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

• DOI: 10.1101/2024.02.06.579177
• Publication Date: 2024-02-09T01:08:43Z
• Authors: Franck Simon; Maria Colomba Comes; Tiziana Tocci; Louise Dupuis; Vincent Cabeli; Nikita Lagrange; Arianna Mencattini; Maria Carla Parrini; Eugenio Martinelli; Hervé Isambert
• Keywords: time-lapse image analysis; granger causality; QH301-705.5; Science; Q; R; Medicine; causal inference; causal discovery; Biology (General); tumor on chip; live-cell imaging; Computational and Systems Biology

AbstractLive-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.