Challenges Related to Analysis of Protein Spot Volumes from Two-Dimensional Gel Electrophoresis As Revealed by Replicate Gels

Proteomics of the heart

Mass spectrometry-based proteomics is a sophisticated identification tool specializing in portraying protein dynamics at a molecular level. Proteomics provides biologists with a snapshot of context-dependent protein and proteoform expression, structural conformations, dynamic turnover, and protein-protein interactions. Cardiac proteomics can offer a broader and deeper understanding of the molecular mechanisms that underscore cardiovascular disease, and it is foundational to the development of future therapeutic interventions. This review encapsulates the evolution, current technologies, and future perspectives of proteomic-based mass spectrometry as it applies to the study of the heart. Key technological advancements have allowed researchers to study proteomes at a single-cell level and employ robot-assisted automation systems for enhanced sample preparation techniques, and the increase in fidelity of the mass spectrometers has allowed for the unambiguous identification of numerous dynamic posttranslational modifications. Animal models of cardiovascular disease, ranging from early animal experiments to current sophisticated models of heart failure with preserved ejection fraction, have provided the tools to study a challenging organ in the laboratory. Further technological development will pave the way for the implementation of proteomics even closer within the clinical setting, allowing not only scientists but also patients to benefit from an understanding of protein interplay as it relates to cardiac disease physiology.

Comparative study of weaning pigs' muscle proteins using two-dimensional electrophoresis

The proteostasis system of animals, including various types of protein modification during the growth stage, leads to an almost incomprehensible number of possible forms of protein, and each can regulate numerous functions. In the presented work, the composition of muscle tissue protein from different portions of piglets was studied to understand the main muscle protein formation. Comparative analysis of weaned piglets' main muscle protein from l. dorsi, biceps femoris, and brachiocephalicus were analyzed using two-dimensional electrophoresis. Changes in the staining intensity of protein fractions inherent in different muscles were revealed. As part of this work, candidate groups of pig muscle proteins have been selected. Eleven protein spots were revealed for the longest muscle of the back, and seven for the biceps; the muscles of the neck are characterized by indicators of low protein fraction volume. Among the proteins found, myosin light chains, phosphoglycerate mutase, troponins, and adenylate kinase is most likely present. The obtained results of protein identification in muscle tissues, obtained during the intensive growth period, will allow a more detailed understanding of protein regulation, function, and interactions in complex biological systems, which will subsequently be significantly important for biomonitoring health and predicting farm animals productivity.

Adaptation of two-dimensional electrophoresis for muscle tissue analysis

It is important to understand the molecular mechanisms that take place in muscle tissues and to predict meat quality characteristics. One of the most popular methods is two-dimensional electrophoresis, which allows us to visualize, share and identify different molecules, including meat proteins. However, the standard conditions of this method are not universal for all types of raw material, so the authors suggest a new variation of two-dimensional electrophoresis for muscle tissue analysis. Samples were tested by the classical version of isoelectric focusing (cathode buffer in the top and anode buffer in the bottom chamber of the electrophoresis cell) and its variation (anode buffer in the top and cathode buffer in the bottom chamber of the electrophoresis cell). Next, extruded gels were incubated in two different buffer systems: the first was equilibration buffer I (6 M urea, 20% w/v glycerol, 2% w/v SDS and 1% w/v Ditiothreitol in 375 mM Tris-HCl buffer, pH 8.8) followed by equilibration buffer II (6 M urea, 20% w/v glycerol, 2% w/v SDS and 4% w/v iodoacetamide in 375 mM Tris-HCl buffer pH 8.8 and the second, buffer А, consisting of 5 M urea, 2% w/v SDS, 5% v/v mercaptoethanol, 62.5 mM Tris-HCl buffer, pH 6.8 and 0.01% w/v bromophenol blue. Electrophoretic studies of muscle tissue revealed the best protein separation after changing the direction of the current (authors' variation), while no differences were detected after changing incubation buffers.

GENOCOP Algorithm and Hierarchical Grid Transformation for Image Warping of Two-Dimensional Gel Electrophoretic Maps

Hierarchical grid transformation is a powerful hierarchical approach to 2-D map warping, able to model both global and local deformations. The algorithm can be stopped when a desired degree of accuracy in the images alignment is obtained. The deformed image is warped and aligned to the target image using a grid where the number of nodes increases in each step of the algorithm. The numerical optimization of the position of the nodes of the grid can be efficiently solved by genetic algorithms, ensuring the achievement of the optimal position of the nodes with a low computational cost with respect to other methods. Here, the optimization of the position of the nodes is carried out by GENOCOP (genetic algorithm for numerical optimization of constrained problems), refined by the following conjugate gradient optimization step. The modeling of the warped space is then achieved by a spline model where some constraints are introduced in the choice of the nodes that are moved. The whole procedure can be intended as an evolutionary method that models the deformation of the gel map at different levels of detail.

Tuber proteome comparison of five potato varieties by principal component analysis

BACKGROUND

Data analysis of omics data should be performed by multivariate analysis such as principal component analysis (PCA). The way data are clustered in PCA is of major importance to develop some classification systems based on multivariate analysis, such as soft independent modeling of class analogy (SIMCA). In a previous study a one-class classifier based on SIMCA was built using microarray data from a set of potatoes. The PCA grouped the transcriptomic data according to varieties. The present work aimed to use PCA to verify the clustering of the proteomic profiles for the same potato varieties.

RESULTS

Proteomic profiles of five potato varieties (Biogold, Fontane, Innovator, Lady Rosetta and Maris Piper) were evaluated by two-dimensional gel electrophoresis (2-DE) performed on two immobilized pH gradient (IPG) strip lengths, 13 and 24 cm, both under pH range 4-7. For each strip length, two gels were prepared from each variety; in total there were ten gels per analysis. For 13 cm strips, 199-320 spots were detected per gel, and for 24 cm strips, 365-684 spots.

CONCLUSION

All four PCAs performed with these datasets presented clear grouping of samples according to the varieties. The data presented here showed that PCA was applicable for proteomic analysis of potato and was able to separate the samples by varieties. © 2016 Society of Chemical Industry.

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.

Changes in SeMSC, glucosinolates and sulforaphane levels, and in proteome profile in broccoli (Brassica oleracea var. Italica) fertilized with sodium selenate

The aim of this work was to analyze the effect of sodium selenate fortification on the content of selenomethyl selenocysteine (SeMSC), total glucosinolates and sulforaphane, as well as the changes in protein profile of the inflorescences of broccoli (Brassica oleracea var. Italica). Two experimental groups were considered: plants treated with 100 μmol/L sodium selenate (final concentration in the pot) and control plants treated with water. Fortification began 2 weeks after transplantation and was repeated once a week during 10 weeks. Broccoli florets were harvested when they reached appropriate size. SeMSC content in broccoli florets increased significantly with sodium selenate fortification; but total glucosinolates and sulforaphane content as well as myrosinase activity were not affected. The protein profile of broccoli florets changed due to fortification with sodium selenate. Some proteins involved in general stress-responses were up-regulated, whereas down-regulated proteins were identified as proteins involved in protection against pathogens. This is the first attempt to evaluate the physiological effect of fortification with sodium selenate on broccoli at protein level. The results of this work will contribute to better understanding the metabolic processes related with selenium uptake and accumulation in broccoli.

Comparative proteomic analysis of casein and whey as prepared by chymosin-induced separation, isoelectric precipitation or ultracentrifugation

Fractionation of bovine milk was performed using chymosin-induced separation, isoelectric precipitation or ultracentrifugation as separation techniques prior to gel-based proteomic analysis. This approach allowed for comparative display and identification of proteins partitioned into casein and whey, respectively. Initially, three different staining methods (silver staining, colloidal Coomassie Blue G-250 or fluorescent Flamingo Pink staining) for two-dimensional gel electrophoresis (2-DGE) analysis were compared for their suitability as staining agent, especially in relation to their suitability to reveal differences in the casein fractions. Fluorescent staining proved to be the most appropriate for this purpose, giving a high sensitivity, and using this staining method, characteristic 2-DGE fingerprints were obtained for each casein and whey fraction from each separation method. A number of protein spots in both casein and whey fractions varied with separation method and these spots were subsequently identified using tandem mass spectrometry (MS). In rennet casein, proteolytic fragmentation of caseins (αs1-, αs2,-, β- and κ-) was identified as a result of chymosin hydrolysis, whereas the 2-DGE profile of acid and ultracentrifuged casein was dominated by the presence of multiple isoforms of κ-caseins. Furthermore, casein remnants were identified in milk serum after ultracentrifugation. This study shows that gel-based proteomic analysis is suitable for characterisation of subtle variations in protein composition of milk fractions that occur as a consequence of different milk fractionation strategies.

A Bayesian model for classifying all differentially expressed proteins simultaneously in 2D PAGE gels

Background

Two-dimensional polyacrylamide gel electrophoresis (2D PAGE) is commonly used to identify differentially expressed proteins under two or more experimental or observational conditions. Wu et al (2009) developed a univariate probabilistic model which was used to identify differential expression between Case and Control groups, by applying a Likelihood Ratio Test (LRT) to each protein on a 2D PAGE. In contrast to commonly used statistical approaches, this model takes into account the two possible causes of missing values in 2D PAGE: either (1) the non-expression of a protein; or (2) a level of expression that falls below the limit of detection.

Results

We develop a global Bayesian model which extends the previously described model. Unlike the univariate approach, the model reported here is able treat all differentially expressed proteins simultaneously. Whereas each protein is modelled by the univariate likelihood function previously described, several global distributions are used to model the underlying relationship between the parameters associated with individual proteins. These global distributions are able to combine information from each protein to give more accurate estimates of the true parameters. In our implementation of the procedure, all parameters are recovered by Markov chain Monte Carlo (MCMC) integration. The 95% highest posterior density (HPD) intervals for the marginal posterior distributions are used to determine whether differences in protein expression are due to differences in mean expression intensities, and/or differences in the probabilities of expression.

Conclusions

Simulation analyses showed that the global model is able to accurately recover the underlying global distributions, and identify more differentially expressed proteins than the simple application of a LRT. Additionally, simulations also indicate that the probability of incorrectly identifying a protein as differentially expressed (i.e., the False Discovery Rate) is very low. The source code is available at https://github.com/stevenhwu/BIDE-2D.

GENOCOP algorithm and hierarchical grid transformation for image warping of two dimensional gel eletrophoretic maps

Hierarchical grid transformation is a powerful approach to SDS 2DPAGE maps warping. The hierarchy of the warping transformation is able to model both global and local deformations of the gels and the algorithm can be stopped when a certain degree of accuracy in the image alignment is obtained. The numerical optimization of the position of the nodes of the grid that are responsible for the image warping is a multivariate task that can be solved efficiently using Genetic Algorithms. The use of Genetic Algorithms ensures that an optimal position of the nodes can be defined with a low computational cost with respect to other methods. The optimal positions of the nodes of the grid can be successfully used for defining a good warping of the gels.

Operator- and software-related post-experimental variability and source of error in 2-DE analysis

In the field of proteomics, several approaches have been developed for separating proteins and analyzing their differential relative abundance. One of the oldest, yet still widely used, is 2-DE. Despite the continuous advance of new methods, which are less demanding from a technical standpoint, 2-DE is still compelling and has a lot of potential for improvement. The overall variability which affects 2-DE includes biological, experimental, and post-experimental (software-related) variance. It is important to highlight how much of the total variability of this technique is due to post-experimental variability, which, so far, has been largely neglected. In this short review, we have focused on this topic and explained that post-experimental variability and source of error can be further divided into those which are software-dependent and those which are operator-dependent. We discuss these issues in detail, offering suggestions for reducing errors that may affect the quality of results, summarizing the advantages and drawbacks of each approach.

Depletion of highly abundant proteins in blood plasma by hydrophobic interaction chromatography for proteomic analysis

The proteomic analysis of plasma is extremely complex due to the presence of few highly abundant proteins. These proteins have to be depleted in order to detect low abundance proteins, which are likely to be of biomedical interest. In this work it was investigated the applicability of hydrophobic interaction chromatography (HIC) as a plasma fractionation method prior to two-dimensional gel electrophoresis (2DGE). The average hydrophobicity of the 56 main plasma proteins was calculated. Plasma proteins were classified as low, medium and highly hydrophobic through a cluster analysis. The highly abundant proteins showed a medium hydrophobicity, and therefore a HIC step was designed to deplete them from plasma. HIC performance was assessed by 2DGE, and it was compared to that obtained by a commercial immuno-affinity (IA) column for albumin depletion. Both methods showed similar reproducibility. HIC allowed partially depleting alpha-1-antitrypsin and albumin, and permitted to detect twice the number of spots than IA. Since albumin depletion by HIC was incomplete, it should be further optimized for its use as a complementary or alternative method to IA.

The consequences of sample pooling in proteomics: an empirical study

Pooling of samples in proteomics experiments might help overcome resource constraints when many individuals are analysed. The measured biological variation should be reduced giving increased power to detect treatment differences. Pooling has been advocated in microarray work but there are few tests of its potential in proteomics. In this study, we examine three issues on which the success of the pooling approach might hinge and provide evidence that: (i) the protein expression in a pool matches the mean expression of the individuals making up the pool for the majority of proteins, although for some proteins the pool expression is different; (ii) the biological variance between pools is reduced compared with that between individuals, as predicted in theory, but this reduction is not as large as expected. A practical consequence of this is that power could be reduced; (iii) proteins detectable in individual samples are usually but not always visible when samples are pooled. We conclude that pooling of samples in proteomics work is a valid and potentially valuable procedure but consideration should be given to these issues in experimental design.

A statistical model to identify differentially expressed proteins in 2D PAGE gels

Two dimensional polyacrylamide gel electrophoresis (2D PAGE) is used to identify differentially expressed proteins and may be applied to biomarker discovery. A limitation of this approach is the inability to detect a protein when its concentration falls below the limit of detection. Consequently, differential expression of proteins may be missed when the level of a protein in the cases or controls is below the limit of detection for 2D PAGE. Standard statistical techniques have difficulty dealing with undetected proteins. To address this issue, we propose a mixture model that takes into account both detected and non-detected proteins. Non-detected proteins are classified either as (a) proteins that are not expressed in at least one replicate, or (b) proteins that are expressed but are below the limit of detection. We obtain maximum likelihood estimates of the parameters of the mixture model, including the group-specific probability of expression and mean expression intensities. Differentially expressed proteins can be detected by using a Likelihood Ratio Test (LRT). Our simulation results, using data generated from biological experiments, show that the likelihood model has higher statistical power than standard statistical approaches to detect differentially expressed proteins. An R package, Slider (Statistical Likelihood model for Identifying Differential Expression in R), is freely available at http://www.cebl.auckland.ac.nz/slider.php.

An insight into high-resolution mass-spectrometry data

Mass spectrometry is a powerful tool with much promise in global proteomic studies. The discipline of statistics offers robust methodologies to extract and interpret high-dimensional mass-spectrometry data and will be a valuable contributor to the field. Here, we describe the process by which data are produced, characteristics of the data, and the analytical preprocessing steps that are taken in order to interpret the data and use it in downstream statistical analyses. Because of the complexity of data acquisition, statistical methods developed for gene expression microarray data are not directly applicable to proteomic data. Areas in need of statistical research for proteomic data include alignment, experimental design, abundance normalization, and statistical analysis.

Proteome changes in wheat subjected to different nitrogen and sulfur fertilizations

Controlling the quality of wheat for breadmaking is a major concern for the milling and baking industry. Wheat flour quality depends on both the genetic background and environmental factors during growth and storage. Amount and timing of application of fertilizer are factors that affect wheat quality. This study investigated the effect of different levels of nitrogen and sulfur on the tris-soluble and glutenin protein fractions by 2D-electrophoresis. Multivariate analysis was performed to study changes in the proteome pattern. In the tris-soluble fraction 20 proteins were changed in abundance due to S fertilization, whereas 16 proteins were changed in the glutenin protein fraction. It was found that induced sulfur deficiency during growth resulted in the most pronounced effect on protein composition. Understanding which proteins are affected by varying levels of fertilizers may help tailor specific traits in various wheat varieties.

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.

An improved pixel-based approach for analyzing images in two-dimensional gel electrophoresis

An improved pixel-based approach for analyzing 2-DE images is presented. The key feature of the method is to create a mask based on all gels in the experiment using image morphology, followed by multivariate analysis on the pixel level. The method reduces the impact of noise and background by identifying regions in the image where protein spots are present, but make no assumption on individual spot boundaries for isolated spots. This makes it possible to detect significant changes in complex regions, and visualize these changes over multiple gels in an easy way. False missing values and spot volumes caused by imposing erroneous spot boundaries are thus circumvented. The approach presented gives improved pixel-based information from the gels, and is also an alternative to existing methods for data-reduction, significance testing and visualization of 2-DE data. Results are compared with software using a common spot boundary approach on an experiment consisting of 35 full size gel images. Gel alignment is required before analysis.

Finding the significant markers: statistical analysis of proteomic data

After separation through two-dimensional gel electrophoresis (2DE), several hundreds of individual protein abundances can be quantified in a cell population or sample tissue. Both a good experimental setup and a valid statistical approach are essential to get insight into the data and to draw correct conclusions. High-throughput 2DE proteomics yield complex and large datasets with a huge disproportion between the hundreds of variables and the restricted number of replicates. However, the most commonly used statistical tests have been designed to cope with a high number of replicates and a restricted number of variables. There is some inconsistency in the proteomics community related to the use of statistics. Two approaches of data analysis can be distinguished: exploratory data analysis and confirmatory data analysis. Currently, most proteomic data are analyzed with the emphasis on confirmatory analysis and do not take into account the exploratory data analysis. This chapter gives an overview of the typical statistical exploratory and confirmatory tools available and suggests case-specific guidelines for a reliable statistical approach that can be used for 2DE analysis. Examples are given for an experimental setup based on classical staining methods as well as for the more advanced difference gel electrophoresis.

Treatment of missing values for multivariate statistical analysis of gel-based proteomics data

The presence of missing values in gel-based proteomics data represents a real challenge if an objective statistical analysis is pursued. Different methods to handle missing values were evaluated and their influence is discussed on the selection of important proteins through multivariate techniques. The evaluated methods consisted of directly dealing with them during the multivariate analysis with the nonlinear estimation by iterative partial least squares (NIPALS) algorithm or imputing them by using either k-nearest neighbor or Bayesian principal component analysis (BPCA) before carrying out the multivariate analysis. These techniques were applied to data obtained from gels stained with classical postrunning dyes and from DIGE gels. Before applying the multivariate techniques, the normality and homoscedasticity assumptions on which parametric tests are based on were tested in order to perform a sound statistical analysis. From the three tested methods to handle missing values in our datasets, BPCA imputation of missing values showed to be the most consistent method.

Comparison of DIGE and post-stained gel electrophoresis with both traditional and SameSpots analysis for quantitative proteomics

2-DE is an important tool in quantitative proteomics. Here, we compare the deep purple (DP) system with DIGE using both a traditional and the SameSpots approach to gel analysis. Missing values in the traditional approach were found to be a significant issue for both systems. SameSpots attempts to address the missing value problem. SameSpots was found to increase the proportion of low volume data for DP but not for DIGE. For all the analysis methods applied in this study, the assumptions of parametric tests were met. Analysis of the same images gave significantly lower noise with SameSpots (over traditional) for DP, but no difference for DIGE. We propose that SameSpots gave lower noise with DP due to the stabilisation of the spot area by the common spot outline, but this was not seen with DIGE due to the co-detection process which stabilises the area selected. For studies where measurement of small abundance changes is required, a cost-benefit analysis highlights that DIGE was significantly cheaper regardless of the analysis methods. For studies analysing large changes, DP with SameSpots could be an effective alternative to DIGE but this will be dependent on the biological noise of the system under investigation.

Multivariate data analysis of two-dimensional gel electrophoresis protein patterns from few samples

One application of 2D gel electrophoresis is to reveal differences in protein pattern between two or more groups of individuals, attributable to their group membership. Multivariate data analytical methods are useful in pinpointing the spots relevant for discrimination by focusing not only on single spot differences, but on the covariance structure between proteins. However, their outcome is dependent on data scaling, and they may fail in producing valid multivariate models due to the much higher number of "irrelevant" spots present in the gels. The case where only few gels are available and where the aim is to find as many as possible of the group-dependent proteins seems particularly difficult to handle. The present paper investigates such a case regarding the effect of scaling and of prefiltering by univariate nonparametric statistics on the selection of spots. Besides, a modified 'autoscaling' of the full data set based on within-group standard deviations is introduced and shown to be advantageous in revealing potential group-dependent proteins additional to those found by prefiltering.

The differential expression of proteins in the cortical cells of wool and hair fibres

Three different cell types have been identified in the cortex of wool: orthocortex, mesocortex and paracortex. Fine wool fibres, particularly Merino sheep, are noted for their bilateral distribution of orthocortical and paracortical cells, with the latter following the concave side of the crimp wave. Furthermore, studies have indicated that the paracortex has a higher concentration of cysteine than the orthocortex. This has been supported by in situ hybridization studies in the follicle that have shown that sulphur-rich proteins are initially expressed on the paracortical side of the fibre, with some becoming more uniformly spread, laterally, over the entire fibre as the keratinization process progresses. In contrast, proteins high in glycine and tyrosine tend to be expressed initially on the orthocortical side of the follicle. While these in vitro studies have pointed to where specific proteins are located in the follicle, elucidating the situation for the mature fibre has been less easy. A range of approaches have been used to separate orthocortical and paracortical cells and these have only been able to provide evidence for a higher level of cysteine in the latter. Electrophoretic studies have found a number of differences in protein expression between the two sides but have not specifically identified which proteins. Thus, there appears to be good evidence for the paracortex containing a higher proportion of proteins in the ultra-high sulphur class but there is some uncertainty regarding the exact distribution of proteins high in glycine and tyrosine.

Proteome Changes in Bovine Longissimus Thoracis Muscle During the Early Postmortem Storage Period

Postmortem changes in protein composition up to 24 h in bovine longissimus thoracis muscle were investigated by two-dimensional gel electrophoresis and MALDI-TOF MS/MS. A total of 47 spots were significantly changed the first 24 h postmortem. The 39 identified proteins can be divided into five groups: metabolic enzymes, defense and stress proteins, structural proteins, proteolytic enzymes, and unclassified proteins. The identified metabolic enzymes are all associated with ATP-generating pathways, either the glycolytic pathway or energy metabolism. In addition, several defense and stress proteins were changed in abundance in this study. These findings contribute to a better understanding of the biochemical processes during postmortem storage of meat.

Multivariate analysis of 2-DE protein patterns--practical approaches

Practical approaches to the use of multivariate data analysis of 2-DE protein patterns are demonstrated by three independent strategies for the image analysis and the multivariate analysis on the same set of 2-DE data. Four wheat varieties were selected on the basis of their baking quality. Two of the varieties were of strong baking quality and hard wheat kernel and two were of weak baking quality and soft kernel. Gliadins at different stages of grain development were analyzed by the application of multivariate data analysis on images of 2-DEs. Patterns related to the wheat varieties, harvest times and quality were detected on images of 2-DE protein patterns for all the three strategies. The use of the multivariate methods was evaluated in the alignment and matching procedures of 2-DE gels. All the three strategies were able to discriminate the samples according to quality, harvest time and variety, although different subsets of protein spots were selected. The explorative approach of using multivariate data analysis and variable selection in the analyses of 2-DEs seems to be promising as a fast, reliable and convenient way of screening and transforming many gel images into spot quantities.