High Resolution Mass Spectrometry Metabolic Profiling of Human Urine Using the SELECT SERIES™ MRT

Importance of the Topic

High-resolution mass spectrometry (HRMS) metabolomic profiling of human urine enables comprehensive detection of endogenous metabolites and xenobiotics. This expands our understanding of metabolic pathways, disease biomarkers, and the impact of external factors such as drugs and environmental exposures.

Objectives and Study Overview

This study demonstrates the SELECT SERIES™ MRT mass spectrometer for untargeted metabolic profiling of pooled human urine samples (smokers, non-smokers, and passive smokers). The goals include achieving sub-ppm mass accuracy, resolving fine isotopic patterns, and integrating with chromatography and informatics workflows to confidently annotate unknown metabolites.

Methodology and Instrumentation

An overview of sample preparation, chromatography, and mass spectrometry conditions was provided:

• Pooled NIST human urine samples diluted 1:4, centrifuged and vortexed.
• UPLC separation on ACQUITY Premier HSS T3 column (2.1×100 mm, 1.8 µm) at 45 °C with a water/acetonitrile gradient (0.1% formic acid) and 0.6 mL/min flow.
• SELECT SERIES™ MRT operated in MRT mode with MSE acquisition from m/z 50–2400, both positive and negative ESI, capillary voltage 3.0 kV, cone voltage 40 V, collision energy 10–40 eV, lockmass correction using leucine enkephalin.
• Data processed with MassLynx V4.2 and Progenesis QI for feature extraction, alignment, normalization, and database annotation; statistical analysis performed in MetaboAnalyst.

Main Results and Discussion

The SELECT SERIES™ MRT delivered consistent sub-ppm mass accuracy and high resolving power at UPLC acquisition speeds. Quality control (QC) samples demonstrated low retention time and mass variance, ensuring analytical robustness. Multivariate analysis (PCA) showed clear separation of smokers, non-smokers, and passive smokers, while OPLS-DA identified top discriminating features.
Key annotated metabolites included acetaminophen glucuronide, ranitidine, sulfamethoxazole metabolites, and biomarkers of cocaine and nicotine metabolism (e.g., benzoylecgonine, cotinine glucuronides). High resolution enabled confident elemental composition through fine isotopic pattern matching, achieving isotope mass errors <2 ppm and >99% isotope similarity.
Removing exogenous drug and nicotine features refined the biological interpretation, revealing underlying metabolic differences between sample groups beyond smoking exposure.

Benefits and Practical Applications

• Sub-ppm mass accuracy reduces false identifications and validation workload.
• High resolution resolves isobaric and co-eluting compounds, improving metabolome coverage.
• Fine isotopic distribution analysis strengthens elemental composition assignments.
• Integrated informatics workflows facilitate streamlined data interpretation and large-scale studies.

Future Trends and Opportunities

• Integration of HRMS with multi-omic platforms (proteomics, lipidomics) for holistic biological insights.
• Application of machine learning and AI in feature annotation and pattern recognition.
• Advancements in ion mobility and ultra-high field instruments to further enhance separation and identification.
• Development of standardized QC and database libraries for cross-laboratory reproducibility.

Conclusion

The SELECT SERIES™ MRT coupled with UPLC and advanced informatics delivers reliable, high-resolution metabolic profiling of human urine. Its capability for sub-ppm mass accuracy, fine isotopic resolution, and robust statistical integration supports confident compound annotation and deeper understanding of metabolic alterations in health and disease.

Reference

• Smith CA, Want EJ, O’Malley G, Abagyan R, Siuzdak G. XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification. Anal Chem. 2006;78(3):779–787.
• Cooper-Shepherd DA, Wildgoose J, Kozlov B, et al. Novel Hybrid Quadrupole-Multireflecting Time-of-Flight Mass Spectrometry System. J Am Soc Mass Spectrom. 2023;34(2):264–272.
• Pang Z, Zhou G, Ewald J, et al. Using MetaboAnalyst 5.0 for LC-HRMS Spectra Processing, Multi-Omics Integration and Covariate Adjustment of Global Metabolomics Data. Nat Protoc. 2022;17(8):1735–1761.
• Xia J, Psychogios N, Young N, Wishart DS. MetaboAnalyst: A Web Server for Metabolomic Data Analysis and Interpretation. Nucleic Acids Res. 2009;37(Web Server issue):W652–W660.
• Mwenya DM, Charalambous BM, Phillips PP, et al. Impact of Cotrimoxazole on Carriage and Antibiotic Resistance of Streptococcus pneumoniae and Haemophilus influenzae in HIV-infected Children in Zambia. Antimicrob Agents Chemother. 2010;54(9):3756–3762.
• Sohy C, Pilette C, Niederman MS, Sibille Y. Acute Exacerbation of Chronic Obstructive Pulmonary Disease and Antibiotics: What Studies Are Still Needed? Eur Respir J. 2002;19(5):966–975.