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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

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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.

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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.