Geometric-aware deep learning for deciphering tissue structure from spatially resolved transcriptomics

Recent advances in spatially resolved transcriptomics have enabled large-scale measurement of gene expression while preserving spatial context, facilitating the investigation of spatial heterogeneity within tissues. In this study, we propose SpatialGEO, a geometric-aware deep learning framework that integrates gene expression profiles with spatial coordinates to generate biologically meaningful low-dimensional embeddings, enabling the dissection of complex tissue architectures. We systematically evaluate SpatialGEO across multiple tissue types and diverse SRT platforms. Results show that SpatialGEO achieves superior performance in tissue structure dissection and data denoising compared to state-of-the-art methods. Moreover, when applied to human breast cancer samples, SpatialGEO precisely delineates the tumor microenvironment and uncovers molecular heterogeneity within tumors and intercellular communication between invasive ductal carcinoma and tumor edge. In mouse embryogenesis, SpatialGEO accurately reconstructs spatiotemporal tissue architectures, highlighting organ-specific developmental programs and elucidating molecular drivers of early neural development.