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Decoding 2-D Maps by Autocovariance Function

  • Maria Chiara Pietrogrande
  • Nicola Marchetti
  • Francesco Dondi
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1384)

Abstract

This chapter describes a mathematical approach based on the study of the 2-D autocovariance function (2-D ACVF) useful for decoding the complex signals resulting from the separation of protein mixtures. The method allows to obtain fundamental analytical information hidden in 2-D PAGE maps by spot overlapping, such as the number of proteins present in the sample and the mean standard deviation of the spots, describing the separation performance. In addition, it is possible to identify ordered patterns potentially present in spot positions, which can be related to the chemical composition of the protein mixture, such as post-translational modifications.

The procedure was validated on computer-simulated maps and successfully applied to reference maps obtained from literature sources.

Key words

2-D PAGE (2-D polyacrylamide gel electrophoresis) maps Chemometric methods Bidimensional autocovariance function Spot overlapping Bioinformatics 

References

  1. 1.
    Rotilio D, Della Corte A, D’Imperio M, Coletta W, Marcone S, Silvestri C, Giordano L, Di Michele M, Donati MB (2012) Proteomics: bases for protein complexity understanding. Thromb Res 129:257–262CrossRefPubMedGoogle Scholar
  2. 2.
    Simula MP, Notarpietro A, Toffoli G, De Re V (2012) 2-D gel electrophoresis: constructing 2D-gel proteome reference maps. Methods Mol Biol 815:163–173, Functional Genomics: methods and Protocols, 2nd Edition, Book SeriesCrossRefPubMedGoogle Scholar
  3. 3.
    Clement CC, Aphkhazava D, Nieves E, Callaway M, Olszewski W, Rotzschke O, Santambrogio L (2013) Protein expression profiles of human lymph and plasma mapped by 2D-DIGE and 1D SDS-PAGE coupled with nanoLC-ESI-MS/MS bottom-up proteomics. J Proteomics 78:172–187PubMedCentralCrossRefPubMedGoogle Scholar
  4. 4.
    Szabo Z, Szomor JS, Foeldi I, Janaky T (2012) Mass spectrometry-based label free quantification of gel separated proteins. J Proteomics 75:5544–5553CrossRefPubMedGoogle Scholar
  5. 5.
    Colignon B, Raes M, Dieu M, Delaive E, Mauro S (2013) Evaluation of three-dimensional gel electrophoresis to improve quantitative profiling of complex proteomes. Proteomics 13:2077–2082CrossRefPubMedGoogle Scholar
  6. 6.
    Nakano K, Tamura S, Otuka K, Niizeki N et al. (2013) Development of a highly sensitive three-dimensional gel electrophoresis method for characterization of monoclonal protein heterogeneity. Anal Biochem 438:117–123CrossRefPubMedGoogle Scholar
  7. 7.
    Lee BS, Gupta S, Morozova I (2003) High-resolution separation of proteins by a three-dimensional sodium dodecyl sulfate polyacrylamide cube gel electrophoresis. Anal Biochem 317:271–275CrossRefPubMedGoogle Scholar
  8. 8.
    Moche M, Albrecht D, Maass S, Hecker M, Westermeier R, Buttner K (2013) The new horizon in 2D electrophoresis: new technology to increase resolution and sensitivity. Electrophoresis 34:1510–1518CrossRefPubMedGoogle Scholar
  9. 9.
    Li F, Seillier-Moiseiwitsch F, Korostysheyskiy VR (2011) Region-based statistical analysis of 2D PAGE images. Comput Stat Data Anal 55:3059–3072PubMedCentralCrossRefPubMedGoogle Scholar
  10. 10.
    Marengo E, Robotti E (2012) A new algorithm for the simulation of SDS 2D-PAGE datasets. Methods Mol Biol 869:407–425CrossRefPubMedGoogle Scholar
  11. 11.
    Pietrogrande MC, Marchetti N, Dondi F, Righetti PG (2003) Spot overlapping in 2D-PAGE maps. Relevance to proteomics. Electrophoresis 24:217–221CrossRefPubMedGoogle Scholar
  12. 12.
    Campostrini N, Areces L, Rappsilber J, Pietrogrande MC, Dondi F, Pastorino F, Ponzoni M, Righetti PG (2005) Spot overlapping in two dimensional maps: a serious problem ignored for much too long time! Proteomics 5:2385–2395CrossRefPubMedGoogle Scholar
  13. 13.
    Rabilloud T, Vaezzadeh AR, Potier N, Lelong C et al. (2009) Power and limitations of electrophoretic separations in proteomics strategies. Mass Spectrom Rev 28:816–843CrossRefPubMedGoogle Scholar
  14. 14.
    Rabilloud T (2013) When 2D is not enough, go for an extra dimension. Proteomics 13:2065–2068Google Scholar
  15. 15.
    Marchetti N, Felinger A, Pasti L, Pietrogrande MC, Dondi F (2004) Decoding two-dimensional complex multicomponent separations by Autocovariance Function. Anal Chem 76:3055–3068CrossRefPubMedGoogle Scholar
  16. 16.
    Pietrogrande MC, Marchetti N, Tosi A, Dondi F, Righetti PG (2005) Decoding 2-D PAGE complex maps by Autocovariance function: a simplified approach useful for proteomics. Electrophoresis 26:2739–2748CrossRefPubMedGoogle Scholar
  17. 17.
    Pietrogrande MC, Marchetti N, Dondi F, Righetti PG (2006) Decoding 2-D PAGE complex maps by Autocovariance function: relevance to proteomics. J Chromatogr B 833:51–62CrossRefGoogle Scholar
  18. 18.
    Dondi F, Pietrogrande MC, Marchetti N, Felinger A (2008) Decoding complex 2-D separations in multidimensional liquid chromatography. In: Cohen SA, Schure MR (eds) Theory and applications in industrial chemistry and the life science. John Wiley & Sons, New York, pp 59–90, ISBN: 978-0-471-73847-3Google Scholar
  19. 19.
    Human 2-D PAGE database of Danish Centre for Human Genome Research, http://www.biobase.dk/cgi-bin/celis
  20. 20.
    Pietrogrande MC, Marchetti N, Dondi F, Righetti PG (2002) Spot overlapping in 2D-PAGE separations: a statistical study of complex protein maps. Electrophoresis 23:283–289CrossRefPubMedGoogle Scholar
  21. 21.
    Press WH, Teukosky SA, Vetterling WT, Flannery BP (1986) Numerical recipes in Fortran. Cambridge University Press, Cambridge, UK. ISBN 978-0521309585Google Scholar
  22. 22.
    Melanie II, Geneva BioInformatics, GeneBio S.A., http//www.genebio.com.
  23. 23.
    Davis JM, Giddings JC (1984) Origin and characterization of departures from the statistical model of component-peak overlap in chromatography. J Chromatogr 289:277–298CrossRefPubMedGoogle Scholar
  24. 24.
    Righetti PG, Candiano G (2011) Recent advances in electrophoretic techniques for the characterization of protein biomolecules: A poker of aces. J Chromatogr A 1218:8727–8737CrossRefPubMedGoogle Scholar
  25. 25.
    Felinger A (1998) Data analysis and signal processing in chromatography. Elsevier, Amsterdam. ISBN 978-0444820662Google Scholar
  26. 26.
    Corfe BM, Evans CA (2014) Are proteins a redundant ontology? Epistemological limitations in the analysis of multistate species. Mol Biosyst 10:1228–1235CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Maria Chiara Pietrogrande
    • 1
  • Nicola Marchetti
    • 1
  • Francesco Dondi
    • 1
  1. 1.Department of Chemical and Pharmaceutical SciencesUniversity of FerraraFerraraItaly

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