A new method of comparing 2D-PAGE maps based on the computation of Zernike moments and multivariate statistical tools

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.

Imaging and Differentiation of Retinal Ganglion Cells in Ex Vivo Experimental Optic Nerve Degeneration by Differential Interference Contrast Microscopy

Purpose: Apoptotic loss of retinal ganglion cells (RGCs) is involved in various optic neuropathies, and its extent is closely related to visual impairment. Direct imaging and counting of RGCs is beneficial to the evaluation of RGC loss, but these processes are challenging with the conventional techniques, due to the transparency and hypo-reflectivity of RGCs as light-transmitting structures of the retina. Differential interference contrast (DIC) microscopy, which can provide real-time images of transparent specimens, is utilized to image neuronal cells including RGCs in the ganglion cell layer (GCL). Methods: Herein, we show that the neuronal cells within each GCL in an explanted rat retina, including the inner nuclear layer and the outer nuclear layer, can be imaged selectively by transmission-type DIC microscopy. RGCs were also differentiated from non-RGCs by the objective method. Results: RGCs were differentiated from non-RGCs in the GCL by their morphological features on DIC images with the aid of retrograde fluorescence labeling. Loss of RGCs was detected in optic-nerve-transection and retinal-ischemia-reperfusion models by DIC imaging. The images obtained from the reflection-type DIC microscopy were comparable to those from the transmission-type DIC microscopy. Conclusions: This method enables direct optical visualization of RGCs in experimental optic-nerve degeneration, thus providing the opportunity for more accurate evaluation of optic neuropathies as well as more effective investigation of diseases.

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.

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.