Deciphering spatial heterogeneity by multimodal spatial transcriptomics modelling with SpatialModal

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ID: 321907
2026
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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 The source code used in this study has been archived on Zenodo at DOI: https://doi.org/10.5281/zenodo.21264356.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.
Reference Key
openalex_W7170084557 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors X X Li, Dongmin Zhao, Xiangting Jia, Gaoming Du, Jialuo Xu, Yang Qi, Yiqi Chen, Yingfu Wu, Jia Gu, Junnan Zhu, Xuequn Shang
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag540
URL
https://doi.org/10.1093/bioinformatics/btag540
Keywords Keywords not found

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