Metabolomics

, Volume 6, Issue 2, pp 207–218 | Cite as

Development and validation of a UPLC/MS method for a nutritional metabolomic study of human plasma

  • Hélène Pereira
  • Jean-François Martin
  • Charlotte Joly
  • Jean-Louis Sébédio
  • Estelle Pujos-Guillot
Original Article

Abstract

In order to study the effect of a diet on metabolites found in body fluids such as plasma, we have developed and validated a UPLC/MS method. While methods using NMR have been well established to analyse different biological tissues, recent studies have described robust untargeted UPLC-MS methods for plasma analysis. One major concern when profiling plasma is the presence of an important quantity of proteins which have to be precipitated without any loss of metabolites prior to LC/MS analysis. The utilization of untargeted approaches in nutritional metabolomics still suffers from the lack of identification of specific biomarkers. We therefore suggest an alternative method still using a global approach but focusing at the same time on metabolites previously described in human plasma in order to detect biomarkers of metabolic dysregulations. Thus, to fulfil our objectives, analytical parameters were tested (i) the anticoagulant type for sample collection, (ii) the protein precipitation method and (iii) UPLC/MS analytical conditions. Three protein precipitation methods and two anticoagulants were tested and compared. The method utilizing blood collection on heparin and methanol precipitation was chosen for giving the most reproducible results while keeping the complexity of the sample. Finally, a validation was proposed to evaluate the stability of this analytical method applied to a large batch of samples for nutritional metabolomic studies.

Keywords

Metabolomics Human nutrition Plasma UPLC/MS Anticoagulant Protein precipitation Validation Matrix effect 

References

  1. FDA. (May 2001) Guidance for industry, Bioanalytical method validation. Rockville: Food and drug administration, Center for Drug Evaluation and research (CDER).Google Scholar
  2. America, A. H. P., Cordewener, J. H. G., van Geffen, M. H. A., Lommen, A., Vissers, J. P. C., Bino, R. J., et al. (2006). Alignment and statistical difference analysis of complex peptide data sets generated by multidimensional LC-MS. Proteomics, 6, 641–653.CrossRefPubMedGoogle Scholar
  3. Benton, H. P., Wong, D. M., Trauger, S. A., & Siuzdak, G. (2008). XCMS2: Processing tandem mass spectrometry data for metabolite identification and structural characterization. Analytical Chemistry, 80, 6382–6389.CrossRefPubMedGoogle Scholar
  4. Boernsen, K. O., Gatzek, S., & Imbert, G. (2005). Controlled protein precipitation in combination with chip-based nanospray infusion mass spectrometry. An approach for metabolomics profiling of plasma. Analytical Chemistry, 77, 7255–7264.CrossRefPubMedGoogle Scholar
  5. Bruce, S. J., Jonsson, P., Antti, H., Cloarec, O., Trygg, J., Marklund, S. L., et al. (2008). Evaluation of a protocol for metabolic profiling studies on human blood plasma by combined ultra-performance liquid chromatography/mass spectrometry: From extraction to data analysis. Analytical Biochemistry, 372, 237–249.CrossRefPubMedGoogle Scholar
  6. Bruce, S. J., Tavazzi, I., Parisod, V., Rezzi, S., Kochhar, S., & Guy, P. A. (2009). Investigation of human blood plasma sample preparation for performing metabolomics using ultrahigh performance liquid chromatography/mass spectrometry. Analytical Chemistry, 81, 3285–3296.CrossRefPubMedGoogle Scholar
  7. Chambers, E., Wagrowski-Diehl, D. M., Lu, Z., & Mazzeo, J. R. (2007). Systematic and comprehensive strategy for reducing matrix effects in LC/MS/MS analyses. Journal of Chromatography B, 852, 22–34.CrossRefGoogle Scholar
  8. Chernushevich, I. V., Loboda, A. V., & Thomson, B. A. (2001). An introduction to quadrupole-time-of-flight mass Spectrometry. Journal of Mass Spectrometry and Ion Physics, 36, 849–865.Google Scholar
  9. Churchwell, M. I., Twaddle, N. C., Meeker, L. R., & Doerge, D. R. (2005). Improving LC-MS sensitivity through increases in chromatographic performance: Comparisons of UPLC-ES/MS/MS to HPLC-ES/MS/MS. Journal of Chromatography B, 825, 134–143.CrossRefGoogle Scholar
  10. De Villiers, A., Lestremau, F., Szucs, R., Gélébart, S., David, F., & Sandra, P. (2006). Evaluation of ultra performance liquid chromatography: Part I. Possibilities and limitations. Journal of Chromatography A, 1127, 60–69.CrossRefPubMedGoogle Scholar
  11. De Vos, C. H. R., 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, 778–791.CrossRefPubMedGoogle Scholar
  12. Dettmer, K., Pavel, A. A., & Hammock, B. D. (2007). Mass spectrometry based metabolomics. Mass Spectrometry Reviews, 26, 51–78.CrossRefPubMedGoogle Scholar
  13. Dumas, M.-E., Barton, R. H., Toye, A., Cloarec, O., Blancher, C., Rothwell, A., et al. (2006). Metabolic profiling reveals a contribution of gut microbiota to fatty liver phenotype in insulin-resistant mice. PNAS, 103, 12511–12516.CrossRefPubMedGoogle Scholar
  14. Fardet, A., Llorach, R., Martin, J. F., Besson, C., Lyan, B., Pujos-Guillot, E., et al. (2008). A liquid chromatography-quadrupole time-of-flight (LC-QTOF)-based metabolomic approach reveals new metabolic effects of catechin in rats fed high-fat diets. Journal of Proteome Research, 7, 2388–2398.CrossRefPubMedGoogle Scholar
  15. Fiehn, O., Kopka, J., Dörmann, P., Altmann, T., Trethewey, R. N., & Willmitzer, L. (2000). Metabolite profiling for plant functional genomics. Nature, 18, 1157–1161.CrossRefGoogle Scholar
  16. German, J. B., Roberts, M. A., Fay, L., & Watkins, S. M. (2002). Metabolomics and individual metabolic assessment: The next great challenge for nutrition. Journal of Nutrition, 132, 2486–2487.PubMedGoogle Scholar
  17. Gibney, M. J., & Gibney, E. R. (2004). Symposium on ‘implications for dietary guidelines of genetic influences on requirements’ diet, genes and disease: Implications for nutrition policy. Proceedings of the Nutrition Society, 63, 491–500.CrossRefPubMedGoogle Scholar
  18. Gibney, M. J., Walsh, M., Brennan, L., Roche, H. M., German, J. B., & Van Ommen, B. (2005). Metabolomics in human nutrition: Opportunities and challanges. American Journal of Clinical Nutrition, 82, 497–503.PubMedGoogle Scholar
  19. Gika, H., Macpherson, E., Theodoridis, G., & Wilson, I. D. (2008). Evaluation of the repeatability of ultra-performance liquid chromatography-TOF-MS for global metabolic profiling of human urine samples. Journal of Chromatography B, 871, 299–305.CrossRefGoogle Scholar
  20. Guo, Z., Yarasheski, K., & Jensen, M. D. (2006). High-precision isotopic analysis of palmitoylcarnitine by liquid chromatography/electrospray ionization ion-trap tandem mass spectrometry. Rapid Communications in Mass Spectrometry, 20, 3361–3366.CrossRefPubMedGoogle Scholar
  21. Guy, P. A., Tavazzi, I., Bruce, S. J., Ramadan, Z., & Kochhar, S. (2008). Global metabolic profiling analysis on human urine by UPLC-TOFMS: Issues and method validation in nutritional metabolomics. Journal of Chromatography B, 871, 253–260.CrossRefGoogle Scholar
  22. Houjou, T., Kotoko, Y., Masayoshi, I., Takao, S., & Ryo, T. (2005). A shotgun tandem mass spectrometric analysis of phospholipids with normal-phase and/or reverse-phase liquid chromatography/electrospray ionization mass spectrometry. Rapid Communications in Mass Spectrometry, 19, 654–666.CrossRefPubMedGoogle Scholar
  23. Idle, J. R., & Gonzalez, F. J. (2007). Metabolomics. Cell Metabolism, 6, 348–351.CrossRefPubMedGoogle Scholar
  24. Jonsson, P., Gullberg, J., Nordstrom, A., Kusano, M., Kowalczyk, M., Sjostrom, T., et al. (2004). A strategy for identifying differences in large series of metabolomic samples analyzed by GC/MS. Analytical Chemistry, 76, 1738–1745.CrossRefPubMedGoogle Scholar
  25. Koutsari, C., & Jensen, M. D. (2006). Thematic review series: Patient-oriented research. Free fatty acid metabolism in human obesity. Journal of Lipid Research, 47, 1643–1650.CrossRefPubMedGoogle Scholar
  26. Lawton, K. A., Berger, A., Mitchell, M., Milgram, K. E., Evans, A. M., Guo, L. N., et al. (2008). Analysis of the adult human plasma metabolome. Pharmacogen, 9, 383–397.CrossRefGoogle Scholar
  27. Lenz, W. (2007). Analytical strategies in metabonomics. Journal of Proteome Research, 6, 443–458.CrossRefPubMedGoogle Scholar
  28. Liebisch, G., Drobnik, W., Lieser, B., & Schmitz, G. (2002). High-throughput quantification of lysophosphatidylcholine by electrospray ionization tandem mass spectrometry. Clinical Chemistry, 48, 2217–2224.PubMedGoogle Scholar
  29. Liebisch, G., Lieser, B., Rathenberg, J., Drobnik, W., & Schmitz, G. (2004). High-throughput quantification of phosphatidylcholine and sphingomyelin by electrospray ionization tandem mass spectrometry coupled with isotope correction algorithm. Biochimica et Biophysica Acta—Molecular and Cell Biology of Lipids, 1686, 108–117.CrossRefGoogle Scholar
  30. Lindon, J. C., & Nicholson, J. K. (2008). Analytical technologies for metabonomics and metabolomics, and multi-omic information recovery. TrAC, 27, 194–204.Google Scholar
  31. Ma, L., Luo, H., Dai, J., & Carr, P. W. (2006). Development of acid stable, hyper-crosslinked, silica-based reversed-phase liquid chromatography supports for the separation of organic bases. Journal of Chromatography A, 1114, 21–28.CrossRefPubMedGoogle Scholar
  32. Maher, A. D., Zirah, S. F. M., Holmes, E., & Nicholson, J. K. (2007). Experimental and analytical variation in human urine in 1H NMR spectroscopy-based metabolic phenotyping studies. Analytical Chemistry, 79, 5204–5211.CrossRefPubMedGoogle Scholar
  33. Major, H., Williams, R., Wilson, A., & Wilson, I. (2006). A metabonomic analysis of plasma from Zucker rat strains using gas chromatography/mass spectrometry and pattern recognition. Rapid Communications in Mass Spectrometry, 20, 3295–3302.CrossRefPubMedGoogle Scholar
  34. Michopoulos, F., Lai, L., Gika, H., Theodoridis, G., & Wilson, I. (2009). UPLC-MS-based analysis of human plasma for metabonomics using solvent precipitation or solid phase extraction. Journal of Proteome Research, 8, 2114–2121.CrossRefPubMedGoogle Scholar
  35. Moco, S., Bino, R. J., Vos, R. C. H. D., & Vervoort, J. (2007). Metabolomics technologies and metabolite identification. TrAC, 26, 855–866.Google Scholar
  36. Nicholson, J. K., & Foxall, P. (1995). 750 MHz 1H and 1H 13C NMR spectroscopy of human blood plasma. Analytical Chemistry, 67, 793–811.CrossRefPubMedGoogle Scholar
  37. Nicholson, J. K., Lindon, J. C., & Holmes, E. (1999). Metabonomics’: Understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica, 29, 1181–1189.CrossRefPubMedGoogle Scholar
  38. Nordstrom, A., O’Maille, G., Qin, C., & Siuzdak, G. (2006). Nonlinear data alignment for UPLC-MS and HPLC-MS based metabolomics: Quantitative analysis of endogenous and exogenous metabolites in human serum. Analytical Chemistry, 78, 3289–3295.CrossRefPubMedGoogle Scholar
  39. Pietilainen, K. H., Sysi-Aho, M., Rissanen, A., Seppanen-Laakso, T., Yki-Jarvinen, H., Kaprio, J., et al. (2007). Acquired obesity is associated with changes in the serum lipidomic profile independent of genetic effects–a monozygotic twin study. PLoS ONE, 5, 472–483.Google Scholar
  40. Piraud, M., Vianey-Saban, C., Petritis, K., Elfakir, C., Steghens, J. P., Morla, A., et al. (2003). ESI-MS/MS analysis of underivatised amino acids: A new tool for the diagnosis of inherited disorders of amino acid metabolism. Fragmentation study of 79 molecules of biological interest in positive and negative ionisation mode. Rapid Communications in Mass Spectrometry, 17, 1297–1311.CrossRefPubMedGoogle Scholar
  41. Plumb, R. S., Johnson, K. A., Rainville, P. D., Shockcor, J. P., Williams, R., Granger, J. H., et al. (2006a). 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 Communications in Mass Spectrometry, 20, 2800–2806.CrossRefPubMedGoogle Scholar
  42. Plumb, R. S., Johnson, K. A., Rainville, P. D., Smith, B. W., Wilson, I. D., Castro-Perez, J. M., et al. (2006b). UPLC/MS; a new approach for generating molecular fragment information for biomarker structure elucidation. Rapid Communications in Mass Spectrometry, 20, 1989–1994.CrossRefPubMedGoogle Scholar
  43. Rezzi, S., Ramadan, Z., Fay, L. B., & Kochlar, S. (2006). Nutritional metabonomics: Applications and perspectives. Journal of Proteome Research. Reviews, 6, 513–525.CrossRefGoogle Scholar
  44. Sadagopan, N. P., Li, W., Cook, J. A., Galvan, J. A., Weller, D. L., Fountain, S. T., et al. (2003). Investigation of EDTA anticoagulant in plasma to improve the throughput of liquid chromatography/tandem mass spectrometric assays. Rapid Communications in Mass Spectrometry, 17, 1065–1070.CrossRefPubMedGoogle Scholar
  45. Sébedio, J. L., Martin, J. F., & Pujos, E. (2008). Nutritional metabolomics: What are the perspectives? OCL, 15, 341–345.Google Scholar
  46. Skeaff, C. M., Hodson, L., & McKenzie, J. E. (2006). Dietary-induced changes in fatty acid composition of human plasma, platelet, and erythrocyte lipids follow a similar time course. Journal of Nutrition, 136, 565–569.PubMedGoogle Scholar
  47. Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R., & Siuzdak, G. (2006). XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Analytical Chemistry, 78, 779–787.CrossRefPubMedGoogle Scholar
  48. Tikunov, Y., Lommen, A., de Vos, C. H. R., 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, 1125–1137.CrossRefPubMedGoogle Scholar
  49. Van De Steene, J. C., & Lambert, W. E. (2008). Comparaison of matrix effects in HPLC-MS/MS and UPLC-MS/MS analysis of nine basic pharmaceuticals in surface waters. American Society for Mass Spectrometry, 19, 713–718.CrossRefGoogle Scholar
  50. Want, E., O’Maille, G., Smith, C., Brandon, T., Uritboonthai, W., Qin, C., et al. (2006). Solvent-dependent metabolite distribution, clustering, and protein extraction for serum profiling with mass spectrometry. Analytical Chemistry, 78, 743–752.CrossRefPubMedGoogle Scholar
  51. Watkins, S. M., Hammock, B. D., Newman, J. W., & German, J. B. (2001). Individual metabolism should guide agriculture toward foods for improved health and nutrition. American Journal of Clinical Nutrition, 74, 283–286.PubMedGoogle Scholar
  52. Wilson, I. D., Plumb, R., Granger, J., Major, H., Williams, R., & Lenz, E. M. (2005). HPLC-MS-based methods for the study of metabonomics. Journal of Chromatography B, 817, 67–76.CrossRefGoogle Scholar
  53. Wong, M. C. Y., Lee, W. T. K., Wong, J. S. Y., Frost, G., & Lodge, J. (2008). An approach towards method development for untargeted urinary metabolite profiling in metabonomic research using UPLC/QToF MS. Journal of Chromatography B, 871, 341–348.CrossRefGoogle Scholar
  54. Zelena, E., Dunn, W. B., Broadhurst, D., Francis-McIntyre, S., Carroll, K. M., Begley, P., et al. (2009). Development of a robust and repeatable UPLC; MS method for the long-term metabolomic study of human serum. Analytical Chemistry, 81, 1357–1364.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Hélène Pereira
    • 1
    • 2
  • Jean-François Martin
    • 1
    • 2
  • Charlotte Joly
    • 1
    • 2
  • Jean-Louis Sébédio
    • 1
    • 2
  • Estelle Pujos-Guillot
    • 1
    • 2
  1. 1.Plateforme d’Exploration du MétabolismeINRA, UMR 1019, Unité de Nutrition Humaine, Centre de Recherche de Clermont-Ferrand/TheixSt-Genès-ChampanelleFrance
  2. 2.Clermont Université, UFR Médecine, UMR 1019 Nutrition HumaineClermont-FerrandFrance

Personalised recommendations