Skip to main content
SpringerLink
Log in

Multiplexed Profiling and Data Processing Methods to Identify Temperature-Regulated Primary Metabolites Using Gas Chromatography Coupled to Mass Spectrometry

  • Protocol
  • First Online:
Plant Cold Acclimation

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2156))

Abstract

This book chapter describes the analytical procedures required for the profiling of a metabolite fraction enriched for primary metabolites. The profiling is based on routine gas chromatography coupled to mass spectrometry (GC-MS). The generic profiling method is adapted to plant material, specifically to the analysis of plant material that was exposed to temperature stress. The method can be combined with stable isotope labeling and tracing experiments and is equally applicable to preparations of plant material and microbial photosynthetic organisms. The described methods are modular and can be multiplexed, that is, the same sample or a paired identical backup sample can be analyzed sequentially by more than one of the described procedures. The modules include rapid sampling and metabolic inactivation protocols for samples in a wide weight range, sample extraction procedures, chemical derivatization steps that are required to make the metabolite fraction amenable to gas chromatographic analysis, routine GC-MS methods, and procedures of data processing and data mining. A basic and extendable set of standardizations for metabolite recovery and retention index alignment of the resulting GC-MS chromatograms is included. The methods have two applications: (1) The rapid screening for changes of relative metabolite pools sizes under temperature stress and (2) the verification by exact quantification using GC-MS protocols that are extended by internal and external standardization.

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 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
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

Institutional subscriptions

Similar content being viewed by others

References

  1. Fiehn O, Kopka J, Dörmann P et al (2000) Metabolite profiling for plant functional genomics. Nat Biotechnol 18:1157–1161

    CAS  PubMed  Google Scholar 

  2. Roessner U, Wagner C, Kopka J et al (2000) Simultaneous analysis of metabolites in potato tuber by gas chromatography–mass spectrometry. Plant J 23:131–142

    CAS  PubMed  Google Scholar 

  3. 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 5:479–496

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Wagner C, Sefkow M, Kopka J (2003) 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

    CAS  PubMed  Google Scholar 

  5. Schauer N, Steinhauser D, Strelkov S et al (2005) GC–MS libraries for the rapid identification of metabolites in complex biological samples. FEBS Lett 579:1332–1337

    CAS  PubMed  Google Scholar 

  6. Kopka J, Schauer N, Krueger S et al (2004) GMD@CSB.DB: the Golm Metabolome Database. Bioinformatics 21:1635–1638

    PubMed  Google Scholar 

  7. Hummel J, Strehmel N, Selbig J et al (2010) Decision tree supported substructure prediction of metabolites from GC-MS profiles. Metabolomics 6:322–333

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Kaplan F, Kopka J, Haskell DW et al (2004) Exploring the temperature-stress metabolome of Arabidopsis. Plant Physiol 136:4159–4168

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Kaplan F, Kopka J, Sung DY et al (2007) Transcript and metabolite profiling during cold acclimation of Arabidopsis reveals an intricate relationship of cold-regulated gene expression with modifications in metabolite content. Plant J 50:967–981

    CAS  PubMed  Google Scholar 

  10. Guy C, Kaplan F, Kopka J et al (2008) Metabolomics of temperature stress. Physiol Plant 132:220–235

    CAS  PubMed  Google Scholar 

  11. Korn M, Gärtner T, Erban A et al (2010) Predicting Arabidopsis freezing tolerance and heterosis in freezing tolerance from metabolite composition. Mol Plant 3:224–235

    CAS  PubMed  Google Scholar 

  12. Dunn WB, Erban A, Weber RJM et al (2013) Mass appeal: metabolite identification in mass spectrometry-focused untargeted metabolomics. Metabolomics 9:44–66

    CAS  Google Scholar 

  13. Sumner LW, Amberg A, Barrett D et al (2007) Proposed minimum reporting standards for chemical analysis. Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 3:211–221

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Fernie AR, Aharoni A, Willmitzer L et al (2011) Recommendations for reporting metabolite data. Plant Cell 23:2477–2482

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Lisec J, Schauer N, Kopka J et al (2006) Gas chromatography mass spectrometry–based metabolite profiling in plants. Nat Protoc 1:387–396

    CAS  PubMed  Google Scholar 

  16. Erban A, Schauer N, Fernie AR et al (2007) Nonsupervised construction and application of mass spectral and retention time index libraries from time-of-flight gas chromatography-mass spectrometry metabolite profiles. In: Metabolomics: methods and protocols. Humana Press, Totowa, NJ, pp 19–38

    Google Scholar 

  17. Strehmel N, Hummel J, Erban A et al (2008) Retention index thresholds for compound matching in GC–MS metabolite profiling. J Chromatogr B 871:182–190

    CAS  Google Scholar 

  18. Murashige T, Skoog F (1962) A revised medium for rapid growth and bio assays with tobacco tissue cultures. Physiol Plant 15:473–497

    CAS  Google Scholar 

  19. Zhong HH, Painter JE, Salomé PA et al (1998) Imbibition, but not release from stratification, sets the circadian clock in Arabidopsis seedlings. Plant Cell 10:2005–2017

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Boyes DC, Zayed AM, Ascenzi R et al (2001) Growth stage–based phenotypic analysis of Arabidopsis. A model for high throughput functional genomics in plants. Plant Cell 13:1499–1510

    CAS  PubMed  PubMed Central  Google Scholar 

  21. van den Dool H, Kratz P (1963) A generalization of the retention index system including linear temperature programmed gas—liquid partition chromatography. J Chromatogr A 11:463–471

    Google Scholar 

  22. Birkemeyer C, Kolasa A, Kopka J (2003) Comprehensive chemical derivatization for gas chromatography–mass spectrometry-based multi-targeted profiling of the major phytohormones. J Chromatogr A 993:89–102

    CAS  PubMed  Google Scholar 

  23. Luedemann A, Strassburg K, Erban A et al (2008) TagFinder for the quantitative analysis of gas chromatography—mass spectrometry (GC-MS)-based metabolite profiling experiments. Bioinformatics 24:732–737

    CAS  PubMed  Google Scholar 

  24. Luedemann A, von Malotky L, Erban A et al (2012) TagFinder: preprocessing software for the fingerprinting and the profiling of gas chromatography–mass spectrometry based metabolome analyses, in Plant metabolomics: methods and protocols. Humana Press, Totowa, NJ

    Google Scholar 

  25. Lawas LMF, Li X, Erban A et al (2019) Metabolic responses of rice cultivars with different tolerance to combined drought and heat stress under field conditions. GigaScience 8:giz050. https://doi.org/10.1093/gigascience/giz050

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Wolfender JL, Rudaz S, Choi YH et al (2014) Plant metabolomics: from holistic data to relevant biomarkers. Curr Med Chem 20:1056–1090

    Google Scholar 

  27. Zhang Q, Bhattacharya S, Andersen ME (2013) Ultrasensitive response motifs: basic amplifiers in molecular signalling networks. Open Biol 3:130031

    PubMed  PubMed Central  Google Scholar 

  28. de Abreu e Lima F, Leifels L, Nikoloski Z (2018) Regression-based modeling of complex plant traits based on metabolomics data. In: Plant metabolomics: methods and protocols. Springer, New York, NY, pp 321–327

    Google Scholar 

  29. van den Berg RA, Hoefsloot HCJ et al (2006) Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 7:142

    PubMed  PubMed Central  Google Scholar 

  30. Borgognone MAG, Bussi J, Hough G (2001) Principal component analysis in sensory analysis: covariance or correlation matrix? Food Qual Prefer 12:323–326

    Google Scholar 

  31. Rogers JK, Guzman CD, Taylor ND et al (2015) Synthetic biosensors for precise gene control and real-time monitoring of metabolites. Nucleic Acids Res 43:7648–7660

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Kacser H, Burns JA, Kacser H et al (1995) The control of flux: 21 years on. Biochem Soc Trans 23:341

    CAS  PubMed  Google Scholar 

  33. Lopez-Fontal E, Milanesi L, Tomas S (2016) Multivalence cooperativity leading to “all-or-nothing” assembly: the case of nucleation-growth in supramolecular polymers. Chem Sci 7:4468–4475

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Hastie T, Tibshirani R, Narasimhan B et al. (2018) Impute: imputation for microarray data. R package version 1.56.0.

    Google Scholar 

  35. Zhang S (2012) Nearest neighbor selection for iteratively kNN imputation. J Syst Softw 85:2541–2552

    Google Scholar 

  36. Josse J, Husson F (2016) missMDA: a package for handling missing values in multivariate data analysis. J Stat Softw 70:1–31

    Google Scholar 

  37. Do K, Wahl S, Raffler J et al (2018) Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies. Metabolomics 14:128–128

    PubMed  PubMed Central  Google Scholar 

  38. Wehrens R, Hageman JA, van Eeuwijk F et al (2016) Improved batch correction in untargeted MS-based metabolomics. Metabolomics 12:88

    PubMed  PubMed Central  Google Scholar 

  39. Delignette-Muller ML, Dutang C (2015) fitdistrplus: an R package for fitting distributions. J Stat Softw 64:1–34

    Google Scholar 

  40. Massey FJ (1951) The Kolmogorov-Smirnov test for goodness of fit. J Am Stat Assoc 46:68–78

    Google Scholar 

  41. Levene H (1960) Robust tests for equality of variances. In: Contributions to probability and statistics: essays in honor of Harold Hotelling. Stanford University Press, Stanford, CA, pp 278–292

    Google Scholar 

  42. Gooch JW (2011) Kruskal-Wallis test. In: Encyclopedic dictionary of polymers. Springer Science & Business Media, New York, NY, pp 984–985

    Google Scholar 

  43. Breslow NE (1995) Generalized linear models: checking assumptions and strengthening conclusions. In: Congresso Nazionale Societa’ Italiana di Biometria Centro Convegni S. Agostino, Cortona, Italy

    Google Scholar 

  44. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol 57:289–300

    Google Scholar 

  45. Suzuki R, Shimodaira H (2006) Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics 22(12):1540–1542

    CAS  PubMed  Google Scholar 

  46. Grapov D, Wanichthanarak K, Fiehn O (2015) MetaMapR: pathway independent metabolomic network analysis incorporating unknowns. Bioinformatics 31(16):2757–27604

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Breiman L (2001) Random Forests. Mach Learn 45:5–32

    Google Scholar 

  48. Touw WG, Bayjanov JR et al (2012) Data mining in the life sciences with Random Forest: a walk in the park or lost in the jungle? Brief Bioinform 14:315–326

    PubMed  PubMed Central  Google Scholar 

  49. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297

    Google Scholar 

  50. Westerhuis JA, van Velzen EJJ, Hoefsloot HCJ et al (2010) Multivariate paired data analysis: multilevel PLSDA versus OPLSDA. Metabolomics 6:119–128

    CAS  PubMed  Google Scholar 

  51. Huege J, Sulpice R, Gibon Y et al (2007) GC-EI-TOF-MS analysis of in vivo carbon-partitioning into soluble metabolite pools of higher plants by monitoring isotope dilution after 13CO2 labelling. Phytochemistry 68:2258–2272

    CAS  PubMed  Google Scholar 

  52. Strassburg K, Walther D, Takahashi H et al (2010) Dynamic transcriptional and metabolic responses in yeast adapting to temperature stress. OMICS 14:249–259

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Huege J, Goetze J, Schwarz D et al (2011) Modulation of the major paths of carbon in photorespiratory mutants of Synechocystis. PLoS One 6:e16278

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Strehmel N, Kopka J, Scheel D et al (2014) Annotating unknown components from GC/EI-MS-based metabolite profiling experiments using GC/APCI(+)-QTOFMS. Metabolomics 10:324–336

    CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Applied Metabolome Analysis Research Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany

    Alexander Erban, Federico Martinez-Seidel, Yogeswari Rajarathinam, Frederik Dethloff, Isabel Orf, Ines Fehrle, Jessica Alpers, Olga Beine-Golovchuk & Joachim Kopka

  2. Proteomics and Biomarkers, Max Planck Institute of Psychiatry, München, Germany

    Frederik Dethloff

  3. Owlstone Medical Ltd, Cambridge, UK

    Isabel Orf

  4. Nuclear Pore Complex and Ribosome Assembly, Biochemie-Zentrum, Universität Heidelberg, Heidelberg, Germany

    Olga Beine-Golovchuk

Authors
  1. Alexander Erban
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Federico Martinez-Seidel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yogeswari Rajarathinam
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Frederik Dethloff
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Isabel Orf
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ines Fehrle
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jessica Alpers
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Olga Beine-Golovchuk
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Joachim Kopka
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Joachim Kopka .

Editor information

Editors and Affiliations

  1. Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam, Germany

    Dirk K. Hincha

  2. Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam, Germany

    Ellen Zuther

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Erban, A. et al. (2020). Multiplexed Profiling and Data Processing Methods to Identify Temperature-Regulated Primary Metabolites Using Gas Chromatography Coupled to Mass Spectrometry. In: Hincha, D., Zuther, E. (eds) Plant Cold Acclimation. Methods in Molecular Biology, vol 2156. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0660-5_15

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-0660-5_15

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0659-9

  • Online ISBN: 978-1-0716-0660-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation