Identifying multicellular spatiotemporal organization of cells with SpaceFlow

TLDR

Spatial transcriptomics must jointly consider transcriptomic similarity and spatial positioning. The study introduces SpaceFlow, a flexible deep learning framework that incorporates spatiotemporal information into spatial transcriptomic analysis. SpaceFlow uses spatially regularized deep graph networks to produce low‑dimensional embeddings that combine expression similarity and spatial proximity, then builds a pseudo‑spatiotemporal map linking pseudotime with spatial coordinates. SpaceFlow robustly segments domains and uncovers biologically meaningful spatiotemporal patterns, including evolving lineages in heart development and tumor‑immune interactions in breast cancer.

Abstract

One major challenge in analyzing spatial transcriptomic datasets is to simultaneously incorporate the cell transcriptome similarity and their spatial locations. Here, we introduce SpaceFlow, which generates spatially-consistent low-dimensional embeddings by incorporating both expression similarity and spatial information using spatially regularized deep graph networks. Based on the embedding, we introduce a pseudo-Spatiotemporal Map that integrates the pseudotime concept with spatial locations of the cells to unravel spatiotemporal patterns of cells. By comparing with multiple existing methods on several spatial transcriptomic datasets at both spot and single-cell resolutions, SpaceFlow is shown to produce a robust domain segmentation and identify biologically meaningful spatiotemporal patterns. Applications of SpaceFlow reveal evolving lineage in heart developmental data and tumor-immune interactions in human breast cancer data. Our study provides a flexible deep learning framework to incorporate spatiotemporal information in analyzing spatial transcriptomic data.