Improved Methodology for Metabolomics Data Acquisition Workflow: Utilization of Electron Impact and Chemical Ionization High Resolution Time-of-Flight Mass Spectrometry

Importance of the Topic

Liver disease affects over 1.5 million people each year worldwide. Comprehensive metabolomic profiling of biological tissues is critical for understanding disease mechanisms, discovering biomarkers, and developing targeted therapies.

Objectives and Study Overview

This study presents an improved GC-MS workflow combining electron impact high-resolution time-of-flight (EI-HRT) and chemical ionization high-resolution time-of-flight (CI-HRT) mass spectrometry. The main goal is to generate robust, high-quality spectral data for a wide range of metabolites in mouse liver extracts, including labile compounds that often lack molecular ions under EI conditions.

Methodology and Instrumentation

• Sample Preparation: Tissue extracts were dried, subjected to a two-step silylation derivatization, and transferred to GC vials.
• Gas Chromatography: Agilent 7890 GC with Restek Rxi-5Sil MS column (30 m × 0.25 mm ID, 0.25 µm film), splitless injection (1 µL), helium carrier at 1.0 mL/min, oven program from 50 °C to 300 °C in multiple ramps.
• Mass Spectrometry: LECO Pegasus® GC-HRT operated in EI mode at 250 °C and CI mode (5 % ammonia in methane) at 200 °C, folded flight path resolution of 25 000 FWHM, mass ranges 60–520 m/z (EI) and 50–1200 m/z (CI), 6 spectra/s, internal PFTBA calibration.
• Data Processing: Deconvolution using ChromaTOF HRT, spectral library searches against commercial and in-house libraries, accurate mass formula assignment with ≤1 ppm error.

Main Results and Discussion

The combined EI-HRT and CI-HRT approach enabled detection and confident identification of diverse metabolite classes, including monosaccharides, amino acids, fatty acids, sterols, and organic acids. Key findings include:

• High spectral similarity scores (average >800/1000) and mass accuracy (mean 0.69 ppm) for representative compounds.
• CI-HRT spectra provided clear [M + H]+ ions for labile sugars like mannose (m/z 570.29431) and maltose, overcoming limitations of EI data.
• Complete spectral coverage allowed retrospective interrogation of datasets for novel biomarkers.

Benefits and Practical Applications

• Robust high-throughput workflow with accurate peak deconvolution.
• High resolving power minimizes background interferences and coelution effects.
• Accurate elemental composition determination enhances confidence in structural assignments.
• Rich spectral libraries support reliable metabolite identification in complex matrices.

Future Trends and Potential Applications

Advancements may include coupling GC-HRT with automated sample preparation, integration with LC-MS and ion mobility separation, adoption of machine learning algorithms for pattern discovery, and real-time metabolomic monitoring in clinical and industrial settings.

Conclusion

The integrated EI-HRT/CI-HRT GC-MS workflow delivers comprehensive, high-quality metabolomic profiles in mouse liver extracts. This approach enhances detection of low-abundance and labile compounds, supporting biomarker discovery, QA/QC, and systems biology research.

References

1. Xie B., Waters M.J., Schirra H.J. Investigating Potential Mechanisms of Obesity by Metabolomics. J. Biomed Biotechnol. 2012;2012:805683.
2. Mouse liver extracts were provided by Dr. Xiang Zhang (Department of Chemistry, University of Louisville, KY, USA).