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Related Experiment Video

Updated: Jul 5, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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A guidebook of spatial transcriptomic technologies, data resources and analysis approaches.

Liangchen Yue1, Feng Liu2, Jiongsong Hu3

  • 1Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China.

Computational and Structural Biotechnology Journal
|January 12, 2024
PubMed
Summary
This summary is machine-generated.

Spatial transcriptomics offers unbiased access to gene expression and spatial information within tissues. This review guides technology selection, data integration, and computational analysis for spatial transcriptomics research.

Keywords:
Spatial transcriptomic technologies

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Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • Transcriptomic technologies have advanced the study of cellular gene expression and disease.
  • Traditional bulk and single-cell RNA sequencing lack spatial context within tissue microenvironments.

Purpose of the Study:

  • To provide a comprehensive overview of spatial transcriptomic technologies.
  • To guide researchers in selecting appropriate technologies and integrating complex datasets.
  • To discuss computational approaches for analyzing spatial transcriptomic data and its applications, particularly in tumor tissues.

Main Methods:

  • Review and elaboration of various spatial transcriptomic technologies.
  • Discussion of computational methods for spatial transcriptomic data analysis.
  • Exploration of spatial multimodal omics and its potential in tumor research.

Main Results:

  • Spatial transcriptomics enables bias-free access to both transcriptional and spatial information.
  • Various computational approaches are available for analyzing spatial transcriptomic data for diverse research objectives.
  • Spatial multimodal omics shows promise for applications in tumor tissue analysis.

Conclusions:

  • Spatial transcriptomics is a powerful tool for understanding tissue at a molecular and spatial level.
  • This review provides a guide for technology selection, data integration, and analysis.
  • Future research directions and potential applications in various biological fields, including oncology, are discussed.