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Sponsored Artificial Intelligence: A Modern Approach, Global Edition Artificial Intelligence: A Modern Approach, Global Edition

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Xiangting Jia

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Open access Jul 2026

Deciphering spatial heterogeneity by multimodal spatial transcriptomics modelling with SpatialModal

Abstract Motivation Advances in spatial transcriptomics (ST) technologies have made it possible to jointly acquire gene expression and histological image information while preserving spatial coordinates. This breakthrough presents unprecedented opportunities for the precise dissection of spatial heterogeneity in complex tissues. However, existing computational methods remain limited in their capacity for effective integration and synergistic modelling of multimodal ST data. Results We propose SpatialModal, a multimodal graph learning framework that learns robust joint representations by combining a hierarchical representation strategy with a dual-level contrastive learning mechanism. We perform extensive validation of SpatialModal across diverse ST datasets spanning human and mouse tissues. The results demonstrate that SpatialModal effectively reveals intricate brain architectures in humans and mice, dissects tumour microenvironment heterogeneity in breast cancer, delineates Alzheimer’s disease patterns, and characterizes spatiotemporal developmental trajectories within the embryonic heart, underscoring its capability to decipher the spatial heterogeneity of biological tissues. Furthermore, SpatialModal exhibits remarkable versatility and robustness, maintaining superior efficacy even on unimodal datasets devoid of histological images, thereby ensuring its broad applicability across diverse ST platforms. Availability and Implementation SpatialModal is implemented in Python and is freely available at https://github.com/xingyili/SpatialModal. The source code used in this study has been archived on Zenodo at DOI: https://doi.org/10.5281/zenodo.21264356. All datasets used in this study are publicly available at https://doi.org/10.5281/zenodo.18220735.

Xingyi Li, Dongmin Zhao, Xiangting Jia et al. · 0 citations
Open access Jul 2026

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.

Xingyi Li, Xiangting Jia, Dongmin Zhao et al. · 1 citation