ArticleNature communications2024
METASPACE-ML: Context-specific metabolite annotation for imaging mass spectrometry using machine learning.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
29 citing papers in PubMed.
- Exploring the spatial distribution of biomolecules inBiofilm · 2026Article
- MALDI mass spectrometry imaging across spatial scales in toxicology: tissue evidence, cellular heterogeneity, and emerging single-cell opportunities.Analytical and bioanalytical chemistry · 2026Review
- Review
- Computational Mass Spectrometry Imaging in the Era of AI.Chemical reviews · 2026Review
- An m/z- and Intensity-Based HRMS Clustering Algorithm Targeted toward Single-Cell Metabolomics.Journal of the American Society for Mass Spectrometry · 2026Article
- Trends in Computational Metabolomics: A Perspective on Five Years of Software Development, Challenges, and Opportunities (2021-2025).Analytical chemistry · 2026Review
- PLANCK: super-multiplex optical imaging without labeling.bioRxiv : the preprint server for biology · 2026Article
- A Step-by-Step Protocol From METASPACE to Biological Interpretation.Journal of mass spectrometry : JMS · 2026Article
- Spatial metabolomics: design, pitfalls and data interpretation.The EMBO journal · 2026Article
- Progress of metabolomics-centric multi-omics research in medicine.iMetaOmics · 2026Review
- Achieving Single-Cell Resolution via Desorption Electrospray Ionization Mass Spectrometry Imaging (DESI-MSI) on Different Platforms.Analytical chemistry · 2026Article
- Review
- Spatial metabolomics and multiomics integration for breakthroughs in precision medicine for kidney disease.Nature reviews. Nephrology · 2026Review
- HT SpaceM enables high-throughput mapping of metabolic diversity at the single-cell level.Cell reports methods · 2026Article
- Machine learning spectroscopy to advance computation and analysis.Chemical science · 2025Review
- Review
- Multiomics analysis of the Silkworm cocoon shell.Scientific data · 2025Article
- Single-cell metabolite annotation by tandem mass spectrometry imaging andRSC advances · 2025Article
- Subcellular-Resolution Molecular Pathology by Laser Ablation-Rapid Evaporative Ionization Mass Spectrometry.Analytical chemistry · 2025Article
- Sketching T cell atlases in the single-cell era: challenges and recommendations.Immunology and cell biology · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.