SpatioCell: deep integration of histology and spatial transcriptomics for profiling the cellular microenvironment at single-cell level.

Spatial transcriptomics (ST) is a powerful assay for capturing gene expression in tissues. However, due to inherent limitations of spatial resolution, most existing ST datasets remain at a multicellular resolution, which hinders a comprehensive understanding of spatial organization. We propose SpatioCell, a computational algorithm to automatically extract both cell type and expression information at single-cell resolution from ST data, through a morpho-transcriptomic spatial reconstruction framework solved via dynamic programming, integrating morphological and transcriptomic information. This framework enables deterministic single-cell spatial reconstruction, assigning cell identities to precise locations rather than inferring the spot-level cell type composition and expression. It further uncovers overlooked microenvironmental features while correcting deconvolution errors. Using ST data from triple-negative breast cancer as an example, SpatioCell reveals the significance of the distance between cancer-associated fibroblasts (CAFs) and tumor or immune cells in terms of disease progression. Thus, the establishment of SpatioCell will broaden the biomedical applications of ST and facilitate investigations of single-cell spatial organization.