Inferring trajectories from longitudinal spatially-resolved omics data is fundamental to understanding the dynamics of structural and functional tissue changes in development, regeneration and repair, disease progression, and response to treatment. We propose ContextFlow, a novel context-aware flow matching framework that incorporates prior knowledge to guide the inference of structural tissue dynamics from spatially resolved omics data. Specifically, ContextFlow integrates local tissue organization and ligand-receptor communication patterns into a transition plausibility matrix that regularizes the optimal transport objective. By embedding these contextual constraints, ContextFlow generates trajectories that are not only statistically consistent but also biologically meaningful, making it a generalizable framework for modeling spatiotemporal dynamics from longitudinal, spatially resolved omics data. Evaluated on three datasets, ContextFlow consistently outperforms state-of-the-art flow matching methods across multiple quantitative and qualitative metrics of inference accuracy and biological coherence. Our code is available at: \href{https://github.com/santanurathod/ContextFlow}{ContextFlow}

翻译：从纵向空间分辨组学数据中推断轨迹，对于理解发育、再生与修复、疾病进展以及治疗响应过程中组织结构与功能变化的动态机制至关重要。本文提出ContextFlow，一种新颖的上下文感知流匹配框架，该框架通过融入先验知识来指导从空间分辨组学数据中推断组织结构的动态变化。具体而言，ContextFlow将局部组织结构与配体-受体通讯模式整合为一个转移合理性矩阵，用于正则化最优传输目标函数。通过嵌入这些上下文约束，ContextFlow生成的轨迹不仅具有统计一致性，同时具备生物学意义，使其成为一个可推广的框架，适用于从纵向空间分辨组学数据中建模时空动态。在三个数据集上的评估表明，ContextFlow在推断准确性与生物学一致性等多个定量与定性指标上，均持续优于当前最先进的流匹配方法。我们的代码公开于：\href{https://github.com/santanurathod/ContextFlow}{ContextFlow}