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Related Concept Videos

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Updated: Jul 30, 2025

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

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Advances in spatial transcriptomics and related data analysis strategies.

Jun Du1, Yu-Chen Yang2, Zhi-Jie An2

  • 1Department of Hematology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, 160 Pujiang Road, Shanghai, 200127, China.

Journal of Translational Medicine
|May 18, 2023
PubMed
Summary

Spatial transcriptomics offers unprecedented insights into tissue heterogeneity and cell interactions within their native environment. This review explores technologies, applications, and computational methods for advancing biological and medical research.

Keywords:
MethodologySpatial transcriptomicsTissue heterogeneity

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Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNA-seq) lacks spatial context.
  • Spatial transcriptomics provides gene expression data in intact tissue sections.
  • Understanding tissue architecture and cell microenvironment interactions is crucial.

Purpose of the Study:

  • To review current spatial transcriptomics technologies.
  • To explore their applications in biological and medical research.
  • To discuss computational strategies and future perspectives.

Main Methods:

  • Review of existing spatial transcriptomics technologies.
  • Analysis of their applications in understanding tissue heterogeneity, histogenesis, and disease pathogenesis.
  • Discussion of in silico data analysis using R and Python packages.

Main Results:

  • Spatial transcriptomics enables gene expression analysis with spatial resolution in physiological context.
  • These technologies reveal tissue architecture and cell-microenvironment interactions.
  • Computational tools are essential for data interpretation and overcoming technological limitations.

Conclusions:

  • Spatial transcriptomics is a rapidly advancing field with significant potential.
  • It offers novel insights into complex biological processes and diseases.
  • Integration of advanced computational strategies will drive future discoveries.