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Related Concept Videos

Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Untargeted Spatial Metabolomics and Spatial Proteomics on the Same Tissue Section.

Gregory W Vandergrift1, Marija Veličković1, Le Z Day1

  • 1Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.

Analytical Chemistry
|December 21, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel spatial multiomic workflow using desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) for simultaneous spatial metabolomics and proteomics on a single tissue section, enabling integrated molecular insights.

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Area of Science:

  • Biochemistry
  • Proteomics
  • Metabolomics

Background:

  • Existing spatial multiomic workflows often require serial sections or have incompatible substrate needs.
  • Desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) offers a potential solution for on-tissue metabolite profiling.

Purpose of the Study:

  • To develop and validate a novel, integrated spatial multiomic workflow.
  • To enable simultaneous spatial metabolomics and proteomics from the same tissue section.

Main Methods:

  • Utilized DESI-MSI for on-tissue spatial metabolomics on a rat brain section.
  • Employed METASPACE for metabolite annotation and generated a segmentation map.
  • Performed downstream spatial proteomics on selected regions of interest (ROIs) from the same tissue section.

Main Results:

  • Identified 160 metabolite annotations with DESI-MSI (≤20% FDR).
  • Quantified 3888–4717 proteins per ROI (200 μm × 200 μm) using spatial proteomics.
  • Demonstrated correlation between ceramide localization and SMPD3 protein abundance.
  • Showcased protein abundance resolving metabolite isomeric ambiguity.

Conclusions:

  • The integrated DESI-MSI workflow allows for complementary spatial and molecular information from a single tissue section.
  • This approach optimizes spatial proteomics assays by minimizing sample disruption and substrate limitations.
  • The workflow facilitates deeper understanding of tissue molecular architecture and function.