Multisite assessment of reproducibility in high‐content cell migration imaging data

• DOI: 10.15252/msb.202211490
• Publication Date: 2023-04-17T07:17:05Z
• Authors: Jianjiang Hu; Xavier Serra‐Picamal; Gert‐Jan Bakker; Marleen Van Troys; Sabina Winograd‐Katz; Nil Ege; Xiaowei Gong; Yuliia Didan; Inna Grosheva; Omer Polansky; Karima Bakkali; Evelien Van Hamme; Merijn van Erp; Manon Vullings; Felix Weiss; Jarama Clucas; Anna M Dowbaj; Erik Sahai; Christophe Ampe; Benjamin Geiger; Peter Friedl; Matteo Bottai; Staffan Strömblad
• Keywords: Model organisms; Medicine (General); high-content imaging; cell migration; QH301-705.5; Radboud University Medical Center; Radboudumc 2: Cancer development and immune defence Cell Biology (UMC); General Biochemistry, Genetics and Molecular Biology; high‐content imaging; Imaging; R5-920; Cell Movement; Medicine and Health Sciences; Radboudumc 19: Nanomedicine Cell Biology (UMC); Biology (General); reproducibility; General Immunology and Microbiology; variability; Applied Mathematics; Biology and Life Sciences; Reproducibility of Results; Articles; Cell Biology; Tumour Biology; Computational Theory and Mathematics; batch effect removal; General Agricultural and Biological Sciences; Information Systems

AbstractHigh‐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.