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Updated: Jun 14, 2026

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

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Published on: July 6, 2022

SpatioCell: deep integration of histology and spatial transcriptomics for profiling the cellular microenvironment at

Naiqiao Hou1, Yue Yu1, Zhaorun Wu2

  • 1Institute for Developmental and Regenerative Cardiovascular Medicine, MOE-Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China; Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.

Science Bulletin
|June 12, 2026
PubMed
Summary

SpatioCell reconstructs single-cell resolution from spatial transcriptomics data. This computational algorithm reveals microenvironmental features and cell interactions, advancing tissue analysis in diseases like breast cancer.

Keywords:
Artificial intelligenceSingle-cell annotationSpatial transcriptomesTumor microenvironment

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Area of Science:

  • Genomics
  • Computational Biology
  • Biomedical Imaging

Background:

  • Spatial transcriptomics (ST) provides tissue gene expression but often lacks single-cell resolution.
  • Multicellular resolution in ST data limits understanding of cellular spatial organization and interactions.
  • Accurate cell type and expression mapping is crucial for detailed tissue analysis.

Purpose of the Study:

  • To develop a computational algorithm, SpatioCell, for extracting single-cell resolution data from ST.
  • To enable precise spatial reconstruction of cell types and gene expression within tissues.
  • To uncover microenvironmental features and improve understanding of cellular spatial organization.

Main Methods:

  • Developed SpatioCell, a morpho-transcriptomic spatial reconstruction framework.
  • Utilized dynamic programming to integrate morphological and transcriptomic information.
  • Enabled deterministic single-cell spatial reconstruction and deconvolution error correction.

Main Results:

  • SpatioCell accurately assigns cell identities to precise locations in ST data.
  • The algorithm uncovers previously overlooked microenvironmental features.
  • Analysis of triple-negative breast cancer data highlighted the importance of cancer-associated fibroblast proximity to tumor and immune cells.

Conclusions:

  • SpatioCell provides single-cell resolution from ST data, overcoming current limitations.
  • The algorithm facilitates detailed investigations into single-cell spatial organization and tissue microenvironments.
  • SpatioCell broadens the biomedical applications of spatial transcriptomics for disease research.