Modeling Microenvironment Trajectories on Spatial Transcriptomics with NicheFlow

TL;DR

NicheFlow is a novel flow-based generative model that infers the evolution of cellular microenvironments in tissue over time using spatial transcriptomics data, capturing both global and local tissue dynamics.

Contribution

It introduces NicheFlow, a method that models microenvironment trajectories across spatial slides by integrating optimal transport and flow matching, addressing limitations of single-cell level models.

Findings

Successfully recovers tissue architecture across datasets

Captures local microenvironment changes over time

Applies to embryonic and brain development data

Abstract

Understanding the evolution of cellular microenvironments in spatiotemporal data is essential for deciphering tissue development and disease progression. While experimental techniques like spatial transcriptomics now enable high-resolution mapping of tissue organization across space and time, current methods that model cellular evolution operate at the single-cell level, overlooking the coordinated development of cellular states in a tissue. 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 spatial coordinates using optimal transport and Variational Flow Matching. Our approach successfully recovers both global spatial architecture and local microenvironment composition across diverse spatiotemporal datasets, from embryonic to brain development.