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Gas chromatography mass spectrometry–based metabolite profiling in plants

Nature Protocols volume 1, pages 387–396 (2006)Cite this article

A Corrigendum to this article was published on 27 August 2015

This article has been updated

Abstract

The concept of metabolite profiling has been around for decades, but technical innovations are now enabling it to be carried out on a large scale with respect to the number of both metabolites measured and experiments carried out. Here we provide a detailed protocol for gas chromatography mass spectrometry (GC-MS)-based metabolite profiling that offers a good balance of sensitivity and reliability, being considerably more sensitive than NMR and more robust than liquid chromatography–linked mass spectrometry. We summarize all steps from collecting plant material and sample handling to derivatization procedures, instrumentation settings and evaluating the resultant chromatograms. We also define the contribution of GC-MS–based metabolite profiling to the fields of diagnostics, gene annotation and systems biology. Using the protocol described here facilitates routine determination of the relative levels of 300–500 analytes of polar and nonpolar extracts in ∼400 experimental samples per week per machine.

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Figure 1: Experimental procedure for extract preparation.
Figure 2: Normalization.
Figure 3: Timeline of standard operating procedure.
Figure 4: Retention time variation within sample sets.
Figure 5: Example of a peak deconvolution error.
Figure 6: Median and box-plot spectra.
Figure 7: Spectra annotation correction process.

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  • 05 August 2015

    In the version of this article initially published, Figure 1 contained the words "metoxyamin hydrochloride." These words should have read "methoxyamine hydrochloride." The error has been corrected in the HTML and PDF versions of the article.

References

  1. Fernie, A.R., Trethewey, R.N., Krotzky, A.J. & Willmitzer, L. Innovation—metabolite profiling: from diagnostics to systems biology. Nat. Rev. Mol. Cell Biol. 5, 763–769 (2004).

    Article  CAS  Google Scholar 

  2. Kopka, J., Fernie, A., Weckwerth, W., Gibon, Y. & Stitt, M. Metabolite profiling in plant biology: platforms and destinations. Genome Biol. 5, 109 (2004).

    Article  Google Scholar 

  3. Hirai, M.Y. & Saito, K. Post-genomics approaches for the elucidation of plant adaptive mechanisms to sulphur deficiency. J. Exp. Bot. 55, 1871–1879 (2004).

    Article  CAS  Google Scholar 

  4. Sumner, L.W., Mendes, P. & Dixon, R.A. Plant metabolomics: large-scale phytochemistry in the functional genomics era. Phytochemistry 62, 817–836 (2003).

    Article  CAS  Google Scholar 

  5. Harrigan, G.G. & Goodacre, R. Metabolic Profiling: Its Role in Biomarker Discovery and Gene Function Analysis (Springer, Berlin/Heidelberg, 2003).

    Book  Google Scholar 

  6. Soga, T. et al. Quantitative metabolome analysis using capillary electrophoresis mass spectrometry. J. Proteome Res. 2, 488–494 (2003).

    Article  CAS  Google Scholar 

  7. Roessner, U. et al. Metabolic profiling allows comprehensive phenotyping of genetically or environmentally modified plant systems. Plant Cell 13, 11–29 (2001).

    Article  CAS  Google Scholar 

  8. Fiehn, O. et al. Metabolite profiling for plant functional genomics. Nat. Biotechnol. 18, 1157–1161 (2000).

    Article  CAS  Google Scholar 

  9. Wasim, M., Hassan, M.S. & Brereton, R.G. Evaluation of chemometric methods for determining the number and position of components in high-performance liquid chromatography detected by diode array detector and on-flow H-1 nuclear magnetic resonance spectroscopy. Analyst 128, 1082–1090 (2003).

    Article  CAS  Google Scholar 

  10. Lindon, J.C. HPLC-NMR-MS: past, present and future. Drug Discov. Today 8, 1021–1022 (2003).

    Article  Google Scholar 

  11. Meiler, J. & Will, M. Genius: a genetic algorithm for automated structure elucidation from C-13 NMR spectra. J. Am. Chem. Soc. 124, 1868–1870 (2002).

    Article  CAS  Google Scholar 

  12. Halket, J.M. & Zaikin, V.G. Derivatization in mass spectrometry. 1. Silylation. Eur. J. Mass Spectrom. 9, 1–21 (2003).

    Article  CAS  Google Scholar 

  13. Roessner-Tunali, U. et al. Metabolic profiling of transgenic tomato plants overexpressing hexokinase reveals that the influence of hexose phosphorylation diminishes during fruit development. Plant Physiol. 133, 84–99 (2003).

    Article  CAS  Google Scholar 

  14. Roessner, U., Wagner, C., Kopka, J., Trethewey, R.N. & Willmitzer, L. Simultaneous analysis of metabolites in potato tuber by gas chromatography-mass spectrometry. Plant J. 23, 131–142 (2000).

    Article  CAS  Google Scholar 

  15. Swart, P.J. et al. HPLC-UV atmospheric-pressure ionization mass-spectrometric determination of the dopamine-D2 agonist N-0923 and its major metabolites after oxidative-metabolism by rat-liver, monkey liver, and human liver-microsomes. Toxicol. Methods 3, 279–290 (1993).

    Article  CAS  Google Scholar 

  16. Aharoni, A. et al. Nontargeted metabolome analysis by use of Fourier transform ion cyclotron mass spectrometry. OMICS 6, 217–234 (2003).

    Article  Google Scholar 

  17. Plumb, R.S. et al. Use of liquid chromatography/time-of-flight mass spectrometry and multivariate statistical analysis shows promise for the detection of drug metabolites in biological fluids. Rapid Commun. Mass Spectrom. 17, 2632–2638 (2003).

    Article  CAS  Google Scholar 

  18. Sato, S., Soga, T., Nishioka, T. & Tomita, M. Simultaneous determination of the main metabolites in rice leaves using capillary electrophoresis mass spectrometry and capillary electrophoresis diode array detection. Plant J. 40, 151–163 (2004).

    Article  CAS  Google Scholar 

  19. Unger, M. et al. Analytical characterisation of crude extracts from an African Ancistrocladus species using high-performance liquid chromatography and capillary electrophoresis coupled to ion trap mass spectrometry. Phytochem. Anal. 15, 21–26 (2004).

    Article  CAS  Google Scholar 

  20. Taylor, J., King, R.D., Altmann, T. & Fiehn, O. Application of metabolomics to plant genotype discrimination using statistics and machine learning. Bioinformatics 18, S241–S248 (2002).

    Article  Google Scholar 

  21. Wagner, C., Sefkow, M. & Kopka, J. Construction and application of a mass spectral and retention time index database generated from plant GC/EI-TOF-MS metabolite profiles. Phytochemistry 62, 887–900 (2003).

    Article  CAS  Google Scholar 

  22. Saito, K., Dixon, R.A. & Willmitzer, L. Plant Metabolomics (eds Nagata, T.L.H. & Widholm, J.M.) (Springer, Berlin/Heidelberg, 2006).

    Book  Google Scholar 

  23. Smith, C.A., Want, E.J., O'Maille, G., Abagyan, R. & Siuzdak, G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78, 779–787 (2006).

    Article  CAS  Google Scholar 

  24. Duran, A.L., Yang, J., Wang, L.J. & Sumner, L.W. Metabolomics spectral formatting, alignment and conversion tools (MSFACTs). Bioinformatics 19, 2283–2293 (2003).

    Article  CAS  Google Scholar 

  25. Weckwerth, W., Loureiro, M.E., Wenzel, K. & Fiehn, O. Differential metabolic networks unravel the effects of silent plant phenotypes. Proc. Natl. Acad. Sci. USA 101, 7809–7814 (2004).

    Article  CAS  Google Scholar 

  26. Kaplan, F. et al. Exploring the temperature-stress metabolome of Arabidopsis. Plant Physiol. 136, 4159–4168 (2004).

    Article  CAS  Google Scholar 

  27. Fiehn, O., Kopka, J., Trethewey, R.N. & Willmitzer, L. Identification of uncommon plant metabolites based on calculation of elemental compositions using gas chromatography and quadrupole mass spectrometry. Anal. Chem. 72, 3573–3580 (2000).

    Article  CAS  Google Scholar 

  28. Gullberg, J., Jonsson, P., Nordstrom, A., Sjostrom, M. & Moritz, T. Design of experiments: an efficient strategy to identify factors influencing extraction and derivatization of Arabidopsis thaliana samples in metabolomic studies with gas chromatography/mass spectrometry. Anal. Biochem. 331, 283–295 (2004).

    Article  CAS  Google Scholar 

  29. Sauter, H., Lauer, M. & Fritsch, H. Metabolic profiling of plants—a new diagnostic technique. Am. Chem. Soc. Symp. Ser. 443, 288–299 (1991).

    CAS  Google Scholar 

  30. Desbrosses, G.G., Kopka, J. & Udvardi, M.K. Lotus japonicus metabolic profiling. Development of gas chromatography–mass spectrometry resources for the study of plant-microbe interactions. Plant Physiol. 137, 1302–1318 (2005).

    Article  CAS  Google Scholar 

  31. Roessner, U., Willmitzer, L. & Fernie, A.R. High-resolution metabolic phenotyping of genetically and environmentally diverse potato tuber systems. Identification of phenocopies. Plant Physiol. 127, 749–764 (2001).

    Article  CAS  Google Scholar 

  32. Junker, B.H. et al. Temporally regulated expression of a yeast invertase in potato tubers allows dissection of the complex metabolic phenotype obtained following its constitutive expression. Plant Mol. Biol. 56, 91–110 (2004).

    Article  CAS  Google Scholar 

  33. Catchpole, G.S. et al. Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops. Proc. Natl. Acad. Sci. USA 102, 14458–14462 (2005).

    Article  CAS  Google Scholar 

  34. Defernez, M. et al. NMR and HPLC-UV profiling of potatoes with genetic modifications to metabolic pathways. J. Agric. Food Chem. 52, 6075–6085 (2004).

    Article  CAS  Google Scholar 

  35. Hirai, M. et al. Transcriptome and metabolome analyses reveal a whole adaptive process of plant to sulfur deficiency. Plant Cell Physiol. 45, S122–S122 (2004).

    Article  Google Scholar 

  36. Nikiforova, V.J. et al. Systems rebalancing of metabolism in response to sulfur deprivation, as revealed by metabolome analysis of Arabidopsis plants. Plant Physiol. 138, 304–318 (2005).

    Article  CAS  Google Scholar 

  37. Urbanczyk-Wochniak, E. & Fernie, A.R. Metabolic profiling reveals altered nitrogen nutrient regimes have diverse effects on the metabolism of hydroponically-grown tomato (Solanum lycopersicum) plants. J. Exp. Bot. 56, 309–321 (2005).

    Article  CAS  Google Scholar 

  38. Broeckling, C.D. et al. Metabolic profiling of Medicago truncatula cell cultures reveals the effects of biotic and abiotic elicitors on metabolism. J. Exp. Bot. 56, 323–336 (2005).

    Article  CAS  Google Scholar 

  39. Schnee, C. et al. The products of a single maize sesquiterpene synthase form a volatile defense signal that attracts natural enemies of maize herbivores. Proc. Natl. Acad. Sci. USA 103, 1129–1134 (2006).

    Article  CAS  Google Scholar 

  40. Suzuki, H. et al. Methyl jasmonate and yeast elicitor induce differential transcriptional and metabolic re-programming in cell suspension cultures of the model legume Medicago truncatula. Planta 220, 696–707 (2005).

    Article  CAS  Google Scholar 

  41. Tohge, T. et al. Functional genomics by integrated analysis of metabolome and transcriptome of Arabidopsis plants over-expressing an MYB transcription factor. Plant J. 42, 218–235 (2005).

    Article  CAS  Google Scholar 

  42. Goossens, A. et al. A functional genomics approach toward the understanding of secondary metabolism in plant cells. Proc. Natl. Acad. Sci. USA 100, 8595–8600 (2003).

    Article  CAS  Google Scholar 

  43. Morikawa, T. et al. Cytochrome P450 CYP710A encodes the sterol C-22 desaturase in Arabidopsis and tomato. Plant Cell 18, 1008–1022 (2006).

    Article  CAS  Google Scholar 

  44. Hirai, M.Y. et al. Elucidation of gene-to-gene and metabolite-to-gene networks in Arabidopsis by integration of metabolomics and transcriptomics. J. Biol. Chem. 280, 25590–25595 (2005).

    Article  CAS  Google Scholar 

  45. Tagashira, N. et al. The metabolic profiles of transgenic cucumber lines vary with different chromosomal locations of the transgene. Cell. Mol. Biol. Lett. 10, 697–710 (2005).

    CAS  PubMed  Google Scholar 

  46. Schauer, N. et al. Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement. Nat. Biotechnol. 24, 447–454 (2006).

    Article  CAS  Google Scholar 

  47. Askenazi, M. et al. Integrating transcriptional and metabolite profiles to direct the engineering of lovastatin-producing fungal strains. Nat. Biotechnol. 21, 150–156 (2003).

    Article  CAS  Google Scholar 

  48. Urbanczyk-Wochniak, E. et al. Parallel analysis of transcript and metabolic profiles: a new approach in systems biology. EMBO Rep. 4, 989–993 (2003).

    Article  CAS  Google Scholar 

  49. Jenkins, H. et al. A proposed framework for the description of plant metabolomics experiments and their results. Nat. Biotechnol. 22, 1601–1606 (2004).

    Article  CAS  Google Scholar 

  50. Scholz, M., Gatzek, S., Sterling, A., Fiehn, O. & Selbig, J. Metabolite fingerprinting: detecting biological features by independent component analysis. Bioinformatics 20, 2447–2454 (2004).

    Article  CAS  Google Scholar 

  51. Scholz, M., Kaplan, F., Guy, C.L., Kopka, J. & Selbig, J. Non-linear PCA: a missing data approach. Bioinformatics 21, 3887–3895 (2005).

    Article  CAS  Google Scholar 

  52. Steuer, R., Kurths, J., Fiehn, O. & Weckwerth, W. Observing and interpreting correlations in metabolomic networks. Bioinformatics 19, 1019–1026 (2003).

    Article  CAS  Google Scholar 

  53. Urbanczyk-Wochniak, E. et al. Profiling of diurnal patterns of metabolite and transcript abundance in potato (Solanum tuberosum) leaves. Planta 221, 891–903 (2005).

    Article  CAS  Google Scholar 

  54. Ishizaki, K. et al. The critical role of Arabidopsis electron-transfer flavoprotein: Ubiquinone oxidoreductase during dark-induced starvation. Plant Cell 17, 2587–2600 (2005).

    Article  CAS  Google Scholar 

  55. Ishizaki, K. et al. The functional association between Arabidopsis electron transfer flavoprotein (ETF) and electron transfer flavoprotein ubiquinone oxidereductase (ETFQO) during dark induced starvation. Plant J. (in press).

  56. Stitt, M. & Fernie, A.R. From measurements of metabolites to metabolomics: an ‘on the fly’ perspective illustrated by recent studies of carbon-nitrogen interactions. Curr. Opin. Biotechnol. 14, 136–44 (2003).

    Article  CAS  Google Scholar 

  57. Schad, M., Mungur, R., Fiehn, O. & Kehr, J. Metabolic profiling of laser microdissected vascular bundles of Arabidopsis thaliana. Plant Methods 1, 2 (2005).

    Article  Google Scholar 

  58. Roessner-Tunali, U. et al. Kinetics of labelling of organic and amino acids in potato tubers by gas chromatography-mass spectrometry following incubation in 13C labelled isotopes. Plant J. 39, 668–679 (2004).

    Article  CAS  Google Scholar 

  59. Tieman, D. et al. Tomato aromatic amino acid decarboxylases participate in synthesis of the flavor volatiles 2-phenylethanol and 2-phenylacetaldehyde. Proc. Natl. Acad. Sci. USA 103, 8287–8291 (2006).

    Article  CAS  Google Scholar 

  60. Roessner-Tunali, U. et al. De novo amino acid biosynthesis in potato tubers is regulated by sucrose levels. Plant Physiol. 133, 683–692 (2003).

    Article  CAS  Google Scholar 

  61. Kopka, J. et al. GMD@CSB.DB: the Golm Metabolome Database. Bioinformatics 21, 1635–1638 (2005).

    Article  CAS  Google Scholar 

  62. Schauer, N. et al. GC-MS libraries for the rapid identification of metabolites in complex biological samples. FEBS Lett. 579, 1332–1337 (2005).

    Article  CAS  Google Scholar 

  63. Tolstikov, V.V. & Fiehn, O. Analysis of highly polar compounds of plant origin: combination of hydrophilic interaction chromatography and electrospray ion trap mass spectrometry. Anal. Biochem. 301, 298–307 (2002).

    Article  CAS  Google Scholar 

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Acknowledgements

We thank O. Fiehn and Ä. Eckhardt for intensive collegial discussions on all matters metabolomic.

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Author notes

  1. Jan Lisec and Nicolas Schauer: These authors contributed equally to this work.

Authors and Affiliations

  1. Max-Planck-Institut für Molekulare Pflanzenphysiologie, Am Mühlenberg 1, Potsdam-Golm, 14476, Germany

    Jan Lisec, Nicolas Schauer, Joachim Kopka, Lothar Willmitzer & Alisdair R Fernie

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Correspondence to Alisdair R Fernie.

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Lisec, J., Schauer, N., Kopka, J. et al. Gas chromatography mass spectrometry–based metabolite profiling in plants. Nat Protoc 1, 387–396 (2006). https://doi.org/10.1038/nprot.2006.59

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