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High-content image-based cell phenotyping provides fundamental insights in a broad variety of life science areas. Striving for accurate conclusions and meaningful impact demands high reproducibility standards, even more importantly with the advent of data sharing initiatives. 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, 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 data and meta-analysis depend on standardized procedures and batch correction applied to studies of perturbation effects.

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.

3D in vitro culture models of cancer cells in extracellular matrix (ECM) have been developed to investigate drug targeting and resistance or, alternatively, mechanisms of invasion; however, models allowing analysis of shared pathways mediating invasion and therapy resistance are lacking. To evaluate therapy response associated with cancer cell invasion, we here used 3D invasion culture of tumor spheroids in 3D fibrillar collagen and applied Ethanol-Ethyl cinnamate (EtOH-ECi) based optical clearing to detect both spheroid core and invasion zone by subcellular-resolved 3D microscopy. When subjected to a single dose of irradiation (4 Gy), we detected significant cell survival in the invasion zone. By physical separation of the core and invasion zone, we identified differentially regulated genes preferentially engaged in invading cells controlling cell division, repair, and survival. This imaging-based 3D invasion culture may be useful for the analysis of complex therapy-response patterns in cancer cells in drug discovery and invasion-associated resistance development.

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