Current challenges and developments in GC-MS based metabolite profiling technology.
Gas chromatography-mass spectrometry (GC-MS) based metabolite profiling of biological samples is one of the key technologies for metabolite profiling and substantially contributes to our understanding of the metabolome. While the technology is in increasing use it is challenged with novel demands. Increasing the number of metabolite identifications within existing profiling platforms is prerequisite for a substantially improved scope of profiling studies. Clear, reproducible strategies for metabolite identification and exchange of identifications between laboratories will facilitate further developments, such as the extension of profiling technologies towards metabolic signals and other technically demanding trace compound analysis. Using GC-MS technology as an example the concept of mass spectral tags (MSTs) is presented. A mass spectral tag is defined by the chemometric properties, molecular mass to charge ratio, chromatographic retention index and an induced mass fragmentation pattern such as an electron impact mass spectrum (EI-MS) or secondary fragmentation (MS(2)). These properties if properly documented will allow identification of hitherto non-identified MSTs by standard addition experiments of authenticated reference substances even years after first MST description. Strategies are discussed for MST identification and enhanced MST characterization utilizing experimental schemes such as in vivo stable isotope labelling of whole organisms and open access information distribution, for example the GMG internet platform initiated in 2004 (GMD, http://www.csbdb.mpimp-golm.mpg.de/gmd.html).
Kopka
Joachim
J
Max Planck Institute of Molecular Plant Physiology, Department Molecular Plant Physiology (Prof. L. Willmitzer), Golm, Germany. Kopka@mpimp-golm.mpg.de
D050260:Carbohydrate Metabolism; D016208:Databases, Factual; D008401:Gas Chromatography-Mass Spectrometry / Q000379:methods* / Q000639:trends*; D020869:Gene Expression Profiling / Q000379:methods; D050356:Lipid Metabolism; D010944:Plants / Q000737:chemistry* / Q000378:metabolism*