Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry

Abstract

Metabolism has an essential role in biological systems. Identification and quantitation of the compounds in the metabolome is defined as metabolic profiling, and it is applied to define metabolic changes related to genetic differences, environmental influences and disease or drug perturbations. Chromatography–mass spectrometry (MS) platforms are frequently used to provide the sensitive and reproducible detection of hundreds to thousands of metabolites in a single biofluid or tissue sample. Here we describe the experimental workflow for long-term and large-scale metabolomic studies involving thousands of human samples with data acquired for multiple analytical batches over many months and years. Protocols for serum- and plasma-based metabolic profiling applying gas chromatography–MS (GC-MS) and ultraperformance liquid chromatography–MS (UPLC-MS) are described. These include biofluid collection, sample preparation, data acquisition, data pre-processing and quality assurance. Methods for quality control–based robust LOESS signal correction to provide signal correction and integration of data from multiple analytical batches are also described.

This is a preview of subscription content

Access options

Figure 1
Figure 2: The QC-RLSC protocol for a metabolic feature detected in UPLC-MS (ES+) with signal attenuation across a given analytical batch.
Figure 3: The data preprocessing workflow for UPLC-MS data.
Figure 4: The experimental workflow followed in the HUSERMET project.
Figure 5: Typical chromatograms observed for serum.
Figure 6: Peak area data for 1-methylnicotinamide before and after QC-RLSC.
Figure 7: Gender-specific creatinine distribution.

References

  1. Bruggeman, F.J. & Westerhoff, H.V. The nature of systems biology. Trends Microbiol. 15, 45–50 (2007).

    Article  CAS  PubMed  Google Scholar 

  2. Kell, D.B. Metabolomics, modelling and machine learning in systems biology—towards an understanding of the languages of cells. Delivered on 3 July 2005 at the 30th FEBS Congress and 9th IUBMB conference in Budapest. FEBS J. 273, 873–894 (2006).

    Article  CAS  PubMed  Google Scholar 

  3. van der Greef, J., Hankemeier, T. & McBurney, R.N. Metabolomics-based systems biology and personalized medicine: moving towards n = 1 clinical trials? Pharmacogenomics 7, 1087–1094 (2006).

    Article  CAS  PubMed  Google Scholar 

  4. Fiehn, O. Metabolomics—the link between genotypes and phenotypes. Plant Mol. Biol. 48, 155–171 (2002).

    CAS  PubMed  Google Scholar 

  5. Goodacre, R., Vaidyanathan, S., Dunn, W.B., Harrigan, G.G. & Kell, D.B. Metabolomics by numbers: acquiring and understanding global metabolite data. Trends Biotechnol. 22, 245–252 (2004).

    Article  CAS  PubMed  Google Scholar 

  6. Griffin, J.L. The Cinderella story of metabolic profiling: does metabolomics get to go to the functional genomics ball? Philos. Trans. R. Soc. B Biol. Sci. 361, 147–161 (2006).

    Article  CAS  Google Scholar 

  7. Nicholson, J.K., Lindon, J.C. & Holmes, E. 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29, 1181–1189 (1999).

    Article  CAS  PubMed  Google Scholar 

  8. Allen, J. et al. High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. Nat. Biotechnol. 21, 692–696 (2003).

    Article  CAS  PubMed  Google Scholar 

  9. MacKenzie, D.A. et al. Relatedness of medically important strains of Saccharomyces cerevisiae as revealed by phylogenetics and metabolomics. Yeast 25, 501–512 (2008).

    Article  CAS  PubMed  Google Scholar 

  10. van der Werf, M.J. et al. Comprehensive analysis of the metabolome of Pseudomonas putida S12 grown on different carbon sources. Mol. Biosyst. 4, 315–327 (2008).

    Article  CAS  PubMed  Google Scholar 

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

    CAS  Article  PubMed  Google Scholar 

  12. Hall, R.D. Plant metabolomics: from holistic hope, to hype, to hot topic. New Phytol. 169, 453–468 (2006).

    Article  CAS  PubMed  Google Scholar 

  13. Atherton, H.J. et al. A combined 1H-NMR spectroscopy- and mass spectrometry-based metabolomic study of the PPAR-alpha null mutant mouse defines profound systemic changes in metabolism linked to the metabolic syndrome. Physiol. Genomics 27, 178–186 (2006).

    Article  CAS  PubMed  Google Scholar 

  14. Dunn, W.B. et al. Serum metabolomics reveals many novel metabolic markers of heart failure, including pseudouridine and 2-oxoglutarate. Metabolomics 3, 413–426 (2007).

    Article  CAS  Google Scholar 

  15. Holmes, E. et al. Human metabolic phenotype diversity and its association with diet and blood pressure. Nature 453, 396–400 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. Kenny, L.C. et al. Robust early pregnancy prediction of later preeclampsia using metabolomic biomarkers. Hypertension 56, 741–749 (2010).

    Article  CAS  PubMed  Google Scholar 

  17. Bundy, J.G., Davey, M.P. & Viant, M.R. Environmental metabolomics: a critical review and future perspectives. Metabolomics 5, 3–21 (2009).

    Article  CAS  Google Scholar 

  18. Kell, D.B. Metabolomic biomarkers: search, discovery and validation. Expert Rev. Mol. Diagn. 7, 329–333 (2007).

    Article  CAS  PubMed  Google Scholar 

  19. Ong, K.R. et al. Biomarkers of dietary energy restriction in women at increased risk of breast cancer. Cancer Prev. Res. 2, 720–731 (2009).

    Article  CAS  Google Scholar 

  20. Sabatine, M.S. et al. Metabolomic identification of novel biomarkers of myocardial ischemia. Circulation 112, 3868–3875 (2005).

    Article  CAS  PubMed  Google Scholar 

  21. Holmes, E. et al. Metabolic profiling of CSF: evidence that early intervention may impact on disease progression and outcome in schizophrenia. PLoS Med. 3, e327 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Nicholls, A.W., Nicholson, J.K., Haselden, J.N. & Waterfield, C.J. A metabonomic approach to the investigation of drug-induced phospholipidosis: an NMR spectroscopy and pattern recognition study. Biomarkers 5, 410–423 (2000).

    Article  CAS  Google Scholar 

  23. Kell, D.B. Systems biology, metabolic modelling and metabolomics in drug discovery and development. Drug Discov. Today 11, 1085–1092 (2006).

    Article  CAS  PubMed  Google Scholar 

  24. Schnackenberg, L.K. & Beger, R.D. The role of metabolic biomarkers in drug toxicity studies. Toxicol. Mech. Methods 18, 301–311 (2008).

    Article  CAS  PubMed  Google Scholar 

  25. Lodge, J.K. Targeted and non-targeted approaches for metabolite profiling in nutritional research. Proc. Nutr. Soc. 69, 95–102 (2010).

    Article  CAS  PubMed  Google Scholar 

  26. Gibney, M.J., Walsh, M., Brennan, L., Roche, H.M., German, B. & van Ommen, B. Metabolomics in human nutrition: opportunities and challenges. Am. J. Clin. Nutr. 82, 497–503 (2005).

    Article  CAS  PubMed  Google Scholar 

  27. German, J.B., Gillies, L.A., Smilowitz, J.T., Zivkovic, A.M. & Watkins, S.M. Lipidomics and lipid profiling in metabolomics. Curr. Opin. Lipidol. 18, 66–71 (2007).

    CAS  PubMed  Google Scholar 

  28. Kell, D.B. & Oliver, S.G. Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era. Bioessays 26, 99–105 (2004).

    Article  PubMed  Google Scholar 

  29. Dunn, W.B., Bailey, N.J.C. & Johnson, H.E. Measuring the metabolome: current analytical technologies. Analyst 130, 606–625 (2005).

    Article  CAS  PubMed  Google Scholar 

  30. Fiehn, O. Extending the breadth of metabolite profiling by gas chromatography coupled to mass spectrometry. Trends Analyt. Chem. 27, 261–269 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Dunn, W.B. et al. A GC-TOF-MS study of the stability of serum and urine metabolomes during the UK Biobank sample collection and preparation protocols. Int. J. Epidemiol. 37, 23–30 (2008).

    Article  Google Scholar 

  32. Denkert, C. et al. Mass spectrometry-based metabolic profiling reveals different metabolite patterns in invasive ovarian carcinomas and ovarian borderline tumors. Cancer Res. 66, 10795–10804 (2006).

    Article  CAS  PubMed  Google Scholar 

  33. Zelena, E. et al. Development of a robust and repeatable UPLC-MS method for the long-term metabolomic study of human serum. Anal. Chem. 81, 1357–1364 (2009).

    Article  CAS  PubMed  Google Scholar 

  34. Gika, H.G., Theodoridis, G.A. & Wilson, I.D. Liquid chromatography and ultra-performance liquid chromatography-mass spectrometry fingerprinting of human urine. Sample stability under different handling and storage conditions for metabonomics studies. J. Chromatogr. A 1189, 314–322 (2008).

    Article  CAS  PubMed  Google Scholar 

  35. Wilson, I.D. et al. HPLC-MS-based methods for the study of metabonomics. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 817, 67–76 (2005).

    Article  CAS  PubMed  Google Scholar 

  36. Ramautar, R., Somsen, G.W. & de Jong, G.J. CE-MS in metabolomics. Electrophoresis 30, 276–291 (2009).

    Article  CAS  PubMed  Google Scholar 

  37. Monton, M.R.N. & Soga, T. Metabolome analysis by capillary electrophoresis-mass spectrometry. J. Chromatogr. A 1168, 237–246 (2007).

    Article  CAS  PubMed  Google Scholar 

  38. Bjerrum, J.T. et al. Metabonomics in ulcerative colitis: diagnostics, biomarker identification, and insight into the pathophysiology. J. Proteome Res. 9, 954–962 (2009).

    Article  CAS  Google Scholar 

  39. Barton, R.H., Nicholson, J.K., Elliott, P. & Holmes, E. High-throughput H-1 NMR-based metabolic analysis of human serum and urine for large-scale epidemiological studies: validation study. Int. J. Epidemiol. 37, 31–40 (2008).

    Article  Google Scholar 

  40. Salek, R.M. et al. A metabolomic comparison of urinary changes in type 2 diabetes in mouse, rat, and human. Physiol. Genomics 29, 99–108 (2007).

    Article  CAS  PubMed  Google Scholar 

  41. Ellis, D.I. & Goodacre, R. Metabolic fingerprinting in disease diagnosis: biomedical applications of infrared and Raman spectroscopy. Analyst 131, 875–885 (2006).

    Article  CAS  PubMed  Google Scholar 

  42. Bogdanov, M. et al. Metabolomic profiling to develop blood biomarkers for Parkinson's disease. Brain 131, 389–396 (2008).

    Article  PubMed  Google Scholar 

  43. Southam, A.D., Payne, T., Cooper, H.J., Arvanitis, T.N. & Viant, M.R. A novel strategy to increase the number of metabolites detected in fish liver extracts using direct infusion FT-RCR mass spectrometry based metabolomics. Mar. Environ. Res 66, 29–29 (2008).

    Google Scholar 

  44. Gieger, C. et al. Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet. 4, e1000282 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Lawton, K.A. et al. Analysis of the adult human plasma metabolome. Pharmacogenomics 9, 383–397 (2008).

    Article  CAS  PubMed  Google Scholar 

  46. Want, E.J. et al. Global metabolic profiling procedures for urine using UPLC–MS. Nat. Protoc. 5, 1005–1018 (2010).

    Article  CAS  PubMed  Google Scholar 

  47. Subramanian, A. et al. Proton MR CSF analysis and a new software as predictors for the differentiation of meningitis in children. NMR Biomed. 18, 213–225 (2005).

    Article  CAS  PubMed  Google Scholar 

  48. Kaplan, K. et al. Monitoring dynamic changes in lymph metabolome of fasting and fed rats by electrospray ionization-ion mobility mass spectrometry (ESI-IMMS). Anal. Chem. 81, 7944–7953 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Plumb, R.S. et al. Application of ultra performance liquid chromatography-mass spectrometry to profiling rat and dog bile. J. Proteome Res. 8, 2495–2500 (2009).

    Article  CAS  PubMed  Google Scholar 

  50. Wu, J.F., An, Y.P., Yao, J.W., Wang, Y.L. & Tang, H.R. An optimised sample preparation method for NMR-based faecal metabonomic analysis. Analyst 135, 1023–1030 (2010).

    Article  CAS  PubMed  Google Scholar 

  51. Walsh, M.C., Brennan, L., Malthouse, J.P.G., Roche, H.M. & Gibney, M.J. Effect of acute dietary standardization on the urinary, plasma, and salivary metabolomic profiles of healthy humans. Am. J. Clin. Nutr. 84, 531–539 (2006).

    Article  CAS  PubMed  Google Scholar 

  52. Pandher, R., Ducruix, C., Eccles, S.A. & Raynaud, F.I. Cross-platform Q-TOF validation of global exo-metabolomic analysis: application to human glioblastoma cells treated with the standard PI 3-Kinase inhibitor LY294002. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 877, 1352–1358 (2009).

    Article  CAS  PubMed  Google Scholar 

  53. Munger, J. et al. Systems-level metabolic flux profiling identifies fatty acid synthesis as a target for antiviral therapy. Nat. Biotechnol. 26, 1179–1186 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Pietilainen, K.H. et al. Global metabolomics profiles of adipose tissue, serum and urine in weight-discordant monozygotic twin pairs. Obesity 16, S60 (2008).

    Article  Google Scholar 

  55. Welthagen, W. et al. 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, 65–73 (2005).

    Article  CAS  Google Scholar 

  56. Pears, M.R. et al. High resolution H-1 NMR-based metabolomics indicates a neurotransmitter cycling deficit in cerebral tissue from a mouse model of Batten disease. J. Biol. Chem. 280, 42508–42514 (2005).

    Article  CAS  PubMed  Google Scholar 

  57. Dunn, W.B. et al. Changes in the metabolic footprint of placental explant-conditioned culture medium identifies metabolic disturbances related to hypoxia and pre-eclampsia. Placenta 30, 974–980 (2009).

    Article  CAS  PubMed  Google Scholar 

  58. Kell, D.B. et al. Metabolic footprinting and systems biology: the medium is the message. Nat. Rev. Microbiol. 3, 557–565 (2005).

    Article  CAS  PubMed  Google Scholar 

  59. Wishart, D.S. et al. HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res. 37, D603–D610 (2009).

    Article  CAS  PubMed  Google Scholar 

  60. Goodacre, R. Metabolomics of a superorganism. J. Nutr. 137, 259S–266S (2007).

    Article  CAS  PubMed  Google Scholar 

  61. Lindon, J.C. et al. The consortium for metabonomic toxicology (COMET): aims, activities and achievements. Pharmacogenomics 6, 691–699 (2005).

    Article  CAS  PubMed  Google Scholar 

  62. Begley, P. et al. Development and performance of a gas chromatography-time-of-flight mass spectrometry analysis for large-scale nontargeted metabolomic studies of human serum. Anal. Chem. 81, 7038–7046 (2009).

    Article  CAS  PubMed  Google Scholar 

  63. Gika, H.G., Macpherson, E., Theodoridis, G.A. & Wilson, I.D. Evaluation of the repeatability of ultra-performance liquid chromatography-TOF-MS for global metabolic profiling of human urine samples. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 871, 299–305 (2008).

    Article  CAS  PubMed  Google Scholar 

  64. Sangster, T., Major, H., Plumb, R., Wilson, A.J. & Wilson, I.D. A pragmatic and readily implemented quality control strategy for HPLC-MS and GC-MS-based metabonomic analysis. Analyst 131, 1075–1078 (2006).

    Article  CAS  PubMed  Google Scholar 

  65. van der Greef, J. et al. The art and practice of systems biology in medicine: mapping patterns of relationships. J. Proteome Res. 6, 1540–1559 (2007).

    Article  CAS  PubMed  Google Scholar 

  66. van der Kloet, F.M., Bobeldijk, I., Verheij, E.R. & Jellema, R.H. Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping. J. Proteome Res. 8, 5132–5141 (2009).

    Article  CAS  PubMed  Google Scholar 

  67. Yanes, O. et al. Metabolic oxidation regulates embryonic stem cell differentiation. Nat. Chem. Biol. 6, 411–417 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Lee, M.S. et al. Metabolomics study with gas chromatography-mass spectrometry for predicting valproic acid-induced hepatotoxicity and discovery of novel biomarkers in rat urine. Int. J. Toxicol. 28, 392–404 (2009).

    Article  CAS  PubMed  Google Scholar 

  69. Broadhurst, D.I. & Kell, D.B. Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2, 171–196 (2006).

    Article  CAS  Google Scholar 

  70. Dunn, W.B., Broadhurst, D.I., Atherton, H.J., Goodacre, R. & Griffin, J.L. Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chem. Soc. Rev. 40, 387–426 (2011).

    Article  CAS  PubMed  Google Scholar 

  71. Kind, T., Tolstikov, V., Fiehn, O. & Weiss, R.H. A comprehensive urinary metabolomic approach for identifying kidney cancer. Anal. Biochem. 363, 185–195 (2007).

    Article  CAS  PubMed  Google Scholar 

  72. Halket, J.M. & Zaikin, V.G. Derivatization in mass spectrometry—5. Specific derivatization of monofunctional compounds. Eur. J. Mass Spectrom. 11, 127–160 (2005).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  74. Little, J.L. Artifacts in trimethylsilyl derivatization reactions and ways to avoid them. J. Chromatogr. A 844, 1–22 (1999).

    Article  CAS  PubMed  Google Scholar 

  75. Tao, X.M. et al. GC-MS with ethyl chloroformate derivatization for comprehensive analysis of metabolites in serum and its application to human uremia. Anal. Bioanal. Chem. 391, 2881–2889 (2008).

    Article  CAS  PubMed  Google Scholar 

  76. Wilson, I.D. et al. High resolution 'Ultra performance' liquid chromatography coupled to oa-TOF mass spectrometry as a tool for differential metabolic pathway profiling in functional genomic studies. J. Proteome Res. 4, 591–598 (2005).

    Article  CAS  PubMed  Google Scholar 

  77. Dunn, W.B. et al. Metabolic profiling of serum using ultra performance liquid chromatography and the LTQ-Orbitrap mass spectrometry system. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 871, 288–298 (2008).

    Article  CAS  PubMed  Google Scholar 

  78. Kamleh, M.A., Hobani, Y., Dow, J.A.T. & Watson, D.G. Metabolomic profiling of Drosophila using liquid chromatography Fourier transform mass spectrometry. FEBS Lett. 582, 2916–2922 (2008).

    Article  CAS  PubMed  Google Scholar 

  79. Plumb, R.S. et al. The detection of phenotypic differences in the metabolic plasma profile of three strains of Zucker rats at 20 weeks of age using ultra-performance liquid chromatography/orthogonal acceleration time-of-flight mass spectrometry. Rapid Commun. Mass Spectrom. 20, 2800–2806 (2006).

    Article  CAS  PubMed  Google Scholar 

  80. Gika, H.G., Theodoridis, G.A. & Wilson, I.D. Hydrophilic interaction and reversed-phase ultra-performance liquid chromatography TOF-MS for metabonomic analysis of Zucker rat urine. J. Sep. Sci. 31, 1598–1608 (2008).

    Article  CAS  PubMed  Google Scholar 

  81. Cubbon, S., Bradbury, T., Wilson, J. & Thomas-Oates, J. Hydrophilic interaction chromatography for mass spectrometric metabonomic studies of urine. Anal. Chem. 79, 8911–8918 (2007).

    Article  CAS  PubMed  Google Scholar 

  82. Want, E.J., Smith, C.A., Qin, C., VanHorne, K.C. & Siuzdak, G. Phospholipid capture combined with non-linear chromatographic correction for improved serum metabolite profiling. Metabolomics 2, 145–154 (2006).

    Article  CAS  Google Scholar 

  83. Michopoulos, F., Lai, L., Gika, H., Theodoridis, G. & Wilson, I. UPLC-MS-based analysis of human plasma for metabonomics using solvent precipitation or solid phase extraction. J. Proteome Res. 8, 2114–2121 (2009).

    Article  CAS  PubMed  Google Scholar 

  84. Brown, M. et al. Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics. Analyst 134, 1322–1332 (2009).

    Article  CAS  PubMed  Google Scholar 

  85. Want, E.J. et al. Solvent-dependent metabolite distribution, clustering, and protein extraction for serum profiling with mass spectrometry. Anal. Chem. 78, 743–752 (2006).

    Article  CAS  PubMed  Google Scholar 

  86. Jiye, A. et al. Extraction and GC/MS analysis of the human blood plasma metabolome. Anal. Chem. 77, 8086–8094 (2005).

    Article  CAS  Google Scholar 

  87. Bruce, S.J. et al. Investigation of human blood plasma sample preparation for performing metabolomics using ultrahigh performance liquid chromatography/mass spectrometry. Anal. Chem. 81, 3285–3296 (2009).

    Article  CAS  PubMed  Google Scholar 

  88. FDA. Guidance for Industry, Bioanalytical Method Validation. Food and Drug Administration, Centre for Drug Valuation and Research (CDER), 2001.

  89. 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  PubMed  Google Scholar 

  90. Katajamaa, M., Miettinen, J. & Oresic, M. MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics 22, 634–636 (2006).

    Article  CAS  PubMed  Google Scholar 

  91. Lommen, A. MetAlign: interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data preprocessing. Anal. Chem. 81, 3079–3086 (2009).

    Article  CAS  PubMed  Google Scholar 

  92. Baran, R. et al. MathDAMP: a package for differential analysis of metabolite profiles. BMC Bioinformatics 7, 530 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Cleveland, W.S. Robust locally weighted regression and smoothing scatterplots. J. Am. Stat. Assoc. 74, 829–836 (1979).

    Article  Google Scholar 

  94. Huber, P.J. Robust Statistics (John Wiley & Sons, 1981).

  95. Bowman, A.W. & Azzalini, A. Applied Smoothing Techniques for Data Analysis (Oxford Science Publications, 1997).

  96. Sumner, L.W. et al. Proposed minimum reporting standards for chemical analysis. Metabolomics 3, 211–221 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  98. Smith, C.A. et al. METLIN—a metabolite mass spectral database. Ther. Drug Monit. 27, 747–751 (2005).

    Article  CAS  PubMed  Google Scholar 

  99. Draper, J. et al. Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour 'rules'. BMC Bioinformatics 10, 227 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  100. Brown, M. et al. Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets. Bioinformatics 27, 1108–1112 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The human serum metabolome project (HUSERMET) is funded by the UK Biotechnology and Biological Sciences Research Council (BBSRC) (BB/C008219/1), MRC, GlaxoSmithKline and by AstraZeneca. We thank the BBSRC and the Engineering and Physical Sciences Research Council for their financial support to The Manchester Centre for Integrative Systems Biology (BB/C008219/1). W.B.D. wishes to thank the UK National Institute for Health Research for financially supporting the Manchester Biomedical Research Centre.

Author information

Authors and Affiliations

Authors

Consortia

Contributions

W.B.D. developed the experimental design strategy, the quality control strategy, the analytical methods and co-wrote the paper. D.B. developed the experimental design, sample scheduling and quality control strategies, and developed the QC-RSLC algorithm, performed data analysis and co-wrote the paper. P.B. and E.Z. developed the experimental design strategy and methods and acquired data. S.F.-M. and N.A. acquired data. M.B. developed the XCMS deconvolution strategy and performed data analysis. J.D.K. developed the sample stratification algorithm. A.H., J.N.H. and A.W.N. developed the experimental design strategy. I.D.W., D.B.K. and R.G. developed the experimental design strategy and co-wrote the paper.

Corresponding author

Correspondence to Warwick B Dunn.

Supplementary information

Supplementary Method 1

Typical analysis orders for (A) GC-ToF-MS (PDF 5 kb)

Supplementary Method 2

Typical analysis orders for (B) UPLC-ToF-MS (PDF 5 kb)

Supplementary Method 3

Typical analysis orders for (C) UPLC-LTQ/Orbitrap-MS (PDF 6 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Dunn, W., Broadhurst, D., Begley, P. et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc 6, 1060–1083 (2011). https://doi.org/10.1038/nprot.2011.335

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nprot.2011.335

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing