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Related Experiment Video

Updated: May 14, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Integrating Ambient Ionization Mass Spectrometry Imaging and Spatial Transcriptomics on the Same Cancer Tissues to

Trevor M Godfrey1, Yasmin Shanneik1, Wanqiu Zhang2

  • 1Department of Surgery, Baylor College of Medicine, Houston, TX, 77030, USA.

Angewandte Chemie (International Ed. in English)
|April 11, 2025
PubMed
Summary

This study introduces a new method integrating spatial metabolomics and spatial transcriptomics on the same tissue section. This combined approach reveals novel correlations between metabolites and mRNA transcripts in cancer tissues.

Keywords:
Cancer researchDisease markersMass spectrometry imagingSpatial transcriptomicsTissue imaging

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Area of Science:

  • Biotechnology
  • Molecular Biology
  • Cancer Research

Background:

  • Spatial omics technologies like spatial transcriptomics (ST) and mass spectrometry imaging (MSI) have advanced disease research, particularly in cancer.
  • These powerful techniques, however, analyze distinct molecular classes (RNA, metabolites) independently.
  • Integrating these spatial omics datasets is challenging due to section-to-section variability when analyzing adjacent tissue slices.

Purpose of the Study:

  • To develop and validate a novel workflow for simultaneous spatial metabolomics and spatial transcriptomics on identical tissue sections.
  • To assess the compatibility of desorption electrospray ionization mass spectrometry imaging (DESI-MSI) with subsequent Visium spatial transcriptomics.
  • To explore integrated molecular insights and identify novel correlations between metabolites and mRNA in human cancer tissues.

Main Methods:

  • Combined desorption electrospray ionization mass spectrometry imaging (DESI-MSI) for spatial metabolomics and Visium spatial transcriptomics on the same tissue sections.
  • Evaluated RNA integrity and spatial transcriptomics data quality after DESI-MSI analysis under ambient conditions.
  • Applied the integrated workflow to human breast and lung cancer tissue samples.

Main Results:

  • Demonstrated that DESI-MSI does not compromise RNA quality or subsequent spatial transcriptomics data.
  • Successfully generated integrated spatial metabolomics and transcriptomics data from single tissue sections.
  • Identified previously unknown correlations between specific metabolites and mRNA transcripts within distinct cancer-associated regions.

Conclusions:

  • The presented workflow enables the integration of spatial metabolomics and transcriptomics on identical tissue sections, overcoming previous limitations.
  • This integrated approach preserves molecular data integrity, allowing for more holistic biological insights.
  • The findings highlight the potential for discovering novel molecular relationships in cancer biology through multi-omics spatial analysis.