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Expansion omics: from expansion microscopy to spatial omics.

Zhen Dong1,2,3, Weirong Xiang4,5,6, Wenhao Jiang4,5,6

  • 1Affiliated Hangzhou First People's Hospital, State Key Laboratory of Medical Proteomics, School of Medicine, Westlake University, Hangzhou, Zhejiang Province, China. dongzhen@westlake.edu.cn.

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|December 1, 2025
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Summary
This summary is machine-generated.

Expansion omics (ExO) uses physical tissue expansion for high-resolution spatial omics profiling. This technology enables precise biomolecule detection and mapping across multiple omics layers, advancing spatial biology and medicine.

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Area of Science:

  • Spatial biology
  • Molecular imaging
  • Omics technologies

Background:

  • Tissue expansion (ExO) is a technique initially for super-resolution imaging.
  • ExO has evolved into a foundational method for spatially resolved omics profiling.
  • It enables precise biomolecule detection and links them to biological functions.

Purpose of the Study:

  • To explore how expansion omics integrates multi-omics data.
  • To highlight the role of chemical anchoring strategies in ExO.
  • To discuss the advancements in spatial resolution achieved by ExO.

Main Methods:

  • Utilizing physical tissue expansion for enhanced spatial resolution.
  • Employing chemical anchoring strategies for selective molecular retention.
  • Applying ExO across diverse spatial omics modalities like epigenomics, transcriptomics, proteomics, and lipidomics.

Main Results:

  • Achieved subcellular resolution for omics profiling and sub-nanometer scale for imaging.
  • Enabled high-resolution mapping of chromatin states, gene expression, protein localization, and lipid distributions.
  • Facilitated spatial multi-omics approaches for correlating multiple biomolecular dimensions.

Conclusions:

  • Expansion omics is a core technology for whole-slide, single-cell multi-omics.
  • ExO integrates super-resolution imaging with spatial omics for advanced biological insights.
  • Further development is needed to address challenges in resolution, molecular retention, and data analysis for the Artificial Intelligence Virtual Cell.