SpateCV: cross-modality alignment regularization of cell types improves spatial gene imputation for spatial transcriptomics

Abstract Background The integration of single-cell RNA sequencing (scRNA-seq) and high-resolution spatial transcriptomics (ST) could improve our understanding of both tissue architecture and cellular heterogeneity simultaneously. The key to accomplishing this goal mainly relies on effectively co-embedding similar cells with consistent representations from the two types of data. Methods In this paper, we construct a conditional variational autoencoder (CVAE) architecture, named SpateCV, to explicitly regularize the embedding alignment of similar cells from scRNA-seq and ST data in a shared latent through a clustering loss. Results Benchmark results across twelve datasets demonstrate that SpateCV achieves superior performance in spatial gene imputation and spatial patterns reconstruction. Critically, SpateCV translates this technical accuracy into biological insight. With the imputed genome-wide expression, our method enables the identification of novel spatially differentially expressed genes, such as the astrocyte marker Hepacam, and facilitates the inference of layer-specific intercellular communication networks, identifying corpus callosum cells as key signaling hubs in the mouse visual cortex. Additionally, SpateCV enables the in silico spatial mapping of neuronal subtypes by integrating spatial context into scRNA-seq data. Conclusion SpateCV provides a robust framework for extracting biological knowledge from multimodal spatial-omics data.