{"doi":"10.1002/ansa.70012","title":"The Dark Metabolome/Lipidome and In‐Source Fragmentation","abstract":"To the editor, Tandem mass spectrometry (MS/MS) is valued for its ability to facilitate molecular identification and deliver highly consistent data across a wide range of mass spectrometry platforms. Distinct from MS/MS is the fragmentation that occurs during electrospray ionization (ESI), commonly referred to as in-source fragmentation (ISF) (Figure 1). ISF was first observed in the 1950s with electron ionization and has been recognized as an inherent yet often overlooked feature of the ESI process, albeit less prevalent than with electron ionization. Recently, ISF has been associated with the overrepresentation of peaks in liquid chromatography mass spectrometry (LC/MS) data, where it accounts for the majority of observed unfiltered peaks [1]. Due to its overrepresentation in LC/MS data, and the subsequent inability to identify the molecules associated with these peaks using MS/MS data, ISF has been linked to the so-called “dark metabolome” [2, 3] (also encompassing the lipidome), a term used to describe uncharacterized molecular species in metabolomics and lipidomics. This association [1] was determined by an examination of MS/MS data acquired at 0 eV collision energy from METLIN's extensive library of over 931,000 molecular standards. However, while the similarity of ISF and MS/MS at 0 eV data has been described in previous studies [1, 4–6], it has yet to be directly established that they correlate with each other. We explored the consistency between MS/MS (0 eV) data and ISF across various molecular species to assess whether mining METLIN's MS/MS (0 eV) data—comprising over 931,000 molecular standards—can effectively link ISF to the dark metabolome and lipidome. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) with ESI has become a cornerstone in metabolomics, lipidomics, and clinical analysis due to its accuracy in identifying small molecules within complex biological matrices. With LC-MS/MS, after ionization occurs in the ESI source, charged molecules are directed into a collision cell where they undergo fragmentation for structural analysis. This procedure is typically repeated for all charged analytes present in a sample. However, despite its utility, this method has revealed an unexpectedly vast array of spectral features associated with the “dark metabolome.” However, given the limited number of protein-coding genes [7, 8] with only a fraction producing enzymes, the chemical diversity [3, 9, 10] detected through LC-MS/MS—potentially hundreds of thousands or even millions of metabolites—far exceeds biological expectations. Current estimates suggest that less than 2% of observed LC-MS/MS spectra can be annotated, a potentially broad spectrum of unknown compounds [3]. Recent research [1] using the METLIN database and its data at 0 eV has shed light on this discrepancy, and much of the perceived complexity may stem from technological factors, particularly ISF, rather than from biological diversity itself. Our laboratory, along with several others [11], has observed the widespread occurrence of ISF [12, 13]. This process involves the fragmentation of analytes during the initial ionization stage within the ESI source, occurring before they reach the collision cell. Essentially, ISF can transform a single analyte into multiple molecular ions and fragments, creating a complex array of ions from what was initially a single entity. Consequently, the mass analyzer indiscriminately isolates and further fragments whatever enters the collision cell. Given this understanding, we suspect that ISF may play a significant role in contributing to the so-called dark metabolome. In order to correlate the observation of peaks and ISF, we examined the METLIN MS/MS database [14], which consists of over 931,000 molecular standards representing over 350 chemical classes in which we mined METLIN's MS/MS data at 0 eV, an energy designed to simulate the absence of CID. This analysis was performed to assess whether MS/MS spectra acquired","journal":"Analytical Science Advances","year":2025,"id":514273,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9498,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":901656,"name":"Linh Hoang","orcid":"0000-0002-0077-8213","position":1,"is_corresponding":false},{"id":707224,"name":"Aries Aisporna","orcid":null,"position":2,"is_corresponding":false},{"id":113549,"name":"Martin Giera","orcid":"0000-0003-1684-1894","position":3,"is_corresponding":false},{"id":113550,"name":"Gary Siuzdak","orcid":"0000-0002-4749-0014","position":4,"is_corresponding":false},{"id":1027595,"name":"Winnie Uritboonthai","orcid":null,"position":0,"is_corresponding":true}],"reference_count":13,"raw_metadata":null,"created_at":"2026-07-19T02:48:29.165853Z","pmid":"40371267","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}