Katalog Plus
Bibliothek der Frankfurt UAS
Bald neuer Katalog: sichern Sie sich schon vorab Ihre persönlichen Merklisten im Nutzerkonto: Anleitung.
Dieses Ergebnis aus BASE kann Gästen nicht angezeigt werden.  Login für vollen Zugriff.

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

Title: Reproducibility of molecular phenotypes after long-term differentiation to human iPSC-derived neurons: A multi-site omics study
Authors: Volpato, V; Smith, J; Sandor, C; Ried, JS; Baud, A; Handel, A; Newey, S; 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, W; Gissen, P; De Filippis, R; Janssen, K; Reinhardt, P; Adamski, J; Royaux, I; Peeters, P; Terstappen, G; Graf, M; Livesey, F; Akerman, C; Mills, K; Bowden, R; Nicholson, G; Webber, C; Cader, MZ; Lakics, V
Publisher Information: Cell Press
Publication Year: 2018
Collection: Oxford University Research Archive (ORA)
Description: 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.
Document Type: article in journal/newspaper
Language: English
Relation: https://doi.org/10.1016/j.stemcr.2018.08.013
DOI: 10.1016/j.stemcr.2018.08.013
Availability: https://doi.org/10.1016/j.stemcr.2018.08.013; https://ora.ox.ac.uk/objects/uuid:d040c80d-73f8-4612-a8b2-2a2a29660fe9
Rights: info:eu-repo/semantics/openAccess ; CC Attribution (CC BY)
Accession Number: edsbas.DD5C9C5F
Database: BASE