Advertisement

Absolute Quantification of Toxicological Biomarkers via Mass Spectrometry

  • Thomas Y. K. LauEmail author
  • Ben C. Collins
  • Peter Stone
  • Ning Tang
  • William M. Gallagher
  • Stephen R. Pennington
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1641)

Abstract

With the advent of “–omics” technologies there has been an explosion of data generation in the field of toxicology, as well as many others. As new candidate biomarkers of toxicity are being regularly discovered, the next challenge is to validate these observations in a targeted manner. Traditionally, these validation experiments have been conducted using antibody-based technologies such as Western blotting, ELISA, and immunohistochemistry. However, this often produces a significant bottleneck as the time, cost, and development of successful antibodies are often far outpaced by the generation of targets of interest. In response to this, there recently have been several developments in the use of triple quadrupole (QQQ) mass spectrometry (MS) as a platform to provide quantification of proteins. This technology does not require antibodies; it is typically less expensive and quicker to develop assays and has the opportunity for more accessible multiplexing. The speed of these experiments combined with their flexibility and ability to multiplex assays makes the technique a valuable strategy to validate biomarker discovery.

Key words

Catalase Mass spectrometry Quantification Proteomics Biomarkers 

Notes

Acknowledgments

The authors would like to thank Agilent Technologies, Santa Clara, for generating much of the data and figures used in this example. We would like to also thank all members of the PredTox Consortium. Funding is acknowledged under the FP6 Integrated Project, InnoMed. The UCD Conway Institute and the Proteome Research Centre is funded by the Programme for Research in Third Level Institutions (PRTLI), as administered by the Higher Education Authority (HEA) of Ireland.

References

  1. 1.
    Gerber SA, Rush J, Stemman O, Kirschner MW, Gygi SP (2003) Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc Natl Acad Sci U S A 100:6940–6945CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Anderson L, Hunter CL (2005) Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol Cell Proteomics 5:573–588CrossRefPubMedGoogle Scholar
  3. 3.
    Abbatiello SE, Mani DR, Schilling B, MacLean B, Zimmerman LJ, Feng X, Cusack MP, Sedransk N, Hall SC, Addona T, Allen S, Dodder NG, Ghosh M, Held JM, Hedrick V, Inerowicz HD, Jackson A, Keshishian H, Kim JW, Lyssand JS, Riley CP, Rudnick P, Sadowski P, Shaddox K, Smith D, Tomazela D, Wahlander A, Waldemarson S, Whitwell CA, You J, Zhang S, Kinsinger CR, Mesri M, Rodriguez H, Borchers CH, Buck C, Fisher SJ, Gibson BW, Liebler D, MacCoss M, Neubert TA, Paulovich A, Regnier F, Skates SJ, Tempst P, Wang M, Carr SA (2013) Design, implementation and multisite evaluation of a system suitability protocol for the quantitative assessment of instrument performance in Liquid Chromatography-Multiple Reaction Monitoring-MS (LC-MRM-MS). Mol Cell Proteomics 12(9):2623–2639. doi: 10.1074/mcp.M112.027078 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Egertson JD, MacLean B, Johnson R, Xuan Y, MacCoss MJ (2015) Multiplexed peptide analysis using data-independent acquisition and Skyline. Nat Protoc 10(6):887–903. doi: 10.1038/nprot.2015.055 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Kuster B, Schirle M, Mallick P, Aebersold R (2005) Scoring proteomes with proteotypic peptide probes. Nat Rev Mol Cell Biol 6(7):577–583CrossRefPubMedGoogle Scholar
  6. 6.
    Chen EI, Cociorva D, Norris JL, Yates JR 3rd (2007) Optimization of mass spectrometry-compatible surfactants for shotgun proteomics. J Proteome Res 6(7):2529–2538CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Wilhelm M, Schlegl J, Hahne H, Moghaddas Gholami A, Lieberenz M, Savitski MM, Ziegler E, Butzmann L, Gessulat S, Marx H, Mathieson T, Lemeer S, Schnatbaum K, Reimer U, Wenschuh H, Mollenhauer M, Slotta-Huspenina J, Boese JH, Bantscheff M, Gerstmair A, Faerber F, Kuster B (2014) Mass-spectrometry-based draft of the human proteome. Nature 509(7502):582–587. doi: 10.1038/nature13319 CrossRefPubMedGoogle Scholar
  8. 8.
    Desiere F, Deutsch EW, King NL, Nesvizhskii AI, Mallick P, Eng J, Chen S, Eddes J, Loevenich SN, Aebersold R (2006) The PeptideAtlas project. Nucleic Acids Res 34(Database issue):D655–D658CrossRefPubMedGoogle Scholar
  9. 9.
    Craig R, Cortens JP, Beavis RC (2004) Open source system for analyzing, validating, and storing protein identification data. J Proteome Res 3(6):1234–1242CrossRefPubMedGoogle Scholar
  10. 10.
    Mallick P, Schirle M, Chen SS, Flory MR, Lee H, Martin D, Ranish J, Raught B, Schmitt R, Werner T, Kuster B, Aebersold R (2007) Computational prediction of proteotypic peptides for quantitative proteomics. Nat Biotechnol 25(1):125–131CrossRefPubMedGoogle Scholar
  11. 11.
    MacLean B, Tomazela DM, Shulman N, Chambers M, Finney GL, Frewen B, Kern R, Tabb DL, Liebler DC, MacCoss MJ (2010) Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26(7):966–968. doi: 10.1093/bioinformatics/btq054 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Thomas Y. K. Lau
    • 1
    Email author
  • Ben C. Collins
    • 2
  • Peter Stone
    • 3
  • Ning Tang
    • 3
  • William M. Gallagher
    • 4
  • Stephen R. Pennington
    • 5
  1. 1.Pfizer Inc.AndoverUSA
  2. 2.Institute of Molecular Systems BiologyETH ZürichSwitzerland
  3. 3.Agilent TechnologiesSanta ClaraUSA
  4. 4.School of Biomolecular and Biomedical ScienceUCD Conway Institute, University College DublinDublinIreland
  5. 5.UCD School of Medicine and Medical ScienceUCD Conway InstituteDublinIreland

Personalised recommendations