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A Perspective on Unintentional Fragments and Their Impact on the Dark Metabolome, Untargeted Profiling, Molecular Networking, Public Data, and Repository Scale Analysis

Yasin El Abiead, Ipsita Mohanty, Shipei Xing, Adriano Rutz, Vincent Charron-Lamoureux, Tito Damiani, Wenyun Lu, Gary J. Patti, Nicola Zamboni, Óscar Yanes, Pieter C. Dorrestein

2025JACS Au10 citationsDOIOpen Access PDF

Abstract

In/postsource fragments (ISFs) arise during electrospray ionization or ion transfer in mass spectrometry when molecular bonds break, generating ions that can complicate data interpretation. Although ISFs have been recognized for decades, their contribution to untargeted metabolomicsparticularly in the context of the so-called "dark matter" (unannotated MS or MS/MS spectra) and the "dark metabolome" (unannotated molecules)remains unsettled. This ongoing debate reflects a central tension: while some caution against overinterpreting unidentified signals lacking biological evidence, others argue that dismissing them too quickly risks overlooking genuine molecular discoveries. These discussions also raise a deeper question: what exactly should be considered part of the metabolome? As metabolomics advances toward large-scale data mining and high-throughput computational analysis, resolving these conceptual and methodological ambiguities has become essential. In this perspective, we propose a refined definition of the "dark metabolome" and present a systematic overview of ISFs and related ion forms, including adducts and multimers. We examine their impact on metabolite annotation, experimental design, statistical analysis, computational workflows, and repository-scale data mining. Finally, we provide practical recommendationsincluding a set of dos and do nots for researchers and reviewersand discuss the broader implications of ISFs for how the field explores unknown molecular space. By embracing a more nuanced understanding of ISFs, metabolomics can achieve greater rigor, reduce misinterpretation, and unlock new opportunities for discovery.

Topics & Concepts

Context (archaeology)Data scienceSet (abstract data type)Perspective (graphical)Field (mathematics)Computer scienceChemistryMetabolomicsScale (ratio)Computational biologyData sharingElectrospray ionizationGenomicsData miningComponent (thermodynamics)Conceptual frameworkBiochemical engineeringNanotechnologyManagement scienceAssociation (psychology)Metabolomics and Mass Spectrometry StudiesMass Spectrometry Techniques and ApplicationsAdvanced Proteomics Techniques and Applications
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