Nature methods · 2026
High-parameter spatial multi-omics through histology-anchored integration
Liu Y, Wang C, Wang Z, Chen L, Li Z, Song J, Zou Q, Gao R, Qian BZ, Feng X, Guan R, Yuan Z
Show affiliations
- Liu Y — Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.
- Wang C — Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.
- Wang Z — Center 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.
- Chen L — Department of Computer Science and Technology, College of Mathematics and Computer, Shantou University, Shantou, China.
- Li Z — National Clinical Research Center for Laboratory Medicine, Department of Laboratory Medicine, The First Hospital of China Medical University, Shenyang, China.
- Song J — Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria, Australia.
- Zou Q — Center 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.
- Gao R — Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, China.
- Qian BZ — Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, The Human Phenome Institute, Zhangjiang-Fudan International Innovation Center, Fudan University, Shanghai, China.
- Feng X — Key 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.
- Guan R — Key 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.
- Yuan Z — Center 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.
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.
Most-cited papers citing this
- 2026
- 2026
- Bioinformatics · 2026
- 2026
Log in to like, log, and share thoughts on this paper.
Discussion
Share your thoughts above to start a thread. Constructive feedback only — reply to and like others’ posts.
No discussion yet — be the first to share constructive thoughts.