, 12:114 | Cite as

LC–MS based global metabolite profiling: the necessity of high data quality

  • Mikael K. R. EngskogEmail author
  • Jakob Haglöf
  • Torbjörn Arvidsson
  • Curt Pettersson
Review Article


LC–MS based global metabolite profiling currently lacks detailed guidelines to demonstrate that the obtained data is of high enough analytical quality. Insufficient data quality may result in the failure to generate a hypothesis, or in the worst case, a false or skewed hypothesis. After assessing the literature, it is apparent that an analytically focused summary and critical discussion related to this subject would be beneficial for both beginners and experts engaged in this field. A particular focus will be placed on data quality, which we here define as the degree to which a set of parameters fulfills predetermined criteria, similar to the established guidelines for targeted analysis. However, several of these parameters are difficult to assess since holistic approaches measure thousands of metabolites in parallel and seldom include predefined knowledge of which metabolites will differ between sample groups. In this review, the following parameters will be discussed in detail: reproducibility, selectivity, certainty of metabolite identification and metabolite coverage. The review systematically describes the generic workflow for LC–MS based global metabolite profiling and highlights how each separate part may affect data quality. The last part of the review describes how data quality can be evaluated as well as identifies areas where additional improvement is needed. In this review, we provide our own analytical opinions in regards to evaluation and, to some extent, improvement of data quality.


Data quality Global metabolite profiling LC–MS Validation Metabolomic workflow Metabolomics 


Compliance with ethical standards

Conflict of Interest

Mikael K. R. Engskog, Jakob Haglöf, Torbjörn Arvidsson and Curt Pettersson declare that they have no conflict of interest.


  1. Allwood, J. W., Erban, A., de Koning, S., Dunn, W. B., Luedemann, A., Lommen, A., et al. (2009). Inter-laboratory reproducibility of fast gas chromatography–electron impact–time of flight mass spectrometry (GC–EI–TOF/MS) based plant metabolomics. Metabolomics, 5(4), 479–496. doi: 10.1007/s11306-009-0169-z.PubMedPubMedCentralCrossRefGoogle Scholar
  2. Armitage, E. G., Godzien, J., Alonso-Herranz, V., López-Gonzálvez, Á., & Barbas, C. (2015). Missing value imputation strategies for metabolomics data. Electrophoresis, 36, 3050–3060. doi: 10.1002/elps.201500352.PubMedCrossRefGoogle Scholar
  3. Bauer, C., Cramer, R., & Schuchhardt, J. (2011). Data Mining in Proteomics. Methods in Enzymology, 696(1), 93–105. doi: 10.1007/978-1-60761-987-1.Google Scholar
  4. Bell, D. S., Cramer, H. M., & Jones, A. D. (2005). Rational method development strategies on a fluorinated liquid chromatography stationary phase: Mobile phase ion concentration and temperature effects on the separation of ephedrine alkaloids. Journal of Chromatography A, 1095(1–2), 113–118. doi: 10.1016/j.chroma.2005.08.004.PubMedCrossRefGoogle Scholar
  5. Benton, H. P., Want, E., Keun, H. C., Amberg, A., Plumb, R. S., Goldfain-Blanc, F., et al. (2012). Intra- and interlaboratory reproducibility of ultra performance liquid chromatography-time-of-flight mass spectrometry for urinary metabolic profiling. Analytical Chemistry, 84(5), 2424–2432. doi: 10.1021/ac203200x.PubMedCrossRefGoogle Scholar
  6. Bijlsma, S., Bobeldijk, I., Verheij, E. R., Ramaker, R., Kochhar, S., Macdonald, I. A., et al. (2006). Large-scale human metabolomics studies: A strategy for data (pre-) processing and validation. Analytical Chemistry, 78(2), 567–574. doi: 10.1021/ac051495j.PubMedCrossRefGoogle Scholar
  7. Boron, W. F. (2004). Regulation of intracellular pH. Advances in Physiology Education, 28, 160–179. doi: 10.1152/advan.00045.2004.PubMedCrossRefGoogle Scholar
  8. Bowen, B. P., & Northen, T. R. (2010). Dealing with the unknown: Metabolomics and metabolite atlases. Journal of the American Society for Mass Spectrometry, 21(9), 1471–1476. doi: 10.1016/j.jasms.2010.04.003.PubMedCrossRefGoogle Scholar
  9. Brereton, R. G., & Lloyd, G. R. (2014). Partial least squares discriminant analysis: Taking the magic away. Journal of Chemometrics, 28(4), 213–225. doi: 10.1002/cem.2609.CrossRefGoogle Scholar
  10. Broadhurst, D. I., & Kell, D. B. (2006). Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics, 2(4), 171–196. doi: 10.1007/s11306-006-0037-z.CrossRefGoogle Scholar
  11. Brodsky, L., Moussaieff, A., Shahaf, N., Aharoni, A., & Rogachev, I. (2010). Evaluation of peak picking quality in LC-MS metabolomics data. Analytical Chemistry, 82(22), 9177–9187. doi: 10.1021/ac101216e.PubMedCrossRefGoogle Scholar
  12. 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. doi: 10.1039/b901179j.PubMedCrossRefGoogle Scholar
  13. 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. doi: 10.1093/bioinformatics/btr079.PubMedPubMedCentralCrossRefGoogle Scholar
  14. Bruce, S. J., Jonsson, P., Antti, H., Cloarec, O., Trygg, J., Marklund, S. L., & Moritz, T. (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(2), 237–249. doi: 10.1016/j.ab.2007.09.037.PubMedCrossRefGoogle Scholar
  15. Bruce, S. J., Tavazzi, I., 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(9), 3285–3296.PubMedCrossRefGoogle Scholar
  16. Burton, L., Ivosev, G., Tate, S., Impey, G., Wingate, J., & Bonner, R. (2008). Instrumental and experimental effects in LC-MS-based metabolomics. Journal of Chromatography, B: Analytical Technologies in the Biomedical and Life Sciences, 871(2), 227–235. doi: 10.1016/j.jchromb.2008.04.044.PubMedCrossRefGoogle Scholar
  17. Bylesjö, M., Rentalainen, M., Cloarec, O., Nicholson, J. K., Holmes, E., & Trygg, J. (2006). OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification. Journal of Chemometrics, 20(September), 341–351. doi: 10.1002/cem.1006.CrossRefGoogle Scholar
  18. Calbiani, F., Careri, M., Elviri, L., Mangia, A., & Zagnoni, I. (2006). Matrix effects on accurate mass measurements of low-molecular weight compounds using liquid chromatography-electrospray-quadrupole time-of-flight mass spectrometry. Journal of Mass Spectrometry, 41(3), 289–294. doi: 10.1002/jms.984.PubMedCrossRefGoogle Scholar
  19. Castillo, S., Gopalacharyulu, P., Yetukuri, L., & Orešič, M. (2011). Algorithms and tools for the preprocessing of LC-MS metabolomics data. Chemometrics and Intelligent Laboratory Systems, 108(1), 23–32. doi: 10.1016/j.chemolab.2011.03.010.CrossRefGoogle Scholar
  20. Coble, J. B., & Fraga, C. G. (2014). Comparative evaluation of preprocessing freeware on chromatography/mass spectrometry data for signature discovery. Journal of Chromatography A, 1358, 155–164. doi: 10.1016/j.chroma.2014.06.100.PubMedCrossRefGoogle Scholar
  21. Coulier, L., Bas, R., Jespersen, S., Verheij, E., van der Werf, M. J., & Hankemeier, T. (2006). Simultaneous quantitative analysis of metabolites using ion-pair liquid chromatography–electrospray ionization mass spectrometry. Analytical Chemistry, 78(18), 6573–6582. doi: 10.1021/Ac0607616.PubMedCrossRefGoogle Scholar
  22. Creek, D. J., Dunn, W. B., Fiehn, O., Griffin, J. L., Hall, R. D., Lei, Z., et al. (2014). Metabolite identification: are you sure? And how do your peers gauge your confidence? Metabolomics, 10(3), 350–353. doi: 10.1007/s11306-014-0656-8.CrossRefGoogle Scholar
  23. Creek, D. J., Jankevics, A., Burgess, K. E. V., Breitling, R., & Barrett, M. P. (2012). IDEOM: An excel interface for analysis of LC-MS-based metabolomics data. Bioinformatics, 28(7), 1048–1049. doi: 10.1007/s11306-011-0341-0.PubMedCrossRefGoogle Scholar
  24. Cuhadar, S., Koseoglu, M., Atay, A., & Dirican, A. (2013). The effect of storage time and freeze-thaw cycles on the stability of serum samples. Biochem Med (Zagreb), 23(1), 70–77.CrossRefGoogle Scholar
  25. De Livera, A. M., Dias, D. A., Souza, D. De, Rupasinghe, T., Tull, D. L., Roessner, U., et al. (2012). Normalising and integrating metabolomics data normalising and integrating metabolomics data. Analytical Chemistry, 84, 10768–10776.PubMedCrossRefGoogle Scholar
  26. Denery, J. R., Nunes, A. A. K., & Dickerson, T. J. (2011). Characterization of differences between blood sample matrices in untargeted metabolomics. Analytical Chemistry, 83, 1040–1047.PubMedCrossRefGoogle Scholar
  27. Dettmer, K., Aronov, P. A., & Hammock, B. D. (2012). Mass spectrometry-based metabolomics. Mass Spectrometry Reviews, 29(6), 997–1003. doi: 10.1016/j.biotechadv.2011.08.021.Secreted.Google Scholar
  28. Di Guida, R., Engel, J., Allwood, J. W., Weber, R. J. M., Jones, M. R., Sommer, U., et al. (2016). Non-targeted UHPLC-MS metabolomic data processing methods: A comparative investigation of normalisation, missing value imputation, transformation and scaling. Metabolomics, 12(5), 93. doi: 10.1007/s11306-016-1030-9.PubMedPubMedCentralCrossRefGoogle Scholar
  29. Dieterle, F., Ross, A., Schlotterbeck, G., & Senn, H. (2006). Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Analytical Chemistry, 78(13), 4281–4290. doi: 10.1021/ac051632c.PubMedCrossRefGoogle Scholar
  30. Draisma, H. H. M., Reijmers, T. H., & Van Der Kloet, F. (2010). Equating, or correction for between-block effects with application to body fluid LC–MS and NMR metabolomics datasets. Analytical Chemistry, 82(3), 1039–1046.PubMedCrossRefGoogle Scholar
  31. Dunn, W. B., Broadhurst, D., Begley, P., Zelena, E., Francis-McIntyre, S., Anderson, N., et al. (2011). 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. doi: 10.1038/nprot.2011.335.PubMedCrossRefGoogle Scholar
  32. Dunn, W. B., Erban, A., Weber, R. J. M., Creek, D. J., Brown, M., Breitling, R., et al. (2013). Mass appeal: Metabolite identification in mass spectrometry-focused untargeted metabolomics. Metabolomics, 9(suppl. 1), 44–66. doi: 10.1007/s11306-012-0434-4.CrossRefGoogle Scholar
  33. Dunn, W. B., Wilson, I. D., Nicholls, A. W., & Broadhurst, D. (2012). The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans. Bioanalysis, 4(18), 2249–2264. doi: 10.4155/bio.12.204.PubMedCrossRefGoogle Scholar
  34. Eliasson, M., Ränner, S., Madsen, R., Donten, M. A., Marsden-Edwards, E., Moritz, T., et al. (2012). Strategy for optimizing LC–MS data processing in metabolomics: A design of experiments approach. Analytical Chemistry, 84(15), 6869–6876. doi: 10.1016/j.ijpharm.2011.11.009.PubMedCrossRefGoogle Scholar
  35. EMA. Guideline on bioanalytical method validation., EMA Guideline (2012). EMEA/CHMP/EWP/192217/2009.Google Scholar
  36. Engskog, M., Björklund, M., Haglöf, J., Arvidsson, T., Shoshan, M., & Pettersson, C. (2015). Metabolic profiling of epithelial ovarian cancer cell lines: Evaluation of harvesting protocols for profiling using NMR spectroscopy. Bioanalysis, 7(2), 157–166.PubMedCrossRefGoogle Scholar
  37. Eriksson, L., Byrne, T., Johansson, E., Trygg, J., & Vikström, C. (2013). Centering and Scaling. In Multi- and Megavariate Data Analysis (3rd ed., pp. 243–254). Malmö: MKS Umetrics AB.Google Scholar
  38. Fiehn, O. (2002). Metabolomics—The link between genotypes and phenotypes. Plant Molecular Biology, 48(1–2), 155–171. doi: 10.1023/A:1013713905833.PubMedCrossRefGoogle Scholar
  39. Food and Drug Administration. (2001). Guidance for industry: Bioanalytical method validation. U.S. Department of Health and Human Services. doi:
  40. Food and Drug Administration. (2013). Guidance for industry bioanalytical method validation guidance for industry bioanalytical method validation. U.S. Department of Health and Human Services. doi:
  41. Fura, A., Harper, T. W., Zhang, H., Fung, L., & Shyu, W. C. (2003). Shift in pH of biological fluids during storage and processing: Effect on bioanalysis. Journal of Pharmaceutical and Biomedical Analysis, 32(3), 513–522. doi: 10.1016/S0731-7085(03)00159-6.PubMedCrossRefGoogle Scholar
  42. Gertsman, I., Gangoiti, J., & Barshop, B. (2014). Validation of a dual LC-HRMS platform for clinical metabolic diagnosis in serum, bridging quantitative analysis and untargeted metabolomics. Metabolomics, 10(2), 312–323. doi: 10.1016/j.biotechadv.2011.08.021.Secreted.PubMedCrossRefGoogle Scholar
  43. Gika, H. G., Macpherson, E., Theodoridis, G. A., & 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: Analytical Technologies in the Biomedical and Life Sciences, 871(2), 299–305. doi: 10.1016/j.jchromb.2008.05.048.PubMedCrossRefGoogle Scholar
  44. Gika, H. G., Theodoridis, G. A., Earll, M., & Wilson, I. D. (2012a). A QC approach to the determination of day-to-day reproducibility and robustness of LC–MS methods for global metabolite profiling in metabonomics/metabolomics. Bioanalysis, 4(18), 2239–2247. doi: 10.4155/bio.12.212.PubMedCrossRefGoogle Scholar
  45. Gika, H., Theodoridis, G., Mattivi, F., Vrhovsek, U., & Pappa-Louisi, A. (2012b). Retention prediction of a set of amino acids under gradient elution conditions in hydrophilic interaction liquid chromatography. Journal of Separation Science, 35(3), 376–383. doi: 10.1002/jssc.201100795.PubMedCrossRefGoogle Scholar
  46. Gika, H. G., Theodoridis, G. A., Plumb, R. S., & Wilson, I. D. (2014a). Current practice of liquid chromatography–mass spectrometry in metabolomics and metabonomics. Journal of Pharmaceutical and Biomedical Analysis, 87, 12–25. doi: 10.1016/j.jpba.2013.06.032.PubMedCrossRefGoogle Scholar
  47. Gika, H. G., Theodoridis, G. A., Wingate, J. E., & Wilson, I. D. (2007). Within-day reproducibility of an HPLC–MS-based method for metabonomic analysis: Application to human urine research articles. Journal of Proteome Research, 6(8), 3291–3303.PubMedCrossRefGoogle Scholar
  48. Gika, H. G., Wilson, I. D., & Theodoridis, G. A. (2014b). LC-MS-based holistic metabolic profiling. Problems, limitations, advantages, and future perspectives. Journal of Chromatography, B: Analytical Technologies in the Biomedical and Life Sciences, 966, 1–6. doi: 10.1016/j.jchromb.2014.01.054.PubMedCrossRefGoogle Scholar
  49. Gika, H. G., Zisi, C., Theodoridis, G., & Wilson, I. D. (2016). Protocol for quality control in metabolic profiling of biological fluids by U(H)PLC-MS. Journal of Chromatography B, 1008, 15–25. doi: 10.1016/j.jchromb.2015.10.045.CrossRefGoogle Scholar
  50. Goeddel, L., & Patti, G. (2012). Maximizing the value of metabolomic data. Bioanalysis, 4(18), 2199–2201. doi: 10.4155/bio.12.210.PubMedCrossRefGoogle Scholar
  51. Goodacre, R. (2007). Metabolomics of a superorganism. The Journal of Nutrition, 137(suppl. 1), 259S–266S.PubMedGoogle Scholar
  52. Gromski, P. S., Muhamadali, H., Ellis, D. I., Xu, Y., Correa, E., Turner, M. L., & Goodacre, R. (2015). A tutorial review: Metabolomics and partial least squares-discriminant analysis—A marriage of convenience or a shotgun wedding. Analytica Chimica Acta, 879, 10–23. doi: 10.1016/j.aca.2015.02.012.PubMedCrossRefGoogle Scholar
  53. Gromski, P. S., Xu, Y., Kotze, H. L., Correa, E., Ellis, D. I., Armitage, E. G., et al. (2014). Influence of missing values substitutes on multivariate analysis of metabolomics data. Metabolites, 4(2), 433–452. doi: 10.3390/metabo4020433.PubMedPubMedCentralCrossRefGoogle Scholar
  54. Gürdeniz, G., Kristensen, M., Skov, T., & Dragsted, L. O. (2012). The effect of LC–MS data preprocessing methods on the selection of plasma biomarkers in fed versus fasted rats. Metabolites, 2(1), 77–99. doi: 10.3390/metabo2010077.PubMedPubMedCentralCrossRefGoogle Scholar
  55. Hebels, D. G. A., Georgiadis, P., Keun, H. C., Athersuch, T. J., Vineis, P., Vermeulen, R., et al. (2013). Performance in omics analyses of blood samples in long-term storage: Opportunities for the exploitation of existing biobanks in environmental. Environmental Health Perspectives, 480(4), 480–487.Google Scholar
  56. Hendriks, G., Uges, D. R., & Franke, J. P. (2007). Reconsideration of sample pH adjustment in bioanalytical liquid-liquid extraction of ionisable compounds. Journal of Chromatography, B: Analytical Technologies in the Biomedical and Life Sciences, 853(1–2), 234–241. doi: 10.1016/j.jchromb.2007.03.017.PubMedCrossRefGoogle Scholar
  57. Hendriks, M. M. W. B., van Eeuwijk, F. A., Jellema, R. H., Westerhuis, J. A., Reijmers, T. H., Hoefsloot, H. C. J., & Smilde, A. K. (2011). Data-processing strategies for metabolomics studies. TrAC—Trends in Analytical Chemistry, 30(10), 1685–1698. doi: 10.1016/j.trac.2011.04.019.CrossRefGoogle Scholar
  58. Hrydziuszko, O., & Viant, M. R. (2012). Missing values in mass spectrometry based metabolomics: An undervalued step in the data processing pipeline. Metabolomics, 8, 161–174. doi: 10.1007/s11306-011-0366-4.CrossRefGoogle Scholar
  59. Ismaiel, O., Zhang, T., Jenkins, R., & Karnes, H. T. (2011). Determination of octreotide and assessment of matrix effects in human plasma using ultra high performance liquid chromatography-tandem mass spectrometry. Journal of Chromatography, B: Analytical Technologies in the Biomedical and Life Sciences, 879(22), 2081–2088. doi: 10.1016/j.jchromb.2011.05.039.PubMedCrossRefGoogle Scholar
  60. Issaq, H. J., Waybright, T. J., & Veenstra, T. D. (2011). Cancer biomarker discovery: Opportunities and pitfalls in analytical methods. Electrophoresis, 32(9), 967–975. doi: 10.1002/elps.201000588.PubMedCrossRefGoogle Scholar
  61. Ivanisevic, J., Zhu, Z. J., Plate, L., Tautenhahn, R., Chen, S., O’Brien, P. J., et al. (2013). Toward’Omic scale metabolite profiling: A dual separation-mass spectrometry approach for coverage of lipid and central carbon metabolism. Analytical Chemistry, 85(14), 6876–6884. doi: 10.1021/ac401140h.PubMedPubMedCentralCrossRefGoogle Scholar
  62. Jackson, J. E. (1991). A user’s guide to principal components. New York: Wiley. doi: 10.1002/0471725331.CrossRefGoogle Scholar
  63. Jørgenrud, B., Jäntti, S. S., Mattila, I., Pöhö, P. P., Rønningen, K. S., Yki-Järvinen, H., et al. (2015). The influence of sample collection methodology and sample preprocessing on the blood metabolic profile. Bioanalysis, 7(8), 991–1006. doi: 10.4155/bio.15.16.PubMedCrossRefGoogle Scholar
  64. Kamlage, B., Maldonado, S. G., Bethan, B., Peter, E., Schmitz, O., Liebenberg, V., & Schatz, P. (2014). Quality markers addressing preanalytical variations of blood and plasma processing identified by broad and targeted metabolite profiling. Clinical Chemistry, 60(2), 399–412. doi: 10.1373/clinchem.2013.211979.PubMedCrossRefGoogle Scholar
  65. Kamleh, M. A., Ebbels, T. M. D., Spagou, K., Masson, P., & Want, E. J. (2012). Optimizing the use of quality control samples for signal drift correction in large-scale urine metabolic profiling studies. Analytical Chemistry, 84, 2670–2677.PubMedCrossRefGoogle Scholar
  66. Katajamaa, M., Miettinen, J., & Orešič, M. (2006). MZmine: Toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics, 22(5), 634–636. doi: 10.1093/bioinformatics/btk039.PubMedCrossRefGoogle Scholar
  67. Kell, D. B. (2004). Metabolomics and systems biology: Making sense of the soup. Current Opinion in Microbiology, 7(3), 296–307. doi: 10.1016/j.mib.2004.04.012.PubMedCrossRefGoogle Scholar
  68. Keun, H. C., Ebbels, T. M. D., Antti, H., Bollard, M. E., Beckonert, O., Holmes, E., et al. (2003). Improved analysis of multivariate data by variable stability scaling: Application to NMR-based metabolic profiling. Analytica Chimica Acta, 490(1–2), 265–276. doi: 10.1016/S0003-2670(03)00094-1.CrossRefGoogle Scholar
  69. Kind, T., & Fiehn, O. (2010). Advances in structure elucidation of small molecules using mass spectrometry. Bioanalytical Reviews, 2(1), 23–60. doi: 10.1007/s12566-010-0015-9.PubMedPubMedCentralCrossRefGoogle Scholar
  70. Kirwan, J. A., Broadhurst, D. I., Davidson, R. L., & Viant, M. R. (2013). Characterising and correcting batch variation in an automated direct infusion mass spectrometry (DIMS) metabolomics workflow. Analytical and Bioanalytical Chemistry, 405(15), 5147–5157. doi: 10.1007/s00216-013-6856-7.PubMedCrossRefGoogle Scholar
  71. Kloos, D. P., Lingeman, H., Niessen, W. M. A., Deelder, A. M., Giera, M., & Mayboroda, O. A. (2013). Evaluation of different column chemistries for fast urinary metabolic profiling. Journal of Chromatography, B: Analytical Technologies in the Biomedical and Life Sciences, 927, 90–96. doi: 10.1016/j.jchromb.2013.02.017.PubMedCrossRefGoogle Scholar
  72. Kuhl, C., Tautenhahn, R., Böttcher, C., Larson, T., & Neumann, S. (2012). CAMERA: An integrated strategy for compound spectra extraction and annotation of LC/MS data sets. Analytical Chemistry, 84(1), 283–289. doi: 10.1021/ac202450g.PubMedCrossRefGoogle Scholar
  73. Kuligowski, J., Sanchez-Illana, A., Sanjuan-Herraez, D., Vento, M., & Quintas, G. (2015). Intra-batch effect correction in liquid chromatography-mass spectrometry using quality control samples and support vector regression (QC-SVRC). Analyst, 140(22), 7810–7817. doi: 10.1039/c5an01638j.PubMedCrossRefGoogle Scholar
  74. Kultima, K., Nilsson, A., Scholz, B., Rossbach, U. L., Fälth, M., & Andrén, P. E. (2009). Development and evaluation of normalization methods for label-free relative quantification of endogenous peptides. Molecular & Cellular Proteomics: MCP, 8(10), 2285–2295. doi: 10.1074/mcp.M800514-MCP200.PubMedCentralCrossRefGoogle Scholar
  75. Lahaie, M., Mess, J.-N., Furtado, M., & Garofolo, F. (2010). Elimination of LC–MS/MS matrix effect due to phospholipids using specific solid-phase extraction elution conditions. Bioanalysis, 2(6), 1011–1021. doi: 10.4155/bio.10.65.PubMedCrossRefGoogle Scholar
  76. León, Z., García-Cañaveras, J. C., Donato, M. T., & Lahoz, A. (2013). Mammalian cell metabolomics: Experimental design and sample preparation. Electrophoresis, 34(19), 2762–2775. doi: 10.1002/elps.201200605.PubMedGoogle Scholar
  77. Lorenz, M. A., Burant, C. F., & Kennedy, R. T. (2011). Reducing time and increasing sensitivity in sample preparation for adherent mammalian cell metabolomics. Analytical Chemistry, 83(9), 3406–3414. doi: 10.1021/ac103313x.PubMedPubMedCentralCrossRefGoogle Scholar
  78. Lu, W., Clasquin, M. F., Melamud, E., Amador-Noguez, D., Caudy, A. A., & Rabinowitz, J. D. (2011). NIH public access. Analytical Chemistry, 82(8), 3212–3221. doi: 10.1021/ac902837x.Metabolomic.CrossRefGoogle Scholar
  79. Madsen, R., Lundstedt, T., & Trygg, J. (2010). Chemometrics in metabolomics—A review in human disease diagnosis. Analytica Chimica Acta, 659(1–2), 23–33. doi: 10.1016/j.aca.2009.11.042.PubMedCrossRefGoogle Scholar
  80. Martano, G., Delmotte, N., Kiefer, P., Christen, P., Kentner, D., Bumann, D., & Vorholt, J. A. (2014). Fast sampling method for mammalian cell metabolic analyses using liquid chromatography–mass spectrometry. Nature Protocols, 10(1), 1–11. doi: 10.1038/nprot.2014.198.PubMedCrossRefGoogle Scholar
  81. Martin, J.-C., Maillot, M., Mazerolles, G., Verdu, A., Lyan, B., Migné, C., et al. (2015). Can we trust untargeted metabolomics? Results of the metabo-ring initiative, a large-scale, multi-instrument inter-laboratory study. Metabolomics, 11(4), 807–821. doi: 10.1007/s11306-014-0740-0.PubMedCrossRefGoogle Scholar
  82. 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(4), 2114–2121. doi: 10.1021/pr801045q.PubMedCrossRefGoogle Scholar
  83. Moco, S., Vervoort, J., Moco, S., Bino, R. J., De Vos, R. C. H., & Bino, R. (2007). Metabolomics technologies and metabolite identification. TrAC —Trends in Analytical Chemistry, 26(9), 855–866. doi: 10.1016/j.trac.2007.08.003.CrossRefGoogle Scholar
  84. Naz, S., García, A., & Barbas, C. (2013a). Multiplatform analytical methodology for metabolic fingerprinting of lung tissue. Analytical Chemistry, 85(22), 10941–10948. doi: 10.1021/ac402411n.PubMedCrossRefGoogle Scholar
  85. Naz, S., Garcia, A., Rusak, M., & Barbas, C. (2013b). Method development and validation for rat serum fingerprinting with CE-MS: Application to ventilator-induced-lung-injury study. Analytical and Bioanalytical Chemistry, 405(14), 4849–4858. doi: 10.1007/s00216-013-6882-5.PubMedCrossRefGoogle Scholar
  86. Naz, S., Vallejo, M., García, A., & Barbas, C. (2014). Method validation strategies involved in non-targeted metabolomics. Journal of Chromatography A, 1353, 99–105. doi: 10.1016/j.chroma.2014.04.071.PubMedCrossRefGoogle Scholar
  87. Nicholson, J. K., & Lindon, J. C. (2008). Metabonomics. Nature, 455(October), 1054–1056.PubMedCrossRefGoogle Scholar
  88. 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; The Fate of Foreign Compounds in Biological Systems, 29(11), 1181–1189. doi: 10.1080/004982599238047.PubMedCrossRefGoogle Scholar
  89. Nilsson, L. B. (2013). The bioanalytical challenge of determining unbound concentration and protein binding for drugs. Bioanalysis, 5(24), 3033–3050. doi: 10.4155/bio.13.274.PubMedCrossRefGoogle Scholar
  90. Nilsson, L. B., & Schmidt, S. (2001). Simultaneous determination of total and free drug plasma concentrations combined with batch-wise pH-adjustment for the free concentration determinations. Journal of Pharmaceutical and Biomedical Analysis, 24(5–6), 921–927. doi: 10.1016/S0731-7085(00)00560-4.PubMedCrossRefGoogle Scholar
  91. Ogata, H., Goto, S., Sato, K., Fujubuchi, W., Bono, H., & Kanehisa, M. (1999). KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research, 27(1), 29–34.PubMedPubMedCentralCrossRefGoogle Scholar
  92. Paglia, G., Magnúsdóttir, M., Thorlacius, S., Sigurjónsson, Ó. E., Gudmundsson, S., Palsson, B., & Thiele, I. (2012). Intracellular metabolite profiling of platelets: Evaluation of extraction processes and chromatographic strategies. Journal of Chromatography, B: Analytical Technologies in the Biomedical and Life Sciences, 898, 111–120. doi: 10.1016/j.jchromb.2012.04.026.PubMedCrossRefGoogle Scholar
  93. Pandher, R., Ducruix, C., Eccles, S. A., & Raynaud, F. I. (2009). Cross-platform Q-TOF validation of global exo-metabolomic analysis: Application to human glioblastoma cells treated with the standard PI 3-Kinase inhibitor LY294002. Journal of Chromatography, B: Analytical Technologies in the Biomedical and Life Sciences, 877(13), 1352–1358. doi: 10.1016/j.jchromb.2008.12.001.PubMedCrossRefGoogle Scholar
  94. Pedreschi, R., Hertog, M. L. A. T. M., Carpentier, S. C., Lammertyn, J., Robben, J., Noben, J. P., et al. (2008). Treatment of missing values for multivariate statistical analysis of gel-based proteomics data. Proteomics, 8(7), 1371–1383. doi: 10.1002/pmic.200700975.PubMedCrossRefGoogle Scholar
  95. Pereira, H., Martin, J.-F., Joly, C., Sébédio, J. L., & Pujos-Guillot, E. (2010). Development and validation of a UPLC/MS method for a nutritional metabolomic study of human plasma. Metabolomics, 6(2), 207–218. doi: 10.1007/s11306-009-0188-9.CrossRefGoogle Scholar
  96. Phinney, K. W., Ballihaut, G., Bedner, M., Benford, B. S., Camara, J. E., Christopher, S. J., et al. (2013). Development of a standard reference material for metabolomics research. Analytical Chemistry, 85(24), 11732–11738. doi: 10.1021/ac402689t.PubMedPubMedCentralCrossRefGoogle Scholar
  97. Pinto, J., Domingues, M. R. M., Galhano, E., Pita, C., Almeida, M. D. C., Carreira, I. M., & Gil, A. M. (2014). Human plasma stability during handling and storage: Impact on NMR metabolomics. The Analyst, 139(5), 1168–1177. doi: 10.1039/c3an02188b.PubMedCrossRefGoogle Scholar
  98. Psychogios, N., Hau, D. D., Peng, J., Guo, A. C., Mandal, R., Bouatra, S., et al. (2011). The human serum metabolome. PLoS One, 6(2), e16957. doi: 10.1371/journal.pone.0016957.PubMedPubMedCentralCrossRefGoogle Scholar
  99. Qi, X., Zhang, Y., Gao, J., Chen, T., Zhao, A., Yan, Y., & Jia, W. (2011). Metabolite profiling of hemodialysate using gas chromatography time-of-flight mass spectrometry. Journal of Pharmaceutical and Biomedical Analysis, 55(5), 1142–1147. doi: 10.1016/j.jpba.2011.04.001.PubMedCrossRefGoogle Scholar
  100. Rafiei, A., & Sleno, L. (2014). Comparison of peak-picking workflows for untargeted liquid chromatography/high-resolution mass spectrometry metabolomics data analysis. Rapid Communications in Mass Spectrometry, 29(1), 119–127. doi: 10.1002/rcm.7094.CrossRefGoogle Scholar
  101. Ramakrishnan, P., Nair, S., & Rangiah, K. (2016). A method for comparative metabolomics in urine using high resolution mass spectrometry. Journal of Chromatography A, 1443, 83–92. doi: 10.1016/j.chroma.2016.02.080.PubMedCrossRefGoogle Scholar
  102. Ramautar, R., & de Jong, G. J. (2014). Recent developments in liquid-phase separation techniques for metabolomics. Bioanalysis, 6, 1011–1026. doi: 10.4155/bio.14.51.PubMedCrossRefGoogle Scholar
  103. Rico, E., González, O., Blanco, M. E., & Alonso, R. M. (2014). Evaluation of human plasma sample preparation protocols for untargeted metabolic profiles analyzed by UHPLC-ESI-TOF-MS. Analytical and Bioanalytical Chemistry, 406(29), 7641–7652. doi: 10.1007/s00216-014-8212-y.PubMedCrossRefGoogle Scholar
  104. Robert, O., Sabatier, J., Desoubzdanne, D., Lalande, J., Balayssac, S., Gilard, V., et al. (2011). pH optimization for a reliable quantification of brain tumor cell and tissue extracts with (1)H NMR: focus on choline-containing compounds and taurine. Analytical and Bioanalytical Chemistry, 399(2), 987–999. doi: 10.1007/s00216-010-4321-4.PubMedCrossRefGoogle Scholar
  105. Rusilowicz, M., Dickinson, M., Charlton, A., O’Keefe, S., & Wilson, J. (2016). A batch correction method for liquid chromatography–mass spectrometry data that does not depend on quality control samples. Metabolomics, 12(3), 1–11. doi: 10.1007/s11306-016-0972-2.CrossRefGoogle Scholar
  106. Saccenti, E., Hoefsloot, H. C. J., Smilde, A. K., Westerhuis, J. A., & Hendriks, M. M. W. B. (2014). Reflections on univariate and multivariate analysis of metabolomics data. Metabolomics, 10(3), 361–374. doi: 10.1007/s11306-013-0598-6.CrossRefGoogle Scholar
  107. Salek, R. M., Steinbeck, C., Viant, M. R., Goodacre, R., & Dunn, W. B. (2013). The role of reporting standards for metabolite annotation and identification in metabolomic studies. GigaScience, 2(1), 13. doi: 10.1186/2047-217X-2-13.PubMedPubMedCentralCrossRefGoogle Scholar
  108. 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.PubMedPubMedCentralGoogle Scholar
  109. Sangster, T., Major, H., Plumb, R., Wilson, A. J., & Wilson, I. D. (2006). A pragmatic and readily implemented quality control strategy for HPLC-MS and GC–MS-based metabonomic analysis. The Analyst, 131(10), 1075–1078. doi: 10.1039/b604498k.PubMedCrossRefGoogle Scholar
  110. Sarafian, M. H., Gaudin, M., Lewis, M. R., Martin, F. P., Holmes, E., Nicholson, J. K., & Dumas, M. E. (2014). Objective set of criteria for optimization of sample preparation procedures for ultra-high throughput untargeted blood plasma lipid profiling by ultra performance liquid chromatography–mass spectrometry. Analytical Chemistry, 86(12), 5766–5774. doi: 10.1021/ac500317c.PubMedCrossRefGoogle Scholar
  111. Scheel, I., Aldrin, M., Glad, I. K., Sørum, R., Lyng, H., & Frigessi, A. (2005). The influence of missing value imputation on detection of differentially expressed genes from microarray data. Bioinformatics, 21(23), 4272–4279. doi: 10.1093/bioinformatics/bti708.PubMedCrossRefGoogle Scholar
  112. 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. doi: 10.1021/ac2000994.PubMedCrossRefGoogle Scholar
  113. Simón-Manso, Y., Lowenthal, M. S., Kilpatrick, L. E., Sampson, M. L., Telu, K. H., Rudnick, P. A., et al. (2013). Metabolite profiling of a NIST standard reference material for human plasma (SRM 1950): GC–MS, LC–MS, NMR, and clinical laboratory analyses, libraries, and web-based resources. Analytical Chemistry, 85(24), 11725–11731. doi: 10.1021/ac402503m.PubMedCrossRefGoogle Scholar
  114. Smilde, A. K., Van der Werf, M. J., & Bijlsma, S. (2005). Fusion of mass spectrometry-based metabolomics data. Analytical Chemistry, 77(20), 6729. papers3://publication/uuid/D4413DC1-F642-419B-9706-6E027D8014A8.Google Scholar
  115. Smilde, A. K., van der Werf, M. J., Schaller, J.-P., & Kistemaker, C. (2009). Characterizing the precision of mass-spectrometry-based metabolic profiling platforms. The Analyst, 134(11), 2281. doi: 10.1039/b902242b.PubMedCrossRefGoogle Scholar
  116. Smith, C., Elizabeth, J., O’Maille, G., Abagyan, R., & Siuzdak, G. (2006). XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. ACS Publications, 78(3), 779–787.Google Scholar
  117. Spagou, K., Tsoukali, H., Raikos, N., Gika, H., Wilson, I. D., & Theodoridis, G. (2010). Hydrophilic interaction chromatography coupled to MS for metabonomic/metabolomic studies. Journal of Separation Science, 33(6–7), 716–727. doi: 10.1002/jssc.200900803.PubMedCrossRefGoogle Scholar
  118. Sud, M., Fahy, E., Cotter, D., Brown, A., Dennis, E. A., Glass, C. K., et al. (2007). LMSD: LIPID MAPS structure database. Nucleic Acids Research, 35(suppl. 1), 527–532. doi: 10.1093/nar/gkl838.CrossRefGoogle Scholar
  119. Sumner, L. W., Samuel, T., Noble, R., Gmbh, S. D., Barrett, D., Beale, M. H., & Hardy, N. (2007). Proposed minimum reporting standards for chemical analysis chemical analysis working group (CAWG) metabolomics standards initiative (MSI). Metabolomics, 3(3), 211–221. doi: 10.1007/s11306-007-0082-2.Proposed.PubMedPubMedCentralCrossRefGoogle Scholar
  120. Sysi-Aho, M., Katajamaa, M., Yetukuri, L., & Oresic, M. (2007). Normalization method for metabolomics data using optimal selection of multiple internal standards. BMC Bioinformatics, 8, 93. doi: 10.1186/1471-2105-8-93.PubMedPubMedCentralCrossRefGoogle Scholar
  121. Szymańska, E., Saccenti, E., Smilde, A. K., & Westerhuis, J. A. (2012). Double-check: Validation of diagnostic statistics for PLS-DA models in metabolomics studies. Metabolomics, 8(1), 3–16. doi: 10.1007/s11306-011-0330-3.PubMedCrossRefGoogle Scholar
  122. T’Kindt, R., Alaerts, G., Heyden, Y. Vander, Deforce, D., & Van Bocxlaer, J. (2007). Broad-spectrum separations in metabolomics using enhanced polar LC stationary phases: A dedicated evaluation using plant metabolites. Journal of Separation Science, 30(13), 2002–2011. doi: 10.1002/jssc.200700077.PubMedCrossRefGoogle Scholar
  123. Taguchi, R., Nishijima, M., & Shimizu, T. (2007). Basic analytical systems for lipidomics by mass spectrometry in Japan. Methods in Enzymology, 432(07), 185–211. doi: 10.1016/S0076-6879(07)32008-9.PubMedCrossRefGoogle Scholar
  124. Teahan, O., Gamble, S., Holmes, E., Waxman, J., Nicholson, J. K., Bevan, C., & Keun, H. C. (2006). Impact of analytical bias in metabonomic studies of human blood serum and plasma. Analytical Chemistry, 78(13), 4307–4318. doi: 10.1021/ac051972y.PubMedCrossRefGoogle Scholar
  125. Telu, K. H., Yan, X., Wallace, W. E., Stein, S. E., & Simón-Manso, Y. (2016). Analysis of human plasma metabolites across different liquid chromatography/mass spectrometry platforms: Cross-platform transferable chemical signatures. Rapid Communications in Mass Spectrometry, 30(5), 581–593. doi: 10.1002/rcm.7475.PubMedCrossRefGoogle Scholar
  126. Teng, Q., Huang, W., Collette, T. W., Ekman, D. R., & Tan, C. (2009). A direct cell quenching method for cell-culture based metabolomics. Metabolomics, 5(2), 199–208. doi: 10.1007/s11306-008-0137-z.CrossRefGoogle Scholar
  127. Theodoridis, G. A., Gika, H. G., Want, E. J., & Wilson, I. D. (2012). Liquid chromatography-mass spectrometry based global metabolite profiling: A review. Analytica Chimica Acta, 711, 7–16. doi: 10.1016/j.aca.2011.09.042.PubMedCrossRefGoogle Scholar
  128. Trygg, J., Holmes, E., & Lundstedt, T. (2007). Chemometrics in metabonomics. Journal of Proteome Research, 6(2), 469–479. doi: 10.1021/pr060594q.PubMedCrossRefGoogle Scholar
  129. Tulipani, S., Llorach, R., Urpi-Sarda, M., & Andres-Lacueva, C. (2013). Comparative analysis of sample preparation methods to handle the complexity of the blood fluid metabolome: When less is more. Analytical Chemistry, 85(1), 341–348. doi: 10.1021/ac302919t.PubMedCrossRefGoogle Scholar
  130. Tulipani, S., Mora-Cubillos, X., Jáuregui, O., Llorach, R., García-Fuentes, E., Tinahones, F. J., & Andres-Lacueva, C. (2015). New and vintage solutions to enhance the plasma metabolome coverage by LC-ESI-MS untargeted metabolomics. The not-so-simple process of method performance evaluation. Analytical Chemistry, 87(5), 2639–2647. doi: 10.1021/ac503031d.PubMedCrossRefGoogle Scholar
  131. van den Berg, R. A., Hoefsloot, H. C., Westerhuis, J. A., Smilde, A. K., & van der Werf, M. J. (2006). Centering, scaling, and transformations: Improving the biological information content of metabolomics data. BMC Genomics, 7, 142. doi: 10.1186/1471-2164-7-142.PubMedPubMedCentralCrossRefGoogle Scholar
  132. Van Der Kloet, F. M., Bobeldijk, I., Verheij, E. R., & Jellema, R. H. (2009). Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping. Journal of Proteome Research, 8(11), 5132–5141. doi: 10.1021/pr900499r.PubMedCrossRefGoogle Scholar
  133. Veselkov, K. A., Vingara, L. K., Masson, P., Robinette, S. L., Want, E., Li, J. V., et al. (2011). Optimized preprocessing of ultra-performance liquid chromatography/mass spectrometry urinary metabolic profiles for improved information recovery. Analytical Chemistry, 83, 5864–5872.PubMedCrossRefGoogle Scholar
  134. Viant, M. R., Bearden, D. W., Bundy, J. G., Burton, I. W., Collette, T. W., Ekman, D. R., et al. (2008). International NMR-based environmental metabolomics intercomparison exercise. Environmental Science and Technology, 43(1), 219–225. doi: 10.1021/es802198z.CrossRefGoogle Scholar
  135. Vorkas, P. A., Isaac, G., Anwar, M. A., Davies, A. H., Want, E. J., & Holmes, E. (2015). Untargeted UPLC-MS profiling pipeline to expand tissue metabolome coverage: Application to cardiovascular disease. Analytical Chemistry, 87(8), 4184–4193. doi: 10.1021/ac503775m.PubMedPubMedCentralCrossRefGoogle Scholar
  136. Vuckovic, D. (2012). Current trends and challenges in sample preparation for global metabolomics using liquid chromatography-mass spectrometry. Analytical and Bioanalytical Chemistry, 403(6), 1523–1548. doi: 10.1007/s00216-012-6039-y.PubMedCrossRefGoogle Scholar
  137. Want, E. J. (2009). Challenges in applying chemometrics to LC-MS-based global metabolite profile data. Bioanalysis, 1(4), 805–819. doi: 10.4155/bio.09.64.PubMedCrossRefGoogle Scholar
  138. Want, E. J., & Masson, P. (2011). Processing and analysis of GC/LC-MS-based metabolomics data. Methods in Molecular Biology, 708(4), 321–334. doi: 10.1007/978-1-61737-985-7.Google Scholar
  139. Want, E. J., Wilson, I. D., Gika, H., Theodoridis, G., Plumb, R. S., Shockcor, J., et al. (2010). Global metabolic profiling procedures for urine using UPLC-MS. Nature Protocols, 5(6), 1005–1018. doi: 10.1038/nprot.2010.50.PubMedCrossRefGoogle Scholar
  140. Ward, J. L., Baker, J. M., Miller, S. J., Deborde, C., Maucourt, M., Biais, B., et al. (2010). An inter-laboratory comparison demonstrates that [1H]-NMR metabolite fingerprinting is a robust technique for collaborative plant metabolomic data collection. Metabolomics, 6(2), 263–273. doi: 10.1007/s11306-010-0200-4.PubMedPubMedCentralCrossRefGoogle Scholar
  141. Wedge, D. C., Allwood, J. W., Dunn, W. B., Vaughan, A. A., Simpson, K., Brown, M., et al. (2011). Is serum or plasma more appropriate for inter-subject assessment in patients with small-cell lung cancer. Analytical Chemistry, 83, 6689–6697.PubMedCrossRefGoogle Scholar
  142. Wehrens, R., Jos Hageman, B. A., Fred van Eeuwijk, B., Rik Kooke, B., Pádraic Flood, B. J., Erik Wijnker, B., et al. (2016). Improved batch correction in untargeted MS-based metabolomics. Metabolomics,. doi: 10.1007/s11306-016-1015-8.PubMedPubMedCentralGoogle Scholar
  143. Westerhuis, J. A., Hoefsloot, H. C. J., Smit, S., Vis, D. J., Smilde, A. K., Velzen, E. J. J., et al. (2008a). Assessment of PLSDA cross validation. Metabolomics, 4(1), 81–89. doi: 10.1007/s11306-007-0099-6.CrossRefGoogle Scholar
  144. Westerhuis, J. A., van Velzen, E. J. J., Hoefsloot, H. C. J., & Smilde, A. K. (2008b). Discriminant Q2 (DQ2) for improved discrimination in PLSDA models. Metabolomics, 4(4), 293–296. doi: 10.1007/s11306-008-0126-2.CrossRefGoogle Scholar
  145. Westerhuis, J. A., van Velzen, E. J. J., Hoefsloot, H. C. J., & Smilde, A. K. (2010). Multivariate paired data analysis: Multilevel PLSDA versus OPLSDA. Metabolomics, 6(1), 119–128. doi: 10.1007/s11306-009-0185-z.PubMedCrossRefGoogle Scholar
  146. Wheelock, Å. M., & Wheelock, C. E. (2013). Trials and tribulations of’omics data analysis: Assessing quality of SIMCA-based multivariate models using examples from pulmonary medicine. Molecular BioSystems, 9(11), 2589–2596. doi: 10.1039/c3mb70194h.PubMedCrossRefGoogle Scholar
  147. Whiley, L., Godzien, J., Ruperez, F. J., Legido-Quigley, C., & Barbas, C. (2012). In-vial dual extraction for direct LC-MS analysis of plasma for comprehensive and highly reproducible metabolic fingerprinting. Analytical Chemistry, 84(14), 5992–5999. doi: 10.1021/ac300716u.PubMedCrossRefGoogle Scholar
  148. Wishart, D. S. (2009). Computational strategies for metabolite identification in metabolomics. Bioanalysis, 1(9), 1579–1596. doi: 10.4155/bio.09.138.PubMedCrossRefGoogle Scholar
  149. Wishart, D. S. (2011). Advance in metabolite identification. Bioanalysis, 3(15), 1769–1782.PubMedCrossRefGoogle Scholar
  150. Wishart, D. S., Jewison, T., Guo, A. C., Wilson, M., Knox, C., Liu, Y., et al. (2013). HMDB 3.0—The human metabolome database in 2013. Nucleic Acids Research, 41(D1), 801–807. doi: 10.1093/nar/gks1065.CrossRefGoogle Scholar
  151. Wishart, D. S., Knox, C., Guo, A. C., Eisner, R., Young, N., Gautam, B., et al. (2009). HMDB: A knowledgebase for the human metabolome. Nucleic Acids Research, 37(suppl. 1), 603–610. doi: 10.1093/nar/gkn810.CrossRefGoogle Scholar
  152. Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109–130. doi: 10.1016/S0169-7439(01)00155-1.CrossRefGoogle Scholar
  153. Xia, J., Psychogios, N., Young, N., & Wishart, D. S. (2009). MetaboAnalyst: A web server for metabolomic data analysis and interpretation. Nucleic Acids Research, 37(suppl. 2), 652–660. doi: 10.1093/nar/gkp356.CrossRefGoogle Scholar
  154. Xia, J., & Wishart, D. S. (2011). Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nature Protocols, 6(6), 743–760. doi: 10.1038/nprot.2011.319.PubMedCrossRefGoogle Scholar
  155. Yang, W., Chen, Y., Xi, C., Zhang, R., Song, Y., Zhan, Q., et al. (2013). Liquid chromatography−tandem mass spectrometry-based plasma metabonomics delineate the effect of metabolites’ stability on reliability of potential biomarkers. Analytical Chemistry, 85, 2606–2610.PubMedCrossRefGoogle Scholar
  156. Yang, J., Zhao, X., Lu, X., Lin, X., & Xu, G. (2015). A data preprocessing strategy for metabolomics to reduce the mask effect in data analysis. Frontiers in Molecular Biosciences, 2(February), 4. doi: 10.3389/fmolb.2015.00004.PubMedPubMedCentralGoogle Scholar
  157. Yin, P., Lehmann, R., & Xu, G. (2015). Effects of pre-analytical processes on blood samples used in metabolomics studies. Analytical and Bioanalytical Chemistry, 407, 4879–4892. doi: 10.1007/s00216-015-8565-x.PubMedPubMedCentralCrossRefGoogle Scholar
  158. Yu, Z., Kastenmüller, G., He, Y., Belcredi, P., Möller, G., Prehn, C., et al. (2011). Differences between human plasma and serum metabolite profiles. PLoS One, 6(7), 1–6. doi: 10.1371/journal.pone.0021230.Google Scholar
  159. 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(4), 1357–1364. doi: 10.1021/ac8019366.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Mikael K. R. Engskog
    • 1
    Email author
  • Jakob Haglöf
    • 1
  • Torbjörn Arvidsson
    • 1
    • 2
  • Curt Pettersson
    • 1
  1. 1.Division of Analytical Pharmaceutical ChemistryUppsala UniversityUppsalaSweden
  2. 2.Medical Product AgencyUppsalaSweden

Personalised recommendations