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

• DOI: 10.7554/elife.95485.3
• Publication Date: 2025-01-17T13:15:41Z
• Authors: Franck Simon; Maria Colomba Comes; Tiziana Tocci; Louise Dupuis; Vincent Cabeli; Nikita Lagrange; Arianna Mencattini; Maria Carla Parrini; Eugenio Martinelli; Herve Isambert
• Keywords: Live cell imaging; Causality

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