Giera et al. (2024) served as a powerful reminder of the challenges in LC-MS/MS data interpretation, which were already well known to us. Their work underscored the role of in-source fragmentation (ISF) and how its misinterpretation can inflate the apparent metabolome. However, their bold claim that ISF accounts for most of the dark metabolome caused a lot of debate, with some opponents pointing to lack of methodological transparency and disagreement with real-world datasets.
The response by Yasin et al. (2025) highlighted a more nuanced view: ISF is one contributor to signal complexity but does not preclude the discovery of new metabolites. Evidence from biological samples shows that many unannotated features persist even after accounting for fragments and adducts, indicating they can not be dismissed as artifacts alone. Thus, while Giera et al. escalated the debate, Yasin et al. reinforced a key point: the dark metabolome remains a genuine space for discovery, not merely an artifact of instrumentation.
The Key Papers
Giera et al. (2024): The Hidden Impact of In-Source Fragmentation
Using over 930,000 molecular standards from the METLIN database, Giera, Siuzdak and colleagues found that more than 70 percent of LC-MS/MS peaks could arise from in-source fragmentation (ISF) – where analytes partially break apart during ionization before collision-induced dissociation. They proposed that this phenomenon could artificially inflate the number of features in metabolomic datasets, suggesting that much of the so-called dark metabolome may not represent unknown metabolites but fragment ions misidentified as distinct compounds.
Yasin et al. (2025) – Discovery of Metabolites Prevails amid In-Source Fragmentation
In a large-scale reanalysis of ~30,000 standards and biological datasets, Yasin, Dorrestein, and co-authors reported far lower ISF rates – typically 2–25 percent of ions, depending on instrument and tuning parameters. Even after accounting for fragments, adducts, and isotopes, they found that 82 percent of features in human fecal samples remained unannotated, underscoring that a substantial dark metabolome still exists. Their findings argue that ISF contributes to data complexity but does not negate ongoing metabolite discovery.
This debate underscores a core tension in analytical chemistry: sensitivity versus specificity. Pieter Dorrestein and colleagues emphasize that careful instrument tuning or the use of dedicated soft ionization/transfer modes can reduce ISF, improving data clarity and minimizing miss-annotation. Their perspective prioritizes data quality, which is essential for accurate structural elucidation and biological interpretation. By contrast, Gary Siuzdak takes a pragmatic view, rooted in the realities of untargeted metabolomics. He points out that reducing ISF often comes at the cost of ion transmission efficiency and overall sensitivity. In complex biological samples, where many metabolites are present at low abundance, even small sensitivity losses can result in missing key features.
When we discuss this issue in our company, we tend to relate it to a particular application: discovery-driven metabolomics. Here, where sensitivity is critical and many features are novel or low in abundance, some level of ISF is an acceptable compromise if it enables broader detection. Conversely, in targeted or structure-focused studies, minimizing ISF may be more valuable, even if it means sacrificing some sensitivity.
Mitigation strategies
The solution likely lies in adaptive acquisition strategies (e.g., using a softer ion source, such as the Orbitrap Excedion Pro MS) combined with more advanced software tools – like the Compound Discoverer, which automatically detects and groups ISF. If algorithms can reliably annotate ISF patterns, we can avoid sacrificing sensitivity just to control ISF – and high peak capacity of Orbitrap and Astral spectra allows for the discernment of ISF patterns in the vicinity of other signals. Rather than leaning toward one extreme, the field should aim for approaches that balance sensitivity and spectral clarity through smarter data processing and more flexible instrument settings.
For metabolomics researchers who want to minimize ISF in their own experiments, careful optimization of ion source parameters, such as sheath gas, vaporizer temperature, or ion transfer tube temperature, can help reduce thermal fragmentation. That said, one needs to avoid over-optimizing against ISF to the extent that sensitivity is compromised. In untargeted workflows, some degree of ISF is both unavoidable and generally acceptable.
Metabolomics researchers could also borrow technical approaches used in the proteomics field. One area that comes to mind would be data independent acquisition to extend the dynamic range of analysis and provide a non-biased method for compound fragmentation. That said, proteomics researchers could learn from small molecule mass spectrometry researchers to better understand the impact ISF has on false discovery and quantitation. This would be most relevant in the emerging areas of immunopeptidomes where singly charged peptide species could be impacted by ISF.
Looking to the future, we expect that most software used in metabolomics will eventually be able to detect ISF automatically. Instruments will then be able to adjust their settings on the fly, optimizing ionization, transmission and fragmentation for each eluting compound. ISF could even become a valuable source of complementary information, for example informing about a particular molecular structure or conformation.
Overall, from a scientific standpoint, we find this debate highly productive. However, it has the potential to enter the political dimension where manuscript submissions and grant applications are reviewed with non-scientific bias. If this occurs, it would mean a scientific discussion has been turned into a political tool, which would be deeply troubling.
Previous Articles in the Series
Pieter Dorrestein and Yasin El Abiead:The Dark Metabolome: No Mere Figment?
Martin Giera and Gary Siuzdak:The Dark Metabolome Debate Continues
Shuzhao Li:A Call for Context
Gary Patti:Metabolomics Is Not in Crisis
Jan-Christoph Wolf:Does In-Source Fragmentation Require a Soft Touch?
Oliver Jones: The Past, Present, and Future of the “Dark Metabolome”
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