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

Area of Science:

• * Cell Biology
• * Computational Biology
• * Bioinformatics

Background:

• * Live-cell microscopy generates vast datasets of cellular dynamics.
• * Interpreting causal effects within complex cellular systems from imaging data is challenging.
• * Existing causality methods may miss subtle or time-lagged effects.

Purpose of the Study:

• * To introduce CausalXtract, a computational pipeline for discovering causal effects in live-cell imaging.
• * To identify time-lagged causal relationships from cellular morphodynamics and interactions.
• * To apply CausalXtract to a tumor-on-chip model for uncovering therapeutic insights.

Main Methods:

• * Integration of network-based and information-based analytical frameworks.
• * Analysis of morphodynamic features and cell-cell interactions from time-lapse microscopy.
• * Comparison with traditional Granger and Schreiber causality methods.

Main Results:

• * CausalXtract identified causal effects missed by classical approaches.
• * Novel causal interactions within a tumor-on-chip model were uncovered.
• * Cancer-associated fibroblasts were found to directly inhibit cancer cell apoptosis, independent of treatment.

Conclusions:

• * CausalXtract is a powerful tool for interpreting complex live-cell imaging data.
• * The pipeline reveals previously unrecognized causal relationships and time-delayed effects.
• * Findings have implications for fundamental and translational research in cellular ecosystems.