Comparison of the SYNAPT XS™ and SELECT SERIES™ Cyclic™ IMS for the Analysis of Human Urine

Importance of the Topic

Untargeted metabolomic profiling of complex biological fluids such as human urine is a critical tool for biomarker discovery and clinical research. The capacity of analytical platforms to separate and detect thousands of molecular features directly impacts the quality of data interpretation and identification confidence. Advances in orthogonal separation techniques, particularly ion mobility spectrometry (IMS) hyphenated with liquid chromatography–mass spectrometry (LC-MS), have provided enhanced resolution, increased peak capacity, and improved spectral clarity, enabling more comprehensive metabolic phenotyping.

Study Objectives and Overview

This application note compares two ion mobility-enabled mass spectrometers—the SYNAPT XS and the SELECT SERIES Cyclic IMS—when analyzing a single human urine sample. The primary objectives are to evaluate differences in feature detection, IMS resolution, and annotation potential, and to illustrate how extended IMS path lengths influence overall performance in an untargeted metabolomic workflow.

Methodology and Instrumentation

Sample Preparation:

• Human urine (20 μL) diluted with 30 μL water and 350 μL acetonitrile.
• Shaken for 10 minutes, centrifuged at 13 000 rpm, supernatant collected for injection.

Chromatography:

• ACQUITY Premier UPLC system with BEH Amide column (2.1 × 100 mm, 1.7 μm).
• Gradient of acetonitrile–water with 0.1% formic acid and 10 mM ammonium formate.
• Column at 40 °C, injection volume 2 μL, autosampler at 8 °C.

Mass Spectrometry and Ion Mobility:

• Two instruments compared: SYNAPT XS and SELECT SERIES Cyclic IMS (Waters Corporation).
• Electrospray ionization in positive mode; capillary voltage 2.0 kV; desolvation at 600 °C; cone gas 50 L/h; desolvation gas 800 L/h.
• Time-of-flight mass range 50–1200 Da; scan time 0.3 s; HDMSE acquisition for drift-time-resolved precursor and fragment data.

Main Results and Discussion

Feature Detection:

• The SELECT SERIES Cyclic IMS detected on average >3.5× more low-energy ions than the SYNAPT XS.
• Peak picking in Progenesis QI revealed ~50% more m/z–retention time features with the Cyclic IMS platform.

Ion Mobility Separation:

• Extended IMS path length in the SELECT SERIES Cyclic IMS doubled mobility separation compared to SYNAPT XS, resolving co-eluting compounds by drift time.
• Three-dimensional drift time plots demonstrated separation of isobaric features at identical retention times, yielding cleaner fragmentation spectra.

Database Annotation:

• Using HMDB (±10 ppm mass tolerance), the SYNAPT XS data yielded 1 450 tentative annotations from 2 363 features.
• The SELECT SERIES Cyclic IMS data produced 2 394 annotated features—944 more annotations under identical search criteria.

Benefits and Practical Applications

The Cyclic IMS platform’s improved mobility resolution and peak capacity drive more comprehensive feature detection, higher‐quality fragmentation spectra, and a larger number of database hits. These enhancements increase confidence in biomarker discovery, accelerate untargeted metabolomic workflows, and support high-throughput studies without compromising data depth.

Future Trends and Potential Applications

Emerging directions include multipass IMS separations for even greater resolving power, integration with artificial intelligence–driven data processing to streamline annotation, and application to a broader range of complex matrices (e.g., plasma, tissue extracts). Continuous improvements in IMS cell design and software algorithms will further enhance sensitivity, throughput, and structural elucidation capabilities in metabolomics and lipidomics.

Conclusion

The SELECT SERIES Cyclic IMS outperforms the SYNAPT XS in untargeted urine metabolomics by substantially increasing ion detection, feature picking, and annotation yield. Its extended mobility path length delivers superior resolution and spectral clarity, empowering researchers to uncover deeper insights into metabolic profiles and potential biomarkers.

Reference

1. Rainville PD, et al. Ion Mobility Spectrometry Combined With Ultra Performance Liquid Chromatography/Mass Spectrometry for Metabolic Phenotyping of Urine: Effects of Column Length, Gradient Duration and Ion Mobility Spectrometry on Metabolite Detection. Anal Chim Acta. 2017;982:1–8.
2. King AM, et al. Rapid Profiling Method for the Analysis of Lipids in Human Plasma Using Ion Mobility-enabled Reversed-phase Ultra-high Performance Liquid Chromatography/Mass Spectrometry. J Chromatogr A. 2020;1611:460597.