Analytical and Bioanalytical Chemistry

, Volume 410, Issue 2, pp 483–490 | Cite as

Potential of dynamically harmonized Fourier transform ion cyclotron resonance cell for high-throughput metabolomics fingerprinting: control of data quality

  • Baninia Habchi
  • Sandra Alves
  • Delphine Jouan-Rimbaud Bouveresse
  • Brice Appenzeller
  • Alain Paris
  • Douglas N. Rutledge
  • Estelle Rathahao-Paris
Research Paper

Abstract

Due to the presence of pollutants in the environment and food, the assessment of human exposure is required. This necessitates high-throughput approaches enabling large-scale analysis and, as a consequence, the use of high-performance analytical instruments to obtain highly informative metabolomic profiles. In this study, direct introduction mass spectrometry (DIMS) was performed using a Fourier transform ion cyclotron resonance (FT-ICR) instrument equipped with a dynamically harmonized cell. Data quality was evaluated based on mass resolving power (RP), mass measurement accuracy, and ion intensity drifts from the repeated injections of quality control sample (QC) along the analytical process. The large DIMS data size entails the use of bioinformatic tools for the automatic selection of common ions found in all QC injections and for robustness assessment and correction of eventual technical drifts. RP values greater than 106 and mass measurement accuracy of lower than 1 ppm were obtained using broadband mode resulting in the detection of isotopic fine structure. Hence, a very accurate relative isotopic mass defect (RΔm) value was calculated. This reduces significantly the number of elemental composition (EC) candidates and greatly improves compound annotation. A very satisfactory estimate of repeatability of both peak intensity and mass measurement was demonstrated. Although, a non negligible ion intensity drift was observed for negative ion mode data, a normalization procedure was easily applied to correct this phenomenon. This study illustrates the performance and robustness of the dynamically harmonized FT-ICR cell to perform large-scale high-throughput metabolomic analyses in routine conditions.

Graphical abstract

Analytical performance of FT-ICR instrument equipped with a dynamically harmonized cell.

Keywords

High throughput Metabolomics Direct introduction mass spectrometry Fourier transform ion cyclotron resonance Analytical repeatability 

Notes

Acknowledgments

The authors thank the department of Public Health, University of Liège for providing human urine samples, the “Region Ile-de-France” and Dim Analytics for financial support of the PhD of Baninia Habchi and the Dim ASTREA program for financing the acquisition of the dynamically harmonized cell. The National FT-ICR network (FR 3624 CNRS) providing financial support for conducting the research is also gratefully acknowledged.

Compliance with ethical standards

Disclosure of potential conflicts of interest

The authors declare that they have no conflict of interest.

Research involving human participants

All procedures performed in this study were in accordance with the ethical standards of the national research committee and with the 1964 Helsinki declaration and its later amendments. The protocol of the NESCaV study was approved by the Ethics Committee of the Faculty of Medicine of the University of Liège (Wallonia) as institutional review board representing the Wallonia part of the study (B70720097541).

Supplementary material

216_2017_738_MOESM1_ESM.pdf (1.4 mb)
ESM 1 (PDF 1414 kb)

References

  1. 1.
    Oikawa A, Nakamura Y, Ogura T, Kimura A, Suzuki H, Sakurai N, et al. Clarification of pathway-specific inhibition by Fourier transform ion cyclotron resonance/mass spectrometry-based metabolic phenotyping studies. Plant Physiol. 2006;142:398–413.  https://doi.org/10.1104/pp.106.080317.CrossRefGoogle Scholar
  2. 2.
    Ritchie SA, Ahiahonu PW, Jayasinghe D, Heath D, Liu J, Lu Y, et al. Reduced levels of hydroxylated, polyunsaturated ultra long-chain fatty acids in the serum of colorectal cancer patients: implications for early screening and detection. BMC Med. 2010;8:1–20.  https://doi.org/10.1186/1741-7015-8-13.CrossRefGoogle Scholar
  3. 3.
    Lucio M, Fekete A, Weigert C, Wägele B, Zhao X, Chen J, et al. Insulin sensitivity is reflected by characteristic metabolic fingerprints-a Fourier transform mass spectrometric non-targeted metabolomics approach. PLoS One. 2010;5:e13317.  https://doi.org/10.1371/journal.pone.0013317.CrossRefGoogle Scholar
  4. 4.
    Hughey CA, Rodgers RP, Marshall AG. Resolution of 11,000 compositionally distinct components in a single electrospray ionization Fourier transform ion cyclotron resonance mass spectrum of crude oil. Anal Chem. 2002;74:4145–9.  https://doi.org/10.1021/ac020146b.CrossRefGoogle Scholar
  5. 5.
    Habchi B, Alves S, Paris A, Rutledge DN, Rathahao-Paris E. How to really perform high throughput metabolomic analyses efficiently? TrAC Trends Anal Chem. 2016;85:128–39.  https://doi.org/10.1016/j.trac.2016.09.005.CrossRefGoogle Scholar
  6. 6.
    González-Domínguez R, Castilla-Quintero R, García-Barrera T, Gómez-Ariza JL. Development of a metabolomic approach based on urine samples and direct infusion mass spectrometry. Anal Biochem. 2014;465:20–7.  https://doi.org/10.1016/j.ab.2014.07.016.CrossRefGoogle Scholar
  7. 7.
    Marshall AG, Hendrickson CL, Jackson GS. Fourier transform ion cyclotron resonance mass spectrometry: a primer. Mass Spectrom Rev. 17:1–35.Google Scholar
  8. 8.
    Marshall AG. Milestones in Fourier transform ion cyclotron resonance mass spectrometry technique development. Int J Mass Spectrom 1998. 2000;200:331–56.  https://doi.org/10.1016/S1387-3806(00)00324-9.CrossRefGoogle Scholar
  9. 9.
    Marshall AG, Hendrickson CL. Fourier transform ion cyclotron resonance detection: principles and experimental configurations. Int J Mass Spectrom. 2002;215:59–75.  https://doi.org/10.1016/S1387-3806(01)00588-7.CrossRefGoogle Scholar
  10. 10.
    Boldin IA, Nikolaev EN. Fourier transform ion cyclotron resonance cell with dynamic harmonization of the electric field in the whole volume by shaping of the excitation and detection electrode assembly. Rapid Commun Mass Spectrom. 2011;25:122–6.  https://doi.org/10.1002/rcm.4838.CrossRefGoogle Scholar
  11. 11.
    Nikolaev EN, Boldin IA, Jertz R, Baykut G. Initial experimental characterization of a new ultra-high resolution FTICR cell with dynamic harmonization. J Am Soc Mass Spectrom. 2011;22:1125–33.  https://doi.org/10.1007/s13361-011-0125-9.CrossRefGoogle Scholar
  12. 12.
    Nikolaev E, Jertz R, Grigoryev A, Baykut G. Fine structure in isotopic peak distributions measured using a dynamically harmonized Fourier transform ion cyclotron resonance cell at 7 T. Anal Chem. 2012;84:2275–83.  https://doi.org/10.1021/ac202804f.CrossRefGoogle Scholar
  13. 13.
    Marshall AG, Blakney GT, Chen T, Kaiser NK, McKenna AM, Rodgers RP, et al. Mass resolution and mass accuracy: how much is enough? Mass Spectrom. 2013;2:S0009.  https://doi.org/10.5702/massspectrometry.S0009.CrossRefGoogle Scholar
  14. 14.
    Xian F, Hendrickson CL, Marshall AG. High resolution mass spectrometry. Anal Chem. 2012;84:708–19.  https://doi.org/10.1021/ac203191t.CrossRefGoogle Scholar
  15. 15.
    Thurman EM, Ferrer I. The isotopic mass defect: a tool for limiting molecular formulas by accurate mass. Anal Bioanal Chem. 2010;397:2807–16.  https://doi.org/10.1007/s00216-010-3562-6.CrossRefGoogle Scholar
  16. 16.
    Padilla-Sánchez JA, Plaza-Bolaños P, Romero-González R, Grande-Martínez Á, Thurman EM, Garrido-Frenich A. Innovative determination of polar organophosphonate pesticides based on high-resolution Orbitrap mass spectrometry. J Mass Spectrom. 2012;47:1458–65.  https://doi.org/10.1002/jms.3107.CrossRefGoogle Scholar
  17. 17.
    Rathahao-Paris E, Alves S, Junot C, Tabet J-C. High resolution mass spectrometry for structural identification of metabolites in metabolomics. Metabolomics. 2016;12:10.  https://doi.org/10.1007/s11306-015-0882-8.CrossRefGoogle Scholar
  18. 18.
    Romano S, Dittmar T, Bondarev V, Weber RJM, Viant MR, Schulz-Vogt HN. Exo-metabolome of Pseudovibrio sp. FO-BEG1 analyzed by ultra-high resolution mass spectrometry and the effect of phosphate limitation. PLoS One. 2014;9:1–11.  https://doi.org/10.1371/journal.pone.0096038.Google Scholar
  19. 19.
    Jansson J, Willing B, Lucio M, Fekete A, Dicksved J, Halfvarson J, et al. Metabolomics reveals metabolic biomarkers of Crohn’s disease. PLoS One. 2009;4:e6386.  https://doi.org/10.1371/journal.pone.0006386.CrossRefGoogle Scholar
  20. 20.
    Han J, Danell RM, Patel JR, Gumerov DR, Scarlett CO, Speir JP, et al. Towards high-throughput metabolomics using ultrahigh-field Fourier transform ion cyclotron resonance mass spectrometry. Metabolomics. 2008;4:128–40.  https://doi.org/10.1007/s11306-008-0104-8.CrossRefGoogle Scholar
  21. 21.
    Rosselló-Mora R, Lucio M, Peña A, Brito-Echeverría J, López-López A, Valens-Vadell M, et al. Metabolic evidence for biogeographic isolation of the extremophilic bacterium Salinibacter ruber. ISME J. 2008;2:242–53.  https://doi.org/10.1038/ismej.2007.93.CrossRefGoogle Scholar
  22. 22.
    Kirwan JA, Broadhurst DI, Davidson RL, Viant MR. Characterising and correcting batch variation in an automated direct infusion mass spectrometry (DIMS) metabolomics workflow. Anal Bioanal Chem. 2013;405:5147–57.  https://doi.org/10.1007/s00216-013-6856-7.CrossRefGoogle Scholar
  23. 23.
    Taylor NS, Weber RJM, Southam AD, Payne TG, Hrydziuszko O, Arvanitis TN, et al. A new approach to toxicity testing in Daphnia Magna: application of high throughput FT-ICR mass spectrometry metabolomics. Metabolomics. 2009;5:44–58.  https://doi.org/10.1007/s11306-008-0133-3.CrossRefGoogle Scholar
  24. 24.
    Kirwan JA, Weber RJM, Broadhurst DI, Viant MR. Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control. Sci Data. 2014;1:1–27.  https://doi.org/10.1038/sdata.2014.12.CrossRefGoogle Scholar
  25. 25.
    Wehrens R, Hageman JA, van Eeuwijk F, Kooke R, Flood PJ, Wijnker E, Keurentjes JJB, Lommen A, van Eekelen HDLM, Hall RD, Mumm R, de Vos RCH. Improved batch correction in untargeted MS-based metabolomics. Metabolomics 2016;12. doi:  https://doi.org/10.1007/s11306-016-1015-8.
  26. 26.
    Van Der Kloet FM, Bobeldijk I, Verheij ER, Jellema RH. Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping. J Proteome Res. 2009;8:5132–41.  https://doi.org/10.1021/pr900499r.CrossRefGoogle Scholar
  27. 27.
    Lin L, Yu Q, Yan X, Hang W, Zheng J, Xing J, et al. Direct infusion mass spectrometry or liquid chromatography mass spectrometry for human metabonomics? A serum metabonomic study of kidney cancer. R Soc Chem. 2010;135:2970–8.  https://doi.org/10.1039/c0an00265h.Google Scholar
  28. 28.
    Gika HG, Theodoridis GA, Wingate JE, Wilson ID. Within-day reproducibility of an HPLC MS based method for Metabonomic analysis: application to human urine. J Proteome Res. 2007;6:3291–303.  https://doi.org/10.1021/pr070183p.CrossRefGoogle Scholar
  29. 29.
    Li B, Tang J, Yang Q, Li S, Cui X, Li Y, et al. NOREVA: Normalization and evaluation of MS-based metabolomics data. Nucleic Acids Res. 45:W162–70.  https://doi.org/10.1093/nar/gkx449.
  30. 30.
    Qannari EM, Wakeling I, Courcoux P, MacFie HJH. Defining the underlying sensory dimensions. Food Qual Prefer. 2000;11:151–4.  https://doi.org/10.1016/S0950-3293(99)00069-5.CrossRefGoogle Scholar
  31. 31.
    Dubin E, Spiteri M, Dumas A, Ginet J, Lees M, Rutledge DN. Common components and specific weights analysis : a tool for metabolomic data pre-processing. Chemom Intell Lab Syst. 2016;150:41–50.  https://doi.org/10.1016/j.chemolab.2015.11.005.CrossRefGoogle Scholar
  32. 32.
    Alkerwi A, Guillaume M, Zannad F, Laufs U, Lair M-L. Nutrition, environment and cardiovascular health (NESCAV): protocol of an inter-regional cross-sectional study. BMC Public Health. 2010;10:698.  https://doi.org/10.1186/1471-2458-10-698.CrossRefGoogle Scholar
  33. 33.
    Streel S, Donneau A-F, Hoge A, Majerus S, Kolh P, Chapelle J-P, et al. Socioeconomic impact on the prevalence of cardiovascular risk factors in Wallonia, Belgium: a population-based study. Biomed Res Int. 2015;2015:580849.  https://doi.org/10.1155/2015/580849.CrossRefGoogle Scholar
  34. 34.
    Weber RJM, Southam AD, Sommer U, Viant MR. Characterization of isotopic abundance measurements in high resolution FT-ICR and Orbitrap mass spectra for improved confidence of metabolite identification. Anal Chem. 2011;83:3737–43.  https://doi.org/10.1021/ac2001803.CrossRefGoogle Scholar
  35. 35.
    Marshall AG, Verdun FR. Fourier transforms in NMR, optical, and mass spectrometry. A user’s handbook. Amsterdam: Elsevier; 1989.Google Scholar
  36. 36.
    Matuszewski BK, Constanzer ML, Chavez-Eng CM. Matrix effect in quantitative LC/MS/MS analyses of biological fluids: a method for determination of finasteride in human plasma at picogram per milliliter concentrations. Anal Chem. 1998;70:882–9.  https://doi.org/10.1021/ac971078+.CrossRefGoogle Scholar
  37. 37.
    Southam AD, Payne TG, Cooper HJ, Arvanitis TN, Viant MR. Dynamic range and mass accuracy of wide-scan direct infusion nanoelectrospray Fourier transform ion cyclotron resonance mass spectrometry-based metabolomics increased by the spectral stitching method. Anal Chem. 2007;79:4595–602.  https://doi.org/10.1021/ac062446p.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Baninia Habchi
    • 1
    • 2
  • Sandra Alves
    • 2
  • Delphine Jouan-Rimbaud Bouveresse
    • 1
  • Brice Appenzeller
    • 3
  • Alain Paris
    • 4
  • Douglas N. Rutledge
    • 1
  • Estelle Rathahao-Paris
    • 1
    • 2
  1. 1.UMR Ingénierie Procédés Aliments, AgroParisTech, InraUniversité Paris-SaclayMassyFrance
  2. 2.Sorbonne Universités, UPMC Univ Paris 06, CNRSInstitut Parisien de Chimie Moléculaire (IPCM)ParisFrance
  3. 3.Human Biomonitoring Research UnitLuxembourg Institute of Health (LIH)Esch-sur-AlzetteLuxembourg
  4. 4.Muséum National d’Histoire Naturelle, CNRS, UMR7245 MCAMSorbonne UniversitésParisFrance

Personalised recommendations