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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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Related Experiment Video

Updated: Mar 25, 2026

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Co-localization analysis of spatial transcriptomics in ligand-receptor pairs from tumor microenvironment.

Xiaoxuan Fan1, Lixia Yue2, Yabin Gong3

  • 1School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200032, China.

Critical Reviews in Oncology/Hematology
|March 23, 2026
PubMed
Summary

Co-localization analysis integrates spatial transcriptomics to accurately identify cell-cell interactions and ligand-receptor pairs. This approach enhances disease target discovery and advances precision medicine.

Keywords:
Cell-cell interactionCo-localizationLigand-receptor pairsSpatial transcriptomicsTumor microenvironment

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Area of Science:

  • Cellular biology
  • Bioinformatics
  • Genomics

Background:

  • Cell-to-cell communication is crucial for biological processes.
  • Current methods for analyzing cell interactions often lack spatial context, leading to inaccuracies.
  • Spatial transcriptomics offers a solution by combining gene expression with cellular location.

Purpose of the Study:

  • To review data types and computational methods for co-localization analysis.
  • To explore applications of co-localization analysis in identifying ligand-receptor pairs, particularly in the tumor microenvironment.
  • To discuss the future of co-localization analysis in therapeutic target discovery and precision medicine.

Main Methods:

  • Systematic literature search on co-localization analysis and spatial transcriptomics.
  • Analysis of computational approaches for integrating spatial data with gene expression.
  • Review of case studies focusing on ligand-receptor interactions in the tumor microenvironment.

Main Results:

  • Co-localization analysis significantly improves the accuracy of ligand-receptor pair identification by incorporating spatial information.
  • Spatial transcriptomics-based co-localization is effective for mapping cell-cell interactions within complex tissues like tumors.
  • The integration of artificial intelligence further refines the accuracy of these analyses.

Conclusions:

  • Co-localization analysis is a powerful tool for uncovering novel ligand-receptor pairs and disease targets.
  • This approach facilitates the development of precision medicine strategies.
  • Future applications include enhanced therapeutic target discovery and personalized treatment development.