METASPACE-ML: Context-specific metabolite annotation for imaging mass spectrometry using machine learning

Bishoy Wadie, Lachlan Stuart, Christopher M. Rath, Bernhard Drotleff, Sergii Mamedov and Theodore Alexandrov ()
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Bishoy Wadie: European Molecular Biology Laboratory (EMBL)
Lachlan Stuart: European Molecular Biology Laboratory (EMBL)
Christopher M. Rath: European Molecular Biology Laboratory (EMBL)
Bernhard Drotleff: EMBL
Sergii Mamedov: European Molecular Biology Laboratory (EMBL)
Theodore Alexandrov: European Molecular Biology Laboratory (EMBL)

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: Abstract Imaging mass spectrometry is a powerful technology enabling spatial metabolomics, yet metabolites can be assigned only to a fraction of the data generated. METASPACE-ML is a machine learning-based approach addressing this challenge which incorporates new scores and computationally-efficient False Discovery Rate estimation. For training and evaluation, we use a comprehensive set of 1710 datasets from 159 researchers from 47 labs encompassing both animal and plant-based datasets representing multiple spatial metabolomics contexts derived from the METASPACE knowledge base. Here we show that, METASPACE-ML outperforms its rule-based predecessor, exhibiting higher precision, increased throughput, and enhanced capability in identifying low-intensity and biologically-relevant metabolites.

Date: 2024
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DOI: 10.1038/s41467-024-52213-9

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