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Updated: Sep 13, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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A Meta-Review of Spatial Transcriptomics Analysis Software.

Jessica Gillespie1,2, Maciej Pietrzak1, Min-Ae Song3

  • 1Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.

Cells
|July 25, 2025
PubMed
Summary
This summary is machine-generated.

Choosing spatial transcriptomics software is complex. This review details key metrics for selecting tools for RNA localization, tissue architecture, and cell communication analysis, aiding researchers in spatial transcriptomics studies.

Keywords:
benchmarkingcell–cell communicationdeconvolutionspatial transcriptomicsspatially variable genetissue architecture identification

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Spatial transcriptomics integrates gene expression with spatial data for RNA localization, developmental studies, tumor microenvironment analysis, and tissue atlasing.
  • Numerous spatial transcriptomics software tools exist, but guidance on selecting appropriate software for specific datasets and computational environments is limited.

Purpose of the Study:

  • To review and detail essential metrics for selecting suitable software for spatial transcriptomics analysis.
  • To provide guidance for researchers navigating the diverse landscape of spatial transcriptomics software.

Main Methods:

  • A comprehensive review of benchmarking studies comparing spatial transcriptomics software was conducted.
  • Software performance was evaluated across four critical analysis areas: tissue architecture identification, spatially variable gene discovery, cell-cell communication analysis, and deconvolution.

Main Results:

  • Key metrics for evaluating software performance in tissue architecture identification were identified.
  • Effective methods for assessing spatially variable gene discovery tools were detailed.
  • Approaches for comparing cell-cell communication analysis software were outlined.
  • Criteria for selecting deconvolution tools in spatial transcriptomics were established.

Conclusions:

  • This review consolidates benchmarking results to offer practical guidance for choosing spatial transcriptomics software.
  • Researchers can utilize these metrics to make informed decisions based on their specific analytical needs and computational resources.
  • The findings aim to streamline the selection process for spatial transcriptomics analysis tools, enhancing research efficiency.