Skip to main content
SpringerLink
Log in
Book cover

Biomarker Methods in Drug Discovery and Development pp 317–340Cite as

GC-MS-Based Metabolomics

  • Protocol

Part of the book series: Methods in Pharmacology and Toxicology™ ((MIPT))

Summary

Gas chromatography–mass spectrometry (GC/MS)-based metabolomics profiling methods have been developed and used for plant metabolite profiling since the 1980s. Only during the past few years has the technology been more widely used for metabolomics studies in animals and humans with the aim of toxicology and biomarker discovery, disease diagnosis and classification.

GC-MS is ideally suited and has traditionally been used for analysis of nonpolar analytes like synthetic organic compounds and hydrophobic natural products. Nonpolar compounds have to be derivatized to make them amenable to analysis by GC-MS. This means that care has to be taken to ensure that the variability inevitably introduced by this preprocessing step is kept to an absolute minimum. Over the past few years, robust sample preparation and analysis methods have been developed, which will be described in this chapter. In addition, a brief introduction to gas chromatography and mass spectrometry will be given for readers who are less familiar with these subjects.

GC-MS is distinctly different from the two other more established analytical methods that are used in metabolomics: nuclear magnetic resonance (NMR) and liquid chromatography (LC)-MS, in that it covers a unique range of analyte polarities. It has been found that the overlap between GC-MS and LC-MS metabolomic profiling data is limited because of the differences in separation and ionization mechanisms.

GC-MS has very high sensitivity and can therefore be used for the analysis of less commonly encountered types of samples that might only be available in minute amounts. Examples for this application area will also be given in this chapter.

This is a preview of subscription content, log in via an institution.

Protocol
USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Denkert C, Budczies J, Kind T, et al. Mass spectrometry-based metabolic profiling reveals different metabolite patterns in invasive ovarian carcinomas and ovarian borderline tumours. Cancer Res 2006;66:10795–10804.

    Article  CAS  PubMed  Google Scholar 

  2. Förster J, Famili I, Fu P, Palsson B, Nielsen J. Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res 2003;13:244–253.

    Article  PubMed  Google Scholar 

  3. Griffin J. The Cinderella story of metabolic profiling: does metabolomics get to go to the functional genomics ball? Philos Trans R Soc Lond B Biol Sci 2006;361:147–161.

    Article  PubMed  Google Scholar 

  4. Jellum E, Stokke O, Eldjarn L. Application of gas chromatography, mass spectrometry, and computer methodsin clinical biochemistry. Anal Chem 1973;45:1099–1106.

    Article  CAS  Google Scholar 

  5. Shoemaker J, Elliott W. Automated screening of urine samples for carbohydrates, organic and amino acids after treatment with urease. J Chromatogr 1991;562:125–138.

    Article  CAS  PubMed  Google Scholar 

  6. Kuhara T. Gas chromatographic-mass spectrometric urinary metabolome analysis to study mutations of inborn errors of metabolism. Mass Spectrom Rev 2005;24:814–827.

    Article  CAS  PubMed  Google Scholar 

  7. Lisec J, Schauer N, Kopka J, Willmitzer L, Fernie A. Gas chromatography mass spectrometry-based metabolite profiling in plants. Nature Protocols 2006;1:387–396.

    Article  CAS  PubMed  Google Scholar 

  8. James A, Martin A. Gas-liquid partition chromatography: the separation and micro-estimation of volatile fatty acids from formic acid to dodecanoic acid. Biochem J 1952;50:679–690.

    CAS  PubMed  Google Scholar 

  9. Kopka J. Gas chromatography mass spectrometry. In: Saito K, Dixon R, Willmitzer L, eds. Biotechnology in Agriculture and Forestry. Berlin, Heidelberg: Springer-Verlag, 2006.

    Google Scholar 

  10. Grob R, Barry E. Modern Practice of Gas Chromatography. 4th ed. New York: John Wiley & Sons, 2004.

    Book  Google Scholar 

  11. Skoog D, Holler F, Nieman T. Gas chromatography. In: Principles of Instrumental Analysis. 5th ed. Philadelphia: Saunders College Publishing, 1998:702–722.

    Google Scholar 

  12. Sangster T, Major H, Plumb R, Wilson A, Wilson I. A pragmatic and readily implemented quality control strategy for HPLC-MS and GC-MS based metabanomic analysis. The Analyst 2006;131:1075–1078.

    Article  CAS  PubMed  Google Scholar 

  13. De Hoffmann E, Stroobant V. Mass Spectrometry—Principles and Applications. 3rd ed. New York: John Wiley & Sons, 2007.

    Google Scholar 

  14. Williamson L, Bartlett M. Quantitative gas chromatography/time-of-flight mass spectrometry: a review. Biomed Chromatogr 2007;21:664–669.

    Article  CAS  PubMed  Google Scholar 

  15. Kind T, Fiehn O. Metabolomic database annotations via query of elemental compositions: mass accuracy is insufficient even at less than 1 ppm. BMC Bioinformatics 2006;7:234.

    Article  PubMed  Google Scholar 

  16. Brown S, Kruppa G, Dasseux JL. Metabolomics applications of FT-ICR mass spectrometry. Mass Spectrom Rev 2005;24:223–231.

    Article  CAS  PubMed  Google Scholar 

  17. Kopka J. Current challenges and developments in GC-MS based metabolite profiling technology. J Biotechnol 2006;124:312–322.

    Article  CAS  PubMed  Google Scholar 

  18. Halket J, Zaikin V. Derivatization in mass spectrometry – 1. Silylation. Eur J Mass Spectrom 2003;9:1–21.

    Article  CAS  Google Scholar 

  19. Blau K, Halket J. Handbook of Derivatives for Chromatography. New York: John Wiley & Sons, 1993.

    Google Scholar 

  20. Fiehn O, Kind T. Metabolite profiling in blood plasma. In: Methods in Molecular Biology 358 (Metabolomics). Totowa, NJ: Humana Press Inc., 2007:3–17.

    Google Scholar 

  21. A Trygg J, Gullberg J, et al. Extraction and GC/MS analysis of the human blood plasma metabolome. Anal Chem 2005;77:8086–8094.

    Article  Google Scholar 

  22. Pohjanen E, Thysell E, Jonsson P, et al. A multivariate screening strategy for investigating metabolic effects of strenuous physical exercise in human serum. J Proteome Res 2007;6:2113–2120.

    Article  CAS  PubMed  Google Scholar 

  23. Zhang Q, Wang G, Du Y, Zhu L, A. GC/MS analysis of rat urine for metabonomic research. J Chromatogr B 2007;854:20–25.

    Article  CAS  Google Scholar 

  24. Fancy S, Beckonert O, Darbon G, et al. Gas chromatography/flame ionisation detection mass spectrometry for the detection of endogenous urine metabolites for metabonomics studies and its use as a complementary tool to nuclear magnetic resonance spectroscopy. Rapid Commun Mass Spectrom 2006;20:2271–2280.

    Article  CAS  PubMed  Google Scholar 

  25. Kind T, Tolstikov V, Fiehn O, Weiss R. A comprehensive urinary metabolomic approach for identifying kidney cancer. Anal Biochem 2007;363:185–195.

    Article  CAS  PubMed  Google Scholar 

  26. Atherton H, Bailey N, Zhang W, et al. A combined 1H-NMR spectroscopy- and mass spectrometry-based metabolomic study of the PPAR-\UPalpha null mutant mouse defines profound systemic changes in metabolism linked to the metabolic syndrome. Physiol Genomics 2006;27:178–186.

    Article  CAS  PubMed  Google Scholar 

  27. Welthagen W, Shellie R, Spranger J, Ristow M, Zimmermann R, Fiehn O. Comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry (GCxGC-TOF) for high resolution metabolomics: biomarker discovery on spleen tissue extracts of obese NZO compared to lean C57BL/6 mice. Metabolomics 2005;1:65–73.

    Article  CAS  Google Scholar 

  28. Weckwerth W, Loureiro M, Wenzel K, Fiehn O. Differential metabolic networks unravel the effects of silent plant phenotypes. Proc Natl Acad Sci U S A 2004;101:7809–7814.

    Google Scholar 

  29. Katajamaa M, Miettinen J, Orešic M. MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics 2006;22:634–636.

    Article  CAS  PubMed  Google Scholar 

  30. Smith C, Want E, O’Maille G, Abagyan R, Siuzdak G. XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 2006;78:779–787.

    Article  CAS  PubMed  Google Scholar 

  31. Wold S, Esbensen K, Geladi P. Principal components analysis. Chemom Intell Lab Syst 1987;2:37–52.

    Article  CAS  Google Scholar 

  32. Wold S, Albano C, Dunn W, et al. Multivariate analysis in chemometrics. In: Kowalsi B, ed. Chemometrics: Mathematics and Statistics in Chemistry. Dordrecht, The Netherlands: D Reidel Publishing Company, 1984.

    Google Scholar 

  33. Phillips J, Beens J. Comprehensive two-dimensional gas chromatography: a hyphenated method with strong coupling between two dimensions. J Chromatogr A 1999;856:327–334.

    Article  Google Scholar 

  34. Penn D, Oberzaucher E, Grammer K, et al. Individual and gender fingerprints in human body odour. J R Soc Interface 2007;4:331–340.

    Article  PubMed  Google Scholar 

  35. Soini H, Bruce K, Wiesler D, David F, Sandra P, Novotny M. Stir bar sorptive extraction: a new quantitative and comprehensive sampling technique for determination of chemical signal profiles from biological media. J Chem Ecol 2005;31:377–392.

    Article  CAS  PubMed  Google Scholar 

  36. Emmert-Buck M, Bonner R, Smith P, et al. Laser capture microdissection. Science 1996;274:998–1001.

    Article  CAS  PubMed  Google Scholar 

  37. Umar A, Luider T, Foekens J, Paša-Tolic L. NanoLC-FT-ICR MS improves proteome coverage attainable for ∼3000 laser-microdissected breast carcinoma cells. Proteomics 2007;7:323–329.

    Article  CAS  PubMed  Google Scholar 

  38. Schad M, Mungur R, Fiehn O, Kehr J. Metabolic profiling of laser microdissected vascular bundles of Arabidopsis thaliana. Plant Methods 2005;1:1–10.

    Article  Google Scholar 

  39. Tikunov Y, Verstappen F, Hall R. Metabolomic profiling of natural volatiles: headspace trapping: GC-MS. In: Weckwerth W, ed. Methods in Molecular Biology. Totowa, NJ: Humana Press Inc., 2007:39–53.

    Google Scholar 

  40. Wahl H, Hoffmann A, Luft D, Liebich H. Analysis of volatile organic compounds in human urine by headspace gas chromatography-mass spectrometry with a multipurpose sampler. J Chromatogr A 1999;847:117–125.

    Article  CAS  PubMed  Google Scholar 

  41. Lechner M, Rieder J. Mass spectrometric profiling of low-molecular-weight volatile compounds—diagnostic potential and latest applications. Curr Med Chem 2007;14:987–995.

    Article  CAS  PubMed  Google Scholar 

  42. Musteata F, Pawliszyn J. In vivo sampling with solid phase microextraction. J Biochem Biophys Methods 2007;70:181–193.

    Article  CAS  PubMed  Google Scholar 

  43. Sumner L, Mendes P, Dixon R. Plant metabolomics: large-scale phytochemistry in the functional genomics era. Phytochemistry 2003;62:817–836.

    Article  CAS  PubMed  Google Scholar 

  44. Nordström A, ÓMaille G, Qin C, Siuzdak G. Nonlinear data alignment for UPLC-MS and HPLC-MS based metabolomics: Qunatitative analysis of endogenous and exogenous metabolites in human serum. Anal Chem 2006;78:3289–3295.

    Article  PubMed  Google Scholar 

  45. Masood A, Stark K, Salem N. A simplified and efficient method for the analysis of fatty acid methyl esters suitable for large clincial studies. J Lipid Res 2005;46:2299–3305.

    Article  CAS  PubMed  Google Scholar 

  46. Ekins S, Nikolsky Y, Burgrim A, Kirillov E, Nikolskaya T. Pathway mapping tools for analysis of high content data. Methods Mol Biol 2007;356:319–350.

    CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Exploratory Medicinal Sciences, Pfizer Global Research and Development, Sandwich, Kent, United Kingdom

    Sally-Ann Fancy PhD & Klaus Rumpel

Authors
  1. Sally-Ann Fancy PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Klaus Rumpel
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Procter & Gamble Pharmaceutical Inc, Mason, OH, USA, 8700 Mason-Montgomery Road

    Feng Wang PhD

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Humana Press, a part of Springer Science+Business Media, LLC

About this protocol

Cite this protocol

Fancy, SA., Rumpel, K. (2008). GC-MS-Based Metabolomics. In: Wang, F. (eds) Biomarker Methods in Drug Discovery and Development. Methods in Pharmacology and Toxicology™. Humana Press. https://doi.org/10.1007/978-1-59745-463-6_15

Download citation

  • DOI: https://doi.org/10.1007/978-1-59745-463-6_15

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-934115-23-7

  • Online ISBN: 978-1-59745-463-6

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation