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

At the presentation of the ECNP Negative Data Award (see above), there was an interesting point made by one of the presenters – about the important role of large multi-national multi-lab collaboration projects for the scientific progress. More specifically, it was emphasized that initiatives such as the IMI projects have created an open communication culture that was unimaginable 10-15 years ago.

A recent publication serves as a great illustration for the significance of such collaborative efforts. An IMI consortium called StemBANCC (Stem cells for biological assays of novel drugs and predictive toxicology) is aimed to assess the inter- and intra-laboratory reproducibility of transcriptomic and proteomic readouts using two iPSC lines at five independent laboratories in parallel. Despite acceptable intra-laboratory reproducibility of “omics readouts” and surprisingly good cross-site reproducibility of a previously identified cellular phenotype, “omics datasets” from different sites had large variation that masked specific differences, rendering it impossible to distinguish these two lines from each other in a combined dataset. The computational biology approaches employed in this project revealed and removed the site-specific biases, enabled access to the underlying biology, and identified publication best practices. Besides strongly recommending to disclose the identified variation-inflating confounders in published iPSC differentiation protocols, this study also shows that collaborative approaches with larger sample numbers in cross-laboratory studies are valuable to detect and remove unwanted variation. These conclusions are particularly worth emphasizing as it becomes more and more obvious that the transparent reporting and collaborative efforts are essential in any field, whether in vitro or in vivo.

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