DELVING DEEPER INTO THE METABOLOME WITH CYCLIC IMS, SUB PPM MASS ACCURACY AND HIGH RESOLUTION MASS SPECTROMETRY

Significance of the Topic

Metabolomics provides a global view of small molecules in biological samples and plays a crucial role in biomarker discovery and understanding disease mechanisms. Achieving deep coverage of the metabolome requires advanced separation and detection techniques to resolve co-eluting compounds, isomers and ensure reproducible mass accuracy, especially in studies involving complex matrices such as plasma from COVID-19 patients.

Objectives and Study Overview

This study aimed to evaluate two high-resolution mass spectrometry platforms, a cyclic ion mobility spectrometer (Cyclic IMS) and an extended flight path time-of-flight instrument (MRT), for untargeted metabolomic profiling of COVID-19 patient plasma. Key goals included comparing feature detection, mass accuracy, resolution and the ability to distinguish isomeric species under realistic clinical sample conditions.

Methodology and Instrumentation

Samples and Extraction

• Plasma from six male COVID-19 patients divided into mild and severe groups; multiple time-point samples collected for the severe cohort.
• MTBE extraction for lipids and polar metabolites; separate organic and aqueous fractions prepared for lipid-RPC and HILIC profiling.

Chromatography and Acquisition

• HILIC separation on a BEH Amide column; gradient elution over 10 minutes at 40 °C.
• RPC separation for lipids on a C18 column under an appropriate gradient.
• Cyclic IMS: HDMSe mode with single-pass ion mobility separation and CID fragmentation; scan range 50–1200 m/z.
• MRT: Continuum MSe in extended flight path mode for high resolution; scan range 50–2400 m/z.

Instrumentation Used

• SELECT SERIES Cyclic IMS (Waters Corporation)
• SELECT SERIES MRT high-resolution ToF (Waters Corporation)
• ACQUITY I-Class UPLC system (Waters)
• Progenesis QI and MetaboAnalyst software for data processing and statistical analysis

Results and Discussion

Feature Detection and Separation

• Cyclic IMS detected ~1,000 additional features compared to MRT by resolving co-eluting compounds and isomers through mobility separation.
• PCA of both platforms demonstrated clear grouping by disease severity and tight QC clustering, indicating robust reproducibility.

Mass Accuracy and Resolution

• Cyclic IMS delivered mass errors below 4 ppm for example annotated compounds.
• MRT achieved ultra-stable mass accuracy below 140 ppb and resolution exceeding 160,000 at 204 m/z, enhancing confidence in compound identification.
• Multipass IMSn on the cyclic system separated isomeric bile acids and methylated histidines in under 200 ms with high mobility resolution.

Compound Annotation

• Database searches (HMDB) identified biologically relevant lipid species and small molecules, including conjugated bile acids and acetylcarnitine variants.
• The combination of mobility, high mass resolution and stable mass accuracy improved discrimination of isomers and structural elucidation.

Benefits and Practical Applications

• Enhanced coverage of the metabolome supports comprehensive biomarker discovery in clinical and pharmaceutical research.
• Improved confidence in annotation facilitates study of disease-related metabolic pathways, as demonstrated for COVID-19 plasma.
• Fast scan speeds and high resolution accommodate narrow chromatographic peaks, enabling high-throughput profiling.

Future Trends and Applications

Advances in ion mobility and extended flight path technologies will continue to expand metabolomic depth and throughput. Integrating multipass IMS workflows with real-time data analysis and machine learning promises even greater resolution of complex isomeric mixtures. Applications may extend to personalized medicine, rapid clinical diagnostics, and large-scale epidemiological studies.

Conclusion

This comparative study demonstrates that combining cyclic ion mobility separation with high-resolution mass spectrometry significantly enhances feature detection, mass accuracy and isomer resolution over conventional ToF platforms. The improved analytical performance supports robust biomarker discovery workflows and offers clear benefits for clinical metabolomics applications.

References

1. Becker S et al. LC-MS-based metabolomics in the clinical laboratory. J Chromatogr B. 2012;883-884:68-75.
2. Dunn WB et al. Molecular phenotyping of a UK population. Metabolomics. 2015;11:9-26.
3. Dunn WB et al. Procedures for large-scale metabolic profiling. Nat Protoc. 2011;6(7):1060-83.
4. Paglia G & Astarita G. Metabolomics and lipidomics using traveling-wave ion mobility MS. Nat Protoc. 2017;12(4):797-813.
5. Danlos FX et al. Metabolomic analyses of COVID-19 patients. Cell Death Dis. 2021;12(3):258.
6. Matyash V et al. Lipid extraction by methyl-tert-butyl ether. J Lipid Res. 2008;49(5):1137-46.
7. Giles K et al. A Cyclic Ion Mobility-Mass Spectrometry System. Anal Chem. 2019;91(13):8564-73.
8. Xia J et al. MetaboAnalyst: a web server for metabolomic data analysis. Nucleic Acids Res. 2009;37:W652-60.
9. Wishart DS et al. HMDB 4.0: the human metabolome database. Nucleic Acids Res. 2018;46(D1):D608-17.