Non-Targeted Mass Isotopolome Analysis Using Stable Isotope Patterns to Identify Metabolic Changes

  • Christian-Alexander Dudek
  • Lisa Schlicker
  • Karsten HillerEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 2088)


Gas chromatography coupled with mass spectrometry can provide an extensive overview of the metabolic state of a biological system. Analysis of raw mass spectrometry data requires powerful data processing software to generate interpretable results. Here we describe a data processing workflow to generate metabolite levels, mass isotopomer distribution, similarity and variability analysis of metabolites in a nontargeted manner, using stable isotope labeling. Using our data analysis software, no bioinformatic or programming background is needed to generate results from raw mass spectrometry data.

Key words

Gas chromatography Mass spectrometry GCMS Data analysis Metabolism Mass isotopomer distribution Stable isotope labeling Nontargeted metabolomics 


  1. 1.
    Birkemeyer C, Luedemann A, Wagner C, Erban A, Kopka J (2005) Metabolome analysis: the potential of in vivo labeling with stable isotopes for metabolite profiling. Trends Biotechnol 23(1):28–33CrossRefGoogle Scholar
  2. 2.
    Zaitsu K, Hayashi Y, Kusano M, Tsuchihashi H, Ishii A (2016) Application of metabolomics to toxicology of drugs of abuse: a mini review of metabolomics approach to acute and chronic toxicity studies. Drug Metab Pharmacokinet 31(1):21–26CrossRefGoogle Scholar
  3. 3.
    Klapa MI, Stephanopoulos G (2000) Metabolic flux analysis. In: Bioreaction engineering. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 106–124CrossRefGoogle Scholar
  4. 4.
    Creek DJ, Chokkathukalam A, Jankevics A, Burgess KEV, Breitling R, Barrett MP (2012) Stable isotope-assisted metabolomics for network-wide metabolic pathway elucidation. Anal Chem 84(20):8442–8447CrossRefGoogle Scholar
  5. 5.
    Hiller K, Metallo C, Stephanopoulos G (2011) Elucidation of cellular metabolism via metabolomics and stable-isotope assisted metabolomics. Curr Pharm Biotechnol 12(7):1075–1086CrossRefGoogle Scholar
  6. 6.
    Hiller K, Metallo CM, Kelleher JK, Stephanopoulos G (2010) Nontargeted elucidation of metabolic pathways using stable-isotope tracers and mass spectrometry. Anal Chem 82(15):6621–6628CrossRefGoogle Scholar
  7. 7.
    Weindl D, Wegner A, Hiller K (2016) MIA: non-targeted mass isotopolome analysis. Bioinformatics 32(18):2875–2876CrossRefGoogle Scholar
  8. 8.
    Hiller K et al (2013) NTFD--a stand-alone application for the non-targeted detection of stable isotope-labeled compounds in GC/MS data. Bioinformatics 29(9):1226–1228CrossRefGoogle Scholar
  9. 9.
    Weindl D et al (2016) Bridging the gap between non-targeted stable isotope labeling and metabolic flux analysis. Cancer Metab 4(1):10CrossRefGoogle Scholar
  10. 10.
    Huang X, Chen Y-J, Cho K, Nikolskiy I, Crawford PA, Patti GJ (2014) X 13 CMS: global tracking of isotopic labels in untargeted metabolomics. Anal Chem 86(3):1632–1639CrossRefGoogle Scholar
  11. 11.
    Bueschl C et al (2017) MetExtract II: a software suite for stable isotope-assisted untargeted metabolomics. Anal Chem 89(17):9518–9526CrossRefGoogle Scholar
  12. 12.
    Jennings ME, Matthews DE (2005) Determination of complex Isotopomer patterns in Isotopically labeled compounds by mass spectrometry. Anal Chem 77(19):6435–6444CrossRefGoogle Scholar
  13. 13.
    Hiller K, Hangebrauk J, Jäger C, Spura J, Schreiber K, Schomburg D (2009) Metabolite detector: comprehensive analysis tool for targeted and nontargeted GC/MS based metabolome analysis. Anal Chem 81(9):3429–3439CrossRefGoogle Scholar
  14. 14.
    Hummel J, Strehmel N, Selbig J, Walther D, Kopka J (2010) Decision tree supported substructure prediction of metabolites from GC-MS profiles. Metabolomics 6(2):322–333CrossRefGoogle Scholar
  15. 15.
    Dietmair S, Timmins NE, Gray PP, Nielsen LK, Krömer JO (2010) Towards quantitative metabolomics of mammalian cells: development of a metabolite extraction protocol. Anal Biochem 404(2):155–164CrossRefGoogle Scholar
  16. 16.
    Gonzalez B, François J, Renaud M (1997) A rapid and reliable method for metabolite extraction in yeast using boiling buffered ethanol. Yeast 13(14):1347–1355CrossRefGoogle Scholar
  17. 17.
    Naz S, Moreira dos Santos DC, García A, Barbas C (2014) Analytical protocols based on LC–MS, GC–MS and CE–MS for nontargeted metabolomics of biological tissues. Bioanalysis 6(12):1657–1677CrossRefGoogle Scholar
  18. 18.
    Lisec J, Schauer N, Kopka J, Willmitzer L, Fernie AR (2006) Gas chromatography mass spectrometry–based metabolite profiling in plants. Nat Protoc 1(1):387–396CrossRefGoogle Scholar
  19. 19.
    Meiser J, Weindl D, Hiller K (2013) Complexity of dopamine metabolism. Cell Commun Signal 11(1):34CrossRefGoogle Scholar
  20. 20.
    Meiser J et al (2016) Pro-inflammatory macrophages sustain pyruvate oxidation through pyruvate dehydrogenase for the synthesis of Itaconate and to enable cytokine expression. J Biol Chem 291(8):3932–3946CrossRefGoogle Scholar
  21. 21.
    Stein SE (1999) An integrated method for spectrum extraction and compound identification from gas chromatography/mass spectrometry data. Journal of the American Society for Mass Spectrometry 10(8):770–781CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Christian-Alexander Dudek
    • 1
  • Lisa Schlicker
    • 1
  • Karsten Hiller
    • 1
    • 2
    Email author
  1. 1.Department of Bioinformatics and Biochemistry, Braunschweig Integrated Centre of Systems Biology (BRICS)Technische Universität BraunschweigBraunschweigGermany
  2. 2.Computational Biology of Infection ResearchHelmholtz Centre for Infection ResearchBraunschweigGermany

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