Title: Low-rank Interpretable Cell–Cell Hidden Interactions from Embeddings
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: cell–cell interactions, live-cell imaging, interaction networks, dynamical systems, data-driven modeling, interpretable representation, low-rank optimization

Abstract: Multicellular organisms rely on continuously changing cell–cell interactions that govern critical biological processes as cells modify their internal states and trajectories in space over time. Studying these interactions is critical to understand development, homeostasis, and disease progression. Live-cell imaging provides a unique opportunity to directly observe these dynamical events; however, current computational approaches often fail to model complex, time-varying events involving diverse populations and spatial contexts. Here, we present LICCHIE, a model designed to infer time-changing, feature-based cell-cell interactions, applicable across systems and conditions. Our approach represents each cell with a dynamic multi-feature vector, and interactions are modeled as spatially constrained, directed influences between cell pairs, evolving over time. We optimize the model using an iterative scheme balancing data fidelity, interactions smoothness, and low-rank sparse structure. We validated LICCHIE’s ability to capture cellular interactions across populations in a controlled synthetic setting and applied it to real-world 3D live-cell imaging of patient-derived tumor organoids to (1) identify components with interpretable structures that capture interaction type and directionality, and (2) suggest modulation strategies that may accelerate NK polarization and tumor cell death.

TL;DR: LICCHIE, a data-driven method for interpretable cell–cell interactions inference, revealing their underlying mechanisms