Advertisement

Metabolomics

, Volume 9, Supplement 1, pp 44–66 | Cite as

Mass appeal: metabolite identification in mass spectrometry-focused untargeted metabolomics

  • Warwick B. Dunn
  • Alexander Erban
  • Ralf J. M. Weber
  • Darren J. Creek
  • Marie Brown
  • Rainer Breitling
  • Thomas Hankemeier
  • Royston Goodacre
  • Steffen Neumann
  • Joachim Kopka
  • Mark R. Viant
Original Article

Abstract

Metabolomics has advanced significantly in the past 10 years with important developments related to hardware, software and methodologies and an increasing complexity of applications. In discovery-based investigations, applying untargeted analytical methods, thousands of metabolites can be detected with no or limited prior knowledge of the metabolite composition of samples. In these cases, metabolite identification is required following data acquisition and processing. Currently, the process of metabolite identification in untargeted metabolomic studies is a significant bottleneck in deriving biological knowledge from metabolomic studies. In this review we highlight the different traditional and emerging tools and strategies applied to identify subsets of metabolites detected in untargeted metabolomic studies applying various mass spectrometry platforms. We indicate the workflows which are routinely applied and highlight the current limitations which need to be overcome to provide efficient, accurate and robust identification of metabolites in untargeted metabolomic studies. These workflows apply to the identification of metabolites, for which the structure can be assigned based on entries in databases, and for those which are not yet stored in databases and which require a de novo structure elucidation.

Keywords

Capillary electrophoresis Metabolomics Metabolite identification Structure elucidation Mass spectrometry Gas chromatography Liquid chromatography Ultra performance liquid chromatography DIMS 

Notes

Acknowledgments

WD and MB gratefully acknowledge support from the National Institute for Health Research (NIHR) Manchester Biomedical Research Centre and the UK NorthWest Development Agency (NWDA). RW thanks both the British Heart Foundation (PG/10/036/28341) and UK Engineering and Physical Sciences Research Council (EP/J501414/1) for support. RG is very grateful to the UK BBSRC for financial support. DJC is funded by an Australian National Health and Medical Research Council (NHMRC) Training Fellowship.

References

  1. An, Z., Chen, Y., Zhang, R., Song, Y., Sun, J., He, J., et al. (2010). Integrated ionization approach for RRLC-MS/MS-based metabonomics: Finding potential biomarkers for lung cancer. Journal of Proteome Research, 9(8), 4071–4081.PubMedGoogle Scholar
  2. Beckmann, M., Parker, D., Enot, D. P., Duval, E., & Draper, J. (2008). High-throughput, nontargeted metabolite fingerprinting using nominal mass flow injection electrospray mass spectrometry. Nature Protocols, 3(3), 486–504.PubMedGoogle Scholar
  3. Bird, S. S., Marur, V. R., Sniatynski, M. J., Greenberg, H. K., & Kristal, B. S. (2011). Serum lipidomics profiling using LC-MS and high-energy collisional dissociation fragmentation: Focus on triglyceride detection and characterization. Analytical Chemistry, 83(17), 6648–6657.PubMedGoogle Scholar
  4. Birkemeyer, C., Kolasa, A., & Kopka, J. (2003). Comprehensive chemical derivatization for gas chromatography-mass spectrometry-based multi-targeted profiling of the major phytohormones. Journal of Chromatography A, 993(1–2), 89–102.PubMedGoogle Scholar
  5. 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 in Biotechnology, 23(1), 28–33.PubMedGoogle Scholar
  6. Boroujerdi, A. F., Vizcaino, M. I., Meyers, A., Pollock, E. C., Huynh, S. L., Schock, T. B., et al. (2009). NMR-based microbial metabolomics and the temperature-dependent coral pathogen Vibrio coralliilyticus. Environmental Science and Technology, 43(20), 7658–7664.PubMedGoogle Scholar
  7. Breitling, R., Pitt, A. R., & Barrett, M. P. (2006a). Precision mapping of the metabolome. Trends in Biotechnology, 24(12), 543–548.PubMedGoogle Scholar
  8. Breitling, R., Ritchie, S., Goodenowe, D., Stewart, M. L., & Barrett, M. P. (2006b). Ab initio prediction of metabolic networks using Fourier transform mass spectrometry data. Metabolomics, 2(3), 155–164.Google Scholar
  9. Brown, M., Dunn, W. B., Dobson, P., Patel, Y., Winder, C. L., Francis-McIntyre, S., et al. (2009). Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics. Analyst, 134(7), 1322–1332.PubMedGoogle Scholar
  10. Brown, M., Wedge, D. C., Goodacre, R., Kell, D. B., Baker, P. N., Kenny, L. C., et al. (2011). Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets. Bioinformatics, 27(8), 1108–1112.PubMedGoogle Scholar
  11. Caspi, R., Foerster, H., Fulcher, C. A., Kaipa, P., Krummenacker, M., Latendresse, M., et al. (2008). The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Research, 36(Database issue), D623–D631.PubMedGoogle Scholar
  12. Castro-Perez, J., Roddy, T. P., Nibbering, N. M., Shah, V., McLaren, D. G., Previs, S., et al. (2011). Localization of fatty acyl and double bond positions in phosphatidylcholines using a dual stage CID fragmentation coupled with ion mobility mass spectrometry. Journal of the American Society for Mass Spectrometry, 22(9), 1552–1567.PubMedGoogle Scholar
  13. Creek, D. J., Jankevics, A., Breitling, R., Watson, D. G., Barrett, M. P., & Burgess, K. E. (2011). Toward global metabolomics analysis with hydrophilic interaction liquid chromatography-mass spectrometry: Improved metabolite identification by retention time prediction. Analytical Chemistry, 83(22), 8703–8710.PubMedGoogle Scholar
  14. Creek, D. J., Jankevics, A., Burgess, K. E., Breitling, R., & Barrett, M. P. (2012). IDEOM: An Excel interface for analysis of LC-MS based metabolomics data. Bioinformatics, 28(7), 1048–1049.PubMedGoogle Scholar
  15. Crockford, D. J., Maher, A. D., Ahmadi, K. R., Barrett, A., Plumb, R. S., Wilson, I. D., et al. (2008). 1H NMR and UPLC-MS(E) statistical heterospectroscopy: Characterization of drug metabolites (xenometabolome) in epidemiological studies.” Analytical Chemistry, 80(18), 6835–6844.PubMedGoogle Scholar
  16. de Hoffmann, E., & Stroobant, V. (2007). Mass spectrometry—Principle and applications. Chichester: Wiley.Google Scholar
  17. De Vos, R. C., Moco, S., Lommen, A., Keurentjes, J. J., Bino, R. J., & Hall, R. D. (2007). Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry. Nature Protocols, 2(4), 778–791.PubMedGoogle Scholar
  18. Dear, G. J., Plumb, R. S., Sweatman, B. C., Ismail, I. M., & Ayrton, J. (1999). Tandem mass spectrometry linked fraction collection for the isolation of drug metabolites from biological matrices. Rapid Communications in Mass Spectrometry, 13(10), 886–894.PubMedGoogle Scholar
  19. Desbrosses, G. G., Kopka, J., & Udvardi, M. K. (2005). Lotus japonicus metabolic profiling. Development of gas chromatography-mass spectrometry resources for the study of plant–microbe interactions. Plant Physiology, 137(4), 1302–1318.PubMedGoogle Scholar
  20. Dettmer, K., Aronov, P. A., & Hammock, B. D. (2007). Mass spectrometry-based metabolomics. Mass Spectrometry Reviews, 26(1), 51–78.PubMedGoogle Scholar
  21. Draper, J., Enot, D. P., Parker, D., Beckmann, M., Snowdon, S., Lin, W., et al. (2009). Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour ‘rules’. BMC Bioinformatics, 10(1), 227.PubMedGoogle Scholar
  22. Dunn, W. B. (2008). Current trends and future requirements for the mass spectrometric investigation of microbial, mammalian and plant metabolomes. Physical Biology, 5(1), 011001.PubMedGoogle Scholar
  23. Dunn, W. B., Broadhurst, D. I., Atherton, H. J., Goodacre, R., & Griffin, J. L. (2011a). Systems level studies of mammalian metabolomes: The roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chemical Society Reviews, 40(1), 387–426.PubMedGoogle Scholar
  24. Dunn, W. B., Broadhurst, D., Begley, P., Zelena, E., Francis-McIntyre, S., Anderson, N., et al. (2011b). Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols, 6(7), 1060–1083.PubMedGoogle Scholar
  25. Dunn, W. B., Brown, M., Worton, S. A., Crocker, I. P., Broadhurst, D., Horgan, R., et al. (2009). Changes in the metabolic footprint of placental explant-conditioned culture medium identifies metabolic disturbances related to hypoxia and pre-eclampsia. Placenta, 30(11), 974–980.PubMedGoogle Scholar
  26. Erve, J. C., Gu, M., Wang, Y., DeMaio, W., & Talaat, R. E. (2009). Spectral accuracy of molecular ions in an LTQ/Orbitrap mass spectrometer and implications for elemental composition determination. Journal of the American Society for Mass Spectrometry, 20(11), 2058–2069.PubMedGoogle Scholar
  27. Eyres, G. T., Urban, S., Morrison, P. D., Dufour, J. P., & Marriott, P. J. (2008). Method for small-molecule discovery based on microscale-preparative multidimensional gas chromatography isolation with nuclear magnetic resonance spectroscopy. Analytical Chemistry, 80(16), 6293–6299.PubMedGoogle Scholar
  28. Farag, M. A., Deavours, B. E., de Fátima, A., Naoumkina, M., Dixon, R. A., & Sumner, L. W. (2009). Integrated metabolite and transcript profiling identify a biosynthetic mechanism for hispidol in Medicago truncatula cell cultures. Plant Physiology, 151(3), 1096–1113.PubMedGoogle Scholar
  29. Feldberg, L., Venger, I., Malitsky, S., Rogachev, I., & Aharoni, A. (2009). Dual labeling of metabolites for metabolome analysis (DLEMMA): A new approach for the identification and relative quantification of metabolites by means of dual isotope labeling and liquid chromatography-mass spectrometry. Analytical Chemistry, 81(22), 9257–9266.PubMedGoogle Scholar
  30. Fenn, J. B., Mann, M., Meng, C. K., Wong, S. F., & Whitehouse, C. M. (1989). Electrospray ionization for mass spectrometry of large biomolecules. Science, 246(4926), 64–71.PubMedGoogle Scholar
  31. Fernie, A. R., Aharoni, A., Willmitzer, L., Stitt, M., Tohge, T., Kopka, J., et al. (2011). Recommendations for reporting metabolite data. The Plant Cell, 23(7), 2477–2482.PubMedGoogle Scholar
  32. Fiehn, O. (2001). Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks. Comparative and Functional Genomics, 2(3), 155–168.PubMedGoogle Scholar
  33. Fiehn, O. (2002). Metabolomics—The link between genotypes and phenotypes. Plant Molecular Biology, 48(1–2), 155–171.PubMedGoogle Scholar
  34. Fiehn, O., Kopka, J., Dörmann, P., Altmann, T., Trethewey, R. N., & Willmitzer, L. (2000a). Metabolite profiling for plant functional genomics. Nature Biotechnology, 18(11), 1157–1161.PubMedGoogle Scholar
  35. Fiehn, O., Kopka, J., Trethewey, R. N., & Willmitzer, L. (2000b). Identification of uncommon plant metabolites based on calculation of elemental compositions using gas chromatography and quadrupole mass spectrometry. Analytical Chemistry, 72(15), 3573–3580.PubMedGoogle Scholar
  36. Fuhrer, T., Heer, D., Begemann, B., & Zamboni, N. (2011). High-throughput, accurate mass metabolome profiling of cellular extracts by flow injection-time-of-flight mass spectrometry. Analytical Chemistry, 83(18), 7074–7080.PubMedGoogle Scholar
  37. Giavalisco, P., Hummel, J., Lisec, J., Inostroza, A. C., Catchpole, G., & Willmitzer, L. (2008). High-resolution direct infusion-based mass spectrometry in combination with whole 13C metabolome isotope labeling allows unambiguous assignment of chemical sum formulas. Analytical Chemistry, 80(24), 9417–9425.PubMedGoogle Scholar
  38. Giavalisco, P., Köhl, K., Hummel, J., Seiwert, B., & Willmitzer, L. (2009). 13C isotope-labeled metabolomes allowing for improved compound annotation and relative quantification in liquid chromatography-mass spectrometry-based metabolomic research. Analytical Chemistry, 81(15), 6546–6551.PubMedGoogle Scholar
  39. Gipson, G. T., Tatsuoka, K. S., Sokhansanj, B. A., Ball, R. J., & Connor, S. C. (2008). Assignment of MS-based metabolomic datasets via compound interaction pair mapping. Metabolomics, 4(1), 94–103.Google Scholar
  40. Goodacre, R. (2007). Metabolomics of a superorganism. Journal of Nutrition, 137(1 Suppl), 259S–266S.PubMedGoogle Scholar
  41. Halket, J. M., & Zaikin, V. G. (2003). Derivatization in mass spectrometry—1. Silylation. European Journal of Mass Spectrometry, 9(1), 1–21.PubMedGoogle Scholar
  42. Heinonen, M., Rantanen, A., Mielikäinen, T., Kokkonen, J., Kiuru, J., Ketola, R. A., et al. (2008). FiD: A software for ab initio structural identification of product ions from tandem mass spectrometric data. Rapid Communications in Mass Spectrometry, 22(19), 3043–3052.PubMedGoogle Scholar
  43. Herrgård, M. J., Swainston, N., Dobson, P., Dunn, W. B., Arga, K. Y., Arvas, M., et al. (2008). A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nature Biotechnology, 26(10), 1155–1160.PubMedGoogle Scholar
  44. Hildebrandt, C., Wolf, S., & Neumann, S. (2011). Database supported candidate search for metabolite identification. Journal of Integrative Bioinformatics, 8(2), 157.PubMedGoogle Scholar
  45. Hill, D. W., Kertesz, T. M., Fontaine, D., Friedman, R., & Grant, D. F. (2008). Mass spectral metabonomics beyond elemental formula: Chemical database querying by matching experimental with computational fragmentation spectra. Analytical Chemistry, 80(14), 5574–5582.PubMedGoogle Scholar
  46. Hill, A. W., & Mortishire-Smith, R. J. (2005). Automated assignment of high-resolution collisionally activated dissociation mass spectra using a systematic bond disconnection approach. Rapid Communications in Mass Spectrometry, 19(21), 3111–3118.Google Scholar
  47. Hoekman, B., Breitling, R., Suits, F., Bischoff, R., & Horvatovich, P. (2012). msCompare: A framework for quantitative analysis of label-free LC-MS data for comparative biomarker studies. Molecular & Cellular Proteomics. doi: 10.1074/mcp.M111.015974.
  48. Hoopmann, M. R., Merrihew, G. E., von Haller, P. D., & MacCoss, M. J. (2009). Post analysis data acquisition for the iterative MS/MS sampling of proteomics mixtures. Journal of Proteome Research, 8(4), 1870–1875.PubMedGoogle Scholar
  49. Horai, H., Arita, M., Kanaya, S., Nihei, Y., Ikeda, T., Suwa, K., et al. (2010). MassBank: A public repository for sharing mass spectral data for life sciences. Journal of Mass Spectrometry, 45(7), 703–714.PubMedGoogle Scholar
  50. Huege, J., Goetze, J., Schwarz, D., Bauwe, H., Hagemann, M., & Kopka, J. (2011). Modulation of the major paths of carbon in photorespiratory mutants of synechocystis. PLoS ONE, 6(1), e16278.PubMedGoogle Scholar
  51. Huege, J., Sulpice, R., Gibon, Y., Lisec, J., Koehl, K., & Kopka, J. (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(16–18), 2258–2272.PubMedGoogle Scholar
  52. 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), 322–333.PubMedGoogle Scholar
  53. Iijima, Y., Nakamura, Y., Ogata, Y., Tanaka, K., Sakurai, N., Suda, K., et al. (2008). Metabolite annotations based on the integration of mass spectral information. The Plant Journal, 54(5), 949–962.PubMedGoogle Scholar
  54. Kahar, P., Taku, K., & Tanaka, S. (2011). Enhancement of xylose uptake in 2-deoxyglucose tolerant mutant of Saccharomyces cerevisiae. Journal of Bioscience and Bioengineering, 111(5), 557–563.PubMedGoogle Scholar
  55. Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M., & Hirakawa, M. (2010). KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Research, 38(Database issue), D355–D360.PubMedGoogle Scholar
  56. Kaufmann, A. (2010). Strategy for the elucidation of elemental compositions of trace analytes based on a mass resolution of 100,000 full width at half maximum. Rapid Communications in Mass Spectrometry, 24(14), 2035–2045.PubMedGoogle Scholar
  57. Kenny, L. C., Broadhurst, D. I., Dunn, W., Brown, M., North, R. A., McCowan, L., et al. (2010). Robust early pregnancy prediction of later preeclampsia using metabolomic biomarkers. Hypertension, 56(4), 741–749.PubMedGoogle Scholar
  58. Kind, T., & Fiehn, O. (2006). Metabolomic database annotations via query of elemental compositions: Mass accuracy is insufficient even at less than 1 ppm. BMC Bioinformatics, 7, 234.PubMedGoogle Scholar
  59. Kind, T., & Fiehn, O. (2007). Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. BMC Bioinformatics, 8, 105.PubMedGoogle Scholar
  60. Kind, T., & Fiehn, O. (2011). Advances in structure elucidation of small molecules using mass spectrometry. Bioanalytical Reviews, 2(1–4), 23–60.Google Scholar
  61. Kind, T., Wohlgemuth, G., Lee, D. Y., Lu, Y., Palazoglu, M., Shahbaz, S., et al. (2009). FiehnLib: Mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Analytical Chemistry, 81(24), 10038–10048.PubMedGoogle Scholar
  62. Kirchmair, J., Williamson, M. J., Tyzack, J. D., Tan, L., Bond, P. J., Bender, A., et al. (2012). Computational prediction of metabolism: Sites, products, SAR, P450 enzyme dynamics, and mechanisms. Journal of Chemical Information and Modeling, 52(3), 617–648.PubMedGoogle Scholar
  63. Koch, B. P., Dittmar, T., Witt, M., & Kattner, G. (2007). Fundamentals of molecular formula assignment to ultrahigh resolution mass data of natural organic matter. Analytical Chemistry, 79(4), 1758–1763.PubMedGoogle Scholar
  64. Komatsu, M., Uchiyama, T., Omura, S., Cane, D. E., & Ikeda, H. (2010). Genome-minimized Streptomyces host for the heterologous expression of secondary metabolism. The Proceedings of the National Academy of Sciences of the United States of America, 107(6), 2646–2651.Google Scholar
  65. Konishi, Y., Kiyota, T., Draghici, C., Gao, J. M., Yeboah, F., Acoca, S., et al. (2007). Molecular formula analysis by an MS/MS/MS technique to expedite dereplication of natural products. Analytical Chemistry, 79(3), 1187–1197.PubMedGoogle Scholar
  66. Kopka, J. (2006). Current challenges and developments in GC-MS based metabolite profiling technology. Journal of Biotechnology, 124(1), 312–322.PubMedGoogle Scholar
  67. Kopka, J., Schauer, N., Krueger, S., Birkemeyer, C., Usadel, B., Bergmüller, E., et al. (2005). GMD@CSB.DB: The Golm Metabolome Database. Bioinformatics, 21(8), 1635–1638.PubMedGoogle Scholar
  68. Kuhl, C., Tautenhahn, R., Böttcher, C., Larson, T. R., & Neumann, S. (2011). CAMERA: An integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Analytical Chemistry, 84(1), 283–289.PubMedGoogle Scholar
  69. Kumari, S., Stevens, D., Kind, T., Denkert, C., & Fiehn, O. (2011). Applying in silico retention index and mass spectra matching for identification of unknown metabolites in accurate mass GC-TOF mass spectrometry. Analytical Chemistry, 83(15), 5895–5902.PubMedGoogle Scholar
  70. Lei, Z., Huhman, D. V., & Sumner, L. W. (2011). Mass spectrometry strategies in metabolomics. Journal of Biological Chemistry, 286(29), 25435–25442.PubMedGoogle Scholar
  71. Lisec, J., Schauer, N., Kopka, J., Willmitzer, L., & Fernie, A. R. (2006). Gas chromatography mass spectrometry-based metabolite profiling in plants. Nature Protocols, 1(1), 387–396.PubMedGoogle Scholar
  72. Lloyd, A. J., Beckmann, M., Favé, G., Mathers, J. C., & Draper, J. (2011). Proline betaine and its biotransformation products in fasting urine samples are potential biomarkers of habitual citrus fruit consumption. British Journal of Nutrition, 106(6), 812–824.PubMedGoogle Scholar
  73. Loo, R. L., Chan, Q., Brown, I. J., Robertson, C. E., Stamler, J., Nicholson, J. K., et al. (2012). A comparison of self-reported analgesic use and detection of urinary ibuprofen and acetaminophen metabolites by means of metabonomics: The INTERMAP study. American Journal of Epidemiology, 175(4), 348–358.PubMedGoogle Scholar
  74. Lu, X., Zhao, X., Bai, C., Zhao, C., Lu, G., & Xu, G. (2008). LC-MS-based metabonomics analysis. Journal of Chromatography B—Analytical Technologies in the Biomedical and Life Sciences, 866(1–2), 64–76.Google Scholar
  75. Lugan, R., Niogret, M. F., Leport, L., Guégan, J. P., Larher, F. R., Savouré, A., et al. (2010). Metabolome and water homeostasis analysis of Thellungiella salsuginea suggests that dehydration tolerance is a key response to osmotic stress in this halophyte. The Plant Journal, 64(2), 215–229.PubMedGoogle Scholar
  76. Malvoisin, E., Evrard, E., Roberfroid, M., & Mercier, M. (1979). Determination of Kovats retention indices with a capillary column and electron-capture detection: Application to the assay of the enzymatic conversion of 3,4-epoxy-1-butene into diepoxybutane. Journal of Chromatography, 186, 81–87.PubMedGoogle Scholar
  77. Matsuda, F., Shinbo, Y., Oikawa, A., Hirai, M. Y., Fiehn, O., Kanaya, S., et al. (2009). Assessment of metabolome annotation quality: A method for evaluating the false discovery rate of elemental composition searches. PLoS ONE, 4(10), e7490.PubMedGoogle Scholar
  78. Mihaleva, V. V., Verhoeven, H. A., de Vos, R. C., Hall, R. D., & van Ham, R. C. (2009). Automated procedure for candidate compound selection in GC-MS metabolomics based on prediction of Kovats retention index. Bioinformatics, 25(6), 787–794.PubMedGoogle Scholar
  79. Miura, D., Tsuji, Y., Takahashi, K., Wariishi, H., & Saito, K. (2010). A strategy for the determination of the elemental composition by Fourier transform ion cyclotron resonance mass spectrometry based on isotopic peak ratios. Analytical Chemistry, 82(13), 5887–5891.PubMedGoogle Scholar
  80. Neumann, S., Thum, A., & Böttcher, S. (2012). Nearline acquisition and processing of liquid chromatography-tandem mass spectrometry data. Metabolomics. doi: 10.1007/s11306-012-0401-0.
  81. Ochiai, N., & Sasamoto, K. (2010). Selectable one-dimensional or two-dimensional gas chromatography-olfactometry/mass spectrometry with preparative fraction collection for analysis of ultra-trace amounts of odor compounds. Journal of Chromatography A, 1218(21), 3180–3185.PubMedGoogle Scholar
  82. Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999). KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research, 27(1), 29–34.PubMedGoogle Scholar
  83. Oresic, M., Simell, S., Sysi-Aho, M., Näntö-Salonen, K., Seppänen-Laakso, T., Parikka, V., et al. (2008). Dysregulation of lipid and amino acid metabolism precedes islet autoimmunity in children who later progress to type 1 diabetes. Journal of Experimental Medicine, 205(13), 2975–2984.PubMedGoogle Scholar
  84. Pechlivanis, A., Kostidis, S., Saraslanidis, P., Petridou, A., Tsalis, G., Mougios, V., et al. (2010). (1)H-NMR-based metabonomic investigation of the effect of two different exercise sessions on the metabolic fingerprint of human urine. Journal of Proteome Research, 9(12), 6405–6416.PubMedGoogle Scholar
  85. Plumb, R. S., Johnson, K. A., Rainville, P., Smith, B. W., Wilson, I. D., Castro-Perez, J. M., et al. (2006). UPLIC/MS(E); A new approach for generating molecular fragment information for biomarker structure elucidation. Rapid Communications in Mass Spectrometry, 20(13), 1989–1994.PubMedGoogle Scholar
  86. Pope, G. A., MacKenzie, D. A., Defernez, M., Aroso, M. A., Fuller, L. J., Mellon, F. A., et al. (2007). Metabolic footprinting as a tool for discriminating between brewing yeasts. Yeast, 24(8), 667–679.PubMedGoogle Scholar
  87. Ramautar, R., Somsen, G. W., & de Jong, G. J. (2009). CE-MS in metabolomics. Electrophoresis, 30(1), 276–291.PubMedGoogle Scholar
  88. Roessner, U., Luedemann, A., Brust, D., Fiehn, O., Linke, T., Willmitzer, L., et al. (2001). Metabolic profiling allows comprehensive phenotyping of genetically or environmentally modified plant systems. Plant Cell, 13(1), 11–29.PubMedGoogle Scholar
  89. Roessner, U., Wagner, C., Kopka, J., Trethewey, R. N., & Willmitzer, L. (2000). Technical advance: Simultaneous analysis of metabolites in potato tuber by gas chromatography-mass spectrometry. The Plant Journal, 23(1), 131–142.PubMedGoogle Scholar
  90. Rogers, S., Scheltema, R. A., Girolami, M., & Breitling, R. (2009). Probabilistic assignment of formulas to mass peaks in metabolomics experiments. Bioinformatics, 25(4), 512–518.PubMedGoogle Scholar
  91. Rojas-Chertó, M., Kasper, P. T., Willighagen, E. L., Vreeken, R. J., Hankemeier, T., & Reijmers, T. H. (2011). Elemental composition determination based on MS(n). Bioinformatics, 27(17), 2376–2383.PubMedGoogle Scholar
  92. Sana, T. R., Roark, J. C., Li, X., Waddell, K., & Fischer, S. M. (2008). Molecular formula and METLIN Personal Metabolite Database matching applied to the identification of compounds generated by LC/TOF-MS. Journal of Biomolecular Techniques, 19(4), 258–266.PubMedGoogle Scholar
  93. Sansone, S.-A., Schober, D., Atherton, H. J., Fiehn, O., Jenkins, H., Rocca-Serra, P., et al. (2007). Metabolomics standards initiative—Ontology working group work in progress. Metabolomics, 3(3), 249–256.Google Scholar
  94. Schauer, N., Steinhauser, D., Strelkov, S., Schomburg, D., Allison, G., Moritz, T., et al. (2005). GC-MS libraries for the rapid identification of metabolites in complex biological samples. FEBS Letters, 579(6), 1332–1337.PubMedGoogle Scholar
  95. Scheltema, R. A., Jankevics, A., Jansen, R. C., Swertz, M. A., & Breitling, R. (2011). PeakML/mzMatch: A file format, Java library, R library, and tool-chain for mass spectrometry data analysis. Analytical Chemistry, 83(7), 2786–2793.PubMedGoogle Scholar
  96. Scheltema, R. A., Kamleh, A., Wildridge, D., Ebikeme, C., Watson, D. G., Barrett, M. P., et al. (2008). Increasing the mass accuracy of high-resolution LC-MS data using background ions: A case study on the LTQ-Orbitrap. Proteomics, 8(22), 4647–4656.PubMedGoogle Scholar
  97. Schmidt, B., Joussen, N., Bode, M., & Schuphan, I. (2006). Oxidative metabolic profiling of xenobiotics by human P450s expressed in tobacco cell suspension cultures. Biochemical Society Transactions, 34(Pt 6), 1241–1245.PubMedGoogle Scholar
  98. Schug, K., & McNair, H. M. (2002). Adduct formation in electrospray ionization. Part 1: Common acidic pharmaceuticals. Journal of Separation Science, 25(12), 759–766.Google Scholar
  99. Schug, K., & McNair, H. M. (2003). Adduct formation in electrospray ionization mass spectrometry II. Benzoic acid derivatives. Journal of Chromatography A, 985(1–2), 531–539.PubMedGoogle Scholar
  100. Schymanski, E. L., Gallampois, C. M., Krauss, M., Meringer, M., Neumann, S., Schulze, T., et al. (2012). Consensus structure elucidation combining GC/EI-MS, structure generation and calculated properties. Analytical Chemistry, 84(7), 3287–3295.PubMedGoogle Scholar
  101. Schymanski, E. L., Meringer, M., & Brack, W. (2011). Automated strategies to identify compounds on the basis of GC/EI-MS and calculated properties. Analytical Chemistry, 83(3), 903–912.PubMedGoogle Scholar
  102. Sheldon, M. T., Mistrik, R., & Croley, T. R. (2009). Determination of ion structures in structurally related compounds using precursor ion fingerprinting. Journal of the American Society for Mass Spectrometry, 20(3), 370–376.PubMedGoogle Scholar
  103. Siegel, M. M., & Gill, G. (1990). MASSPEC: A graphics-based data system for correlating a mass spectrum with a proposed structure. Analytica Chimica Acta, 237, 459–472.Google Scholar
  104. Smart, K. F., Aggio, R. B., Van Houtte, J. R., & Villas-Bôas, S. G. (2010). Analytical platform for metabolome analysis of microbial cells using methyl chloroformate derivatization followed by gas chromatography-mass spectrometry. Nature Protocols, 5(10), 1709–1729.PubMedGoogle Scholar
  105. Soga, T., Ohashi, Y., Ueno, Y., Naraoka, H., Tomita, M., & Nishioka, T. (2003). Quantitative metabolome analysis using capillary electrophoresis mass spectrometry. Journal of Proteome Research, 2(5), 488–494.PubMedGoogle Scholar
  106. Southam, A. D., Payne, T. G., Cooper, H. J., Arvanitis, T. N., & Viant, M. R. (2007). Dynamic range and mass accuracy of wide-scan direct infusion nanoelectrospray Fourier transform ion cyclotron resonance mass spectrometry-based metabolomics increased by the spectral stitching method. Analytical Chemistry, 79(12), 4595–4602.PubMedGoogle Scholar
  107. Spagou, K., Wilson, I. D., Masson, P., Theodoridis, G., Raikos, N., Coen, M., et al. (2010). HILIC-UPLC-MS for exploratory urinary metabolic profiling in toxicological studies. Analytical Chemistry, 83(1), 382–390.PubMedGoogle Scholar
  108. Stoll, N., Schmidt, E., & Thurow, K. (2006). Isotope pattern evaluation for the reduction of elemental compositions assigned to high-resolution mass spectral data from electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry. Journal of the American Society for Mass Spectrometry, 17(12), 1692–1699.PubMedGoogle Scholar
  109. Strehmel, N., Hummel, J., Erban, A., Strassburg, K., & Kopka, J. (2008). Retention index thresholds for compound matching in GC-MS metabolite profiling. The Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, 871(2), 182–190.Google Scholar
  110. Sud, M., Fahy, E., Cotter, D., Brown, A., Dennis, E. A., Glass, C. K., et al. (2007). LMSD: LIPID MAPS structure database. Nucleic Acids Res, 35(Database issue), D527–D532.PubMedGoogle Scholar
  111. Sugimoto, M., Kikuchi, S., Arita, M., Soga, T., Nishioka, T., & Tomita, M. (2005). Large-scale prediction of cationic metabolite identity and migration time in capillary electrophoresis mass spectrometry using artificial neural networks. Analytical Chemistry, 77(1), 78–84.PubMedGoogle Scholar
  112. Sumner, L. W., Amberg, A., Barrett, D., Beale, M. H., Beger, R., Daykin, C. A., et al. (2007). Proposed minimum reporting standards for chemical analysis. Metabolomics, 3(3), 211–221.Google Scholar
  113. Taylor, N. S., Weber, R. J. M., Southam, A. D., Payne, T. G., Hrydziuszko, O., Arvanitis, T. N., et al. (2009). A new approach to toxicity testing in Daphnia magna: Application of high throughput FT-ICR mass spectrometry metabolomics. Metabolomics, 5(1), 44–58.Google Scholar
  114. Theodoridis, G., Gika, H. G., & Wilson, I. D. (2008). LC-MS-based methodology for global metabolite profiling in metabonomics/metabolomics. TrAC—Trends in Analytical Chemistry, 27(3), 251–260.Google Scholar
  115. Tikunov, Y., Lommen, A., de Vos, C. H., Verhoeven, H. A., Bino, R. J., Hall, R. D., et al. (2005). A novel approach for nontargeted data analysis for metabolomics. Large-scale profiling of tomato fruit volatiles. Plant Physiology, 139(3), 1125–1137.PubMedGoogle Scholar
  116. Tong, H., Bell, D., Tabei, K., & Siegel, M. M. (1999). Automated data massaging, interpretation, and e-mailing modules for high throughput open access mass spectrometry. Journal of the American Society for Mass Spectrometry, 10(11), 1174–1187.Google Scholar
  117. van der Hooft, J. J., Vervoort, J., Bino, R. J., Beekwilder, J., & de Vos, R. C. (2010). Polyphenol identification based on systematic and robust high-resolution accurate mass spectrometry fragmentation. Analytical Chemistry, 83(1), 409–416.PubMedGoogle Scholar
  118. van der Werf, M. J., Overkamp, K. M., Muilwijk, B., Coulier, L., & Hankemeier, T. (2007). Microbial metabolomics: Toward a platform with full metabolome coverage. Analytical Biochemistry, 370(1), 17–25.PubMedGoogle Scholar
  119. Viant, M. R. (2008). Recent developments in environmental metabolomics. Molecular BioSystems, 4(10), 980–986.PubMedGoogle Scholar
  120. Wachsmuth, C. J., Almstetter, M. F., Waldhier, M. C., Gruber, M. A., Nürnberger, N., Oefner, P. J., et al. (2011). Performance evaluation of gas chromatography-atmospheric pressure chemical ionization-time-of-flight mass spectrometry for metabolic fingerprinting and profiling. Analytical Chemistry, 83(19), 7514–7522.PubMedGoogle Scholar
  121. 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(6), 887–900.PubMedGoogle Scholar
  122. Wang, X., Liang, Y., Zhu, L., Xie, H., Li, H., He, J., et al. (2010). Preparative isolation and purification of flavone c-glycosides from the leaves of Ficus microcarpa L. f by medium-pressure liquid chromatography, high-speed countercurrent chromatography, and preparative liquid chromatography. Journal of Liquid Chromatography & Related Technologies, 33(4), 462–480.Google Scholar
  123. Weber, R. J., Southam, A. D., Sommer, U., & Viant, M. R. (2011). Characterization of isotopic abundance measurements in high resolution FT-ICR and orbitrap mass spectra for improved confidence of metabolite identification. Analytical Chemistry, 83(10), 3737–3743.PubMedGoogle Scholar
  124. Weber, R. J. M., & Viant, M. R. (2010). MI-Pack: Increased confidence of metabolite identification in mass spectra by integrating accurate masses and metabolic pathways. Chemometrics and Intelligent Laboratory Systems, 104(1), 75–82.Google Scholar
  125. Welthagen, W., Shellie, R. A., Spranger, J., Ristow, M., Zimmermann, R., & Fiehn, O. (2005). Comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOF) for high resolution metabolomics: Biomarker discovery on spleen tissue extracts of obese NZO compared to lean C57BL/6 mice. Metabolomics, 1(1), 65–73.Google Scholar
  126. Wikoff, W. R., Anfora, A. T., Liu, J., Schultz, P. G., Lesley, S. A., Peters, E. C., et al. (2009). Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. The Proceedings of the National Academy of Sciences of the United States of America, 106(10), 3698–3703.Google Scholar
  127. Winder, C. L., Dunn, W. B., & Goodacre, R. (2011). TARDIS-based microbial metabolomics: Time and relative differences in systems. Trends in Microbiology, 19(7), 315–322.PubMedGoogle Scholar
  128. Wishart, D. S. (2011). Advances in metabolite identification. Bioanalysis, 3(15), 1769–1782.PubMedGoogle Scholar
  129. Wishart, D. S., Knox, C., Guo, A. C., Eisner, R., Young, N., Gautam, B., et al. (2009). HMDB: A knowledgebase for the human metabolome. Database issue, 37(3), D603–D610.Google Scholar
  130. Wishart, D. S., Knox, C., Guo, A. C., Shrivastava, S., Hassanali, M., Stothard, P., et al. (2006). DrugBank: A comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Research, 34(Database issue), D668–D672.PubMedGoogle Scholar
  131. Wolf, S., Schmidt, S., Müller-Hannemann, M., & Neumann, S. (2010). In silico fragmentation for computer assisted identification of metabolite mass spectra. BMC Bioinformatics, 11, 148.PubMedGoogle Scholar
  132. Xu, Y., Heilier, J. F., Madalinski, G., Genin, E., Ezan, E., Tabet, J. C., et al. (2010). Evaluation of accurate mass and relative isotopic abundance measurements in the LTQ-orbitrap mass spectrometer for further metabolomics database building. Analytical Chemistry, 82(13), 5490–5501.PubMedGoogle Scholar
  133. Yuan, J., Doucette, C. D., Fowler, W. U., Feng, X. J., Piazza, M., Rabitz, H. A., et al. (2009). Metabolomics-driven quantitative analysis of ammonia assimilation in E. coli. Molecular Systems Biology, 5, 302.PubMedGoogle Scholar
  134. Zhu, J., & Cole, R. B. (2000). Formation and decompositions of chloride adduct ions. Journal of the American Society for Mass Spectrometry, 11(11), 932–941.PubMedGoogle Scholar
  135. Zhudamo, J. S., Qunfa Hong, R. L., Lu, P., & Wang, L. (1988). ASES/MS: An automatic structure elucidation system for organic compounds using mass spectrometric data. The Analyst, 113, 1261–1265.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Warwick B. Dunn
    • 1
    • 2
  • Alexander Erban
    • 3
  • Ralf J. M. Weber
    • 4
    • 5
  • Darren J. Creek
    • 6
    • 7
  • Marie Brown
    • 1
    • 2
  • Rainer Breitling
    • 8
    • 9
  • Thomas Hankemeier
    • 10
    • 11
  • Royston Goodacre
    • 12
    • 13
  • Steffen Neumann
    • 14
  • Joachim Kopka
    • 3
  • Mark R. Viant
    • 4
    • 5
  1. 1.Centre for Advanced Discovery & Experimental Therapeutics (CADET), Central Manchester NHS Foundation Trust, Manchester Academic Health Sciences CentreUniversity of ManchesterManchesterUK
  2. 2.School of BiomedicineUniversity of ManchesterManchesterUK
  3. 3.Max Planck Institute for Molecular Plant Physiology (MPIMP)Potsdam-GolmGermany
  4. 4.Centre for Systems BiologyUniversity of BirminghamBirminghamUK
  5. 5.School of BiosciencesUniversity of BirminghamBirminghamUK
  6. 6.Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life SciencesUniversity of GlasgowGlasgowUK
  7. 7.Department of Biochemistry and Molecular BiologyUniversity of MelbourneParkvilleAustralia
  8. 8.Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life SciencesUniversity of GlasgowGlasgowUK
  9. 9.Groningen Bioinformatics Centre, Groningen Biomolecular Sciences and Biotechnology InstituteUniversity of GroningenGroningenThe Netherlands
  10. 10.Division of Analytical Biosciences, LACDRLeiden UniversityLeidenThe Netherlands
  11. 11.Netherlands Metabolomics Centre, LACDRLeiden UniversityLeidenThe Netherlands
  12. 12.Manchester Centre for Integrative Systems BiologyUniversity of ManchesterManchesterUK
  13. 13.School of Chemistry, Manchester Interdisciplinary BiocentreUniversity of ManchesterManchesterUK
  14. 14.Department of Stress and Developmental BiologyLeibniz Institute of Plant BiochemistryHalleGermany

Personalised recommendations