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Updated: Oct 6, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Computational elucidation of spatial gene expression variation from spatially resolved transcriptomics data.

Ke Li1, Congcong Yan1, Chenghao Li1

  • 1School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P. R. China.

Molecular Therapy. Nucleic Acids
|January 17, 2022
PubMed
Summary
This summary is machine-generated.

Spatially resolved transcriptomics (SRT) offers new insights into tissues. This study surveys computational tools for spatial expression (SE) analysis in SRT, aiding researchers in finding resources and methods.

Keywords:
SE analysissingle-cell RNA sequencingspatially resolved transcriptomesspatially variable genes

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Spatially resolved transcriptomics (SRT) provides in situ tissue and organ insights.
  • Spatial expression (SE) analysis is crucial for understanding SRT data.
  • A need exists for accessible public repositories and computational techniques for SE analysis.

Purpose of the Study:

  • To survey and summarize public resources for SE analysis in SRT.
  • To provide an overview of state-of-the-art computational approaches and tools for SE analysis.
  • To explore future perspectives and challenges of in silico techniques in SE analysis.

Main Methods:

  • Systematic literature review of computational approaches for SE analysis.
  • Cataloging and summarizing public resources and tools.
  • Analysis of recent advances and future trends in the field.

Main Results:

  • A comprehensive survey of public resources for SE analysis in SRT is presented.
  • An updated overview of current computational tools and methods for SE analysis is provided.
  • Key challenges and future directions for in silico SE analysis are identified.

Conclusions:

  • This study guides medical and life scientists to dedicated resources and competent tools for spatial gene expression analysis.
  • The findings facilitate better utilization of SRT data through improved computational methods.
  • Addressing the identified challenges will advance the field of spatial transcriptomics analysis.