Multiple Testing and Pattern Recognition in 2-DE Proteomics

  • Sebastien C. CarpentierEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1384)


After separation through two-dimensional gel electrophoresis (2-DE), several hundreds of individual protein abundances can be quantified in a cell population or sample tissue. However, gel-based proteomics has the reputation of being a slow and cumbersome art. But art is not dead! While 2-DE may no longer be the tool of choice in high-throughput differential proteomics, it is still very effective to identify and quantify protein species caused by genetic variations, alternative splicing, and/or PTMs. This chapter reviews some typical statistical exploratory and confirmatory tools available and suggests case-specific guidelines for (1) the discovery of potentially interesting protein spots, and (2) the further characterization of protein families and their possible PTMs.

Key words

2-DE Multivariate statistics Protein correlations Clustering Protein isoforms 



The author would like to thank Annick De Troyer and Anne-Catherine Vanhove for technical assistance. Prof. Etienne Waelkens and his group (Laboratory of Protein Phosphorylation and Proteomics, KU Leuven), are gratefully acknowledged for the MALDI-TOF/TOF measurements. Financial support from “CIALCA” and the Bioversity International project “ITC characterization” (research projects financed by the Belgian Directorate-General for Development Cooperation (DGDC)) is gratefully acknowledged.


  1. 1.
    Nesvizhskii AI, Aebersold R (2005) Interpretation of shotgun proteomics data: the protein inference problem. Mol Cell Proteomics 4(10):1419–1440CrossRefPubMedGoogle Scholar
  2. 2.
    Carpentier S, Panis B, Renaut J, Samyn B, Vertommen A, Vanhove A, Swennen R, Sergeant K (2011) The use of 2D-electrophoresis and de novo sequencing to characterize inter- and intra-cultivar protein polymorphisms in an allopolyploid crop. Phytochemistry 72:1243–1250CrossRefPubMedGoogle Scholar
  3. 3.
    Henry I, Carpentier S, Pampurova S, Van Hoylandt A, Panis B, Swennen R, Remy S (2011) Structure and regulation of the ASR gene family in banana. Planta 234:785–798PubMedCentralCrossRefPubMedGoogle Scholar
  4. 4.
    Carpentier S, Swennen R, Panis B (2009) Plant protein sample preparation for 2DE. In: Walker J (ed) The protein protocols handbook. Humana Press, Totowa, NJ, pp 109–119CrossRefGoogle Scholar
  5. 5.
    Carpentier S, Witters E, Laukens K, Deckers P, Swennen R, Panis B (2005) Preparation of protein extracts from recalcitrant plant tissues: an evaluation of different methods for two-dimensional gel electrophoresis analysis. Proteomics 5:2497–2507CrossRefPubMedGoogle Scholar
  6. 6.
    Alm R, Johansson P, Hjernø K, Emanuelsson C, Ringnér M, Häkkinen J (2006) Detection and identification of protein isoforms using cluster analysis of MALDI-MS mass spectra. J Proteome Res 5:785–792CrossRefPubMedGoogle Scholar
  7. 7.
    Benjamin Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B 57:289–300Google Scholar
  8. 8.
    Carpentier S, Panis B, Swennen R, Lammertyn J (2008) Finding the significant markers: statistical analysis of proteomic data. In: Vlahou A (ed) Methods in molecular biology, vol 428. Humana Press Inc., Totowa, NJ, pp 327–347Google Scholar
  9. 9.
    Marengo E, Robotti E, Bobba M, Liparota MC, Rustichelli C, Zamoo A, Chilosi M, Righetti PG (2006) Electrophoresis 27:484–494CrossRefPubMedGoogle Scholar
  10. 10.
    Sharma S (1996) Applied multivariate techniques. Wiley, New York ISBN 0-471-31064-6Google Scholar
  11. 11.
    Jackson JE (2003) A user’s guide to principal components. Wiley, New YorkGoogle Scholar
  12. 12.
    Tarroux P (1983) Analysis of protein-patterns during differentiation using 2-D electrophoresis and computer multidimensional classification. Electrophoresis 4:63–70CrossRefGoogle Scholar
  13. 13.
    Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D, Altman RB (2001) Missing value estimation methods for DNA microarrays. Bioinformatics 17:520–525CrossRefPubMedGoogle Scholar
  14. 14.
    Scheel I, Aldrin M, Glad IK, Sorum R, Lyng H, Frigessi A (2005) The influence of missing value imputation on detection of differentially expressed genes from microarray data. Bioinformatics 21:4272–4279CrossRefPubMedGoogle Scholar
  15. 15.
    Oba S, Sato M, Takemasa I, Monden M, Matsubara K, Ishii S (2003) A Bayesian missing value estimation method for gene expression profile data. Bioinformatics 19:2088–2096CrossRefPubMedGoogle Scholar
  16. 16.
    Wold S (1985) Partial least squares. Encyc Stat Sci 6:581–591Google Scholar
  17. 17.
    Schmidt F, Schmid M, Jungblut PR, Mattowb J, Faciusc A, Pleissner K (2003) Iterative data analysis is the key for exhaustive analysis of peptide mass fingerprints from proteins separated by two-dimensional electrophoresis. J Am Soc Mass Spectrom 14:943–956CrossRefPubMedGoogle Scholar
  18. 18.
    Vanhove A (2014) The quest for osmotic stress markers in Musa: from protein to gene and back in a non-model crop. Dissertation presented for the degree of Doctor in Bioscience Engineering KUleuven, LeuvenGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Biosystems, Faculty of Bioscience EngineeringK.U. LeuvenLeuvenBelgium
  2. 2.SYBIOMA: Facility for Systems Biology Based Mass SpectrometryLeuvenBelgium

Personalised recommendations