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Updated: May 2, 2026

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Reference-Informed Spatial Domain Detection Using Weak Supervision for Spatial Transcriptomics.

Xin Ma1,2, Weijia Jin1, Qing Lu1

  • 1Department of Biostatistics College of Public Health and Health Professions & College of Medicine, University of Florida, Gainesville, FL, USA.

Biorxiv : the Preprint Server for Biology
|September 26, 2025
PubMed
Summary
This summary is machine-generated.

GraphScrDom, a new model, accurately segments tissues in spatial transcriptomics (ST) studies using limited manual annotations and gene expression data. It offers a user-friendly toolkit for advanced spatial domain analysis.

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Area of Science:

  • Spatial transcriptomics
  • Computational biology
  • Bioinformatics

Background:

  • Spatial transcriptomics (ST) enables mapping tissue organization and function.
  • Accurate tissue segmentation is crucial for ST data analysis.
  • Existing methods often require extensive manual annotation or lack generalizability.

Purpose of the Study:

  • To develop a novel, reference-informed, weakly supervised contrastive learning model for tissue segmentation in ST studies.
  • To integrate manual annotations (scribbles) with gene expression profiles for improved segmentation accuracy.
  • To provide a user-friendly software toolkit for spatial domain detection.

Main Methods:

  • Introduced GraphScrDom, a contrastive learning model.
  • Integrated expert-provided scribbles on spatial grids/histology images.
  • Utilized cell type-specific gene expression profiles from reference single-cell RNA-seq data.
  • Developed an integrative software toolkit with annotation and training modules.

Main Results:

  • GraphScrDom achieved superior performance in tissue segmentation across various ST platforms and resolutions (bulk and single-cell).
  • The model demonstrated strong generalizability and robustness, outperforming existing methods with limited annotations.
  • Performance was validated using six widely adopted metrics.

Conclusions:

  • GraphScrDom offers a robust and efficient solution for spatial domain detection in ST data.
  • The developed toolkit facilitates user-friendly spatial domain analysis.
  • This approach enhances the mapping of complex tissue organization and function.