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

Yonghao Liu ¹, Chuyao Wang ¹, Zhikang Wang ^2,3,4

¹Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.
²Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Center for Integrative Spatial-Omics Research, Fudan University, Shanghai, China.
³Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China.
⁴Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria, Australia.
⁵Department of Computer Science and Technology, College of Mathematics and Computer, Shantou University, Shantou, China.
⁶National Clinical Research Center for Laboratory Medicine, Department of Laboratory Medicine, The First Hospital of China Medical University, Shenyang, China.
⁷Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, China.
⁸Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, The Human Phenome Institute, Zhangjiang-Fudan International Innovation Center, Fudan University, Shanghai, China.
⁹Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China. fengxy@jlu.edu.cn.
¹⁰Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China. guanrenchu@jlu.edu.cn.
¹¹Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Center for Integrative Spatial-Omics Research, Fudan University, Shanghai, China. zhiyuan@fudan.edu.cn.
¹²Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China. zhiyuan@fudan.edu.cn.

⁰Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.

Nature Methods +

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December 17, 2025

View abstract on PubMed

Summary

SpatialEx and SpatialEx+ integrate spatial omics data using histology images. These computational frameworks enable high-parameter multi-omics profiling across tissue sections, enhancing accessibility.

Area Of Science

Computational biology
Genomics
Histopathology

Background

Spatial omics technologies aim for high-parameter, multi-omics coprofiling but face integration challenges.
Serial-section profiling introduces the spatial diagonal integration problem when combining complementary panels.

Purpose Of The Study

To develop computational frameworks (SpatialEx and SpatialEx+) for integrating spatial molecular data across tissue sections.
To leverage histology as a universal anchor for multi-omics data integration in spatial profiling.

Main Methods

SpatialEx utilizes a pretrained hematoxylin and eosin foundation model with hypergraph and contrastive learning to predict single-cell omics from histology.
SpatialEx+ incorporates an omics cycle module for cross-omics consistency via slice-invariant mappings, enabling integration without comeasured data.
The frameworks encode multi-neighborhood spatial dependencies and global tissue context.

Main Results

Demonstrated superior hematoxylin and eosin-to-omics prediction and diagonal integration of panels and omics.
Validated across various biological scenarios, showing robustness with nonoverlapping or heterogeneous sections.
Frameworks scale to over 1 million cells and support unlimited omics layers.

Conclusions

SpatialEx and SpatialEx+ provide a broadly accessible solution for multimodal spatial profiling.
Histology-guided integration overcomes key challenges in serial-section spatial omics.
The developed frameworks facilitate seamless and accurate multi-omics data integration.