GC-MS-Based Metabolic Phenotyping

The term metabolomics or metabonomics, “Biochemistry's new side of view,” refers to the study of small molecules derived from biochemical reactions in cells, organs, and organisms and has more recently been generalized to metabolic phenotyping. Quantitative and qualitative metabolic phenotyping (providing either absolute or relative quantitation) delivers a map sometimes with detailed cartographic information of the small molecule content in biological samples. GC-MS metabolic phenotyping is considered a “gold standard” for the analysis of small (semi)volatile molecules. The high separation capacity, the reliable identification based on spectral libraries, and the precise quantification of metabolites provide advantages that overcome the hurdle of derivatization reactions needed for the enhancement of the chromatographic performance of polar metabolites. The existence of validated workflows of a plethora of specimens bolsters the use of GC-MS. For the successful application of such workflows, derivatization and chromatographic parameters should be fully controllable, otherwise errors and biases may be introduced, affecting data quality and the validity of the obtained results. In data treatment, peak annotation and metabolite identification remains a challenging task; however, it is by far at the most advanced stage compared to the other metabolic phenotyping platforms. This is due to the high reproducibility of the ionization mode, which renders spectra directly comparable among various instruments or laboratories. As a consequence, validated spectral libraries have been generated (both commercial and web-based) and are widely used, enhancing the identification of unknown peaks in complex data. Data handling remains a demanding process but sophisticated platforms facilitate the process from peak picking to peak alignment and deconvolution of the signals. Validated multivariate statistics is usually performed to efficiently express the extracted information. Biochemical interpretation is assisted by online metabolome and pathway databases. Finally, GC × GC-MS technology provides an interesting alternative, which offers the highest separation capabilities among the various analytical separation technologies.