Contour area filtering of two-dimensional electrophoresis images

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 electrophoresis image brightness correction based on gradient interval histogram

Background

Two-dimensional electrophoresis (2DE) is one of the most widely applied techniques in comparative proteomics. The basic task of 2DE is to identify differential protein expression by quantitative analysis of 2DE images. To reduce the errors of spot quantification in 2DE images, a novel brightness correction method based on gradient interval histogram (GIH) is proposed in this paper.

Results

First, GIH equalization is proposed to enhance the protein spot edges, especially the weak protein spots in the 2DE image. Second, to eliminate the overall brightness shift, GIH matching is applied to the 2DE images that need to be compared. Finally, the proposed method is verified by subjective quality evaluation and quantitative analysis of protein spots in real 2DE images.

Conclusions

The experimental results show that the average error of the quantification of corresponding protein spots in the resulting image pairs is less than 3%, which is significantly superior to that of the existing methods.

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.

A new method for assigning common spot boundaries for multiple gels in two-dimensional gel electrophoresis

The benefits of defining common spot boundaries when several gels from 2-DE are compared and analyzed have lately been stressed by both commercial software producers and users of this software. Though the importance of common spot boundaries is clearly stated, few reports exist that target this issue explicitly. In this study a method for defining common spots boundaries is developed, called the spot density method. The method consists of the following steps: segmentation and spot identification on each individual gel, transferring the spot-center coordinates for all gels onto a single new gel, collecting spot centers clustered together in the new gel and finally assigning pixels and new spot boundaries based on the spots in each cluster. The method is compared to a synthetic gel approach, and validated by visual inspection of three representative areas in the gels. The gel images need to be aligned prior to segmentation and spot identification, but the method can be used regardless of the choice of segmentation procedure. This makes the method an easy extension to existing methods for spot identification and matching. Conclusions based on the visual inspection are that the spot density method identifies partly overlapping spots and low-intensity spots better than the synthetic gel approach.

Restriction landmark genomic scanning (RLGS) spot identification by second generation virtual RLGS in multiple genomes with multiple enzyme combinations

Background

Restriction landmark genomic scanning (RLGS) is one of the most successfully applied methods for the identification of aberrant CpG island hypermethylation in cancer, as well as the identification of tissue specific methylation of CpG islands. However, a limitation to the utility of this method has been the ability to assign specific genomic sequences to RLGS spots, a process commonly referred to as "RLGS spot cloning."

Results

We report the development of a virtual RLGS method (vRLGS) that allows for RLGS spot identification in any sequenced genome and with any enzyme combination. We report significant improvements in predicting DNA fragment migration patterns by incorporating sequence information into the migration models, and demonstrate a median Euclidian distance between actual and predicted spot migration of 0.18 centimeters for the most complex human RLGS pattern. We report the confirmed identification of 795 human and 530 mouse RLGS spots for the most commonly used enzyme combinations. We also developed a method to filter the virtual spots to reduce the number of extra spots seen on a virtual profile for both the mouse and human genomes. We demonstrate use of this filter to simplify spot cloning and to assist in the identification of spots exhibiting tissue-specific methylation.

Conclusion

The new vRLGS system reported here is highly robust for the identification of novel RLGS spots. The migration models developed are not specific to the genome being studied or the enzyme combination being used, making this tool broadly applicable. The identification of hundreds of mouse and human RLGS spot loci confirms the strong bias of RLGS studies to focus on CpG islands and provides a valuable resource to rapidly study their methylation.