Advertisement

Intact Protein Analysis by LC-MS for Characterizing Biomarkers in Cerebrospinal Fluid

  • Jérôme Vialaret
  • Sylvain Lehmann
  • Christophe HirtzEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1959)

Abstract

In the field of proteomics, the emerging “top-down” MS-based proteomics approach can be used to obtain a bird’s eye view of all intact proteoforms present in a sample. This alternative to the “bottom-up” approach based on tryptic protein digestion processes has some unique advantages for assessing PTMs and sequence variations. However, it requires some dedicated tools for sample preparation and LC-MS analysis, which makes it more complex to handle than the bottom-up approach. In this study, a simple methodology is presented for characterizing intact proteins in biological fluid. This method yields quantitative information using an MS1 profiling approach and makes it possible to identify the proteins regulated under various clinical conditions.

Key words

Biomarker Top-down LC-MS Cerebrospinal fluid Protein precipitation SPE 

References

  1. 1.
    Huhmer AF, Biringer RG, Amato H et al (2006) Protein analysis in human cerebrospinal fluid: physiological aspects, current progress and future challenges. Dis Markers 22(1–2):3–26. https://doi.org/10.1155/2006/158797CrossRefPubMedGoogle Scholar
  2. 2.
    Roche S, Gabelle A, Lehmann S (2008) Clinical proteomics of the cerebrospinal fluid: towards the discovery of new biomarkers. Proteomics Clin Appl 2(3):428–436. https://doi.org/10.1002/prca.200780040CrossRefPubMedGoogle Scholar
  3. 3.
    Lehmann S, Hoofnagle A, Hochstrasser D et al (2013) Quantitative Clinical Chemistry Proteomics (qCCP) using mass spectrometry: general characteristics and application. Clin Chem Lab Med 51(5):919–935. https://doi.org/10.1515/cclm-2012-0723CrossRefPubMedGoogle Scholar
  4. 4.
    Lehmann S, Brede C, Lescuyer P et al (2017) Clinical mass spectrometry proteomics (cMSP) for medical laboratory: what does the future hold? Clin Chim Acta 467:51–58. https://doi.org/10.1016/j.cca.2016.06.001CrossRefPubMedGoogle Scholar
  5. 5.
    Geyer PE, Kulak NA, Pichler G et al (2016) Plasma proteome profiling to assess human health and disease. Cell Syst 2(3):185–195. https://doi.org/10.1016/j.cels.2016.02.015CrossRefPubMedGoogle Scholar
  6. 6.
    Smith LM, Kelleher NL (2018) Proteoforms as the next proteomics currency. Science 359(6380):1106–1107. https://doi.org/10.1126/science.aat1884CrossRefPubMedGoogle Scholar
  7. 7.
    Seckler HDS, Fornelli L, Mutharasan RK et al (2018) A targeted, differential top-down proteomic methodology for comparison of ApoA-I proteoforms in individuals with high and low HDL efflux capacity. J Proteome Res 17(6):2156–2164. https://doi.org/10.1021/acs.jproteome.8b00100CrossRefPubMedGoogle Scholar
  8. 8.
    Schmit PO, Vialaret J, Wessels H et al (2018) Towards a routine application of top-down approaches for label-free discovery workflows. J Proteome 175:12–26. https://doi.org/10.1016/j.jprot.2017.08.003CrossRefGoogle Scholar
  9. 9.
    Lehmann S, Schraen S, Quadrio I et al (2014) Impact of harmonization of collection tubes on Alzheimer's disease diagnosis. Alzheimers Dement 10(5 Suppl):S390–S394.e2. https://doi.org/10.1016/j.jalz.2013.06.008CrossRefPubMedGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Jérôme Vialaret
    • 1
  • Sylvain Lehmann
    • 1
  • Christophe Hirtz
    • 1
    Email author
  1. 1.Clinical Proteomics Platform, LBPC, IRMB, CHU MontpellierMontpellier UniversityMontpellierFrance

Personalised recommendations