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Background  Two-Dimensional Difference In Gel Electrophoresis (2D-DIGE) is a powerful tool for measuring differences in protein expression between samples or conditions. However, to remove systematic variability within and between gels the data has to be normalized.  In this study we examined the ability of four existing and four novel normalization methods to remove systematic bias in data produced with 2D-DIGE. We also propose a modification of an existing method where the statistical framework determines whether a  set  of proteins shows an association with the predefined phenotypes of interest. This method was applied to our data generated from a monkey model ( Macaca fascicularis ) of Parkinson's disease.    Results  Using 2D-DIGE we analysed the protein content of the striatum from 6 control and 21 MPTP-treated monkeys, with or without de novo or long-term L-DOPA administration.  There was an intensity and spatial bias in the data of all the gels examined in this study. Only two of the eight normalization methods evaluated ('2D loess+scale' and 'SC-2D+quantile') successfully removed both the intensity and spatial bias. In 'SC-2D+quantile' we extended the commonly used loess normalization method against dye bias in two-channel microarray systems to suit systems with three or more channels.  Further, by using the proposed method, Differential Expression in Predefined Proteins  Sets  (DEPPS), several  sets  of proteins associated with the priming effects of L-DOPA in the striatum in parkinsonian animals were identified. Three of these  sets  are proteins involved in energy metabolism and one  set  involved proteins which are part of the microtubule cytoskeleton.    Conclusion  Comparison of the different methods leads to a series of methodological recommendations for the normalization and the analysis of data, depending on the experimental design. Due to the nature of 2D-DIGE data we recommend that the p-values obtained in significance tests should be used as rankings only. Individual proteins may be interesting as such, but by studying  sets  of proteins the interpretation of the results are probably more accurate and biologically informative.

bmc bioinformatics

Normalization and expression changes in predefined sets of proteins using 2D gel electrophoresis: A proteomic study of L-DOPA induced dyskinesia in an animal model of Parkinson's disease using DIGE