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Capillary Electrophoresis Mass Spectrometry as a Tool for Untargeted Metabolomics

Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1978)

Abstract

Although capillary electrophoresis (CE) coupled to mass spectrometry (MS) is a separation technique not extensively implemented, it offers differential possibilities in the study of polar and ionic metabolites in complex matrices with minimum sample treatment. However, in order to get successful results, some efforts at early stages and following specific recommendations are necessary.

In this chapter, we describe our updated and well-tested methods for untargeted metabolomics using CE-MS-TOF for common biological samples: urine, serum or plasma, feces, tissues, and cells. Sample treatment, as well as separation and detection conditions are described in detail and other steps in the workflow for untargeted metabolomics are also explained. Special attention is paid to instrumental setup and advices for daily practice.

Characteristic electropherograms obtained with each type of sample are depicted as well as groups of metabolites easily measured by this technique. Their global or individual comparisons have been given undoubtedly important information to unveil altered metabolic pathways, diagnosis, and prognosis or biomarker discovery in the study of diseases or conditions over decades.

Keywords

Fingerprinting Biological samples Polar metabolites Untargeted metabolomics CE-MS 

Notes

Acknowledgments

This work was supported by a grant from the Spanish Ministerio de Economía y Competitividad (CTQ2014-55279-R).

References

  1. 1.
    Garcia A, Godzien J, Lopez-Gonzalvez A, Barbas C (2017) Capillary electrophoresis mass spectrometry as a tool for untargeted metabolomics. Bioanalysis 9(1):99–130.  https://doi.org/10.4155/bio-2016-0216CrossRefPubMedGoogle Scholar
  2. 2.
    Canuto GA, Castilho-Martins EA, Tavares MF, Rivas L, Barbas C, López-Gonzálvez Á (2014) Multi-analytical platform metabolomic approach to study miltefosine mechanism of action and resistance in Leishmania. Anal Bioanal Chem 406(14):3459–3476.  https://doi.org/10.1007/s00216-014-7772-1CrossRefPubMedGoogle Scholar
  3. 3.
    Psychogios N, Hau DD, Peng J, Guo AC, Mandal R, Bouatra S, Sinelnikov I, Krishnamurthy R, Eisner R, Gautam B, Young N, Xia J, Knox C, Dong E, Huang P, Hollander Z, Pedersen TL, Smith SR, Bamforth F, Greiner R, McManus B, Newman JW, Goodfriend T, Wishart DS (2011) The human serum metabolome. PLoS One 6(2):e16957.  https://doi.org/10.1371/journal.pone.0016957CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Yu Z, Kastenmüller G, He Y, Belcredi P, Möller G, Prehn C, Mendes J, Wahl S, Roemisch-Margl W, Ceglarek U, Polonikov A, Dahmen N, Prokisch H, Xie L, Li Y, Wichmann HE, Peters A, Kronenberg F, Suhre K, Adamski J, Illig T, Wang-Sattler R (2011) Differences between human plasma and serum metabolite profiles. PLoS One 6(7):e21230.  https://doi.org/10.1371/journal.pone.0021230CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Dudzik D, Barbas-Bernardos C, García A, Barbas C (2018) Quality assurance procedures for mass spectrometry untargeted metabolomics. A review. J Pharm Biomed Anal 147:149–173.  https://doi.org/10.1016/j.jpba.2017.07.044CrossRefPubMedGoogle Scholar
  6. 6.
    Le Chatelier E, Nielsen T, Qin J, Prifti E, Hildebrand F, Falony G, Almeida M, Arumugam M, Batto JM, Kennedy S, Leonard P, Li J, Burgdorf K, Grarup N, Jørgensen T, Brandslund I, Nielsen HB, Juncker AS, Bertalan M, Levenez F, Pons N, Rasmussen S, Sunagawa S, Tap J, Tims S, Zoetendal EG, Brunak S, Clément K, Doré J, Kleerebezem M, Kristiansen K, Renault P, Sicheritz-Ponten T, de Vos WM, Zucker JD, Raes J, Hansen T, Bork P, Wang J, Ehrlich SD, Pedersen O, MetaHIT consortium (2013) Richness of human gut microbiome correlates with metabolic markers. Nature 500(7464):541–546.  https://doi.org/10.1038/nature12506CrossRefPubMedGoogle Scholar
  7. 7.
    Loftfield E, Vogtmann E, Sampson JN, Moore SC, Nelson H, Knight R, Chia N, Sinha R (2016) Comparison of collection methods for fecal samples for discovery metabolomics in epidemiologic studies. Cancer Epidemiol Biomark Prev 25(11):1483–1490.  https://doi.org/10.1158/1055-9965.EPI-16-0409CrossRefGoogle Scholar
  8. 8.
    Yano JM, Yu K, Donaldson GP, Shastri GG, Ann P, Ma L, Nagler CR, Ismagilov RF, Mazmanian SK, Hsiao EY (2015) Indigenous bacteria from the gut microbiota regulate host serotonin biosynthesis. Cell 161(2):264–276.  https://doi.org/10.1016/j.cell.2015.02.047CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Ferrer M, Raczkowska BA, Martínez-Martínez M, Barbas C, Rojo D (2017) Phenotyping of gut microbiota: focus on capillary electrophoresis. Electrophoresis 38(18):2275–2286.  https://doi.org/10.1002/elps.201700056CrossRefPubMedGoogle Scholar
  10. 10.
    Naz S, Moreira dos Santos DC, García A, Barbas C (2014) Analytical protocols based on LC-MS, GC-MS and CE-MS for nontargeted metabolomics of biological tissues. Bioanalysis 6(12):1657–1677.  https://doi.org/10.4155/bio.14.119CrossRefPubMedGoogle Scholar
  11. 11.
    Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, Lewis GD, Fox CS, Jacques PF, Fernandez C, O’Donnell CJ, Carr SA, Mootha VK, Florez JC, Souza A, Melander O, Clish CB, Gerszten RE (2011) Metabolite profiles and the risk of developing diabetes. Nat Med 17(4):448–453.  https://doi.org/10.1038/nm.2307CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Buescher JM, Liebermeister W, Jules M, Uhr M, Muntel J, Botella E, Hessling B, Kleijn RJ, Le Chat L, Lecointe F, Mäder U, Nicolas P, Piersma S, Rügheimer F, Becher D, Bessieres P, Bidnenko E, Denham EL, Dervyn E, Devine KM, Doherty G, Drulhe S, Felicori L, Fogg MJ, Goelzer A, Hansen A, Harwood CR, Hecker M, Hubner S, Hultschig C, Jarmer H, Klipp E, Leduc A, Lewis P, Molina F, Noirot P, Peres S, Pigeonneau N, Pohl S, Rasmussen S, Rinn B, Schaffer M, Schnidder J, Schwikowski B, Van Dijl JM, Veiga P, Walsh S, Wilkinson AJ, Stelling J, Aymerich S, Sauer U (2012) Global network reorganization during dynamic adaptations of Bacillus subtilis metabolism. Science 335(6072):1099–1103.  https://doi.org/10.1126/science.1206871CrossRefPubMedGoogle Scholar
  13. 13.
    Mastrangelo A, Panadero MI, Pérez LM, Gálvez BG, García A, Barbas C, Rupérez FJ (2016) New insight on obesity and adipose-derived stem cells using comprehensive metabolomics. Biochem J 473(14):2187–2203.  https://doi.org/10.1042/BCJ20160241CrossRefPubMedGoogle Scholar
  14. 14.
    Godzien J, Ciborowski M, Angulo S, Barbas C (2013) From numbers to a biological sense: how the strategy chosen for metabolomics data treatment may affect final results. A practical example based on urine fingerprints obtained by LC-MS. Electrophoresis 34(19):2812–2826.  https://doi.org/10.1002/elps.201300053CrossRefPubMedGoogle Scholar
  15. 15.
    Sumner LW, Amberg A, Barrett D, Beale MH, Beger R, Daykin CA, Fan TW, Fiehn O, Goodacre R, Griffin JL, Hankemeier T, Hardy N, Harnly J, Higashi R, Kopka J, Lane AN, Lindon JC, Marriott P, Nicholls AW, Reily MD, Thaden JJ, Viant MR (2007) Proposed minimum reporting standards for chemical analysis chemical analysis working group (CAWG) metabolomics standards initiative (MSI). Metabolomics 3(3):211–221.  https://doi.org/10.1007/s11306-007-0082-2CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Vinaixa M, Schymanski EL, Neumann S, Navarro M, Salek RM, Yanes O (2015) Mass spectral databases for LC/MS and GC/MS-based metabolomics: state of the field and future prospects. TrAC Trends Anal Chem 78:23–35Google Scholar
  17. 17.
    Gil de la Fuente A, Grace Armitage E, Otero A, Barbas C, Godzien J (2017) Differentiating signals to make biological sense—a guide through databases for MS-based non-targeted metabolomics. Electrophoresis 38(18):2242–2256.  https://doi.org/10.1002/elps.201700070CrossRefPubMedGoogle Scholar
  18. 18.
    Godzien J, Armitage EG, Angulo S, Martinez-Alcazar MP, Alonso-Herranz V, Otero A, Lopez-Gonzalvez A, Barbas C (2015) In-source fragmentation and correlation analysis as tools for metabolite identification exemplified with CE-TOF untargeted metabolomics. Electrophoresis 36(18):2188–2195.  https://doi.org/10.1002/elps.201500016CrossRefPubMedGoogle Scholar
  19. 19.
    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, Consortium 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.  https://doi.org/10.1038/nprot.2011.335CrossRefPubMedGoogle Scholar
  20. 20.
    Domínguez-Álvarez J, Rodríguez-Gonzalo E, Hernández-Méndez J, Carabias-Martínez R (2011) Programmed nebulizing gas pressure for efficient and stable capillary electrophoresis-mass spectrometry analysis of anionic compounds in positive separation mode. Anal Chem 83(7):2834–2839.  https://doi.org/10.1021/ac1032897CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Facultad de Farmacia, Centro de Metabolómica y Bioanálisis (CEMBIO)Universidad CEU San PabloMadridSpain

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