ArticleACS measurement science au2026
Dark Reactions in Microdroplets Explain Widespread Artifacts in Metabolomic Profiling.
Article in ACS measurement science au, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Transformation-Aware Molecular Networking for Interpretation of Untargeted LC-HRMS Data.ACS measurement science au · 2026Article
- Untargeted metabolomics suggests a trade-off between phenolamides and flavonoids in the pollen of Petunia hybrida with contrasting flower colours.BMC plant biology · 2026Article
- Alkamines reveal a hidden layer of steroid and drug metabolism.bioRxiv : the preprint server for biology · 2026Article
- Pan-Metabolomics Repository Mapping of the Carnitine Landscape.bioRxiv : the preprint server for biology · 2026Article
- Navigating the conjugated metabolome.bioRxiv : the preprint server for biology · 2026Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reliable ion annotation and identification remain persistent challenges in mass-spectrometry-based untargeted metabolomics. Here, we elucidate the identities, sources, and formation mechanisms of numerous previously unexplained ions by showing that microdroplets formed during electrospray ionization can promote a wide array of chemical transformations. These include redox, addition, condensation, reductive amination, decarboxylative coupling, and radical reactions, many of which are facilitated at the gas-aqueous interface by reactive oxygen and nitrogen species. Activation of metabolite chemical bonds generates cations, anions, and radical intermediates through the loss of protons, electrons, or functional groups, ultimately leading to bond formation and the generation of artifactual ions or false positive ions. These artifacts are frequently misassigned as endogenous metabolites and account for hundreds of thousands of previously unidentified features. As an example, we show that in a recently published untargeted metabolomics analysis of 1969 ions, the annotation rate was substantially improved to over 50% from the previous value of 9%, showing the importance of this new form of identification. Finally, we describe practical strategies to minimize artifactual ion formation, thereby improving the reliability of metabolomic analyses.
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Registered trials
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