Gas chromatography coupled with mass spectrometry (GC-MS) is a well-established and robust analytical platform for targeted and untargeted metabolomics. Its high chromatographic resolution, reproducible retention indices, and the availability of comprehensive spectral libraries make GC-MS particularly suited for the analysis of volatile and semi-volatile metabolites, especially following derivatization. GC-MS excels in confident identification and quantification of key small molecule classes such as organic acids, amino acids, sugars, sterols and more. Time of Flight (TOF) mass spectrometry is particularly well-suited for the analysis of complex metabolomics samples, owing to its ability to produce clean analyte spectra from multiple coeluting or matrix obscured analytes.
In a recent study focused on biomarker discovery for Type 2 diabetes mellitus, plasma samples from case and control cohorts were analyzed using LECO’s Pegasus® BT GC-TOFMS system in combination with ChromaTOF® Sync software. The workflow encompassed automated sample derivatization, high-throughput data acquisition, and downstream statistical and library-driven data processing.
ChromaTOF Sync facilitated comprehensive data handling, including peak deconvolution, alignment across sample batches, database matching for metabolite identification, and multivariate statistical analyses. This integrated workflow enabled the annotation of key metabolite differences between diseased and healthy plasma samples, including fatty acids, amino acids, and purine derivatives.
Figure 1. ChromaTOF Sync results for the comparison of diseased and control human plasma.
Figure 1 illustrates the output from ChromaTOF Sync, including:
(A) a spectral library match for the metabolite hypoxanthine,
(B) quantitation across groups via bar chart,
(C) PCA plot of selected analytes, and
(D) a summary table of annotated metabolites.
The combined capabilities of the Pegasus BT GC-TOFMS and ChromaTOF Sync software enabled efficient and reproducible metabolomic profiling, contributing to the identification of candidate biomarkers relevant to early disease detection in metabolic disorders.
For a more detailed methodology and results, refer to the full application note describing the complete derivatization, acquisition, and processing protocol.
For further information on GC-MS and metabolomics, visit our metabolomics solutions page and be sure to attend our upcoming webinar, “Comprehensive Omics Profiling for Volatiles and Uncommon Fatty Acids: from Bees to Microbes.”
