Gas Chromatography-Mass Spectrometry (GC-MS)-Based Metabolomics

  • Antonia Garcia
  • Coral BarbasEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 708)


Metabolic fingerprinting, the main tool in metabolomics, is a non-targeted methodology where all detectable peaks (or signals), including those from unknown analytes, are considered to establish sample classification. After pattern comparison, those signals changing in response to a specific situation under investigation are identified to gain biological insight. For this purpose, gas chromatographymass spectrometry (GC-MS) has a drawback in that only volatile compounds or compounds that can be made volatile after derivatization can be analysed, and derivatization often requires extensive sample treatment. However, once the analysis is focused on low molecular weight metabolites, GC-MS is highly efficient, sensitive, and reproducible. Moreover, it is quantitative, and its compound identification capabilities are superior to other separation techniques because GC-MS instruments obtain mass spectra with reproducible fragmentation patterns, which allow for the creation of public databases. This chapter describes well-established protocols for metabolic fingerprinting (i.e. the comprehensive analysis of small molecules) in plasma and urine using GC-MS. Guidelines will also be provided regarding subsequent data pre-treatment, pattern recognition, and marker identification.

Key words

Pattern recognition urine plasma multivariate analysis silylation volatile compounds gas chromatography-mass spectrometry 



The authors acknowledge Joanna Teul for her careful experimental work and funding support from the Comunidad de Madrid, S-GEN-0247-2006 and Ministry of Science and Technology (MCIT) CTQ2008-03779.


  1. 1.
    Kanani, H., Chrysanthopoulos, P. K., Klapa, M. I. (2008) Standardizing GC–MS metabolomics. J Chromatogr B 871, 191–201.CrossRefGoogle Scholar
  2. 2.
    Pasikanti, K. K., Ho, P. C., Chan, E. C. Y. (2008) Gas chromatography/mass spectrometry in metabolic profiling of biological fluids. J Chromatogr B 871, 202–211.CrossRefGoogle Scholar
  3. 3.
    Lenz, E. M., Bright, J., Wilson, I. D., Morgan, S. R., Nash, A. F. (2003) A 1H NMR-based metabonomic study of urine and plasma samples obtained from healthy human subjects. J Pharm Biomed Anal 33, 1103–1115.PubMedCrossRefGoogle Scholar
  4. 4.
    Daykin, C. A., Foxall, P. J. D., Connor, S. C., Lindon, J. C., Nicholson, J. K. (2002) The comparison of plasma deproteinization methods for the detection of low-molecular-weight metabolites by 1 h nuclear magnetic resonance spectroscopy. Anal Biochem 304, 220–230.PubMedCrossRefGoogle Scholar
  5. 5.
    Wu, S. L., Amato, H., Biringer, R., Choudhary, G., Shieh, P., Hancock, W. S. (2002) Targeted proteomics of low-level proteins in human plasma by LC/msn: using human growth hormone as a model system. J Proteome Res 21, 253–262.Google Scholar
  6. 6.
    Jiye, A., Trygg, J., Gullberg, J., Johansson, A. I., Jonsson, P., Antti, H., Marklund, S. L., Moritz, T. (2005) Extraction and GC/MS analysis of the human blood plasma metabolome. Anal Chem 77, 8086–8094.CrossRefGoogle Scholar
  7. 7.
    Tietz, N. W. (1986) Textbook of Clinical Chemistry, Saunders, Philadelphia, PA. p. 590.Google Scholar
  8. 8.
    Halket, J. M., Waterman, D., Przyborowska, A. M., Patel, R. K. P., Fraser, P. D., Bramley, P. M. (2005) Chemical derivatization and mass spectral libraries in metabolic profiling by GC/MS and LC/MS/MS. J Exp Bot 56, 219–243.PubMedCrossRefGoogle Scholar
  9. 9.
    Palisade Corporation web page. Accesed May 2009.
  10. 10.
    Tohge, T., Fernie, A. R. (2009) Web-based resources for mass-spectrometry-based metabolomics: a user’s guide. Phytochemistry 70, 450–456.PubMedCrossRefGoogle Scholar
  11. 11.
    Luque-Garcia, J. L., Neubert, T. A. (2007) Sample preparation for serum/plasma profiling and biomarker identification by mass spectrometry. J Chromatogr A 1153, 259–276.PubMedCrossRefGoogle Scholar
  12. 12.
    Kuhara, T. (2005) Metabolomics: The Frontier of Systems Biology, Springer, Tokyo.Google Scholar
  13. 13.
    Kind, T., Tolstikov, V., Fiehn, O., Weiss, R. H. (2007) A comprehensive urinary metabolomic approach for identifying kidney cancer. Anal Biochem 363, 185–195.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Faculty of PharmacySan Pablo-CEUMadridSpain

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