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Untargeted GC-MS Metabolomics

  • Matthaios-Emmanouil P. Papadimitropoulos
  • Catherine G. Vasilopoulou
  • Christoniki Maga-Nteve
  • Maria I. Klapa
Protocol
Part of the Methods in Molecular Biology book series (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.

Key words

Untargeted metabolomics Gas chromatography-mass spectrometry (GC-MS) metabolomics Metabolic profiling Metabolic network analysis Primary metabolism 

References

  1. 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 CrossRefPubMedGoogle Scholar
  2. 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 CrossRefGoogle Scholar
  3. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 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 CrossRefGoogle Scholar
  6. 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 CrossRefPubMedGoogle Scholar
  7. 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 CrossRefGoogle Scholar
  8. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  10. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  11. 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 CrossRefPubMedGoogle Scholar
  12. 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 CrossRefGoogle Scholar
  13. 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 CrossRefPubMedGoogle Scholar
  14. 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 CrossRefPubMedGoogle Scholar
  15. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 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 CrossRefPubMedGoogle Scholar
  17. 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):374–378PubMedGoogle Scholar
  18. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Raychaudhuri S, Stuart JM, Altman RB (2000) Principal components analysis to summarize microarray experiments: application to sporulation time series. Pac Symp Biocomput 2000:455–466Google Scholar
  21. 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 79–90Google Scholar
  22. 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 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Matthaios-Emmanouil P. Papadimitropoulos
    • 1
    • 2
  • Catherine G. Vasilopoulou
    • 1
    • 3
  • Christoniki Maga-Nteve
    • 1
    • 4
  • Maria I. Klapa
    • 1
    • 5
    • 6
  1. 1.Metabolic Engineering and Systems Biology LaboratoryInstitute of Chemical Engineering Sciences, Foundation for Research & Technology - Hellas (FORTH/ICE-HT)PatrasGreece
  2. 2.Division of Genetics, Cell & Developmental Biology, Department of BiologyUniversity of PatrasPatrasGreece
  3. 3.Human and Animal Physiology Laboratory, Department of BiologyUniversity of PatrasPatrasGreece
  4. 4.School of MedicineUniversity of PatrasPatrasGreece
  5. 5.Department of Chemical and Biomolecular EngineeringUniversity of MarylandCollege ParkUSA
  6. 6.Department of BioengineeringUniversity of MarylandCollege ParkUSA

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