通过细胞间通讯增强基因表达对齐，提升单细胞轨迹推断精度。
CellBRIDGE: Learning Cellular Trajectories via Interaction-Aware Alignment
- 引入基于配体-受体活性的交互代价，改进传统仅依赖基因表达距离的对齐方法。
- 在合成与真实数据上均优于仅用特征的基线模型，轨迹推断更准确。
- 可模拟基因扰动并预测其对细胞发育路径的影响，适合生物机制研究者。
从群体快照中推断动态过程是机器学习与生物学中的基础挑战。在单细胞转录组测序（scRNA-seq）中，破坏性测量使得无法直接追踪细胞随时间变化，导致轨迹推断欠定。最优传输（OT）为快照对齐提供了合理框架，但长期存在的建模问题是：何种代价函数能产生生物上有意义的匹配。标准OT方法依赖基因表达距离，隐含将细胞视为独立点，忽略了由配体-受体信号介导的结构化细胞间通讯。我们提出CellBRIDGE（基于细胞的正则化交互驱动基因表达），在基于特征的OT基础上引入由配体-受体活性导出的有向、类型化的交互代价。通过显式建模细胞间通讯，CellBRIDGE在合成和真实scRNA-seq数据集上提升了跨快照匹配质量与下游轨迹估计性能，优于仅使用特征的基线模型。值得注意的是，CellBRIDGE支持机制可解释的体外扰动模拟：在肺癌数据中，抑制特定配体-受体对会引发轨迹偏移，重现靶向通路抑制的预期效应。
原文摘要 · Abstract (English)
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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