Stepwise multi-scale reconstruction of cell spatial organization from single-cell RNA sequencing data with Cell2space.

Abstract

Elucidating the spatial organization of cells is fundamental to understanding tissue architecture and function, yet tissue dissociation during single-cell RNA sequencing (scRNA-seq) strips cells of their native architectural context. It is essential to integrate scRNA-seq data with spatial transcriptomics (ST) references to infer the spatial information of individual cells. Despite the development of various tools, achieving precise and biologically relevant spatial reconstructions remains challenging. Here, we present Cell2space, a deep learning framework that integrates scRNA-seq data with ST references to reconstruct multi-scale cellular spatial organization in a stepwise manner. Instead of treating spatial reconstruction as coordinate regression, Cell2space learns a universal spatial affinity function and employs a hierarchical inference strategy that integrates domain-level priors to refine cellular neighborhoods. This approach enables accurate assignment of single cells to spatial domains and inference of cell-cell neighborhood relationships. Comprehensive evaluations demonstrate that Cell2space robustly achieves superior performance in both domain assignment and neighborhood inference. Applied to the mouse visual cortex and human skin, Cell2space identifies layer-specific markers, reveals continuous gene expression gradients, and faithfully reconstructs stratified tissue architecture without requiring prior annotations. Together, Cell2space provides a powerful framework for integrating single-cell and spatial modalities, enabling deeper insights into tissue organization and cellular microenvironments.