Article Dans Une Revue eLife Année : 2025

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

Résumé

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.
Fichier principal
Vignette du fichier
elife-95485-v1-1.pdf (6) Télécharger le fichier
Origine Publication financée par une institution
licence

Dates et versions

hal-04957992 , version 1 (20-02-2025)

Licence

Identifiants

Citer

Franck Simon, Maria Colomba Comes, Tiziana Tocci, Louise Dupuis, Vincent Cabeli, et al.. CausalXtract, a flexible pipeline to extract causal effects from live-cell time-lapse imaging data. eLife, 2025, 13, ⟨10.7554/eLife.95485.3⟩. ⟨hal-04957992⟩
0 Consultations
0 Téléchargements

Altmetric

Partager

More