uMRM: Bridging Untargeted Discovery and Quantitative MRM

In this ASMS interview, Gary Siuzdak from the Scripps Research Institute discusses untargeted multiple reaction monitoring (uMRM), which offers a broad, systematic framework for connecting untargeted tandem mass spectrometry (MS/MS) data to quantitative analysis.¹

In this interview clip, Siuzdak discusses:

• What problem is uMRM really trying to solve in metabolomics and quantitative mass spectrometry?   

He traces the approach back to early 2010s work at the Dalian Institute,^2,3 his 2018 Nature Methods paper,⁴and a recent Analytical Chemistry paper on uMRM.⁵ From there, Siuzdak turns to a more pointed concern: a large share of what the field treats as “empirical” biological data isn't coming from real molecules at all, but from in-source fragmentation and other instrument-generated artifacts. Drawing on a Nature Metabolism paper with Martin Giera,⁶ he estimates that roughly 70% of peaks seen at 0 eV across METLIN trace back to in-source fragments rather than true precursors — and points to a January 2026 ACS Measurement Science Au study that shows electrospray ionization itself drives extensive unwanted chemistry (using serotonin as an example).⁷ Siuzdak argues this has quietly cost the field credibility, since it's implausible that human biology generates the sheer number of “novel” structures researchers have reported. He's candid that AI-driven annotation, trained on this same contaminated data, has compounded the problem by generating so-called “phantom metabolites”,⁸ and describes the field as needing to sober up from that “binge” before AI prediction can be trusted. He's also skeptical of MS/MS similarity scoring, noting that spectra can look alike while representing very different molecules. Practical fixes he highlights include retention-time-based filtering and METLIN's built-in 0 eV channel, designed specifically to catch in-source fragments empirically rather than assume them away.⁹

References

1. Uritboonthal, W.; Aisporna, A.; Hoang, L.; et al. Untargeted Multiple Reaction Monitoring (uMRM). Presented at ASMS 2026, in San Diego, California, USA. https://asms.org/docs/default-source/conference/74th-asms-final-program_as-of-may-8-2026.pdf?sfvrsn=1234fc3_0 (accessed 2026-07-03).
2. Chen, S.; Kong, H.; Lu, A.; et al. Pseudotargeted Metabolomics Method and Its Application in Serum Biomarker Discovery for Hepatocellular Carcinoma Based on Ultra High-Performance Liquid Chromatography/Triple Quadrupole Mass Spectrometry. Anal Chem 2013, 85 (17), 8326–8333. DOI: 10.1021/ac4016787
3. Luo, P.; Dai, W.; Yin, P.; Multiple Reaction Monitoring-Ion Pair Finder: A Systematic Approach To Transform Nontargeted Mode to Pseudotargeted Mode for Metabolomics Study Based on Liquid Chromatography–Mass Spectrometry. Anal Chem 2015, 87 (10), 5050–5055. DOI: 10.1021/acs.analchem.5b00615
4. Domingo-Almenara, X.; Montenegro-Burke, J. R.; Ivanisevic, J.; et al. XCMS-MRM and METLIN-MRM: A Cloud Library and Public Resource for Targeted Analysis of Small Molecules. Nat Methods 2018, 15 (9), 681–684. DOI: 10.1038/s41592-018-0110-3
5. Uritboonthai, W.; Aisporna, A.; Hoang, L.; et al. Untargeted Multiple Reaction Monitoring. Anal Chem 2026, 98 (12), 8985–8994. DOI: 10.1021/acs.analchem.5c06838
6. Giera, M.; Aisporna, A.; Uritboonthai, W.; Siuzdak, G. The Hidden Impact of In-Source Fragmentation in Metabolic and Chemical Mass Spectrometry Data Interpretation. Nat Metab 2024, 6 (9), 1647–1648. DOI: 10.1038/s42255-024-01076-x
7. Song, X.; Xu, J.; Sun, C.; et al. Dark Reactions in Microdroplets Explain Widespread Artifacts in Metabolomic Profiling. ACS Meas Sci Au 2026, 6 (2), 311–323. DOI: 10.1021/acsmeasuresciau.5c00146
8. Workman, Jr, J. ASMS 2026: Phantom Metabolites and the Hidden Chemistry of Electrospray Ionization. LCGC International website https://www.chromatographyonline.com/view/asms-2026-phantom-metabolites-and-the-hidden-chemistry-of-electrospray-ionization(accessed 2026-07-03).
9. Uritboonthai, W.; Aisporna, A. E.; Hoang, L.; et al. METLIN 960 K: An Empirical Tandem Mass Spectrometry Data Resource. Anal Chem 2026, 98, 12498–12507. DOI: 10.1021/acs.analchem.5c08031