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Updated: Jun 11, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Spatial Transcriptomics of the Respiratory System.

Stathis Megas1, Anna Wilbrey-Clark1, Aidan Maartens1

  • 1Wellcome Sanger Institute, Hinxton, Cambridge, United Kingdom.

Annual Review of Physiology
|October 1, 2024
PubMed
Summary
This summary is machine-generated.

Single-cell genomics reveals lung cell diversity. New spatial technologies and AI analyze cell interactions in 3D, transforming respiratory system research in health and disease.

Keywords:
bioinformaticsgenomicslungmachine learningrespiratory systemspatial transcriptomics

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Area of Science:

  • Pulmonary Medicine
  • Genomics
  • Computational Biology

Background:

  • Single-cell genomics has uncovered significant heterogeneity and plasticity in lung and airway cell types over the past decade.
  • Understanding the spatial interactions of these diverse cell types is crucial for comprehending lung function, including airflow, gas exchange, and barrier defense against infection.

Purpose of the Study:

  • To review current spatial analysis workflows for gene expression data in the respiratory system.
  • To highlight advancements in machine learning and artificial intelligence for interpreting spatial omics data.
  • To showcase the application of spatially resolved transcriptomics in understanding lung development, COVID-19, lung cancer, and fibrosis.

Main Methods:

  • Review of commonly used spatial analysis workflows for gene expression data.
  • Discussion of novel spatially resolved gene expression technologies.
  • Outline of recent developments in machine learning (ML) and artificial intelligence (AI) for spatial data interpretation.

Main Results:

  • Spatially resolved transcriptomics is providing novel insights into lung development, COVID-19, lung cancer, and fibrosis.
  • Emerging technologies and computational tools are enabling the analysis of cell interactions in three-dimensional space within the respiratory system.
  • Machine learning and AI are augmenting the interpretation of complex spatial gene expression data.

Conclusions:

  • Spatially resolved transcriptomics, powered by advanced computational tools, holds the potential to revolutionize our understanding of the respiratory system.
  • These technologies are crucial for deciphering the intricate spatial organization and interactions of lung cells in both health and disease states.
  • The integration of spatial genomics and AI promises significant advancements in respiratory medicine and disease research.