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Inference of cell-type composition and single-cell spatial maps from spatial transcriptomics data with SWOT.

Lanying Wang1, Yuxuan Hu1, Lin Gao2

  • 1School of Computer Science and Technology, Xidian University, Xi'an, China.

Communications Biology
|November 19, 2025
PubMed
Summary

SWOT is a new method that maps cells to spots, enabling single-cell spatial maps from spatial transcriptomics data. This tool reconstructs cellular neighborhoods and tissue architecture at single-cell resolution.

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Area of Science:

  • Single-cell transcriptomics
  • Spatial transcriptomics
  • Computational biology

Background:

  • Spatial transcriptomics data often lacks single-cell resolution.
  • Existing cell-type deconvolution methods estimate proportions but not individual cell locations.
  • Reconstructing single-cell spatial maps is essential for understanding tissue architecture.

Purpose of the Study:

  • To develop a method for inferring single-cell spatial maps from spot-based spatial transcriptomics data.
  • To overcome the resolution limitations of current spatial transcriptomics analysis.
  • To enable cell-level discoveries within tissues.

Main Methods:

  • Introduced a spatially weighted optimal transport (SWOT) method.
  • Developed a cell-to-spot mapping approach.
  • Applied SWOT to infer cell-type composition and single-cell spatial maps.

Main Results:

  • SWOT accurately estimates cell-type proportions and cell numbers per spot.
  • The method precisely determines spatial coordinates for individual cells.
  • SWOT visualizes cell-type spatial distributions and maps single cells in tissues.
  • SWOT aids in identifying and annotating cellular neighborhoods.

Conclusions:

  • SWOT transforms spot-resolution spatial transcriptomics data into single-cell resolution.
  • This method facilitates detailed analysis of tissue architecture and cellular organization.
  • SWOT is a valuable tool for advancing cell-level discoveries in spatial biology.