Comprehensive Accurate Mass Metabolomics Library and Its Evaluation in Targeted and Nontargeted Data Analysis Workflows

Importance of the topic

Accurate identification of metabolites is fundamental for interpreting biochemical pathways in health, disease, and drug discovery. High-resolution GC–MS libraries enhance confidence in compound annotation by combining electron ionization (EI) spectra with accurate mass measurements and retention indices. The Agilent Fiehn accurate mass metabolomics personal compound database and library (PCDL) addresses the need for comprehensive spectral resources to support both targeted and nontargeted metabolomics analyses in complex biological matrices.

Objectives and study overview

This work aimed to develop and characterize an accurate mass GC–MS spectral library covering primary metabolites, secondary metabolites, and xenobiotics. The goals were:

• To assemble a PCDL with over 900 EI spectra and >670 unique compounds relevant to metabolomics.
• To calibrate retention indices (Kovats and FAME-based) for retention time locking.
• To evaluate library performance in targeted (Screener) and nontargeted (Unknowns Analysis) workflows using derivatized blood plasma and tissue extracts.

Methodology and instrumentation

Sample preparation used methoximation followed by MSTFA+1% TMCS silylation, with D27 myristic acid as internal standard. Metabolites were extracted from plasma and tissues using acetonitrile:isopropanol:water (3:3:2). Data were acquired in EI mode (70 eV) on an Agilent 7250 GC/Q-TOF coupled to a 7890B GC and J&W DB-5ms Ultra Inert column with helium at 1 mL/min. Spectra were processed in Agilent MassHunter Qualitative Analysis (v10) for formula annotation, then imported into PCDL via PCDL Manager (v8.0) and enriched with metadata in ChemVista (v1.0). Targeted quantification and screening used MassHunter Quantitative Analysis (v10.2/11.1) and the Screener summary view.

Main results and discussion

The accurate mass PCDL contains >900 spectra, representing 22 compound classes dominated by carboxylic acids, amino acids, and carbohydrates. Comparison with the unit-mass Fiehn.L library showed 474 shared entities and ~200 unique to the accurate mass PCDL, extending coverage especially at m/z >600. In mouse tissue extracts, 86 metabolites were common across brain, liver, kidney, plasma, and serum, with unique tissue-specific markers identified. In plasma screening, the accurate mass PCDL reduced false positives by ~30% versus NIST17 and identified more compounds per sample. Targeted and nontargeted approaches proved complementary, with >150 high-confidence hits per sample (library match score >80).

Benefits and practical applications of the method

• Enhanced specificity and sensitivity in metabolite identification through accurate mass fragments and extended mass range.
• Reduced false positives using ExactMass filtering in nontargeted workflows.
• Comprehensive coverage of primary and secondary metabolites and xenobiotics.
• Seamless integration into targeted Screener workflows for quantitative studies.

Future trends and potential applications

Expansion of accurate mass libraries will enable broader coverage of lipids, peptides, and novel natural products. Integration with cloud databases and AI-driven deconvolution can automate annotation. Combining GC–MS with orthogonal techniques (LC–MS, ion mobility) will further improve metabolome coverage. Personalized medicine, environmental exposome studies, and high-throughput regulatory screening are promising applications.

Conclusion

The Agilent Fiehn accurate mass metabolomics PCDL provides a robust resource for confident metabolite identification in complex biological samples. By leveraging high-resolution GC/Q-TOF data, retention indices, and extensive metadata, researchers can achieve improved coverage, reliability, and throughput in both targeted and nontargeted metabolomics workflows.

References

1. Schauer N. et al. GC–MS Libraries for the Rapid Identification of Metabolites in Complex Biological Samples. FEBS Lett. 2005;579(6):1332–1337.
2. Stein S.E. Mass Spectral Reference Libraries: an Ever-Expanding Resource for Chemical Identification. Anal. Chem. 2012;84(17):7274–7282.
3. Beale D.J. et al. Review of Recent Developments in GC-MS Approaches to Metabolomics-Based Research. Metabolomics. 2018;14(11):152.
4. Grapp M., Maurer H.H., Desel H. Systematic Forensic Toxicological Analysis by GC-MS in Serum Using Automated Mass Spectral Deconvolution and Identification System. Drug Test Anal. 2016;8(8):816–825.
5. Mihaleva V.V. et al. Automated Procedure for Candidate Compound Selection in GC-MS Metabolomics Based on Prediction of Kovats Retention Index. Bioinformatics. 2009;25(6):787–794.
6. Nieto S. et al. Contaminants Screening Using High-Resolution GC/Q-TOF and an Expanded Accurate Mass Library of Pesticides and Environmental Pollutants. Agilent Technologies Application Note 5994-1346EN; 2017.
7. Fiehn O. Metabolomics by Gas Chromatography-Mass Spectrometry: the Combination of Targeted and Untargeted Profiling. Curr. Protoc. Mol. Biol. 2016;114:30.4.1–30.4.32.