NicheTime: Spatiotemporal Generative Modeling of Cellular Microenvironment Evolution via Dual-Scale Optimal Transport

Learning continuous-time evolution of structured systems from unpaired observations remains a fundamental challenge in generative modeling. Existing trajectory inference and generative approaches often assume conservative dynamics or overlook the hierarchical organization and directional constraints of spatiotemporal biological systems. We introduce NicheTime, a continuous-time generative framework for modeling non-conservative spatiotemporal dynamics through transport-guided flow matching. NicheTime addresses three key challenges: unknown correspondence across temporal observations, multi-scale structural evolution, and irreversible dynamics with variable mass flow. NicheTime develops a dual-scale optimal transport coupling that first aligns coarse-grained structures and then refines local correspondences, while allowing flexible mass variation to capture non-conservative transitions. Based on these transport plans, we propose a direction-aware Graph Transformer flow matching architecture that incorporates temporal ordering into message propagation and jointly generates system states and spatial configurations through neural ODE integration. We evaluate NicheTime on three time-resolved spatial transcriptomics datasets covering embryonic development, neural development, and aging. Extensive experiments show that NicheTime improves trajectory reconstruction, state fidelity, and spatial consistency over existing approaches, highlighting the potential of transport-guided flow matching for learning complex structured dynamics.