Analysis of two-dimensional (2D) electrophoretic images is a multi-step approach, enabling application of a variety of methods at different stages of data processing. The choice of these, as well as input parameters, leads to software-induced variations. Effective preprocessing methods, which do not require optimization of input parameters, are potent in eliminating software-induced variations. As a general method for background elimination and image scaling, robust Orthogonal Regression (rOR) is proposed and compared with Orthogonal Regression. This comparison is based on the univariate and multivariate approaches of feature selection, exploring the idea developed for significance analysis of microarray data [V. Goss Tusher, R. Tibshirani, G. Chu, Significance analysis of microarrays applied to the ionizing radiation response, P. Natl. Acad. Sci. U. S. A., 98 (2001) 5116–5121] and adapted to the analysis of proteomic data. All calculations are performed at the pixel level.

Analysis of proteomic data, presented in form of 2D gel electropherograms, is still a real challenge and requires further improvements. Three main problems in data analysis are associated with (1) ‘software-induced variance’ (due to the different methods of data pre-processing or their different input parameters), (2) univariate significance analysis, and (3) requirements of spots detection and quantification. In our study, the advantages of multivariate significance analysis and a pixel-based approach are demonstrated upon the example of the real data set.

Interleukin-1 (IL-1) and interleukin-6 (IL-6) are principal proinflammatory cytokines inducing the acute phase response of various tissues, including liver. Cultured human hepatoma HepG2 cells were stimulated with IL-1 (10 ng/ml) and IL-6 (10 ng/ml). After 24 h the cells were collected and disrupted by sonication in a lysis buffer containing 8M urea. The extracted cellular proteins were separated by 2D polyacrylamide gel electrophoresis. The gels were stained with Coomassie Brilliant Blue R-250 and the protein spots showing different intensities in comparison to control (unstimulated) cells were excised and subjected to analysis by LC-MS/MS. Alternatively, proteins were stained with SYPRO Ruby. These differentially expressed proteins include seven up-regulated and two down-regulated intracellular proteins of various functions. The identification of three cytokine-responsive proteins was confirmed by biosynthetic labeling with [35S]methionine after incubation of HepG2 cells, and by western blot with specific antisera.

The increasing use of proteomics has created a basis for new strategies to develop methodologies for rapid identification of protein patterns in living organisms. It has also become evident that proteomics has other potential applications than protein and peptide identification, e.g. protein characterization, with the aim of revealing their structure, function(s) and interactions of proteins. In comparative proteomics studies, the protein expression of a certain biological system is compared with another system or the same system under perturbed conditions. Global identification of proteins in neuroscience is extremely complex, owing to the limited availability of biological material and very low concentrations of the molecules. Moreover, in addition to proteins, there are number of peptides that must also be considered in global studies on the central nervous system. In this overview, we focus on and discuss problems related to the different sources of biological material and sample handling, which are part of all preparatory and analytical steps. Straightforward protocols are desirable to avoid excessive purification steps, since loss of material at each step is inevitable. We would like to merge the two worlds of proteomics/peptidomics and neuroscience, and finally we consider different practical and technical aspects, illustrated with examples from our laboratory.

Summary. Proteome is a natural consequence of the post-genome era when the HUGO project (Human Genome Organization) has almost been completed. Here, a specifically aimed proteome in drug dependence – morphinome, is described, including tasks, strategies and pitfalls of the methodology.