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Comparison between surface and bead-based MALDI profiling technologies using a single bioinformatics algorithm

Clinical Proteomics volume 2, pages 145–152 (2006)Cite this article

Abstract

In this manuscript, we compared serum profiles obtained with two related technologies, SELDI-TOF and Clinprot, using a single bioinformatic algorithm. These two approaches rely on mass spectrometry to detect proteins and peptides initially selected by binding to various chromatographic matrices. They are proposed by two different companies, and they are competing for being the reference in high throughput serum profiling for clinical proteomics. This independent evaluation of these two technologies put the light on some of their differences, suggests that they address different proteome fractions and, thus, could be complementary. Taken together, our data could contribute to the parameters relevant for the choice of one technology or the other.

References

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

    Article  PubMed  CAS  Google Scholar 

  2. Pieper, R., Gatlin, C. L., Makusky, A. J., et al. (2003) The human serum proteome: display of nearly 3700 chromatographically separated protein spots on two-dimensional electrophoresis gels and identification of 325 distinct proteins.Proteomics 3, 1345–1364.

    Article  PubMed  CAS  Google Scholar 

  3. Yang, Z., Hancock, W. S., Chew, T. R., and Bonilla, L. (2005) A study of glycoproteins in human serum and plasma reference standards (HUPO) using multilectin affinity chromatography coupled with RPLC-MS/MS.Proteomics 5, 3353–3366.

    Article  PubMed  CAS  Google Scholar 

  4. Hortin, G. L. (2006) The MALDI-TOF mass spectrometric view of the plasma proteome and peptidome.Clin. Chem. 52, 1223–1237.

    Article  PubMed  CAS  Google Scholar 

  5. Petricoin, E. F., Ardekani, A. M., Hitt, B. A., et al. (2002) Use of proteomic patterns in serum to identify ovarian cancer.Lancet 359, 572–577.

    Article  PubMed  CAS  Google Scholar 

  6. Coombes, K. R., Morris, J. S., Hu, J., Edmonson, S. R., and Baggerly, K. A. (2005) Serum proteomics profiling: a young technology begins to mature.Nat. Biotechnol. 23, 291–292.

    Article  PubMed  CAS  Google Scholar 

  7. Zhang, Z., Bast, R. C., Jr. Yu, Y., et al. (2004) Three biomarkers identified from serum proteomic analysis for the detection of early stage ovarian cancer.Cancer Res.64, 5882–5890.

    Article  PubMed  CAS  Google Scholar 

  8. Issaq, H. J., Conrads, T. P., Janini, G. M., and Veenstra, T. D. (2002) Methods for fractionation, separation and profiling of proteins and peptides.Electrophoresis 23, 3048–3061.

    Article  PubMed  CAS  Google Scholar 

  9. Pieper, R., Su, Q., Gatlin, C. L., Huang, S. T. Anderson, N. L., and Steiner, S. (2003) Multicomponent immunoaffinity subtraction chromatography: an innovative step towards a comprehensive survey of the human plasma proteome.Proteomics 3, 422–432.

    Article  PubMed  CAS  Google Scholar 

  10. Guerrier, L., Thulasiraman, V., Castagna, A., et al. (2006) Reducing protein concentration range of biological samples using solid-phase ligand libraries.J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 833, 33–40.

    Article  PubMed  CAS  Google Scholar 

  11. Tuszynski, J. (2006) caMassClass: processing and classification of protein mass spectra (SELDI) data. http://cranr-projectorg/src/contrib/Descriptions/caMassClasshtml (The caMassClass Software License, Version 1.0).

  12. Friedman, J. (1984) A variable span scatterplot smoother: Laboratory for Computational Statistics, Stanford University, report no.: Technical Report No. 5.

  13. Fung, E. T., Wright, G. L., Jr., and Dalmasso, E. A. (2000) Proteomic strategies for biomarker identification: progress and challenges.Curr. Opin. Mol. Ther. 2, 643–650.

    PubMed  CAS  Google Scholar 

  14. Cottrell, J. S. (1994) Protein identification by peptide mass fingerprinting.Pept. Res. 7, 115–124.

    PubMed  CAS  Google Scholar 

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Authors and Affiliations

  1. Laboratoire de Physique Moléculaire et Structurale, Faculté de Pharmacie, Montpellier, France

    Christelle Reynès & Robert Sabatier

  2. CNRS, Institut de Génétique Humaine, Montpellier, France

    Stéphane Roche, Laurent Tiers & Sylvain Lehmann

  3. CHU Montpellier, Laboratoire de Biochimie, Hôpital St. Eloi, Montpellier, France

    Stéphane Roche & Sylvain Lehmann

  4. IGF, CNRS UMR5203, INSERM, U661, Univ Montpellier I, Univ Montpellier II, F-34094, Montpellier, France

    Patrick Jouin

  5. Institut Universitaire de Recherche Clinique, Faculté Médecine, Montpellier

    Nicolas Molinari

  6. Service DIM, CHU Nîmes, Nîmes, France

    Nicolas Molinari

  7. Université Montpellier I, F-34000, France

    Nicolas Molinari & Sylvain Lehmann

Authors
  1. Christelle Reynès
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  2. Stéphane Roche
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  3. Laurent Tiers
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  4. Robert Sabatier
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  5. Patrick Jouin
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  6. Nicolas Molinari
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  7. Sylvain Lehmann
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Corresponding author

Correspondence to Sylvain Lehmann.

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The first two authors contributed equally to this work.

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Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Reynès, C., Roche, S., Tiers, L. et al. Comparison between surface and bead-based MALDI profiling technologies using a single bioinformatics algorithm. Clin Proteom 2, 145–152 (2006). https://doi.org/10.1007/BF02752497

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Keywords

Clinical Proteomics

ISSN: 1559-0275