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A Step-by-Step Protocol From METASPACE to Biological Interpretation
Abigail Moreno-Pedraza1, Brittney Gorman1, Marija Velickovic1
1Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA.
Journal of Mass Spectrometry : JMS
|June 11, 2026
Summary
This tutorial introduces an R-pipeline to analyze mass spectrometry imaging (MSI) data using the METASPACE platform. It helps researchers extract biological insights from complex untargeted metabolomic datasets.
Area of Science:
- Molecular imaging
- Metabolomics
- Bioinformatics
Background:
- Mass spectrometry imaging (MSI) offers spatial molecular analysis of biological systems.
- Large MSI datasets pose challenges for extracting biological insights.
- The METASPACE platform facilitates MSI data processing and interpretation using machine learning.
Purpose of the Study:
- To present a user-friendly R-pipeline for METASPACE users.
- To guide the navigation of untargeted metabolomic annotations.
- To translate MSI data into practical biological insights for complex systems.
Main Methods:
- Development of a detailed R-pipeline.
- Leveraging the METASPACE cloud-based platform.
- Focus on untargeted metabolomic annotation within MSI data.
Main Results:
- The pipeline simplifies the analysis of complex MSI datasets.
- Facilitates the translation of molecular data into biological understanding.
- Demonstrates a practical approach for MSI data interpretation.
Conclusions:
- The R-pipeline enhances the utility of METASPACE for MSI data analysis.
- This approach supports diagnostics, drug discovery, and environmental research.
- The pipeline is valuable for both new and experienced MSI researchers.
