Untargeted Metabolomics Using Orbitrap-Based GC-MS

Importance of the Topic

Untargeted metabolomics seeks to profile all small molecules in a biological sample without prior selection. High chromatographic resolution and accurate mass measurement are essential to discover and quantify hundreds of metabolites in complex matrices. Advanced GC-MS platforms combining gas chromatography with Orbitrap high-resolution detection overcome traditional sensitivity and mass-accuracy limitations, enabling reliable biomarker discovery in fields such as forensics and clinical research.

Objectives and Study Overview

This study demonstrates a full untargeted metabolomics workflow using a Thermo Scientific Q Exactive GC hybrid quadrupole-Orbitrap system. The experimental aim was to identify and quantify metabolic markers of post-mortem interval (PMI) in rat muscle tissue decomposed at room temperature over four days. A complete pipeline from extraction through data analysis was optimized to detect changes in metabolite abundance linked to decomposition time.

Methodology and Instrumentation

Sample Preparation and Derivatization

• Rat thigh muscle was homogenized in chloroform/methanol/water (1:3:1) and centrifuged;
• Supernatants were evaporated and subjected to two-step derivatization: methoximation (methoxyamine HCl in pyridine) followed by trimethylsilylation (MSTFA+1% TMCS);
• Samples were maintained at defined temperatures and times using automated autosampler procedures.

Data Processing Workflow

• Raw data conversion: ProteoWizard MSConvert to mzXML;
• Peak detection: XCMS centWave algorithm;
• Feature filtering and alignment: MzMatch.R;
• Univariate analysis: IDEOM and Student’s t-tests;
• Multivariate modeling: SIMCA PLS-DA;
• Tentative compound identification: NIST/Wiley libraries with accurate-mass confirmation.

Instrumentation Used

• GC: Thermo Scientific TRACE 1310 with TraceGOLD TG-5SilMS column (15 m×0.25 mm×0.25 µm);
• Autosampler: TriPlus RSH for automated injection and derivatization;
• MS: Q Exactive GC hybrid quadrupole-Orbitrap, electron ionization at 70 eV, full-scan m/z 50–750, resolution 60 000 (FWHM at m/z 200), sub-ppm mass accuracy;
• Carrier gas: Helium at 1.2 mL/min; injection split ratio 1:60.

Main Results and Discussion

• A total of 1 193 deconvoluted peak clusters were detected above 100 000 counts;
• Univariate statistics identified 272 features with significant time-dependent changes (P<0.05);
• Volcano plots highlighted metabolites with the largest fold-changes between day 0 and day 3;
• Multivariate PLS-DA clearly separated samples by decomposition time, confirming reproducible metabolic shifts;
• Putative IDs for amino acids and amines (e.g., L-threonine, L-aspartate, L-methionine, glutamine, putrescine, lysine) showed progressive accumulation during decay;
• High mass resolution enabled confident deconvolution of overlapping peaks and detection of low-level species.

Benefits and Practical Applications

• Ultra-high resolution and sub-ppm accuracy ensure reliable mass assignments and formula prediction;
• Wide dynamic range captures both high- and low-abundance metabolites in a single run;
• Fast scan rates enable consistent deconvolution of coeluting species for untargeted profiling;
• The workflow supports forensic PMI estimation by quantifying decay-related biomarkers,
offering a laboratory-based complement to visual inspection.

Future Trends and Opportunities

• Integration of expanded spectral and in-house libraries to improve identification of unknowns;
• Coupling with tandem MS or MSn for structural confirmation of key biomarkers;
• Development of predictive machine-learning models linking metabolic trajectories to PMI;
• Miniaturization and automation advances for field-deployable metabolomics platforms.

Conclusion

This work validates an end-to-end untargeted metabolomics approach using Orbitrap-based GC-MS for discovery and quantitation of decomposition markers. The combination of robust sample preparation, automated derivatization, high chromatographic resolution, and Orbitrap mass accuracy delivers a powerful tool for complex biological investigations, including forensic PMI determination.

Reference

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• Creek DJ, Jankevics A, Burgess KEV, Breitling R, Barrett MP. IDEOM: An Excel Interface for Analysis of LC–MS Based Metabolomics Data. Bioinformatics. 2012;28(7):1048–1049.
• Umetrics AB. SIMCA Software for Multivariate Analysis. 2015.
• ProteoWizard MSConvert. ProteoWizard Software Suite. 2015.