CellBRIDGE: Learning Cellular Trajectories via Interaction-Aware Alignment

• URL: http://arxiv.org/abs/2605.30635v1
• Date: Thu, 28 May 2026 22:37:54 GMT
• Title: CellBRIDGE: Learning Cellular Trajectories via Interaction-Aware Alignment
• Abstract summary: Inferring dynamics from population snapshots is a fundamental challenge in machine learning and biology.<n>We introduce CellBRIDGE, which augments feature-based OT with a directed, typed interaction cost derived from ligand-receptor activity.<n>By explicitly modeling cell-cell communication, CellBRIDGE improves cross-snapshot couplings and downstream trajectory estimates.
• Score: 46.660621971318996
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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