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

Yonghao Liu1, Chuyao Wang1, Zhikang Wang2,3,4

  • 1Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.

Nature Methods
|December 17, 2025
PubMed
Summary
This summary is machine-generated.

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

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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.