, Volume 1, Issue 1, pp 75–85 | Cite as

Analytical precision, biological variation, and mathematical normalization in high data density metabolomics

  • Yevgeniya I. Shurubor
  • Ugo Paolucci
  • Boris F. Krasnikov
  • Wayne R. Matson
  • Bruce S. Kristal

Metabolic serotypes sensitive to caloric intake may enable sera metabolomic profiles to validate epidemiological parameters and predict disease risk in humans. This long-range goal is complicated by the lack of known state markers and the requirement for simultaneous monitoring of multiple small changes. Therefore, analytical precision for appropriate high data density studies using HPLC separations coupled with coulometric array detectors was evaluated over a two month period in pooled rat sera samples (previously collected and stored at −80 °C), and in authentic biochemical standards. In sera, mean coefficients of variation (CV) of retention time and ratio accuracy within the established metabolic serotype varied within ±1% and ±3%, respectively. In sets of purified standards, the same parameters fluctuated, correspondently, in ranges of ±0.1% and ±1%. Median CV of the metabolite concentrations were ~13% in standards and ~11–19% in sera, and varied non-monotonically with the analytical system status and experimental design. These parameters were shown to be sufficiently controlled so as not to dominate intra-group biological variability in serum metabolomics studies. Continuation of experimental runs across an analytical breakpoint (column replacement) was associated with disproportionate changes in metabolite concentrations, independent of maintained analytical precision. These changes were sufficient to shift overall profile localization in megavariate projection analyses. We developed a mathematical approach to normalize this break and use partial least squares projection to latent structure discriminant analysis to confirm validity of this normalization approach. This generally applicable mathematical correction helps enable longer term high data density studies by removing a critical source of systemic variation.

Key words

metabolomics HPLC electrochemical detection analytical precision serum biomarkers metabolic serotype 


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  1. Acworth, I.N. and Gamache, P.H. (1996). The Coulometric Electrode Array for Use in HPLC Analysis, Part 1: Theory. Amer. Lab., MayGoogle Scholar
  2. Alburges, M.E., Narang, N., Wamsley, J.K. 1993A sensitive and rapid HPLC-ECD method for the simultaneous analysis of norepinephrine, dopamine, serotonin and their primary metabolites in brain tissueBiomed. Chromatogr7306310Google Scholar
  3. Beal, M.F., Matson, W.R., Storey, E.,  et al. 1992Kynurenic acid concentrations are reduced in Huntington’s disease cerebral cortexJ. Neurol. Sci1088087Google Scholar
  4. Beal, M.F., Matson, W.R., Swartz, K.J., Gamache, P.H., Bird, E.D. 1990Kynurenine pathway measurements in Huntington’s disease striatum; evidence for reduced formation of kynurenic acidJ. Neurochem5513271339Google Scholar
  5. Erickson, L., Johansson, E., Kettaneh-Wold, N. and Wold, S. (2001). Multi- and Megavariate Data Analysis. Umetrics ABGoogle Scholar
  6. Fiehn, O., Kopka, J., Dormann, P., Altmann, T., Trethewey, R.N., Willmitzer, L. 2000Metabolite profiling for plant functional genomicsNat. Biotechnol1811571161Google Scholar
  7. Futreal, P.A., Liu, Q., Shattuck-Eidens, D.,  et al. 1994BRCA1 mutations in primary breast and ovarian carcinomasScience266120122Google Scholar
  8. Harrigan, G.G., Goodacre, R. 2003. Metabolic profiling: its role in biomarker discovery and gene function analysis. Kluwer Academic PublishersGoogle Scholar
  9. Huettle, P. and Gerhard, A. (1997). Multicomponent quantitations of monoamines and related compounds in biological fluids and CNS tissues by HPLC coupled with multielectrode electrochemical detector. Coulometric electrode array detectors for HPLC in Acworth, I.N., et al. (Eds), Progress in HPLC-PRCE. VSP International Science Publication, Utrecht, pp. 213–223Google Scholar
  10. Kristal, B.S., Vigneau-Callahan, K.E., Matson, W.R. 1998Simultaneous analysis of the majority of low-molecular-weight, redox-active compounds from mitochondriaAnal. Biochem2631825Google Scholar
  11. Kristal, B.S., Vigneau-Callahan, K.E., Matson, W.R. 1999Purine catabolism: links to mitochondrial respiration and antioxidant defensesArch. Biochem. Biophys3702233Google Scholar
  12. LeWitt, P.A., Galloway, M.P., Matson, W.R., Milbury, P.M., McDermott, M., Srivastava, D.K. 1992Markers of dopamine metabolism in parkinson’s disease: the Parkinson’s Study GroupNeurology4221112117Google Scholar
  13. Matson, W.R., Bouckoms, A., Svendson, C., Beal, M.F. and Bird, E.D. (1990). Generating and controlling multiparameter databases for biochemical correlates of disorders in Basic, clinical and therapeutic aspects of Alzheimer’s and Parkinson’s diseases. Plenum, New York, pp. 513–516Google Scholar
  14. Matson, W.R., Gamache, P.H., Beal, M.F., Bird, E.D. 1987EC array sensor concepts and dataLife Sci41905908Google Scholar
  15. Matson, W.R., Langials, P., Volicer, L., Gamache, P.H., Bird, E.D., Mark, K.A. 1984n-Electrode three dimensional liquid chromatography with electrochemical detection for determination of neurotransmittersClin. Chem3014771488Google Scholar
  16. Miki, Y., Swensen, J., Shattuck-Eidens, D.,  et al. 1994A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1Science2666671Google Scholar
  17. Milbury, P.E. (1997). CEAS generation of large multiparameter databases for determining categorical process involvement of biomolecules in Anonymous, Coulometric array detectors for HPLC. VSP International Science Publication, pp. 125–141Google Scholar
  18. Nurmi, T., Adlercreutz, H. 1999Sensitive high-performance liquid chromatographic method for profiling phytoestrogens using coulometric electrode array detection: application to plasma analysisAnal. Biochem274110117Google Scholar
  19. Ogawa, T., Matson, W.R., Beal, M.F.,  et al. 1992Kynurenine pathway abnormalities in Parkinson’s diseaseNeurology4217021706Google Scholar
  20. Paolucci, U., Vigneau-Callahan, K.E., Shi, H., Matson, W.R., Kristal, B.S. 2004aDevelopment of biomarkers based on diet-dependent metabolic serotypes: concerns and approaches for cohort and gender issues in serum metabolome studies.OMICS J. Integr. Biol.8209220Google Scholar
  21. Paolucci, U., Vigneau-Callahan, K.E., Shi, H., Matson, W.R., Kristal, B.S. 2004bDevelopment of biomarkers based on diet-dependent metabolic serotypes: Characteristics of component-based models of metabolic serotype.OMICS J. Integr. Biol.8221238Google Scholar
  22. Shi, H., Vigneau-Callahan, K.E., Milbury, P.E., Matson, W.R., Kristal, B.S. 2004aDevelopment of biomarkers based on diet-dependent metabolic merotypes: practical issues in development of expert system-based classification models in metabolomic studiesOMICS J. Integr. Biol.8197208Google Scholar
  23. Shi, H., Vigneau-Callahan, K.E., Matson, W.R., Kristal, B.S. 2002Attention to relative response across sequential electrodes improves quantitation of coulometric arrayAnal. Biochem302239245Google Scholar
  24. Shi, H., Vigneau-Callahan, K.E., Shestopalov, A.I., Milbury, P.E., Matson, W.R., Kristal, B.S. 2002Characterization of diet-dependent metabolic serotypes: primary validation of male and female serotypes in independent cohorts of ratsJ. Nutr13210391046Google Scholar
  25. Shi, H., Vigneau-Callahan, K.E., Shestopalov, A.I., Milbury, P.E., Matson, W.R., Kristal, B.S. 2002Characterization of diet-dependent metabolic serotypes: Proof of principle in female and male ratsJ. Nutr.13210311038Google Scholar
  26. Vigneau-Callahan, K.E., Shestopalov, A.I., Milbury, P.E., Matson, W.R., Kristal, B.S. 2001Characterization of Diet-Dependent Metabolic Serotypes: Analytical and Biological Variability Issues in RatsJ. Nutr131924S932SGoogle Scholar
  27. Weindruch, R., Walford, R. (1988). The Retardation of Aging and Disease by Dietary Restriction. Charles C. Thomas, St. LouisGoogle Scholar
  28. Wooster, R., Neuhausen, S.L., Mangion, J.,  et al. 1994Localization of a breast cancer susceptibility gene, BRCA2, to chromosome 13q12–13Science26520882090Google Scholar
  29. Yu, B.P. 1994Modulation of Aging Processes by Dietary RestrictionCRC PressBoca RatonGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  • Yevgeniya I. Shurubor
    • 1
  • Ugo Paolucci
    • 1
    • 5
  • Boris F. Krasnikov
    • 1
    • 2
  • Wayne R. Matson
    • 3
  • Bruce S. Kristal
    • 1
    • 2
    • 4
  1. 1.Dementia Research ServiceBurke Medical Research InstituteWhite PlainsUSA
  2. 2.Department of NeuroscienceWeill Medical College of Cornell UniversityUSA
  3. 3.ESA, Inc.ChelmsfordUSA
  4. 4.Department of BiochemistryWeill Medical College of Cornell UniversityNYUSA
  5. 5.Department of NeurologyWestchester Medical CenterValhallaUSA

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