High-parameter spatial multi-omics through histology-anchored integration

Yonghao Liu, Chuyao Wang, Zhikang Wang, Liang Chen, Zhi Li, Jiangning Song, Qi Zou, Rui Gao, Bin-Zhi Qian, Xiaoyue Feng, Renchu Guan, Zhiyuan Yuan

Abstract: Spatial omics face challenges in achieving high-parameter, multi-omics coprofiling. Serial-section profiling of complementary panels mitigates technical trade-offs but introduces the spatial diagonal integration problem. To address this, here we present SpatialEx and its extension SpatialEx+, computational frameworks leveraging histology as a universal anchor to integrate spatial molecular data across tissue sections. SpatialEx combines a pretrained hematoxylin and eosin foundation model with hypergraph and contrastive learning to predict single-cell omics from histology, encoding multi-neighborhood spatial dependencies and global tissue context. SpatialEx+ further introduces an omics cycle module that encourages cross-omics consistency via slice-invariant mappings, enabling seamless integration without comeasured training data. Extensive validations show superior hematoxylin and eosin-to-omics prediction, panel diagonal integration and omics diagonal integration across various biological scenarios. The frameworks scale to datasets exceeding 1 million cells, maintain robustness with nonoverlapping or heterogeneous sections and support unlimited omics layers in principle. Our work makes multimodal spatial profiling broadly accessible. The SpatialEx(+) framework uses histology to generate and integrate complementary spatial multi-omics data across samples.

External IDs:doi:10.1038/s41592-025-02926-6