majlinda Lako
Katharina Janssen
Viktor Lakics
Satyan Chintawar
Kevin Mills
Jerzy Adamski
Thomas Barta
Anna Baud
Caleb Webber
Viola Volpato
Moustafa Attar
Roberta De Filippis
Janina S. Ried
Frederick J. Livesey
Peter Reinhardt
Martin Graf
Cynthia Sandor
Jérôme Nicod
Rory Bowden
M. Zameel Cader
Sarah E. Newey
Ines Royaux
Emma S. Whiteley
Aanna Artati
Victoria Stubbs
Paul Gissen
James Smith
An Verheyen
Christoph Patsch
Klaus Christensen
Colin J. Akerman
Smith, James [0000-0001-9131-5849]
Brownjohn, Phil [0000-0002-4707-3077]
Apollo - University of Cambridge Repository
Source :
Stem Cell Reports, Stem Cell Rep. 11, 897-911 (2018), Stem Cell Reports, Vol 11, Iss 4, Pp 897-911 (2018)
Publication Year :
2018
Publisher :
Elsevier, 2018.

Abstract

Summary 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.<br />Highlights • 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<br />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.

Details

Language :
English
ISSN :
22136711
Volume :
11
Issue :
4
Database :
OpenAIRE
Journal :
Stem Cell Reports
Accession number :
edsair.doi.dedup.....724276b54f17f40c7cee9f19cb22fe44