Use of Legendre moments for the fast comparison of two-dimensional polyacrylamide gel electrophoresis maps 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.

The Use of Legendre and Zernike Moment Functions for the Comparison of 2-D PAGE Maps

The comparison of 2-D maps is not trivial, the main difficulties being the high complexity of the sample and the large experimental variability characterizing 2-D gel electrophoresis. The comparison of maps from control and treated samples is usually performed by specific software, providing the so-called spot volume dataset where each spot of a specific map is matched to its analogous in other maps, and they are described by their optical density, which is supposed to be related to the underlying protein amount. Here, a different approach is presented, based on the direct comparison of 2-D map images: each map is decomposed in terms of moment functions, successively applying the multivariate tools usually adopted in image analysis problems. The moments calculated are then treated with multivariate classification techniques. Here, two types of moment functions are presented (Legendre and Zernike moments), while linear discriminant analysis and partial least squares discriminant analysis are exploited as classification tools to provide the classification of the samples. The procedure is applied to a sample dataset to prove its effectiveness.

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.

Differential Analysis of 2-D Maps by Pixel-Based Approaches

Two approaches to the analysis of 2-D maps are available: the first one involves a step of spot detection on each gel image; the second one is based instead on the direct differential analysis of 2-D map images, following a pixel-based procedure. Both approaches strongly depend on the proper alignment of the gel images, but the pixel-based approach allows to solve important drawbacks of the spot-volume procedure, i.e., the problem of missing data and of overlapping spots. However, this approach is quite computationally intensive and requires the use of algorithms able to separate the information (i.e., spot-related information) from the background. Here, the most recent pixel-based approaches are described.

Biomarkers for pancreatic cancer: Recent achievements in proteomics and genomics through classical and multivariate statistical methods

Pancreatic cancer (PC) is one of the most aggressive and lethal neoplastic diseases. A valid alternative to the usual invasive diagnostic tools would certainly be the determination of biomarkers in peripheral fluids to provide less invasive tools for early diagnosis. Nowadays, biomarkers are generally investigated mainly in peripheral blood and tissues through high-throughput omics techniques comparing control vs pathological samples. The results can be evaluated by two main strategies: (1) classical methods in which the identification of significant biomarkers is accomplished by monovariate statistical tests where each biomarker is considered as independent from the others; and (2) multivariate methods, taking into consideration the correlations existing among the biomarkers themselves. This last approach is very powerful since it allows the identification of pools of biomarkers with diagnostic and prognostic performances which are superior to single markers in terms of sensitivity, specificity and robustness. Multivariate techniques are usually applied with variable selection procedures to provide a restricted set of biomarkers with the best predictive ability; however, standard selection methods are usually aimed at the identification of the smallest set of variables with the best predictive ability and exhaustivity is usually neglected. The exhaustive search for biomarkers is instead an important alternative to standard variable selection since it can provide information about the etiology of the pathology by producing a comprehensive set of markers. In this review, the most recent applications of the omics techniques (proteomics, genomics and metabolomics) to the identification of exploratory biomarkers for PC will be presented with particular regard to the statistical methods adopted for their identification. The basic theory related to classical and multivariate methods for identification of biomarkers is presented and then, the most recent applications in this field are discussed.

A new algorithm for the simulation of SDS 2D-PAGE datasets

This chapter describes a new software for the generation of simulated realistic sodium dodecyl sulfate two-dimensional polyacrylamide gel electrophoresis (SDS 2D-PAGE) images. In order to choose the simulation strategy to provide realistic 2D-PAGE maps the statistical characteristics of such images were taken into account, such as the distributions of sizes, intensities, and volumes of the spots. Also, the low reproducibility typical of replicated SDS 2D-PAGE maps of the same sample was simulated. This approach can be used to generate simulated datasets useful in the development and performance evaluation of new classification and/or image analysis algorithms applied to two-dimensional electrophoresis datasets, given the usually small number of experimental replications available.

Proteomic profiling of cellular targets of lipopolysaccharide-induced signalling in Nicotiana tabacum BY-2 cells

Plants constantly monitor for pathogen challenge and utilize a diverse array of adaptive defense mechanisms, including differential protein regulation, during pathogen attack. A proteomic analysis of Nicotiana tabacum BY-2 cells was performed in order to investigate the dynamic changes following perception of bacterial lipopolysaccharides. A multiplexed proteome analysis, employing two-dimensional difference-in-gel-electrophoresis with CyDye DIGE fluors, as well as Ruthenium II tris (bathophenanthroline disulfonate) fluorescence staining and Pro-Q Diamond phosphoprotein-specific gel staining, monitored over 1500 proteins and resulted in the identification of 88 differentially regulated proteins and phosphoproteins responsive to LPS(B.cep.)-elicitation. Functional clustering of the proteins both at the level of their abundance and phosphorylation status, revealed 9 proteins involved in transport, ion homeostasis and signal transduction. A large number of responsive proteins were found to be involved in metabolism- and energy-related processes (36), representing various metabolic pathways. Another abundant category corresponded to proteins classified as molecular chaperones and involved in protein destination/targeting (12). Other categories of proteins found to be LPS(B.cep.)-responsive and differentially regulated include cell structure- and cytoskeletal rearrangement proteins (8) and proteins involved in transcription and translation as well as degradation (11). The results indicate that LPS(B.cep.) induces metabolic reprogramming and changes in cellular activities supporting protein synthesis, -folding, vesicle trafficking and secretion; accompanied by changes to the cytoskeleton and proteosome function. Many of the identified proteins are known to be interconnected at various levels through a complex web of activation/deactivation, complex formation, protein-protein interactions, and chaperoning reactions. The presented data offers novel insights and further evidence for the biochemical action of LPS(B.cep.) as a resistance elicitor, a pathogen-associated molecular pattern molecule and triggering agent of defense responses associated with innate immunity.

2D-PAGE maps analysis

Due to the low reproducibility affecting 2D gel-electrophoresis and the complex maps provided by this technique, the use of effective and robust methods for the comparison and classification of 2D maps is a fundamental tool for the development of automated diagnostic methods. A review of classical and recently developed methods for the comparison of 2D maps is presented here. The methods proposed regard both the analysis of spot volume datasets through multivariate statistical tools (pattern recognition methods, cluster analysis, and classification methods) and the analysis of 2D map images through fuzzy logic, three-way PCA, and the use of moment functions. The theoretical basis of each procedure is briefly introduced, together with a review of the most interesting applications present in recent literature.