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bioRxiv · 10.1101/2024.02.06.579177

CausalXtract: a flexible pipeline to extract causal effects from live-cell time-lapse imaging data

Simon, F.·Comes, M. C.·Tocci, T.·Dupuis, L.·Cabeli, V.·Lagrange, N.·Mencattini, A.·Parrini, M. C.·Martinelli, E.·Isambert, H.
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Abstract

Live-cell microscopy routinely provides massive amount of time-lapse images of complex cellular systems under various physiological or therapeutic conditions. However, this wealth of data remains difficult to interpret in terms of causal effects. Here, we describe CausalXtract, a flexible computational pipeline that discovers causal and possibly time-lagged effects from morphodynamic features and cell-cell interactions in live-cell imaging data. CausalXtract methodology combines network-based and information-based frameworks, which is shown to discover causal effects overlooked by classical Granger and Schreiber causality approaches. We showcase the use of CausalXtract to uncover novel causal effects in a tumor-on-chip cellular ecosystem under therapeutically relevant conditions. In particular, we find that cancer associated fibroblasts directly inhibit cancer cell apoptosis, independently from anti-cancer treatment. CausalXtract uncovers also multiple antagonistic effects at different time delays. Hence, CausalXtract provides a unique computational tool to interpret live-cell imaging data for a range of fundamental and translational research applications.

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Simon, F., Comes, M. C., Tocci, T., Dupuis, L., Cabeli, V., Lagrange, N., Mencattini, A., Parrini, M. C., Martinelli, E., Isambert, H.. 2024-02-08. CausalXtract: a flexible pipeline to extract causal effects from live-cell time-lapse imaging data. https://doi.org/10.1101/2024.02.06.579177

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