Structural Mass Spectrometry for Metabolomics

  • Richard M. HigashiEmail author
Part of the Methods in Pharmacology and Toxicology book series (MIPT)


Structural mass spectrometry (MS) is a chemical structure-revealing analytical technique that measures mass/charge ratio of ions. Biochemical analyses in the context of metabolomics—which seeks chemical descriptions of cellular, organismal, and even community biology—pose challenges distinct from past applications of MS. The scientific need for both far greater depth and breadth of metabolic analytes is manifested analytically in two general approaches: the “Biomarker” approach and the “Pathway” approach. In this chapter, the basic principles of mass spectrometry relevant to these two approaches to metabolomics are reviewed. The emphasis is on practical aspects of how experimental design interacts with instrument designs and capabilities. Both simulated theoretical and real-life analyses are used to illustrate the key concepts and their ramifications.

Key words

High resolution MS Stable isotope resolved metabolomics Isotopologue analysis Metabolic pathways 



Data-dependent MSn


Fourier transform ion cyclotron resonance


Gas chromatography


High information throughput


Inductively coupled plasma MS




Liquid chromatography


Minimum detectable limits


Minimum quantifiable limits


Multiple reaction monitoring


Mass spectrometry


MS to the 2nd or tandem MS


MS to the nth


Natural abundance


Nuclear magnetic resonance spectroscopy




Radio frequency


Stable isotope-resolved metabolomics


Time of flight


Ultraviolet visible



The unpublished data shown here was supported in part by grants from NSF EPSCoR EPS-0447479, NIH 1R01CA118434-01A2, NCI R21CA133688, and the Susan G. Komen Foundation BCTR0503648 while the published data was supported by the grants listed in the cited publications. The author thanks T. Fan, A.N. Lane, H, Moseley, J. Winiike, P. Lorkiewicz, M. Arita, J. Goran, and T. Cassel, among numerous others, for helpful discussions.


  1. 1.
    Watson JT (1985) Introduction to mass spectrometry. Raven, New YorkGoogle Scholar
  2. 2.
    Fan TWM et al (1997) Anaerobic nitrate and ammonium metabolism in flood-tolerant rice coleoptiles. J Exp Bot 48(314): 1655–1666CrossRefGoogle Scholar
  3. 3.
    Fan TW et al (2009) Altered regulation of metabolic pathways in human lung cancer discerned by (13)C stable isotope-resolved metabolomics (SIRM). Mol Cancer 8:41PubMedCentralPubMedCrossRefGoogle Scholar
  4. 4.
    Fan T et al (2005) Metabolomics-edited transcriptomics analysis of Se anticancer action in human lung cancer cells. Metabolomics J 1(4):325–339CrossRefGoogle Scholar
  5. 5.
    Aharoni A et al (2002) Nontargeted metabolome analysis by use of fourier transform ion cyclotron mass spectrometry. OMICS 6(3):217–234PubMedCrossRefGoogle Scholar
  6. 6.
    Lane AN et al (2009) Isotopomer analysis of lipid biosynthesis by high resolution mass spectrometry and NMR. Anal Chim Acta 651: 201–208PubMedCentralPubMedCrossRefGoogle Scholar
  7. 7.
    Fan TW-M et al (2010) Stable isotope-resolved metabolomic analysis of lithium effects on glial-neuronal metabolism and interactions. Metabolomics 6(2):165–179PubMedCentralPubMedCrossRefGoogle Scholar
  8. 8.
    Lane AN et al (2009) Prospects for clinical cancer metabolomics using stable isotope tracers. Exp Mol Pathol 86(3):165–173PubMedCentralPubMedCrossRefGoogle Scholar
  9. 9.
    Lane AN, Fan TW, Higashi RM (2008) Stable isotope-assisted metabolomics in cancer research. IUBMB Life 60(2):124–129PubMedCrossRefGoogle Scholar
  10. 10.
    Lane AN, Fan TW, Higashi RM (2008) Isotopomer-based metabolomic analysis by NMR and mass spectrometry. Methods Cell Biol 84: 541–588PubMedCrossRefGoogle Scholar
  11. 11.
    Fan T et al (2008) Rhabdomyosarcoma cells show an energy producing anabolic metabolic phenotype compared with primary myocytes. Mol Cancer 7(1):79PubMedCentralPubMedCrossRefGoogle Scholar
  12. 12.
    Fan TWM, Higashi RM, Lane AN (2006) Integrating metabolomics and transcriptomics for probing Se anticancer mechanisms. Drug Metab Rev 38(4):707–732PubMedCrossRefGoogle Scholar
  13. 13.
    Fan TWM, Lane AN, Higashi RM (2003) In vivo and in vitro metabolomic analysis of anaerobic rice coleoptiles revealed unexpected pathways. Russ J Plant Physiol 50(6): 787–793CrossRefGoogle Scholar
  14. 14.
    Moseley HN (2010) Correcting for the effects of natural abundance in stable isotope resolved metabolomics experiments involving ultra-high resolution mass spectrometry. BMC Bioinformatics 11:139PubMedCentralPubMedCrossRefGoogle Scholar
  15. 15.
    Arita M (2003) In silico atomic tracing by substrate-product relationships in Escherichia coli intermediary metabolism. Genome Res 13(11): 2455–2466PubMedCrossRefGoogle Scholar
  16. 16.
    Arita M (2004) Computational resources for metabolomics. Brief Funct Genomic Proteomic 3(1):84–93PubMedCrossRefGoogle Scholar
  17. 17.
    Arita M (2009) What can metabolomics learn from genomics and proteomics? Curr Opin Biotechnol 20(6):610–615PubMedCrossRefGoogle Scholar
  18. 18.
    Cascante M et al (2010) Metabolic network adaptations in cancer as targets for novel therapies. Biochem Soc Trans 38(5): 1302–1306PubMedCrossRefGoogle Scholar
  19. 19.
    Fan TWM, Lane AN, Higashi RM (2004) An electrophoretic profiling method for thiol-rich phytochelatins and metallothioneins. Phytochem Anal 15(3):175–183PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Department of Chemistry, Center for Regulatory and Environmental Analytical Metabolomics (CREAM), and James Graham Brown Cancer CenterUniversity of LouisvilleLouisvilleUSA

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