A Novel Gaussian Extrapolation Approach for 2-D Gel Electrophoresis Saturated Protein Spots

  • Massimo NataleEmail author
  • Alfonso Caiazzo
  • Elisa Ficarra
Part of the Methods in Molecular Biology book series (MIMB, volume 1384)


Analysis of images obtained from two-dimensional gel electrophoresis (2-D GE) is a topic of utmost importance in bioinformatics research, since commercial and academic software currently available have proven to be neither completely effective nor fully automatic, often requiring manual revision and refinement of computer generated matches. In this chapter, we present an effective technique for the detection and the reconstruction of over-saturated protein spots. Firstly, the algorithm reveals overexposed areas, where spots may be truncated, and plateau regions caused by smeared and overlapping spots. Next, it reconstructs the correct distribution of pixel values in these overexposed areas and plateau regions, using a two-dimensional least-squares fitting based on a generalized Gaussian distribution.

Pixel correction in saturated and smeared spots allows more accurate proteins quantification, providing more reliable image analysis results. The method is validated for processing highly exposed 2-D GE images, comparing reconstructed spots with the corresponding non-saturated image. The results demonstrate that the algorithm enables correct spot quantification.

Key words

Image analysis Two-dimensional gel electrophoresis Proteomics Software tools 


  1. 1.
    O’Farrel PH (1975) High resolution two-dimensional electrophoresis of proteins. J Biol Chem 250:4007–4021Google Scholar
  2. 2.
    Gorg A, Weiss W, Dunn MJ (2004) Current two-dimensional electrophoresis technology for proteomics. Proteomics 4:3665–3685CrossRefPubMedGoogle Scholar
  3. 3.
    Miller I, Crawford J, Gianazza E (2006) Protein stains for proteomic applications: which, when, why? Proteomics 6:5385–5408CrossRefPubMedGoogle Scholar
  4. 4.
    Hortin GL, Sviridov D (2010) The dynamic range problem in the analysis of the plasma proteome. J Proteomics 73:629–636CrossRefPubMedGoogle Scholar
  5. 5.
    Millioni R, Puricelli L, Sbrignadello S, Iori E, Murphy E, Tessari P (2012) Operator- and software-related post-experimental variability and source of error in 2-DE analysis. Amino Acids 42:1583–1590CrossRefPubMedGoogle Scholar
  6. 6.
    Clark BN, Gutstein HB (2008) The myth of automated, high-throughput two-dimensional gel analysis. Proteomics 8:1197–1203CrossRefPubMedGoogle Scholar
  7. 7.
    Wheelock AM, Buckpitt AR (2005) Software-induced variance in two-dimensional gel electrophoresis image analysis. Electrophoresis 26:4508–4520CrossRefPubMedGoogle Scholar
  8. 8.
    dos Anjos A, Møller AL, Ersbøll BK, Finnie C, Shahbazkia HR (2011) New approach for segmentation and quantification of two-dimensional gel electrophoresis images. Bioinformatics 27:368–375CrossRefPubMedGoogle Scholar
  9. 9.
    Srinark T, Kambhamettu C (2008) An image analysis suite for spot detection and spot matching in two-dimensional electrophoresis gels. Electrophoresis 29:706–715CrossRefPubMedGoogle Scholar
  10. 10.
    Rashwan S, Faheem T, Sarhan A, Youssef BAB (2009) A fuzzy-watershed based algorithm for protein spot detection in 2DGE images. In: Proceedings of the 9th WSEAS international conference on signal processing, computational geometry and artificial vision (ISCGAV’09), World Scientific and Engineering Academy and Society (WSEAS), Stevens Point, WI, USA, pp. 35–40Google Scholar
  11. 11.
    Daszykowski M, Bierczynska-Krzysik A, Silberring J, Walczak B (2010) Avoiding spots detection in analysis of electrophoretic gel images. Chemometr Intell Lab Syst 104:2–7CrossRefGoogle Scholar
  12. 12.
    Berth M, Moser FM, Kolbe M, Bernhardt J (2007) The state of the art in the analysis of two-dimensional gel electrophoresis images. Appl Microbiol Biotechnol 76:1223–1243PubMedCentralCrossRefPubMedGoogle Scholar
  13. 13.
    Maurer MH (2006) Software analysis of two-dimensional electrophoretic gels in proteomic experiments. Curr Bioinform 1:255–262CrossRefGoogle Scholar
  14. 14.
    Maresca B, Cigliano L, Corsaro MM, Pieretti G, Natale M, Bucci EM et al. (2010) Quantitative determination of haptoglobin glycoform variants in psoriasis. Biol Chem 391:1429–1439CrossRefPubMedGoogle Scholar
  15. 15.
    Schneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9(7):671–675CrossRefPubMedGoogle Scholar
  16. 16.
    Natale M, Maresca B, Abrescia P, Bucci EM (2011) Image analysis workflow for 2-D electrophoresis gels based on ImageJ. Proteomics Insights 4:37–49Google Scholar
  17. 17.
    Sternberg S (1983) Biomedical image processing. IEEE Comput 16:22–34CrossRefGoogle Scholar
  18. 18.
    Ben-Israel A (1996) A Newton-Raphson method for the solution of system of equations. J Math Anal Appl 15:243–252CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Massimo Natale
    • 1
    Email author
  • Alfonso Caiazzo
    • 2
  • Elisa Ficarra
    • 3
  1. 1.Global ICTUnicredit SpAMilanoItaly
  2. 2.WIAS BerlinBerlinGermany
  3. 3.Department of Control and Computer EngineeringTorinoItaly

Personalised recommendations