Modeling Microenvironment Trajectories on Spatial Transcriptomics with NicheFlow

Understanding the evolution of cellular microenvironments is essential for deciphering tissue development and disease progression. While spatial transcriptomics now enables high-resolution mapping of tissue organization across space and time, current techniques that analyze cellular evolution operate at the single-cell level, overlooking critical spatial relationships. We introduce NicheFlow, a flow-based generative model that infers the temporal trajectory of cellular microenvironments across sequential spatial slides. By representing local cell neighborhoods as point clouds, NicheFlow jointly models the evolution of cell states and coordinates using optimal transport and Variational Flow Matching. Our approach successfully recovers both global spatial architecture and local microenvironment composition across diverse spatio-temporal datasets, from embryonic to brain development.