Two-Dimensional Gel Electrophoresis Image Analysis

Gel-based proteomics is still quite widespread due to its high-resolution power; the experimental approach is based on differential analysis, where groups of samples (e.g., control vs diseased) are compared to identify panels of potential biomarkers. However, the reliability of the result of the differential analysis is deeply influenced by 2D-PAGE maps image analysis procedures. The analysis of 2D-PAGE images consists of several steps, such as image preprocessing, spot detection and quantitation, image warping and alignment, spot matching. Several approaches are present in literature, and classical or last-generation commercial software packages exploit different algorithms for each step of the analysis. Here, the most widespread approaches and a comparison of the different strategies are presented.

2D-DIGE and Fluorescence Image Analysis

2D-DIGE is still a very widespread technique in proteomics for the identification of panels of biomarkers, allowing to tackle with some important drawback of classical two-dimensional gel-electrophoresis. However, once 2D-gels are obtained, they must undergo a quite articulated multistep image analysis procedure before the final differential analysis via statistical mono- and multivariate methods. Here, the main steps of image analysis software are described and the most recent procedures reported in the literature are briefly presented.

Image Pretreatment Tools II: Normalization Techniques for 2-DE and 2-D DIGE

Gel electrophoresis is usually applied to identify different protein expression profiles in biological samples (e.g., control vs. pathological, control vs. treated). Information about the effect to be investigated (a pathology, a drug, a ripening effect, etc.) is however generally confounded with experimental variability that is quite large in 2-DE and may arise from small variations in the sample preparation, reagents, sample loading, electrophoretic conditions, staining and image acquisition. Obtaining valid quantitative estimates of protein abundances in each map, before the differential analysis, is therefore fundamental to provide robust candidate biomarkers. Normalization procedures are applied to reduce experimental noise and make the images comparable, improving the accuracy of differential analysis. Certainly, they may deeply influence the final results, and to this respect they have to be applied with care. Here, the most widespread normalization procedures are described both for what regards the applications to 2-DE and 2D Difference Gel-electrophoresis (2-D DIGE) maps.

Gel2DE - a software tool for correlation analysis of 2D gel electrophoresis data

BACKGROUND

Two-dimensional gel electrophoresis (2DE) is a powerful technique for studying protein isoforms and their modifications. Existing commercial 2D image analysis tools rely on spot detection that limits analysis of complex protein profiles, e.g. spot appearance/disappearance or overlapping spots. Pixel-by-pixel correlation analysis, an analysis technique for identifying relations between protein patterns in gel images and external variables, can overcome such limitations in spot analysis.

RESULTS

We have implemented the first publically available pixel-by-pixel correlation analysis tool, the software Gel2DE. 2D immunoblot time course analysis of p53 protein stabilization in response to ionizing irradiation shows that pixel-by-pixel analysis can yield an overall activation biosignature for p53, despite changing spots shape, size and position.

CONCLUSIONS

Pixel-by-pixel correlation of aligned 2D images permits analysis of complex protein patterns. We anticipate that the Gel2DE correlation software will be a useful tool for future bioinformatics discoveries through 2D gel electrophoresis.