CellBRIDGE: Learning Cellular Trajectories via Interaction-Aware Alignment
This work provides an incremental improvement for biologists inferring cellular trajectories from scRNA-seq data, by incorporating cell-cell communication into the alignment process.
This paper addresses the challenge of inferring cellular trajectories from scRNA-seq snapshots by introducing CellBRIDGE, a method that augments Optimal Transport with a directed, typed interaction cost derived from ligand-receptor activity. CellBRIDGE improves cross-snapshot couplings and trajectory estimates on synthetic and real scRNA-seq datasets compared to feature-only baselines, and enables interpretable in silico perturbations.
Inferring dynamics from population snapshots is a fundamental challenge in machine learning and biology. In scRNA-sequencing (scRNA-seq), destructive measurements preclude direct tracking of individual cells across time, making trajectory inference underdetermined. Optimal Transport (OT) provides a principled framework for snapshot alignment, but a long-standing modeling question is which cost functions yield biologically meaningful couplings. Standard OT approaches rely on gene-expression distances, implicitly treating cells as independent points and neglecting structured cell-cell communication mediated by ligand-receptor signaling. We introduce CellBRIDGE (Cell-Based Regularized Interaction-Driven Gene Expression), which augments feature-based OT with a directed, typed interaction cost derived from ligand-receptor activity. By explicitly modeling cell-cell communication, CellBRIDGE improves cross-snapshot couplings and downstream trajectory estimates across synthetic and real scRNA-seq datasets relative to feature-only baselines. Notably, CellBRIDGE enables mechanistically interpretable in silico perturbations: on lung cancer data, silencing specific ligand-receptor pairs induces trajectory shifts that recapitulate expected effects of targeted pathway inhibition.