ArticleNature methods2026
High-parameter spatial multi-omics through histology-anchored integration.
Article in Nature methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Computational Mass Spectrometry Imaging in the Era of AI.Chemical reviews · 2026Review
- Sequential MALDI and Nano-DESI MS Imaging of Lipids and Proteins on Stretched Tissue Samples.Analytical chemistry · 2026Article
- Spotlight on challenges and novel methods in highly multiplexed tissue imaging-based spatial proteomics.Journal of translational medicine · 2026Review
- Histopathology-centered computational evolution of spatial omics: integration, mapping, and foundation models.Briefings in bioinformatics · 2026Review
- PromptSTG: prototype-guided prompting for few-shot spatial transcriptomics annotation.Briefings in bioinformatics · 2026Article
- A masked generative graph representation learning framework empowering precise spatial domain identification.Bioinformatics (Oxford, England) · 2026Article
- MultiSP deciphers tissue structure and multicellular communication from spatial multi-omics data.Cell genomics · 2026Article
- Graph designs for deep learning-based multi-omics integration.Briefings in bioinformatics · 2026Review
- A comprehensive survey of computer vision methods for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- MIMIC: a flexible pipeline to register and summarize IMC-MSI experiments.Communications biology · 2026Article
- miniMTI: minimal multiplex tissue imaging enhances biomarker expression prediction from histology.Research square · 2026Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.