Characterising cancer-stroma interactions through high-content phenotyping from microscopy time-lapses

Understanding how cancer-stromal interactions shape cancer progression requires tools that can capture dynamic phenotypic changes in physiologically relevant conditions. Traditional approaches for studying co-culture interactions, such as transcriptomics and flow cytometry, provide valuable insights but are limited by their static nature and reliance on fixed or dissociated cells. In contrast, label-free time-lapse microscopy preserves temporal and spatial context, enabling observation of live-cell behaviours over time. A major challenge, however, lies in the analysis of the resulting high-dimensional datasets. Using co-cultures of breast cancer cells and cancer-associated fibroblasts (CAFs) as a model system, we show that the CellPhe toolkit enables label-free identification and phenotypic characterisation of different cell types within complex live-cell imaging datasets. Our analysis shows that exposure to CAFs drives marked phenotypic shifts in breast cancer cells, including elongation, loss of cell-cell adhesion, and redistribution of intracellular components - hallmarks of epithelial-mesenchymal transition (EMT). To probe the underlying mechanisms, we performed a Luminex immunoassay on CAF-conditioned media and identified secreted analytes strongly associated with EMT induction. Together, these results highlight how automated phenotyping can be integrated with molecular profiling to identify and characterise cellular processes shaped by stromal interactions and reveal the signalling mediators that drive them.