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Related Experiment Video

Updated: May 22, 2026

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

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

Published on: July 6, 2022

Protocol for spatially resolved pathology scores using optimal transport on spatial transcriptomics data.

Mohammad Nuwaisir Rahman1, James F Martin2, M Saifur Rahman3

  • 1Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh; Computer Science and Engineering, BRAC University, Dhaka 1212, Bangladesh; Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore.

STAR Protocols
|May 20, 2026
PubMed
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This summary is machine-generated.

We developed SPaSE, a novel spatial transcriptomics protocol, to quantify tissue pathology by comparing diseased and healthy tissue gene expression. This method links gene activity to pathological impact, aiding disease research.

Area of Science:

  • Molecular Biology
  • Genomics
  • Computational Biology

Background:

  • Spatial transcriptomics (ST) offers insights into tissue architecture and cellular functions.
  • Understanding tissue pathology requires integrating gene expression data with spatial information.
  • Current methods for quantifying pathology in ST data are limited.

Purpose of the Study:

  • To introduce SPaSE (spatially resolved pathology score), a new protocol for quantifying pathology in spatial transcriptomics data.
  • To develop a computational framework for assessing pathological impact at the spot level within tissues.
  • To establish a link between gene expression patterns and pathological features using ST.

Main Methods:

  • SPaSE utilizes optimal transport to compare gene expression profiles between diseased and healthy spatial transcriptomics spots.
Keywords:
BioinformaticsCancerCell BiologyComputer sciencesGenomicsSystems biology

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Last Updated: May 22, 2026

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  • Pathology scores are assigned to individual ST spots based on the computed optimal transport plan.
  • Support vector regression models are employed to correlate gene expression with pathological impact.

    • Main Results:

    • The SPaSE protocol successfully quantifies pathology across diverse human and mouse ST datasets.
    • SPaSE provides a spatially resolved measure of pathological changes.
    • The method demonstrates the utility of integrating spatial context with gene expression for pathology assessment.

    Conclusions:

    • SPaSE is an effective protocol for quantifying tissue pathology using spatial transcriptomics.
    • This approach enhances the analysis of disease mechanisms by linking gene expression to spatial pathological features.
    • SPaSE offers a valuable tool for researchers studying tissue pathology and disease progression.