Incorporating CCS values to enable 4-dimensional annotation of metabolic features

Significance of the Topic

The reliable identification of metabolites in LC-MS/MS-based workflows is critical for metabolomics, quality control and clinical research. Incorporating collision cross section (CCS) values from trapped ion mobility spectrometry (TIMS) adds a fourth dimension to feature annotation, improving confidence by combining m/z, retention time, isotopic pattern, MS/MS spectra and CCS. High reproducibility and inter-laboratory consistency of CCS enhance transferability and reduce false positives in complex sample analyses.

Objectives and Study Overview

This study aimed to assess the intra- and inter-laboratory reproducibility of CCS measurements on timsTOF Pro instruments, compare measured CCS values with literature data, and demonstrate the practical application of CCS-enhanced annotation in urine sample comparisons. Key goals included:

• Quantify CCS reproducibility across two laboratories (Bremen, Germany and Perth, Australia).
• Compare experimentally determined CCS values with the Unified CCS Compendium.
• Apply CCS-based filtering to discriminate metabolic differences in urine samples using PCA and pathway mapping.

Methodology and Instrumentation

Analytical workflow comprised:

• Sample preparation: centrifugation and filtration of volunteer urine (Bremen) and hydrophilized urine standard (NovaMT). Precipitation with methanol and dilution with LC-MS grade solvents.
• Chromatography: Reversed-phase UHPLC (Bruker Elute or Waters I-Class) with C18 columns, 35 °C, gradient from 1 % to 99 % acetonitrile (0.1 % formic acid) over 20 min.
• Mass spectrometry: timsTOF Pro with PASEF, ESI + 4500 V/ESI – 4200 V, 20–1000 m/z scan range, internal mass calibration (sodium formate) and mobility calibration (Agilent TuneMix).
• Data processing: MetaboScape 2021 for feature extraction (“buckets”), using an analyte list integrating retention times (T-ReX LC-QTOF), CCS values (Unified CCS Compendium), and MS/MS spectra (Bruker HMDB 2.0, MetaboBASE 3.0). Annotation criteria: precursor mass accuracy, retention time, isotopic fit (mSigma), MS/MS score, CCS deviation.
• Statistics and visualization: PCA, t-tests, and pathway mapping (Caffeine and Theobromine pathway from WikiPathways WP3633) in MetaboScape.

Main Results and Discussion

CCS reproducibility:

• Intra-lab standard deviation: <0.4 Å2 (positive mode) and <0.2 Å2 (negative mode).
• Inter-lab average |∆CCS|: 0.25 % (positive) and 0.15 % (negative).
• Comparison with Compendium: average |∆CCS| <1 % for both labs and polarities; all deviations <2 %.
• CCS vs. retention time: CCS showed lower average CV (0.11 %) than retention time (0.25 %), highlighting superior transferability without additional alignment.

PCA of two urine samples revealed clear separation driven by caffeine and its derivatives. Quantitative differences in xanthines (xanthine, 1-, 3-, 7-methylxanthine, theobromine, paraxanthine, caffeine) were confirmed by t-tests and fold-change analysis. Elevated theobromine, caffeine and paraxanthine in the Bremen sample indicated recent coffee intake, while higher xanthine in NovaMT suggested later sampling stage of caffeine metabolism.

Benefits and Practical Applications

Integrating CCS values in annotation workflows offers:

• Enhanced confidence through a genuine four-dimensional filter combining mass, time, isotopic pattern and CCS.
• Improved MS/MS spectral quality via mobility-based noise removal in PASEF acquisition.
• Streamlined cross-laboratory comparisons without extensive retention time alignment.
• Robust feature identification in untargeted metabolomics, biomarker discovery and QC applications.

Future Trends and Applications

Advances likely include expanding CCS libraries for broader metabolite coverage, real-time CCS calibration, and integration with machine-learning annotation tools. Combining CCS with ion mobility-resolved fragmentation and multi-omic datasets will further refine compound identification and pathway mapping in complex biological systems.

Conclusion

This study demonstrates that TIMS-derived CCS values are highly reproducible within and between laboratories, correlate closely with literature values, and serve as a reliable additional filter for metabolite annotation. The integration of CCS enhances spectral clarity and annotation confidence, supporting four-dimensional workflows that outperform traditional LC-MS/MS in transferability and robustness.

References

• Unified CCS Compendium, McLean Research Group.
• Bruker HMDB Metabolite Library 2.0.
• Bruker MetaboBASE Personal Library 3.0.
• WikiPathways, Caffeine and Theobromine Pathway (WP3633).