Gas chromatography mass spectrometry (GC-MS) is an effective system for the analysis of volatile metabolites contributing to vegetable/fruit aromas, plant defense responses, short-chain alcohols, acids, esters and hydrocarbons, and thermally stable compounds. However, relatively few metabolites are truly volatile and so, many metabolites can only be analyzed by GC following chemical derivatization. Following derivatization, a large portion of small molecule metabolites, especially those found centrally in metabolism, enter the range of feasible GC separation.
The main advantages of using GC-MS for metabolomics are its high chromatographic separation power, high peak capacity, reproducible retention times, robust quantitation, high selectivity and sensitivity, and fast compound identification using existing commercial spectral libraries (e.g., NIST, Wiley).
In order to obtain a broad coverage of metabolites, GC-MS analysis usually requires a two-step chemical derivatization [1] of the following functional groups:
Both steps are necessary to reduce polarity and increase thermal stability and volatility.
Modern robotic autosamplers are capable of automating two-step derivatization protocols by using optimum heating temperatures for each step, and by optimizing overall cycle time to overlap the derivatization steps of two consecutive samples.
GC-MS and LC-MS typically use completely different mechanisms for sample ionization. Unlike LC-MS, GC-MS generates reproducible molecular fragmentation patterns, making it an integral tool for metabolite identification.
With GC-MS, the sample is often ionized by electron ionization (EI). EI ion spectra are stable, reproducible and compound-specific; therefore, they can be used to confirm chemical identities by comparing measured spectra with those of existing spectral libraries (e.g., NIST). Sometimes, in addition to EI, chemical ionization (CI) is used as a soft ionization alternative to increase confidence in compound identification.
Accurate and reliable compound identification is one of the most important reasons for the popularity of GC-MS in metabolomics studies. However, because of the diversity of chemical structures and concentrations, identification of metabolites is often challenging.
A comparison of generated spectra with those of commercial libraries, such as the NIST and Wiley libraries, can tentatively identify many metabolites. Nevertheless, co-eluting peaks remain a challenge, as do the limited numbers of compound entries in commercial libraries. To address these hurdles, key research labs have developed their own databases and libraries and made them open source, including such examples as the Fiehn library and Golm Metabolome Database .
With the recent advances in high resolution accurate mass GC-MS systems, (e.g., Orbitrap mass analyzers), it is now possible to calculate the empirical chemical formula of molecular ions. This increases confidence in compound identification by adding accurate mass information as a confirmation criterion.
To truly address unknown metabolite identities, is it often necessary to further fragment the molecular ion obtained from CI experiments via MS/MS. This is followed by automated, software-aided, de novo interpretation and structural elucidation as part of the identification process.
Ion chromatography (IC)-MS is best suited to the analysis of charged or very polar metabolites (e.g., sugar phosphates, amino acids) that are difficult to analyze by LC-MS. IC also features high resolution, enabling the separation of isomers and isotopes prior to mass spectrometry analysis.
Liquid chromatography (LC)-MS offers the broadest coverage due to its ability to work with different column chemistries including reversed phase and hydrophobic interaction liquid chromatography. Common LC-MS compounds include lipids, polyamines and alcohols.
