A method based on bead flows for spot detection on 2-D gel 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-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.

Screening of antigenic vesicular fluid proteins of Echinococcus multilocularis as potential viability biomarkers to monitor drug response in alveolar echinococcosis patients

PURPOSE

The only drugs available to treat alveolar echinococcosis (AE) are mostly parasitostatic and in many cases prescribed for life. Decision criteria for discontinuation rely on the absence of parasitic viability. The aim of the present study is to search for candidate proteins that may exhibit good potential as biomarkers for viability.

EXPERIMENTAL DESIGN

Sixteen serum samples (five healthy controls, 11 patients with AE), are used. AE-patients are classified into three groups "Cured" (n = 2), "ABZ-responders" (n = 4) and "ABZ-nonresponders" (n = 5). Immunoreactive proteins from vesicular fluid (VF) are identified and quantified by LC-MS/MS analysis after immunoprecipitation (IP) using all 16 serum samples.

RESULTS

Shotgun analysis of VF lead to the identification of 107 E. multilocularis proteins. Comparative proteomics reveal nine proteins more abundant in IP eluates from ABZ-nonresponder patients (cathepsin b, prosaposin a preprotein, actin modulator protein, fucosidase alpha L1 tissue, gluthatione-S-tranferase, beta galactosidase, elongation factor 2, H17g protein tegumental antigen, and NiemannPick C2 protein).

CONCLUSIONS AND CLINICAL RELEVANCE

Detection of antibodies against these proteins by ELISA could be helpful to monitor the course of alveolar echinococcosis under albendazole (ABZ) treatment.

Mixture Modeling of 2-D Gel Electrophoresis Spots Enhances the Performance of Spot Detection

2-D gel electrophoresis is the most commonly used method in biomedicine to separate even thousands of proteins in a complex sample on a single gel. Even though the technique is quite known, there is still a need to find an efficient and reliable method for detection of protein spots on gel image. In this paper, a three-step algorithm based on mixture of 2-D normal distribution functions is introduced to improve the efficiency of spot detection performed by the existing algorithms, namely Pinnacle software and watershed segmentation method. Comparison of methods is based on using simulated and real data sets with known true spot positions and different number of spots. Fitting a mixture of components to gel image allows for achieving higher sensitivity in detecting spots, regardless the method used to find initial conditions for the model parameters, and it leads to better overall performance of spot detection. By using mixture model, location of spot centers can be estimated with higher accuracy than using the Pinnacle method. An application of spot shape modeling gives higher sensitivity of obtaining low-intensity spots than the watershed method, which is crucial in the discovery of novel biomarkers.

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.

Spot quantification in two dimensional gel electrophoresis image analysis: comparison of different approaches and presentation of a novel compound fitting algorithm

Background

Various computer-based methods exist for the detection and quantification of protein spots in two dimensional gel electrophoresis images. Area-based methods are commonly used for spot quantification: an area is assigned to each spot and the sum of the pixel intensities in that area, the so-called volume, is used a measure for spot signal. Other methods use the optical density, i.e. the intensity of the most intense pixel of a spot, or calculate the volume from the parameters of a fitted function.

Results

In this study we compare the performance of different spot quantification methods using synthetic and real data. We propose a ready-to-use algorithm for spot detection and quantification that uses fitting of two dimensional Gaussian function curves for the extraction of data from two dimensional gel electrophoresis (2-DE) images. The algorithm implements fitting using logical compounds and is computationally efficient. The applicability of the compound fitting algorithm was evaluated for various simulated data and compared with other quantification approaches. We provide evidence that even if an incorrect bell-shaped function is used, the fitting method is superior to other approaches, especially when spots overlap. Finally, we validated the method with experimental data of urea-based 2-DE of Aβ peptides andre-analyzed published data sets. Our methods showed higher precision and accuracy than other approaches when applied to exposure time series and standard gels.

Conclusion

Compound fitting as a quantification method for 2-DE spots shows several advantages over other approaches and could be combined with various spot detection methods.

The algorithm was scripted in MATLAB (Mathworks) and is available as a supplemental file.

Two-dimensional gel electrophoresis image registration using block-matching techniques and deformation models

Block-matching techniques have been widely used in the task of estimating displacement in medical images, and they represent the best approach in scenes with deformable structures such as tissues, fluids, and gels. In this article, a new iterative block-matching technique-based on successive deformation, search, fitting, filtering, and interpolation stages-is proposed to measure elastic displacements in two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) images. The proposed technique uses different deformation models in the task of correlating proteins in real 2D electrophoresis gel images, obtaining an accuracy of 96.6% and improving the results obtained with other techniques. This technique represents a general solution, being easy to adapt to different 2D deformable cases and providing an experimental reference for block-matching algorithms.

Feature detection techniques for preprocessing proteomic data

Numerous gel-based and nongel-based technologies are used to detect protein changes potentially associated with disease. The raw data, however, are abundant with technical and structural complexities, making statistical analysis a difficult task. Low-level analysis issues (including normalization, background correction, gel and/or spectral alignment, feature detection, and image registration) are substantial problems that need to be addressed, because any large-level data analyses are contingent on appropriate and statistically sound low-level procedures. Feature detection approaches are particularly interesting due to the increased computational speed associated with subsequent calculations. Such summary data corresponding to image features provide a significant reduction in overall data size and structure while retaining key information. In this paper, we focus on recent advances in feature detection as a tool for preprocessing proteomic data. This work highlights existing and newly developed feature detection algorithms for proteomic datasets, particularly relating to time-of-flight mass spectrometry, and two-dimensional gel electrophoresis. Note, however, that the associated data structures (i.e., spectral data, and images containing spots) used as input for these methods are obtained via all gel-based and nongel-based methods discussed in this manuscript, and thus the discussed methods are likewise applicable.