Skip to main content
SpringerLink
Log in

Untargeted GC-MS Metabolomics

  • Protocol
  • First Online:
Metabolic Profiling

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1738))

Abstract

Untargeted metabolomics refers to the high-throughput analysis of the metabolic state of a biological system (e.g., tissue, biological fluid, cell culture) based on the concentration profile of all measurable free low molecular weight metabolites. Gas chromatography-mass spectrometry (GC-MS), being a highly sensitive and high-throughput analytical platform, has been proven a useful tool for untargeted studies of primary metabolism in a variety of applications. As an omic analysis, GC-MS metabolomics is a multistep procedure; thus, standardization of an untargeted GC-MS metabolomics protocol requires the integrated optimization of pre-analytical, analytical, and computational steps. The main difference of GC-MS metabolomics compared to other metabolomics analytical platforms, including liquid chromatography-MS, is the need for the derivatization of the metabolite extracts into volatile and thermally stable derivatives, the latter being quantified in the metabolic profiles. This analytical step requires special care in the optimization of the untargeted GC-MS metabolomics experimental protocol. Moreover, both the derivatization of the original sample and the compound fragmentation that takes place in GC-MS impose specialized GC-MS metabolomic data identification, quantification, normalization and filtering methods. In this chapter, we describe the integrated protocol of untargeted GC-MS metabolomics with both the analytical and computational steps, focusing on the GC-MS specific parts, and provide details on any sample depending differences.

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

Access this chapter

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 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 119.00
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

Institutional subscriptions

References

  1. Fiehn O (2002) Metabolomics–the link between genotypes and phenotypes. Plant Mol Biol 48(1–2):155–171. https://doi.org/10.1007/978-94-010-0448-0_11

    Article  CAS  PubMed  Google Scholar 

  2. Kanani H, Chrysanthopoulos PK, Klapa MI (2008) Standardizing GC-MS metabolomics. J Chromatogr B 871(2):191–201. https://doi.org/10.1016/j.jchromb.2008.04.049

    Article  CAS  Google Scholar 

  3. Patti GJ, Yanes O, Siuzdak G (2012) Innovation: metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol 13(4):263–269. https://doi.org/10.1038/nrm3314

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Vasilopoulou CG, Margarity M, Klapa MI (2016) Metabolomic analysis in brain research: opportunities and challenges. Front Physiol 7:183. https://doi.org/10.3389/fphys.2016.00183

    Article  PubMed  PubMed Central  Google Scholar 

  5. Spagou K, Theodoridis G, Wilson I et al (2011) A GC-MS metabolic profiling study of plasma samples from mice on low- and high-fat diets. J Chromatogr B 879(17–18):1467–1475. https://doi.org/10.1016/j.jchromb.2011.01.028

    Article  CAS  Google Scholar 

  6. Kanani HH, Klapa MI (2007) Data correction strategy for metabolomics analysis using gas chromatography-mass spectrometry. Metab Eng 9(1):39–51. https://doi.org/10.1016/j.ymben.2006.08.001

    Article  CAS  PubMed  Google Scholar 

  7. Maga-Nteve C, Klapa MI (2016) Streamlining GC-MS metabolomic analysis using the M-IOLITE software suite. IFAC-PapersOnLine 49(26):286–288. https://doi.org/10.1016/j.ifacol.2016.12.140

    Article  Google Scholar 

  8. Dutta B, Kanani H, Quackenbush J, Klapa MI (2009) Time-series integrated “omic” analyses to elucidate short-term stress-induced responses in plant liquid cultures. Biotechnol Bioeng 102(1):264–279. https://doi.org/10.1002/Bit.22036

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Kanani H, Dutta B, Klapa MI (2010) Individual vs. combinatorial effect of elevated CO2 conditions and salinity stress on Arabidopsis thaliana liquid cultures: comparing the early molecular response using time-series transcriptomic and metabolomic analyses. BMC Syst Biol 4:177. https://doi.org/10.1186/1752-0509-4-177

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Tooulakou G, Giannopoulos A, Nikolopoulos D et al (2016) “Alarm photosynthesis”: calcium oxalate crystals as an internal CO2 source in plants. Plant Physiol 171(4):2577–2585. https://doi.org/10.1104/pp.16.00111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Constantinou C, Chrysanthopoulos PK, Margarity M, Klapa MI (2011) GC-MS metabolomic analysis reveals significant alterations in cerebellar metabolic physiology in a mouse model of adult onset hypothyroidism. J Proteome Res 10(2):869–879. https://doi.org/10.1021/pr100699m

    Article  CAS  PubMed  Google Scholar 

  12. Maga-Nteve C, Vasilopoulou CG, Constantinou C et al (2017) Sex-comparative study of mouse cerebellum physiology under adult-onset hypothyroidism: the significance of GC-MS metabolomic data normalization in meta-analysis. J Chromatogr B 1041-1042:158–166. https://doi.org/10.1016/j.jchromb.2016.12.016

    Article  CAS  Google Scholar 

  13. Chrysanthopoulos PK, Goudar CT, Klapa MI (2010) Metabolomics for high-resolution monitoring of the cellular physiological state in cell culture engineering. Metab Eng 12(3):212–222. https://doi.org/10.1016/j.ymben.2009.11.001

    Article  CAS  PubMed  Google Scholar 

  14. Vernardis SI, Goudar CT, Klapa MI (2013) Metabolic profiling reveals that time related physiological changes in mammalian cell perfusion cultures are bioreactor scale independent. Metab Eng 19:1–9. https://doi.org/10.1016/j.ymben.2013.04.005

    Article  CAS  PubMed  Google Scholar 

  15. Gkourogianni A, Kosteria I, Telonis AG et al (2014) Plasma metabolomic profiling suggests early indications for predisposition to latent insulin resistance in children conceived by ICSI. PLoS One 9(4):e94001. https://doi.org/10.1371/journal.pone.0094001

    Article  PubMed  PubMed Central  Google Scholar 

  16. Saeed AI, Bhagabati NK, Braisted JC et al (2006) TM4 microarray software suite. Methods Enzymol 411:134–193. https://doi.org/10.1016/S0076-6879(06)11009-5

    Article  CAS  PubMed  Google Scholar 

  17. Saeed AI, Sharov V, White J et al (2003) TM4: a free, open-source system for microarray data management and analysis. BioTechniques 34(2):374–378

    CAS  PubMed  Google Scholar 

  18. Allwood JW, Erban A, de Koning S et al (2009) Inter-laboratory reproducibility of fast gas chromatography-electron impact-time of flight mass spectrometry (GC-EI-TOF/MS) based plant metabolomics. Metabolomics 5(4):479–496. https://doi.org/10.1007/s11306-009-0169-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 95(25):14863–14868. https://doi.org/10.1073/pnas.95.25.14863

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Raychaudhuri S, Stuart JM, Altman RB (2000) Principal components analysis to summarize microarray experiments: application to sporulation time series. Pac Symp Biocomput 2000:455–466

    Google Scholar 

  21. Maitra S, Yan J (2008) Principle component analysis and partial least squares: two dimension reduction techniques for regression. In: 2008 Casualty actuarial society discussion paper program–applying multivariate statistical models. Casualty Actuarial Society, Quebec, pp 79–90

    Google Scholar 

  22. Tusher VG, Tibshirani R, Chu G (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 98(9):5116–5121. https://doi.org/10.1073/pnas.091062498

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Metabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research & Technology - Hellas (FORTH/ICE-HT), Patras, 26504, Greece

    Matthaios-Emmanouil P. Papadimitropoulos, Catherine G. Vasilopoulou, Christoniki Maga-Nteve & Maria I. Klapa

  2. Division of Genetics, Cell & Developmental Biology, Department of Biology, University of Patras, Patras, 26500, Greece

    Matthaios-Emmanouil P. Papadimitropoulos

  3. Human and Animal Physiology Laboratory, Department of Biology, University of Patras, Patras, 26500, Greece

    Catherine G. Vasilopoulou

  4. School of Medicine, University of Patras, Patras, 26500, Greece

    Christoniki Maga-Nteve

  5. Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD 20742, USA

    Maria I. Klapa

  6. Department of Bioengineering, University of Maryland, College Park, MD 20742, USA

    Maria I. Klapa

Authors
  1. Matthaios-Emmanouil P. Papadimitropoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Catherine G. Vasilopoulou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christoniki Maga-Nteve
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maria I. Klapa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maria I. Klapa .

Editor information

Editors and Affiliations

  1. Department of Chemistry, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Georgios A. Theodoridis

  2. School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Helen G. Gika

  3. Department of Surgery and Cancer, Imperial College London, London, United Kingdom

    Ian D. Wilson

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Papadimitropoulos, ME.P., Vasilopoulou, C.G., Maga-Nteve, C., Klapa, M.I. (2018). Untargeted GC-MS Metabolomics. In: Theodoridis, G., Gika, H., Wilson, I. (eds) Metabolic Profiling. Methods in Molecular Biology, vol 1738. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7643-0_9

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7643-0_9

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7642-3

  • Online ISBN: 978-1-4939-7643-0

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation