Skip to main content
SpringerLink
Log in

Batch profiling calibration for robust NMR metabonomic data analysis

  • Research Paper
  • Published:
Analytical and Bioanalytical Chemistry Aims and scope Submit manuscript

Abstract

Metabonomic studies involve the analysis of large numbers of samples to identify significant changes in the metabolic fingerprints of biological systems, possibly with sufficient statistical power for analysis. While procedures related to sample preparation and spectral data acquisition generally include the use of independent sample batches, these might be sources of systematic variation whose effects should be removed to focus on phenotyping the relevant biological variability. In this work, we describe a grouped-batch profile (GBP) calibration strategy to adjust nuclear magnetic resonance (NMR) metabolomic data-sets for batch effects either introduced during NMR experiments or samples work-up. We show how this method can be applied to data calibration in the context of a large-scale NMR epidemiological study where quality control samples are available. We also illustrate the efficiency of a batch profile correction for NMR metabonomic investigation of cell extracts, where GBP can significantly improve the predictive power of multivariate statistical models for discriminant analysis of the cell infection status. The method is applicable to a broad range of NMR metabolomic/metabonomic cohort studies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Dunn WB, Broadhurst DI, Atherton HJ, Goodacre R, Griffin JL (2011) Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chem Soc Rev 40(1):387–426

    Article  CAS  Google Scholar 

  2. Suhre K, Shin SY, Petersen AK, Mohney RP, Meredith D, Wagele B, Altmaier E, Deloukas P, Erdmann J, Grundberg E, Hammond CJ, de Angelis MH, Kastenmuller G, Kottgen A, Kronenberg F, Mangino M, Meisinger C, Meitinger T, Mewes HW, Milburn MV, Prehn C, Raffler J, Ried JS, Romisch-Margl W, Samani NJ, Small KS, Wichmann HE, Zhai GJ, Illig T, Spector TD, Adamski J, Soranzo N, Gieger C, Cardiogram (2011) Human metabolic individuality in biomedical and pharmaceutical research. Nature 477(7362):54–60

    Article  CAS  Google Scholar 

  3. Gieger C, Geistlinger L, Altmaier E, de Angelis MH, Kronenberg F, Meitinger T, Mewes HW, Wichmann HE, Weinberger KM, Adamski J, Illig T, Suhre K (2008) Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet 4(11)

  4. Bao YQ, Zhao T, Wang XY, Qiu YP, Su MM, Jia WP, Jia W (2009) Metabonomic variations in the drug-treated type 2 diabetes mellitus patients and healthy volunteers. J Proteome Res 8(4):1623–1630

    Article  CAS  Google Scholar 

  5. Sreekumar A, Poisson LM, Rajendiran TM, Khan AP, Cao Q, Yu JD, Laxman B, Mehra R, Lonigro RJ, Li Y, Nyati MK, Ahsan A, Kalyana-Sundaram S, Han B, Cao XH, Byun J, Omenn GS, Ghosh D, Pennathur S, Alexander DC, Berger A, Shuster JR, Wei JT, Varambally S, Beecher C, Chinnaiyan AM (2009) Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 457(7231):910–914

    Article  CAS  Google Scholar 

  6. Gibney MJ, Walsh M, Brennan L, Roche HM, German B, van Ommen B (2005) Metabolomics in human nutrition: opportunities and challenges. Am J Clin Nutrit 82(3):497–503

    CAS  Google Scholar 

  7. Teahan O, Gamble S, Holmes E, Waxman J, Nicholson JK, Bevan C, Keun HC (2006) Impact of analytical bias in metabonomic studies of human blood serum and plasma. Anal Chem 78(13):4307–4318

    Article  CAS  Google Scholar 

  8. Gika HG, Theodoridis GA, Wingate JE, Wilson ID (2007) Within-day reproducibility of an HPLC-MS-Based method for metabonomic analysis: application to human urine. J Proteome Res 6(8):3291–3303

    Article  CAS  Google Scholar 

  9. Gika HG, Macpherson E, Theodoridis GA, Wilson ID (2008) Evaluation of the repeatability of ultra-performance liquid chromatography-TOF-MS for global metabolic profiling of human urine samples. J Chromatogr, B: Anal Technol Biomed Life Sci 871(2):299–305

    Article  CAS  Google Scholar 

  10. Lai L, Michopoulos F, Gika H, Theodoridis G, Wilkinson RW, Odedra R, Wingate J, Bonner R, Tate S, Wilson ID (2010) Methodological considerations in the development of HPLC-MS methods for the analysis of rodent plasma for metabonomic studies. Mol Biosyst 6(1):108–120

    Article  CAS  Google Scholar 

  11. Wagner S, Scholz K, Sieber M, Kellert M, Voelkel W (2007) Tools in metabonomics: an integrated validation approach for LC-MS metabolic profiling of mercapturic acids in human urine. Anal Chem 79(7):2918–2926

    Article  CAS  Google Scholar 

  12. Sangster T, Major H, Plumb R, Wilson AJ, Wilson ID (2006) A pragmatic and readily implemented quality control strategy for HPLC-MS and GC-MS-based metabonomic analysis. Analyst 131(10):1075–1078

    Article  CAS  Google Scholar 

  13. Dunn WB, Broadhurst D, Begley P, Zelena E, Francis-McIntyre S, Anderson N, Brown M, Knowles JD, Halsall A, Haselden JN, Nicholls AW, Wilson ID, Kell DB, Goodacre R, C HSMH (2011) Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc 6(7):1060–1083

    Article  CAS  Google Scholar 

  14. van der Greef J, Martin S, Juhasz P, Adourian A, Plasterer T, Verheij ER, McBurney RN (2007) The art and practice of systems biology in medicine: mapping patterns of relationships. J Proteome Res 6(4):1540–1559

    Article  Google Scholar 

  15. Zelena E, Dunn WB, Broadhurst D, Francis-McIntyre S, Carroll KM, Begley P, O'Hagan S, Knowles JD, Halsall A, Wilson ID, Kell DB (2009) Development of a robust and repeatable UPLC-MS method for the long-term metabolomic study of human serum. Anal Chem 81(4):1357–1364

    Article  CAS  Google Scholar 

  16. Wang SY, Kuo CH, Tseng YJ (2013) Batch Normalizer: a fast total abundance regression calibration method to simultaneously adjust batch and injection order effects in liquid chromatography/time-of-flight mass spectrometry-based metabolomics data and comparison with current calibration methods. Anal Chem 85(2):1037–1046

    Article  CAS  Google Scholar 

  17. Kirwan JA, Broadhurst DI, Davidson RL, Viant MR (2013) Characterising and correcting batch variation in an automated direct infusion mass spectrometry (DIMS) metabolomics workflow. Anal Bioanal Chem 405(15):5147–5157

    Article  CAS  Google Scholar 

  18. Kamleh MA, Ebbels TM, Spagou K, Masson P, Want EJ (2012) Optimizing the use of quality control samples for signal drift correction in large-scale urine metabolic profiling studies. Anal Chem 84(6):2670–2677

    Article  CAS  Google Scholar 

  19. van der Kloet FM, Bobeldijk I, Verheij ER, Jellema RH (2009) Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping. J Proteome Res 8(11):5132–5141

    Article  Google Scholar 

  20. Sysi-Aho M, Katajamaa M, Yetukuri L, Oresic M (2007) Normalization method for metabolomics data using optimal selection of multiple internal standards. BMC Bioinforma 8:93

    Article  Google Scholar 

  21. De Livera AM, Dias DA, De Souza D, Rupasinghe T, Pyke J, Tull D, Roessner U, McConville M, Speed TP (2012) Normalizing and integrating metabolomics data. Anal Chem 84(24):10768–10776

    Article  Google Scholar 

  22. Dumas ME, Maibaum EC, Teague C, Ueshima H, Zhou B, Lindon JC, Nicholson JK, Stamler J, Elliott P, Chan Q, Holmes E (2006) Assessment of analytical reproducibility of 1H NMR spectroscopy based metabonomics for large-scale epidemiological research: the INTERMAP Study. Anal Chem 78(7):2199–2208

    Article  CAS  Google Scholar 

  23. Blaise BJ, Giacomotto J, Triba MN, Toulhoat P, Piotto M, Emsley L, Segalat L, Dumas ME, Elena B (2009) Metabolic profiling strategy of Caenorhabditis elegans by whole-organism nuclear magnetic resonance. J Proteome Res 8(5):2542–2550

    Article  CAS  Google Scholar 

  24. Beckonert O, Keun HC, Ebbels TMD, Bundy JG, Holmes E, Lindon JC, Nicholson JK (2007) Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat Protoc 2(11):2692–2703

    Article  CAS  Google Scholar 

  25. Broadhurst DI, Kell DB (2006) Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2(4):171–196

    Article  CAS  Google Scholar 

  26. Lindon JC, Nicholson JK, Holmes E, Keun HC, Craig A, Pearce JTM, Bruce SJ, Hardy N, Sansone SA, Antti H, Jonsson P, Daykin C, Navarange M, Beger RD, Verheij ER, Amberg A, Baunsgaard D, Cantor GH, Lehman-McKeeman L, Earll M, Wold S, Johansson E, Haselden JN, Kramer K, Thomas C, Lindberg J, Schuppe-Koistinen I, Wilson ID, Reily MD, Robertson DG, Senn H, Krotzky A, Kochhar S, Powell J, van der Ouderaa F, Plumb R, Schaefer H, Spraul M, worki SMRS (2005) Summary recommendations for standardization and reporting of metabolic analyses. Nat Biotechnol 23(7):833–838

    Article  CAS  Google Scholar 

  27. Dunn WB, Wilson ID, Nicholls AW, 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

    Article  CAS  Google Scholar 

  28. Draisma HHM, Reijmers TH, van der Kloet F, Bobeldijk-Pastorova I, Spies-Faber E, Vogels JTWE, Meulman JJ, Boomsma DI, van der Greef J, Hankemeier T (2010) Equating, or correction for between-block effects with application to body fluid LC-MS and NMR metabolomics data sets. Anal Chem 82(3):1039–1046

    Article  CAS  Google Scholar 

  29. Chu TM, Bao W, Thomas RS, Wolfinger RD (2009) Batch profile estimation, correction, and scoring. In: Scherer A (ed) Batch effects and noise in microarray experiments: sources and solutions. Wiley, Chichester, pp 155–165

    Chapter  Google Scholar 

  30. Wakita T, Pietschmann T, Kato T, Date T, Miyamoto M, Zhao Z, Murthy K, Habermann A, Krausslich HG, Mizokami M, Bartenschlager R, Liang TJ (2005) Production of infectious hepatitis C virus in tissue culture from a cloned viral genome. Nat Med 11(7):791–796

    Article  CAS  Google Scholar 

  31. Savorani F, Tomasi G, Engelsen SB (2010) icoshift: a versatile tool for the rapid alignment of 1D NMR spectra. J Magn Reson 202(2):190–202

    Article  CAS  Google Scholar 

  32. Blaise BJ, Shintu L, Elena B, Emsley L, Dumas ME, Toulhoat P (2009) Statistical recoupling prior to significance testing in nuclear magnetic resonance based metabonomics. Anal Chem 81(15):6242–6251

    Article  CAS  Google Scholar 

  33. Nicholson JK, Foxall PJD, Spraul M, Farrant RD, Lindon JC (1995) 750-Mhz H-1 and H-1-C-13 Nmr-spectroscopy of human blood-plasma. Anal Chem 67(5):793–811

    Article  CAS  Google Scholar 

  34. Riboli E, Kaaks R (1997) The EPIC project: rationale and study design. Int J Epidemiol 26:S6–S14

    Article  Google Scholar 

  35. Wold S (1978) Cross-validatory estimation of number of components in factor and principal components models. Technometrics 20(4):397–405

    Article  Google Scholar 

Download references

Acknowledgments

We thank the Principle Investigators of the EPIC study for allowing secondary use of their data in the present paper. We thank Dr. Pietro Ferrari (IARC, Lyon) for helpful discussions. We thank Bruker Biospin for financial support.

Author information

Authors and Affiliations

  1. Institut des Sciences Analytiques, Centre de RMN à très hauts champs, CNRS/ENS Lyon/UCB Lyon-1, Université de Lyon, 5 rue de la Doua, 69100, Villeurbanne, France

    Anne Fages, Clément Pontoizeau, Elodie Jobard & Bénédicte Elena-Herrmann

  2. Centre de recherche en cancérologie de Lyon, INSERM U1052, CNRS 5286, Université de Lyon, 151, Cours A Thomas, 69424, Lyon Cedex, France

    Pierre Lévy & Birke Bartosch

Authors
  1. Anne Fages
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Clément Pontoizeau
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Elodie Jobard
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pierre Lévy
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Birke Bartosch
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bénédicte Elena-Herrmann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bénédicte Elena-Herrmann.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(PDF 178 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fages, A., Pontoizeau, C., Jobard, E. et al. Batch profiling calibration for robust NMR metabonomic data analysis. Anal Bioanal Chem 405, 8819–8827 (2013). https://doi.org/10.1007/s00216-013-7296-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00216-013-7296-0

Keywords

Search

Navigation