CNRS UMR168, Institut Curie, Université PSL, Sorbonne Université, Paris, France
Maria Colomba Comes
Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
Tiziana Tocci
CNRS UMR168, Institut Curie, Université PSL, Sorbonne Université, Paris, France; Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
Louise Dupuis
CNRS UMR168, Institut Curie, Université PSL, Sorbonne Université, Paris, France
Vincent Cabeli
CNRS UMR168, Institut Curie, Université PSL, Sorbonne Université, Paris, France
Nikita Lagrange
CNRS UMR168, Institut Curie, Université PSL, Sorbonne Université, Paris, France
Arianna Mencattini
Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
Live-cell microscopy routinely provides massive amounts 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 anticancer 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.
