Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Multi-site assessment of the precision and reproducibility of multiple reaction monitoring–based measurements of proteins in plasma

A Corrigendum to this article was published on 01 September 2009

This article has been updated


Verification of candidate biomarkers relies upon specific, quantitative assays optimized for selective detection of target proteins, and is increasingly viewed as a critical step in the discovery pipeline that bridges unbiased biomarker discovery to preclinical validation. Although individual laboratories have demonstrated that multiple reaction monitoring (MRM) coupled with isotope dilution mass spectrometry can quantify candidate protein biomarkers in plasma, reproducibility and transferability of these assays between laboratories have not been demonstrated. We describe a multilaboratory study to assess reproducibility, recovery, linear dynamic range and limits of detection and quantification of multiplexed, MRM-based assays, conducted by NCI-CPTAC. Using common materials and standardized protocols, we demonstrate that these assays can be highly reproducible within and across laboratories and instrument platforms, and are sensitive to low μg/ml protein concentrations in unfractionated plasma. We provide data and benchmarks against which individual laboratories can compare their performance and evaluate new technologies for biomarker verification in plasma.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Figure 1: Sample preparation workflow for studies I, II and III.
Figure 2: Box plots of variation in MRM quantitative measurements, interlaboratory CV, intralaboratory CV and LOQ.
Figure 3: Interlaboratory reproducibility of linear calibration curve slopes for study II.
Figure 4: Response curves representing deviations from the trend line.

Change history

  • 09 September 2009

    In the version of this article initially published, the following acknowledgment was inadvertently left out: “The UCSF CPTAC team gratefully acknowledges the support of the Canary Foundation for providing funds to purchase a 4000 QTRAP mass spectrometer.” The acknowlegment has been added to the HTML and PDF versions of the article.


  1. Rifai, N., Gillette, M.A. & Carr, S.A. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat. Biotechnol. 24, 971–983 (2006).

    Article  CAS  Google Scholar 

  2. Paulovich, A.G., Whiteaker, J.R., Hoofnagle, A.N. & Wang, P. The interface between biomarker discovery and clinical validation: The tar pit of the protein biomarker pipeline. Proteomics Clin. Appl. 2, 1386–1402 (2008).

    Article  CAS  Google Scholar 

  3. Barr, J.R. et al. Isotope-dilution mass spectrometric quantification of specific proteins: model application with apolipoprotein A-1. Clin. Chem. 42, 1676–1682 (1996).

    CAS  PubMed  Google Scholar 

  4. Barnidge, D.R. et al. Absolute quantification of the G protein-coupled receptor rhodopsin by LC/MS/MS using proteolysis product peptides and synthetic peptide standards. Anal. Chem. 75, 445–451 (2003).

    Article  CAS  Google Scholar 

  5. Gerber, S.A., Rush, J., Stemman, O., Kirschner, M.W. & Gygi, S.P. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem mass spectrometry. Proc. Natl. Acad. Sci. USA 100, 6940–6945 (2003).

    Article  CAS  Google Scholar 

  6. Barnidge, D.R., Goodmanson, M.K., Klee, G.G. & Muddiman, D.C. Absolute quantification of the model biomarker prostate-specific antigen in serum by LC-MS/MS using protein cleavage and isotope dilution MS. J. Proteome Res. 3, 644–652 (2004).

    Article  CAS  Google Scholar 

  7. Kuhn, E. et al. Quantification of C-reactive protein in the serum of patients with rheumatoid arthritis using multiple reaction monitoring mass spectrometry and 13C-labeled peptide standards. Proteomics 4, 1175–1186 (2004).

    Article  CAS  Google Scholar 

  8. Anderson, L. & Hunter, C.L. Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol. Cell. Proteomics 5, 573–588 (2006).

    Article  CAS  Google Scholar 

  9. Keshishian, H., Addona, T., Burgess, M., Kuhn, E. & Carr, S.A. Quantitative, multiplexed assays for low abundance proteins in plasma by targeted mass spectrometry and stable isotope dilution. Mol. Cell. Proteomics 6, 2212–2229 (2007).

    Article  CAS  Google Scholar 

  10. Aguiar, M., Masse, R. & Gibbs, B.F. Mass spectrometric quantitation of C-reactive protein using labeled tryptic peptides. Anal. Biochem. 354, 175–181 (2006).

    Article  CAS  Google Scholar 

  11. Wilcken, B. Wiley, V., Hammond, J. & Carpenter, K. Screening newborns for inborn errors of metabolism by tandem mass spectrometry. N. Engl. J. Med. 348, 2304–2312 (2003).

    Article  CAS  Google Scholar 

  12. Whiteaker, J.R. et al. Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer. J. Proteome Res. 6, 3875–3876 (2007).

    Article  Google Scholar 

  13. Anderson, N.L. et al. The human plasma proteome: a non-redundant list developed by combination of four separate sources. Mol. Cell. Proteomics 3, 311–326 (2004).

    Article  CAS  Google Scholar 

  14. Omenn, G.S. et al. Overview of the HUPO plasma proteome project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database. Proteomics 5, 3226–3245 (2005).

    Article  CAS  Google Scholar 

  15. Kuzyk, M.A. et al. MRM-based, multiplexed, absolute quantitation of 45 proteins in human plasma. Mol. Cell. Proteomics published online, doi:10.1074/mcp.M800540-MCP200 (1 May 2009).

  16. Anderson, N.L. & Anderson, N.G. The human plasma proteome: History, character, and diagnostic prospects. Mol. Cell. Proteomics 1, 845–867 (2002).

    Article  CAS  Google Scholar 

  17. Whiteaker, J.R. et al. Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer. J. Proteome Res. 6, 3962–3975 (2007).

    Article  CAS  Google Scholar 

  18. Berna, M. et al. Quantification of NTproBNP in rat serum using immunoprecipitation and LC/MS/MS: a biomarker of drug-induced cardiac hypertrophy. Anal. Chem. 80, 561–566 (2008).

    Article  CAS  Google Scholar 

  19. Berna, M.J., Zhen, Y., Watson, D.E., Hale, J.E. & Ackermann, B.L. Strategic use of immunoprecipitation and LC/MS/MS for trace-level protein quantification: Myosin light chain 1, a biomarker of cardiac necrosis. Anal. Chem. 79, 4199–4205 (2007).

    Article  CAS  Google Scholar 

  20. Labugger, R. et al. Strategy for analysis of cardiac troponins in biological samples with a combination of affinity chromatography and mass spectrometry. Clin. Chem. 49, 873–879 (2003).

    Article  CAS  Google Scholar 

  21. Nicol, G.R. et al. Use of an immunoaffinity-mass spectrometry-based approach for the quantification of protein biomarkers from serum samples of lung cancer patients. Mol. Cell. Proteomics 7, 1974–1982 (2008).

    Article  CAS  Google Scholar 

  22. Anderson, N.L. et al. Mass spectrometric quantitation of peptides and proteins using stable isotope standards and capture by anti-peptide antibodies (SISCAPA). J. Prot. Res. 3, 235–244 (2004).

    Article  CAS  Google Scholar 

  23. Hoofnagle, A.N., Becker, J.O., Wener, M.H. & Heinecke, J.W. Quantification of thyroglobulin, a low-abundance serum protein, by immunoaffinity peptide enrichment and tandem mass spectrometry. Clin. Chem. 54, 1796–1804 (2008).

    Article  CAS  Google Scholar 

  24. Kuhn, E. et al. Developing multiplexed assays for troponin I and interleukin-33 in plasma by peptide immunoaffinity enrichment and targeted mass spectrometry. Clin. Chem. published online, doi:10.1373/clinchem.2009.123935 (16 April 2009).

  25. Biopharmaceutics Coordinating Committee, Center for Drug Evaluation and Research, Center for Veterinary Medicine, US Food and Drug Administration (FDA). Guidance for Industry: Bioanalytical Method Validation (FDA, Rockville, MD; May 2001) 〈〉.

  26. Slev, P.R., Rawlins, M.L. & Roberts, W.L. Performance characteristics of seven automated CA 15–3 assays. Am. J. Clin. Pathol. 125, 752–757 (2006).

    Article  CAS  Google Scholar 

  27. Sapin, R. Insulin immunoassays: fast approaching 50 years of existence and still calling for standardization. Clin. Chem. 53, 810–812 (2007).

    Article  CAS  Google Scholar 

  28. Burkitt, W. et al. Toward Système International d'Unité-traceable protein quantification: from amino acids to proteins. Anal. Biochem. 376, 242–251 (2008).

    Article  CAS  Google Scholar 

  29. R Development Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2008).

  30. Lavagnini, I. & Magno, F. A statistical overview on univariate calibration, inverse regression, and detection limits: Application to gas chromatography/mass spectrometry technique. Mass Spectrom. Rev. 26, 1–18 (2007).

    Article  CAS  Google Scholar 

  31. Xu, X., Keefer, L.K., Ziegler, R.G. & Veenstra, T.D. A liquid chromatography-mass spectrometry method for the quantitative analysis of urinary endogenous estrogen metabolites. Nat. Protocols 2, 1350–1355 (2007).

    Article  CAS  Google Scholar 

  32. Marazzi, A. Algorithms, Routines and S Functions for Robust Statistics (CRC Press, Boca Raton, Florida, USA, 1993).

    Google Scholar 

  33. Venables, W.N. & Ripley, B.D. Modern Applied Statistics with S edn. 4 (Springer, New York, 2002).

    Book  Google Scholar 

  34. Currie, L.A. Limits for qualitative detection and quantitative determination. Anal. Chem. 40, 586–593 (1968).

    Article  CAS  Google Scholar 

  35. Zorn, M.E., Gibbons, R.D. & Sonzogni, W.C. Weighted least-squares approach to calculating limits of detection and quantification by modeling variability as a function of concentration. Anal. Chem. 69, 3069–3075 (1997).

    Article  CAS  Google Scholar 

  36. Vial, J., Mapihan, K.L. & Jardy, A. What is the best means of estimating the detection and quantification limits of a chromatographic method? Chromatographia 57 Supplement, S303–S306 (2003).

    Article  Google Scholar 

  37. Armbruster, D.A., Tillman, M.D. & Hubbs, L.M. Limit of detection (LOD)/limit of quantitation (LOQ): Comparison of the empirical and the statistical methods exemplified with GC-MS assays of abused Drugs. Clin. Chem. 40, 1233–1238 (1994).

    CAS  PubMed  Google Scholar 

  38. Linnet, K. & Kondratovich, M. Partly nonparametric approach for determining the limit of detection. Clin. Chem. 50, 732–740 (2004).

    Article  CAS  Google Scholar 

Download references


This work was supported by grants from the National Cancer Institute (NCI) (U24 CA126476, U24 126477, U24 126480, U24 CA126485, and U24 126479), part of NCI Clinical Proteomic Technologies for Cancer initiative. A component of this initiative is the Clinical Proteomic Technology Assessment for Cancer (CPTAC) Network and teams, which include the Broad Institute of MIT and Harvard (with the Fred Hutchinson Cancer Research Center, Massachusetts General Hospital, the University of North Carolina at Chapel Hill, the University of Victoria and the Plasma Proteome Institute), Memorial Sloan-Kettering Cancer Center (with the Skirball Institute at New York University), Purdue University (with Monarch Life Sciences, Indiana University, Indiana University-Purdue University Indianapolis and the Hoosier Oncology Group), University of California, San Francisco (with the Buck Institute for Age Research, Lawrence Berkeley National Laboratory, the University of British Columbia and the University of Texas M.D. Anderson Cancer Center) and Vanderbilt University School of Medicine (with the University of Texas M.D. Anderson Cancer Center, the University of Washington and the University of Arizona). A full listing of the CPTAC Team Network can be found at The UCSF CPTAC team gratefully acknowledges the support of the Canary Foundation for providing funds to purchase a 4000 QTRAP mass spectrometer.

Author information

Authors and Affiliations



The CPTAC Network contributed collectively to this study. The following CPTAC Network investigators contributed significant intellectual contributions to work described in this paper.

S.E.A., T.A., N.L.A., D.M.B, S.C.H., A.-J.L.H., H.K., D.R., B.S., S.J.S., L.J.Z. and S.A.C. contributed to study design and SOP development. D.M.B and N.G.D. prepared and shipped samples. S.E.A., T.A., S.A., H.L.C., J.M.H., A.J., E.B.J., H.K., D.S., T.J.T., J.R.W., A.W., S.W., L.Z., and L.J.Z. contributed to generation of data. M.P.C., J.L., D.R.M., R.K.N., S.J.S., T.C.P., P.A.R., C.H.S., D.L.T., A.M.V., and L.J.V.-M. contributed to bioinformatics and statistical analysis. S.E.A, T.A., H.K., D.R.M., S.J.S. and L.J.Z. centrally reviewed data. S.E.A., T.A., N.L.A., S.A.C., S.J.F., S.C.H., A.-J.L.H., H.K., D.R.M, B.S., S.J.S., and L.J.Z. wrote and prepared the manuscript. R.K.B., C.B., C.H.B., S.A.C., S.J.F., B.W.G., T.H., C.R.K., D.C.L., M.M., T.A.N., A.G.P., H.R., F.E.R., P.T., and M.W. contributed to experimental design. S.C.H. chaired the CPTAC Experimental Design and Statistics Verification Studies Working Group that designed interlaboratory studies and generated data.

Corresponding author

Correspondence to Steven A Carr.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6, Supplementary Tables 1–6 and Supplementary Methods and Supplementary Appendix (PDF 5784 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Addona, T., Abbatiello, S., Schilling, B. et al. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring–based measurements of proteins in plasma. Nat Biotechnol 27, 633–641 (2009).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing