LC-QTOF-MSE with MS1-based precursor ion quantification and SiMD-assisted identification enhances human urine metabolite analysis
6
Issued Date
2025-01-01
Resource Type
eISSN
20010370
Scopus ID
2-s2.0-105010557213
Journal Title
Computational and Structural Biotechnology Journal
Volume
27
Start Page
3079
End Page
3089
Rights Holder(s)
SCOPUS
Bibliographic Citation
Computational and Structural Biotechnology Journal Vol.27 (2025) , 3079-3089
Suggested Citation
Kurilung A., Limjiasahapong S., Wanichthanarak K., Manokasemsan W., Kaewnarin K., Duangkumpha K., Manocheewa S., Tansawat R., Chaiteerakij R., Nookaew I., Sirivatanauksorn Y., Khoomrung S. LC-QTOF-MSE with MS1-based precursor ion quantification and SiMD-assisted identification enhances human urine metabolite analysis. Computational and Structural Biotechnology Journal Vol.27 (2025) , 3079-3089. 3089. doi:10.1016/j.csbj.2025.07.009 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/111320
Title
LC-QTOF-MSE with MS1-based precursor ion quantification and SiMD-assisted identification enhances human urine metabolite analysis
Corresponding Author(s)
Other Contributor(s)
Abstract
This study presents the development and validation of a liquid chromatography–quadrupole-time-of-flight mass spectrometry method with data-independent acquisition (LC-QTOF-MS<sup>E</sup>) for targeted quantification, post-targeted screening, and untargeted metabolite profiling. Using MS<sup>1</sup>-based precursor ion quantification, the method demonstrated excellent analytical performance with linearity (R² > 0.99), accuracy (84 %–131 %), and precision (1 %–17 % relative standard deviation (RSD)). Although LC-QTOF‑MS<sup>E</sup> sensitivity is at least nine-fold lower than LC-triple quadrupole MS with multiple reaction monitoring, it remains adequate for quantifying urinary metabolites, particularly those that fragment poorly or yield low‑intensity product ions. For post‑targeted screening and untargeted profiling, an in‑house reference library (the Siriraj Metabolomics Data Warehouse, SiMD), comprising 174 curated metabolite standards, was integrated into the workflow to enhance metabolite identification confidence. The official website for SiMD can be accessed at https://si-simd.com/. To demonstrate the method's utility, 11 amino and organic acids were quantified in urine samples from 100 healthy individuals. Four compounds—L-methionine, L-histidine, L-tryptophan, and trans-ferulic acid—were significantly higher levels in females (P < 0.05), likely reflecting sex-specific physiological or dietary intake differences. Post‑targeted screening identified 29 additional metabolites and assigned them to level 1 (m/z, RT, isotope pattern, and MS/MS spectra matched to reference standards) based on the Metabolomics Standards Initiative guidelines. Untargeted retrospective profiling revealed level 1 seven metabolites, including ribitol, creatine, glucuronic acid, trans-ferulic acid, succinic acid, dimethylglycine, and 3-hydroxyphenylacetic acid related to sex variation (VIP > 1.5). In summary, the LC-QTOF-MS<sup>E</sup> method coupled with SiMD provides a robust and comprehensive workflow for metabolomics analysis. It enables reliable target quantification and enhances confidence in metabolite identification while also reducing sample and instrumental demands. These features make it particularly well-suited for clinical metabolomics studies.