Dataset Information

Multisite assessment of reproducibility in high-content cell migration imaging data.

ABSTRACT: High-content image-based cell phenotyping provides fundamental insights into a broad variety of life science disciplines. Striving for accurate conclusions and meaningful impact demands high reproducibility standards, with particular relevance for high-quality open-access data sharing and meta-analysis. However, the sources and degree of biological and technical variability, and thus the reproducibility and usefulness of meta-analysis of results from live-cell microscopy, have not been systematically investigated. Here, using high-content data describing features of cell migration and morphology, we determine the sources of variability across different scales, including between laboratories, persons, experiments, technical repeats, cells, and time points. Significant technical variability occurred between laboratories and, to lesser extent, between persons, providing low value to direct meta-analysis on the data from different laboratories. However, batch effect removal markedly improved the possibility to combine image-based datasets of perturbation experiments. Thus, reproducible quantitative high-content cell image analysis of perturbation effects and meta-analysis depend on standardized procedures combined with batch correction.

SUBMITTER: Hu J

PROVIDER: S-EPMC10258559 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Multisite assessment of reproducibility in high-content cell migration imaging data.

Hu Jianjiang J Serra-Picamal Xavier X Bakker Gert-Jan GJ Van Troys Marleen M Winograd-Katz Sabina S Ege Nil N Gong Xiaowei X Didan Yuliia Y Grosheva Inna I Polansky Omer O Bakkali Karima K Van Hamme Evelien E van Erp Merijn M Vullings Manon M Weiss Felix F Clucas Jarama J Dowbaj Anna M AM Sahai Erik E Ampe Christophe C Geiger Benjamin B Friedl Peter P Bottai Matteo M Strömblad Staffan S

Molecular systems biology 20230417 6

High-content image-based cell phenotyping provides fundamental insights into a broad variety of life science disciplines. Striving for accurate conclusions and meaningful impact demands high reproducibility standards, with particular relevance for high-quality open-access data sharing and meta-analysis. However, the sources and degree of biological and technical variability, and thus the reproducibility and usefulness of meta-analysis of results from live-cell microscopy, have not been systemati ...[more]

PMID: 37063090

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Project description:BackgroundHigh content live cell imaging experiments are able to track the cellular localisation of labelled proteins in multiple live cells over a time course. Experiments using high content live cell imaging will generate multiple large datasets that are often stored in an ad-hoc manner. This hinders identification of previously gathered data that may be relevant to current analyses. Whilst solutions exist for managing image data, they are primarily concerned with storage and retrieval of the images themselves and not the data derived from the images. There is therefore a requirement for an information management solution that facilitates the indexing of experimental metadata and results of high content live cell imaging experiments.ResultsWe have designed and implemented a data model and information management solution for the data gathered through high content live cell imaging experiments. Many of the experiments to be stored measure the translocation of fluorescently labelled proteins from cytoplasm to nucleus in individual cells. The functionality of this database has been enhanced by the addition of an algorithm that automatically annotates results of these experiments with the timings of translocations and periods of any oscillatory translocations as they are uploaded to the repository. Testing has shown the algorithm to perform well with a variety of previously unseen data.ConclusionOur repository is a fully functional example of how high throughput imaging data may be effectively indexed and managed to address the requirements of end users. By implementing the automated analysis of experimental results, we have provided a clear impetus for individuals to ensure that their data forms part of that which is stored in the repository. Although focused on imaging, the solution provided is sufficiently generic to be applied to other functional proteomics and genomics experiments. The software is available from: fhttp://code.google.com/p/livecellim/

Dataset Information

Multisite assessment of reproducibility in high-content cell migration imaging data.

Publications

Multisite assessment of reproducibility in high-content cell migration imaging data.

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