6. Conclusions ESI-MS analysis of lipid is the most prominent approach and has enjoyed the most success in lipidomics. With great efforts of the researchers in the field, a complete quantitative analysis of lipid classes, subclasses, and individual molecular species by using ESI-MS with or without

chromatographic separation is possible. now However, it is very important to understand the principles of quantitation by MS, learn the limitations of each platform for lipid analysis, and keep the general concerns in mind so that an accurate result can be obtained and a meaningful Inhibitors,research,lifescience,medical conclusion can be drawn. It is our sincere hope that with our precautions, we can successfully meet one of the major challenges (i.e., accurate quantification of individual lipid species by MS) in lipidomics. Acknowledgements This work was supported by National Institute on Aging/National Inhibitors,research,lifescience,medical Institute of Diabetes and Digestive and Kidney Diseases Grant R01 AG31675.

XH has a financial relationship with LipoSpectrum LLC. Special thanks to Ms. Stephanie Dickstein for editorial assistance.
For analysis of volatile compounds, gas-chromatography (GC) coupled to mass spectrometry (MS) allows high analysis throughput at relatively low Inhibitors,research,lifescience,medical cost. GC-MS is the most popular analytical technique in metabolomics today because it separates complex metabolite mixtures with high efficiency. Compound identification by GC-MS is also easier due to the high reproducibility of fragmentation patterns in electron impact (EI) ionization Inhibitors,research,lifescience,medical Inhibitors,research,lifescience,medical mass spectra, and the ready availability of libraries of spectra [1,2]. However, most naturally occurring metabolites are not sufficiently volatile to be analyzed directly on

a GC system. Chemical selleck chem derivatization of the metabolites is therefore required, and high analysis throughput by GC-MS relies on fast and efficient derivatization techniques [1,2]. A large number of derivatization methods for analysis of metabolites have been reported, but only a few are currently used in metabolomics [1,2]. Silylation of organic compounds is the classical and most widely used derivatization procedure for metabolome Drug_discovery analysis by GC-MS (Figure 1) [1-6]. Sugars and their derivatives (sugar alcohols, amino sugars, and others) are the class of metabolites most efficiently derivatized by silylation [1,2,6]. However, some important primary cell metabolites such as the amino acids and some organic acids produce relatively unstable silylated derivatives [7-9], which call for alternative derivatization methods for an efficient analysis of these compounds.