Numerical approaches for quantitative analysis of two-dimensional maps: A review of commercial software and home-made systems

Posttranslational modifications of proteins are key features in the identification of CSF biomarkers of multiple sclerosis

Background

Multiple sclerosis is an inflammatory and degenerative disease of the central nervous system (CNS) characterized by demyelination and concomitant axonal loss. The lack of a single specific test, and the similarity to other inflammatory diseases of the central nervous system, makes it difficult to have a clear diagnosis of multiple sclerosis. Therefore, laboratory tests that allows a clear and definite diagnosis, as well as to predict the different clinical courses of the disease are of utmost importance. Herein, we compared the cerebrospinal fluid (CSF) proteome of patients with multiple sclerosis (in the relapse–remitting phase of the disease) and other diseases of the CNS (inflammatory and non-inflammatory) aiming at identifying reliable biomarkers of multiple sclerosis.

Methods

CSF samples from the discovery group were resolved by 2D-gel electrophoresis followed by identification of the protein spots by mass spectrometry. The results were analyzed using univariate (Student’s t test) and multivariate (Hierarchical Cluster Analysis, Principal Component Analysis, Linear Discriminant Analysis) statistical and numerical techniques, to identify a set of protein spots that were differentially expressed in CSF samples from patients with multiple sclerosis when compared with other two groups. Validation of the results was performed in samples from a different set of patients using quantitative (e.g., ELISA) and semi-quantitative (e.g., Western Blot) experimental approaches.

Results

Analysis of the 2D-gels showed 13 protein spots that were differentially expressed in the three groups of patients: Alpha-1-antichymotrypsin, Prostaglandin-H2-isomerase, Retinol binding protein 4, Transthyretin (TTR), Apolipoprotein E, Gelsolin, Angiotensinogen, Agrin, Serum albumin, Myosin-15, Apolipoprotein B-100 and EF-hand calcium-binding domain—containing protein. ELISA experiments allowed validating part of the results obtained in the proteomics analysis and showed that some of the alterations in the CSF proteome are also mirrored in serum samples from multiple sclerosis patients. CSF of multiple sclerosis patients was characterized by TTR oligomerization, thus highlighting the importance of analyzing posttranslational modifications of the proteome in the identification of novel biomarkers of the disease.

Conclusions

The model built based on the results obtained upon analysis of the 2D-gels and in the validation phase attained an accuracy of about 80% in distinguishing multiple sclerosis patients and the other two groups.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12974-022-02404-2.

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.

Bringing New Methods to the Seed Proteomics Platform: Challenges and Perspectives

For centuries, crop plants have represented the basis of the daily human diet. Among them, cereals and legumes, accumulating oils, proteins, and carbohydrates in their seeds, distinctly dominate modern agriculture, thus play an essential role in food industry and fuel production. Therefore, seeds of crop plants are intensively studied by food chemists, biologists, biochemists, and nutritional physiologists. Accordingly, seed development and germination as well as age- and stress-related alterations in seed vigor, longevity, nutritional value, and safety can be addressed by a broad panel of analytical, biochemical, and physiological methods. Currently, functional genomics is one of the most powerful tools, giving direct access to characteristic metabolic changes accompanying plant development, senescence, and response to biotic or abiotic stress. Among individual post-genomic methodological platforms, proteomics represents one of the most effective ones, giving access to cellular metabolism at the level of proteins. During the recent decades, multiple methodological advances were introduced in different branches of life science, although only some of them were established in seed proteomics so far. Therefore, here we discuss main methodological approaches already employed in seed proteomics, as well as those still waiting for implementation in this field of plant research, with a special emphasis on sample preparation, data acquisition, processing, and post-processing. Thereby, the overall goal of this review is to bring new methodologies emerging in different areas of proteomics research (clinical, food, ecological, microbial, and plant proteomics) to the broad society of seed biologists.

The Identification of Native Epitopes Eliciting a Protective High-Affinity Immunoglobulin Subclass Response to Blood Stages of Plasmodium falciparum: Protocol for Observational Studies

Background

Antibodies to blood stages protective against complications of Plasmodium falciparum infection were found to be of immunoglobulin G 1 (IgG1) and IgG3 subclasses and of high affinity to the target epitopes. These target epitopes cannot be characterized using recombinant antigens because of a lack of appropriate glycosylation, phosphorylation, methylation, and bisulfide bond formation, which determine the structure of conformational and nonlinear epitopes within the tertiary and quaternary structures of native P. falciparum antigens.

Objective

This study aims to develop a method for the comprehensive detection of all P. falciparum schizont antigens, eliciting a protective immune response.

Methods

Purified parasitophorous vacuole membrane–enclosed merozoite structures (PEMSs) containing native schizont antigens are initially generated, separated by two-dimensional (2D) gel electrophoresis and blotted onto nitrocellulose. Antigens eliciting a protective antibody response are visualized by incubation with sera from patients with clinical immunity. This is followed by the elution of low-affinity antibodies with urea and detection of protective antibody responses by incubation with anti-IgG1 and anti-IgG3 antibodies, which were conjugated to horseradish peroxidase. This is followed by visualization with a color reaction. Blot signals are normalized by relating to the intensity of blot staining with a reference antibody and housekeeping antigens. Results are corrected for intensity of exposure by the relation of antibody responses to global P. falciparum antibody titers. Antigens eliciting the protective responses are identified as immunorelevant from the comparison of spot positions, indicating high-affinity IgG1 or IgG3 responses on the western blot, which is unique to or consistently more intensive in clinically immune individuals compared with nonimmune individuals. The results obtained are validated by using affinity chromatography.

Results

Another group previously applied 2D western blotting to analyze antibody responses to P. falciparum. The sera of patients allowed the detection of 42 antigenic spots on the 2D immunoblot. The spots detected were excised and subjected to mass spectrometry for identification. A total of 19 protein spots were successfully identified and corresponded to 13 distinct proteins. Another group used immunoaffinity chromatography to identify antigens bound by IgGs produced by mice with enhanced immunity to Plasmodium yoelii. Immunorelevant antigens were isolated and identified by immobilizing immunoglobulin from immune mice to a Sephadex column and then passing a blood-stage antigen mixture through the column followed by the elution of specific bound antigens with sodium deoxycholate and the identification of those antigens by western blotting with specific antibodies.

Conclusions

2D western blotting using native antigens has the potential to identify antibody responses selective for specific defined isomeric forms of the same protein, including isoforms (protein species) generated by posttranscriptional modifications such as phosphorylation, glycosylation, and methylation. The process involved in 2D western blotting enables highly sensitive detection, high resolution, and preservation of antibody responses during blotting. Validation by immunoaffinity chromatography can compensate for the antigen loss associated with the blotting process. It has the potential for indirect quantification of protective antibody responses by enabling quantification of the amount of eluted antibody bound antigens through mass spectrometry.

International Registered Report Identifier (IRRID)

PRR1-10.2196/15690

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 I: Algorithms for Map Denoising and Background Subtraction Methods

One of the critical steps in two-dimensional electrophoresis (2-DE) image pre-processing is the denoising, that might aggressively affect either spot detection or pixel-based methods. The Median Modified Wiener Filter (MMWF), a new nonlinear adaptive spatial filter, resulted to be a good denoising approach to use in practice with 2-DE. MMWF is suitable for global denoising, and contemporary for the removal of spikes and Gaussian noise, being its best setting invariant on the type of noise. The second critical step rises because of the fact that 2-DE gel images may contain high levels of background, generated by the laboratory experimental procedures, that must be subtracted for accurate measurements of the proteomic optical density signals. Here we discuss an efficient mathematical method for background estimation, that is suitable to work even before the 2-DE image spot detection, and it is based on the 3D mathematical morphology (3DMM) theory.

Nonlinear Dimensionality Reduction by Minimum Curvilinearity for Unsupervised Discovery of Patterns in Multidimensional Proteomic Data

Dimensionality reduction is largely and successfully employed for the visualization and discrimination of patterns, hidden in multidimensional proteomics datasets. Principal component analysis (PCA), which is the preferred approach for linear dimensionality reduction, may present serious limitations, in particular when samples are nonlinearly related, as often occurs in several two-dimensional electrophoresis (2-DE) datasets. An aggravating factor is that PCA robustness is impaired when the number of samples is small in comparison to the number of proteomic features, and this is the case in high-dimensional proteomic datasets, including 2-DE ones. Here, we describe the use of a nonlinear unsupervised learning machine for dimensionality reduction called minimum curvilinear embedding (MCE) that was successfully applied to different biological samples datasets. In particular, we provide an example where we directly compare MCE performance with that of PCA in disclosing neuropathic pain patterns, hidden in a multidimensional proteomic dataset.

Effects of post-electrophoretic analysis on variance in gel-based proteomics

2D electrophoresis (2DE) is a prominent separation method for complex proteomes. Although recent advances have increased the utility of this method in quantitative proteomics studies, many sources of variance still exist. This review discusses the post-electrophoretic sources of variance in current 2DE analysis. The essential improvements in protein visualization and software algorithms that have made 2DE a leading quantitative proteomics method are briefly reviewed. A number of shortcomings in the post-electrophoretic analysis of 2DE data that require further attention are highlighted. Topics discussed include protein visualization and image acquisition, internal standards and normalization methods, background subtraction algorithms, normality of distribution, and the need for standardized tests for the evaluation of 2DE analysis software packages.

Pre-analytical stability of the plasma proteomes based on the storage temperature

Background

This study examined the effect of storage temperature on the protein profile of human plasma. Plasma samples were stored for 13 days at -80°C, -20°C, +4°C and room temperature (20-25°C) prior to proteomic analysis. The proteomic comparisons were based on the differences of mean intensity values of protein spots between fresh plasma samples (named “time zero”) and plasma samples stored at different temperatures. To better understand the thermally induced biochemical changes that may affect plasma proteins during storage we identified proteins with different expressions with respect to the time zero sample.

Results

Using two-dimensional electrophoresis followed by MALDI-TOF MS and /or LC-MS/MS 20 protein spots representing 10 proteins were identified with significant differences in abundance when stored at different temperatures. Our results, in agreement with various authors, indicate that during storage for a short period (13 days) at four different temperatures plasma proteins were more affected by degradation processes at +4°C compared to the other temperatures analysed. However, we founded that numerous protein spots (vitamin D binding protein, alpha-1-antitrypsin, serotransferrin, apoplipoprotein A-I, apolipoprotein E, haptoglobin and complement factor B) decrease in abundance with increasing temperature up to 4°C, but at room temperature their intensity mean values are similar to those of time zero and -80°C. We hypothesize that these proteins are labile at 4°C, but at the same time they are stable at room temperature (20-25°C). Furthermore we have grouped the proteins based on their different sensitivity to the storage temperature. Spots of serum albumin, fibrinogen gamma chain and haptoglobin are more resistant to the higher temperatures tested, as they have undergone changes in abundance only at room temperature; conversely, other spots of serum albumin, fibrinogen beta chain and serotransferrin are more labile as they have undergone changes in abundance at all temperatures except at -80°C.

Conclusions

Although there are many studies concerning protein stability of clinical samples during storage these findings may help to provide a better understanding of the changes of proteins induced by storage temperature.

Putative role of proteins involved in detoxification of reactive oxygen species in the early response to gravitropic stimulation of poplar stems

Gravity perception and gravitropic response are essential for plant development. In herbaceous species it is widely accepted that one of the primary events in gravity perception involves the displacement of amyloplasts within specialized cells. However the signaling cascade leading to stem reorientation is not fully known especially in woody species in which primary and secondary growth occur. Several different second messengers and proteins have been suggested to be involved in signal transduction of gravitropism. Reactive oxygen species (ROS) have been implicated as second messengers in several plant hormone responses. It has been shown that ROS are asymmetrically generated in roots by gravistimulation to regions of reduced growth. Proteins involved in detoxification of ROS and defense were identified by mass spectrometry: i.e., Thioredoxin h (Trx h), CuZn superoxide dismutase (CuZn SOD), ascorbate peroxidase (APX2), oxygen evolving enhancer 1 (OEE1), oxygen evolving enhancer 2 (OEE2), and ATP synthase. These differentially accumulated proteins that correspond to detoxification of ROS were analyzed at the mRNA level. The mRNA levels showed different expression patterns than those of the corresponding proteins, and revealed that transcription levels were not completely concomitant with translation. Our data showed that these proteins may play a role in the early response to gravitropic stimulation.

Image Analysis Workflow for 2-D Electrophoresis Gels Based on ImageJ

A number of commercial software packages are currently available to perform digital two-dimensional electrophoresis (2D-GE) gel analysis. However, both the high cost of the commercial packages and the unavailability of a standard data analysis workflow, have prompted several groups to develop freeware systems to perform certain steps of gel analysis. Unfortunately, to the best of our knowledge none of them offer a package that performs all the steps envisaged in a 2D-GE gel analysis. Here we describe an ImageJ-based procedure, able to manage all the steps of a 2D-GE gel analysis. ImageJ is a free available image processing and analysis application developed by National Institutes of Health (NIH) and widely used in different life sciences fields as medical imaging, microscopy, western blotting and PAGE. Nevertheless no one has yet developed a procedure enabled to compare spots on 2D-GE gels. We collected all used ImageJ tools in a plug-in that allows us to perform the whole 2D-GE analysis. To test it, we performed a set of 2D-GE experiments on plasma samples from 9 patients victims of acute myocardial infarction and 8 controls, and we compared the results obtained by our procedure to those obtained using a widely diffuse commercial package, finding similar performances.

Proteome analysis in cardiovascular pathophysiology using Dahl rat model

Dahl salt-sensitive (DS) and salt-resistant (DR) inbred rat strains represent a well established animal model for cardiovascular research. Upon prolonged administration of high-salt-containing diet, DS rats develop systemic hypertension, and as a consequence they develop left ventricular hypertrophy, followed by heart failure. The aim of this work was to explore whether this animal model is suitable to identify biomarkers that characterize defined stages of cardiac pathophysiological conditions. The work had to be performed in two stages: in the first part proteomic differences that are attributable to the two separate rat lines (DS and DR) had to be established, and in the second part the process of development of heart failure due to feeding the rats with high-salt-containing diet has to be monitored. This work describes the results of the first stage, with the outcome of protein expression profiles of left ventricular tissues of DS and DR rats kept under low salt diet. Substantial extent of quantitative and qualitative expression differences between both strains of Dahl rats in heart tissue was detected. Using Principal Component Analysis, Linear Discriminant Analysis and other statistical means we have established sets of differentially expressed proteins, candidates for further molecular analysis of the heart failure mechanisms.

The inter- and intra-operator variability in manual spot segmentation and its effect on spot quantitation in two-dimensional electrophoresis analysis

Separation of complex mixtures of proteins by 2-DE is a fundamental component of current proteomic technology. Quantitative analysis of the images generated by digitization of such gels is critical for identifying alterations in protein expression within a given biological system. Software packages are designed for this purpose. The accurate definition of protein spot boundaries, using a suitable method of image segmentation, is a key requirement for image analysis. It is often necessary for operators to intervene manually to correct mistakes in spot segmentation; therefore operator subjectivity and differences in ability can weaken the analysis. We estimated the error in spot quantification after manual spot segmentation, which was performed by different operators, using two different software packages. Our results clearly show that this operation was associated with significant inter- and intra-variability and an overestimation of subsequent spot intensity, especially when spots were weak. For comparative studies, we suggest separately analysing spots which have been manually segmented by imposing a requirement for at least a threefold difference in spot intensity in addition to use of statistical tests.

Proteomics of plant pathogenic fungi

Plant pathogenic fungi cause important yield losses in crops. In order to develop efficient and environmental friendly crop protection strategies, molecular studies of the fungal biological cycle, virulence factors, and interaction with its host are necessary. For that reason, several approaches have been performed using both classical genetic, cell biology, and biochemistry and the modern, holistic, and high-throughput, omic techniques. This work briefly overviews the tools available for studying Plant Pathogenic Fungi and is amply focused on MS-based Proteomics analysis, based on original papers published up to December 2009. At a methodological level, different steps in a proteomic workflow experiment are discussed. Separate sections are devoted to fungal descriptive (intracellular, subcellular, extracellular) and differential expression proteomics and interactomics. From the work published we can conclude that Proteomics, in combination with other techniques, constitutes a powerful tool for providing important information about pathogenicity and virulence factors, thus opening up new possibilities for crop disease diagnosis and crop protection.

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.

Biomarkers of chemotherapy resistance in breast cancer identified by proteomics: current status

This review describes and discusses the advantages and limitations of proteomic approaches in the identification of biomarkers associated with chemotherapy resistance. Both gel-based (two-dimensional polyacrylamide gel electrophoresis) and gel-free (shotgun and quantitative) mass spectrometry approaches are discussed. Non-mass spectrometry approaches including antibody microarray platforms are described as complementary proteomic strategies. Methods for technical confirmation and clinical validation of putative biomarkers are presented. Use of this proteomic toolbox in the quest for biomarkers of chemotherapy resistance in breast cancer is reviewed. Technical aspects of sample selection, acquisition, storage and analysis are discussed and putative biomarkers identified through proteomic approaches are presented.

Median-modified Wiener filter provides efficient denoising, preserving spot edge and morphology in 2-DE image processing

Denoising is a fundamental early stage in 2-DE image analysis strongly influencing spot detection or pixel-based methods. A novel nonlinear adaptive spatial filter (median-modified Wiener filter, MMWF), is here compared with five well-established denoising techniques (Median, Wiener, Gaussian, and Polynomial-Savitzky-Golay filters; wavelet denoising) to suggest, by means of fuzzy sets evaluation, the best denoising approach to use in practice. Although median filter and wavelet achieved the best performance in spike and Gaussian denoising respectively, they are unsuitable for contemporary removal of different types of noise, because their best setting is noise-dependent. Vice versa, MMWF that arrived second in each single denoising category, was evaluated as the best filter for global denoising, being its best setting invariant of the type of noise. In addition, median filter eroded the edge of isolated spots and filled the space between close-set spots, whereas MMWF because of a novel filter effect (drop-off-effect) does not suffer from erosion problem, preserves the morphology of close-set spots, and avoids spot and spike fuzzyfication, an aberration encountered for Wiener filter. In our tests, MMWF was assessed as the best choice when the goal is to minimize spot edge aberrations while removing spike and Gaussian noise.

Improving 2-DE gel image denoising using contourlets

One of the most commonly used methods for protein separation is 2-DE. After 2-DE gel scanning, images with a plethora of spot features emerge that are usually contaminated by inherent noise. The objective of the denoising process is to remove noise to the extent that the true spots are recovered correctly and accurately i.e. without introducing distortions leading to the detection of false-spot features. In this paper we propose and justify the use of the contourlet transform as a tool for 2-DE gel images denoising. We compare its effectiveness with state-of-the-art methods such as wavelets-based multiresolution image analysis and spatial filtering. We show that contourlets not only achieve better average S/N performance than wavelets and spatial filters, but also preserve better spot boundaries and faint spots and alter less the intensities of informative spot features, leading to more accurate spot volume estimation and more reliable spot detection, operations that are essential to differential expression proteomics for biomarkers discovery.

Phosphotyrosine protein dynamics in cell membrane rafts of sphingosine-1-phosphate-stimulated human endothelium: role in barrier enhancement

Sphingosine-1-phosphate (S1P), a lipid growth factor, is critical to the maintenance and enhancement of vascular barrier function via processes highly dependent upon cell membrane raft-mediated signaling events. Anti-phosphotyrosine 2 dimensional gel electrophoresis (2-DE) immunoblots confirmed that disruption of membrane raft formation (via methyl-beta-cyclodextrin) inhibits S1P-induced protein tyrosine phosphorylation. To explore S1P-induced dynamic changes in membrane rafts, we used 2-D techniques to define proteins within detergent-resistant cell membrane rafts which are differentially expressed in S1P-challenged (1microM, 5min) human pulmonary artery endothelial cells (EC), with 57 protein spots exhibiting >3-fold change. S1P induced the recruitment of over 20 cell membrane raft proteins exhibiting increasing levels of tyrosine phosphorylation including known barrier-regulatory proteins such as focal adhesion kinase (FAK), cortactin, p85alpha phosphatidylinositol 3-kinase (p85alphaPI3K), myosin light chain kinase (nmMLCK), filamin A/C, and the non-receptor tyrosine kinase, c-Abl. Reduced expression of either FAK, MLCK, cortactin, filamin A or filamin C by siRNA transfection significantly attenuated S1P-induced EC barrier enhancement. Furthermore, S1P induced cell membrane raft components, p-caveolin-1 and glycosphingolipid (GM1), to the plasma membrane and enhanced co-localization of membrane rafts with p-caveolin-1 and p-nmMLCK. These results suggest that S1P induces both the tyrosine phosphorylation and recruitment of key actin cytoskeletal proteins to membrane rafts, resulting in enhanced human EC barrier function.

Proteome analysis of apical and basal regions of poplar stems under gravitropic stimulation

Gravity is a constant force guiding the direction of plant growth. In young poplar stem, reorientation of the apical region is mainly obtained by differential growth of elongating primary tissues. At the base, where elongation is achieved but where the cambium is active, reorientation is due to asymmetrical formation of reaction wood. After 45 min of gravistimulation, the stem showed no reorientation, but 1 week later, reaction wood was observed at the base of the stem. To determine the molecular mechanisms taking place at the top and base of the stem, after 45 min or 1 week of inclination, the changes induced in protein accumulation were studied by two-dimensional polyacrylamide gel electrophoresis and quantitatively analyzed using image analysis software. Around 300 protein spots were reproducibly detected and analyzed. Forty percent of these proteins showed significant changes after inclination. Mass spectrometry analysis of 135 spots led to the identification of 60 proteins involved in a wide range of activities and metabolisms. Very different patterns of protein expression were obtained according to conditions tested, highlighting the complexity of gravitropic responses. Our results suggest that primary and secondary tissues present specific mechanisms to sense reorientation and to respond to inclination. Some selected proteins are discussed.

Influence of image-analysis software on quantitation of two-dimensional gel electrophoresis data

Image analysis of two-dimensional gels is a crucial step in a proteomic workflow and has a direct impact on obtained qualitative and quantitative data. Since the analysis is a complex process and creates large data amounts, the use of a respective software is inevitable. There are only a few papers published addressing the issue of analysis-based variance; therefore, our aim was to highlight the discrepancy of received results when different commercially available image-tools are used for gel analysis especially in terms of comparability of the obtained outcome when the same digital image set is used. A set of six gels (three replicates per group) of real-life samples was created and examined with two different versions of PD-Quest (Bio-Rad) (version 6.1 and its update version 8.0) and with an external image-tool Delta 2D (Decodon) (version 3.6). Replicate groups were analyzed and compared with each other with regard to volume ratios of a group of significantly changed spots. The study points out significant variations among results depending on the software package used, underlining the importance of a careful investigation of post-experimental processes to receive comparable and reliable results.

"Pathogeno-proteomics"

Many scientists working on pathogens (viruses, bacteria, fungi, parasites) are betting heavily on data generated by longitudinal genomic-transcriptomic-proteomic studies to explain biochemical host-vector-pathogen interactions and thus to contribute to disease control. Availability of genome sequences of various organisms, from viruses to complex metazoans, led to the discovery of the functions of the genes themselves. The postgenomic era stimulated the development of proteomic and bioinformatics tools to identify the locations, functions, and interactions of the gene products in tissues and/or cells of living organisms. Because of the diversity of available methods and the level of integration they promote, proteomics tools are potentially able to resolve interesting issues specific not only to host-vector-pathogen interactions in cell immunobiology, but also to ecology and evolution, population biology, and adaptive processes. These new analytical tools, as all new tools, contain pitfalls directly related to experimental design, statistical treatment, and protein identification. Nevertheless, they offer the potency of building large protein-protein interaction networks for in silico analysis of novel biological entities named "interactomes," a way of modeling host-vector-pathogen interactions to define new interference strategies.

Quantitative protein microarrays for time-resolved measurements of protein phosphorylation

The quantitative analysis of signaling networks requires highly sensitive methods for the time-resolved determination of protein phosphorylation. For this reason, we developed a quantitative protein microarray that monitors the activation of multiple signaling pathways in parallel, and at high temporal resolution. A label-free sandwich approach was combined with near infrared detection, thus permitting the accurate quantification of low-level phosphoproteins in limited biological samples corresponding to less than 50,000 cells, and with a very low standard deviation of approximately 5%. The identification of suitable antibody pairs was facilitated by determining their accuracy and dynamic range using our customized software package Quantpro. Thus, we are providing an important tool to generate quantitative data for systems biology approaches, and to drive innovative diagnostic applications.

A new method for 2D gel spot alignment: application to the analysis of large sample sets in clinical proteomics

BACKGROUND

In current comparative proteomics studies, the large number of images generated by 2D gels is currently compared using spot matching algorithms. Unfortunately, differences in gel migration and sample variability make efficient spot alignment very difficult to obtain, and, as consequence most of the software alignments return noisy gel matching which needs to be manually adjusted by the user.

RESULTS

We present Sili2DGel an algorithm for automatic spot alignment that uses data from recursive gel matching and returns meaningful Spot Alignment Positions (SAP) for a given set of gels. In the algorithm, the data are represented by a graph and SAP by specific subgraphs. The results are returned under various forms (clickable synthetic gel, text file, etc.). We have applied Sili2DGel to study the variability of the urinary proteome from 20 healthy subjects.

CONCLUSION

Sili2DGel performs noiseless automatic spot alignment for variability studies (as well as classical differential expression studies) of biological samples. It is very useful for typical clinical proteomic studies with large number of experiments.

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.

Isotope-coded two-dimensional maps: tagging with deuterated acrylamide and 2-vinylpyridine

Isotope-coded two-dimensional maps, with either D(0)/D(3)-acrylamide or D(0)/D(4) 2-vinyl pyridine, are described in detail. They have the advantage of running the two samples under investigation within a single slab gel, thus minimizing errors because of spot matching with software packages when samples are run in parallel maps. Labeling with deuterated acrylamide is very simple and inexpensive, because this chemical is commercially available. The experiment has to be carried out at alkaline pH values (pH 8.5-9.0) and with high molarities of alkylating agent (50-100 mM) to ensure good conversion efficiency. On the contrary, labeling with 2-vinyl pyridine (2-VP) can be performed in much lower alkylant molarities (20 mM) and at neutral pH values, thus ensuring essentially 100% conversion efficiency coupled with 100% specificity, because the reaction is sustained by the partial positive and negative charges on the 2-VP and -SH group, respectively. However, deuterated 2-VP is not commercially available and it has to be synthesized ad hoc.

Proteomic studies in animal models of diabetes

The aim of this review is to provide an overview of proteomic studies in animal models of diabetes and to give some insight into the different methods available today in the rapidly developing field of proteomics. A summary of 31 papers published between 1997 and 2007 is presented. For instance, proteomics has been used to study the development of both type 1 and type 2 diabetes, diabetic complications in tissues like heart, kidney and retina and changes after treatment with anti-diabetic drugs like peroxisome proliferator-activated receptors agonists. Together, these studies give a good overview of a number of experimental approaches. Proteomics holds the promise of providing major contributions to the field of diabetes research. However, to achieve this, a number of issues need to be resolved. Appropriate data representation to facilitate data comparison, exchange, and verification is required, as well as improved statistical assessment of proteomic experiments. In addition, it is important to follow up the results with functional studies to be able to make biologically relevant conclusions. The potential of proteomics to dissect complex human disorders is now beginning to be realized. In the future, this will result in new important information concerning diabetes.

An Integrated Strategy in Two-Dimensional Electrophoresis Analysis Able to Identify Discriminants Between Different Clinical Conditions

Two-dimensional gel electrophoresis (2DE) is an indispensable tool in proteomics for the analysis of protein expression in complex biological systems such as cells and tissues. However, the automatic extraction of information from gel images is still a challenging task. In this paper we propose a strategy that represents a computational procedure of support to the discrimination of different clinical conditions associated with the samples. The analyzed gel images were acquired within the framework of a study of peripheral neuropathies: twenty-four 2DE maps generated from cerebrospinal fluid (16 pathologic and 8 control subjects) were processed. Quantitative features were defined to describe each image and treated with a method of dimensionality reduction. The informativeness of the descriptors allowed us to see the gel of the data set as items in a three-dimensional space, segregating according to the clinical conditions. Moreover, information with prognostic value was obtained for a single outsider gel of a patient who was included in a clinical subgroup at the first diagnosis but whose disease progressed with clinical features belonging to a different clinical subgroup. The method developed may represent an effective tool of classification that can be used repeatedly to capture the essential impression from separation images.

2-DE and MALDI-TOF-MS for a comparative analysis of proteins expressed in different cellular models of amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal, neurodegenerative disorder characterized by the selective loss of motor neurons from the spinal cord and brain. About 10% of ALS cases are familial (FALS), and in 20% of these cases the disease has been linked to mutations in the Cu,Zn-SOD1 gene. Although the molecular mechanisms causing these forms of ALS are still unclear, evidence has been provided that motor neurons injuries associated with mutant superoxide dismutase (SOD1)-related FALS result from a toxic gain-in-fuction of the mutated enzyme. To understand better the role of these mutations in the pathophysiology of FALS we have compared the pattern of proteins expressed in human neuroblastoma SH-SY5Y cell line with those of cell lines transfected with plasmids expressing the wild-type human SOD1 and the H46R and G93A mutants. 2-DE coupled to MALDI-TOF-MS were the proteomic tools used for identification of differentially expressed proteins. These included cytoskeletal proteins, proteins that regulate energetic metabolism and intracellular redox conditions, and the ubiquitin proteasome system. The proteomic approach allowed to expand the knowledge on the pattern of proteins, with altered expression, which we should focus on, for a better understanding of the possible mechanism involved in mutated-SOD1 toxicity. The cellular models considered in this work have also evidenced biochemical characteristics common to other SOD1-mutated cellular lines connected to the pathogenesis of ALS.

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.

Application of partial least squares discriminant analysis and variable selection procedures: a 2D-PAGE proteomic study

2D gel electrophoresis is a tool for measuring protein regulation, involving image analysis by dedicated software (PDQuest, Melanie, etc.). Here, partial least squares discriminant analysis was applied to improve the results obtained by classic image analysis and to identify the significant spots responsible for the differences between two datasets. A human colon cancer HCT116 cell line was analyzed, treated and not treated with a new histone deacetylase inhibitor, RC307. The proteins regulated by RC307 were detected by analyzing the total lysates and nuclear proteome profiles. Some of the regulated spots were identified by tandem mass spectrometry. The preliminary data are encouraging and the protein modulation reported is consistent with the antitumoral effect of RC307 on the HCT116 cell line. Partial least squares discriminant analysis coupled with backward elimination variable selection allowed the identification of a larger number of spots than classic PDQuest analysis. Moreover, it allows the achievement of the best performances of the model in terms of prediction and provides therefore more robust and reliable results. From this point of view, the multivariate procedure applied can be considered a good alternative to standard differential analysis, also taking into account the interdependencies existing among the variables.

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.

Body fluid proteomics: Prospects for biomarker discovery

Many diseases are caused by perturbations of cellular signaling pathways and related pathway networks as a result of genetic aberrations. These perturbations are manifested by altered cellular protein profiles in the fluids bathing tissue/organs (i.e., the tissue interstitial fluid, TIF). A major challenge of clinical chemistry is to quantitatively map these perturbed protein profiles - the so-called "signatures of disease" - using modern proteomic technologies. This information can be utilized to design protein biomarkers for the early detection of disease, monitoring disease progression and efficacy of drug action. Here, we discuss the use of body fluids in the context of prospective biomarker discovery, and the marked 1000-1500-fold dilution of body fluid proteins, during their passage from TIF to the circulatory system. Further, we discuss proteomics strategies aimed at depleting major serum proteins, especially albumin, in order to focus on low-abundance protein/peptides in plasma. A major limitation of depletion strategies is the removal of low-molecular weight protein/peptides which specifically bind major plasma proteins. We present a prototype model, using albumin, for understanding the multifaceted nature of biomarker research, highlighting the involvement of albumin in Alzheimer's disease. This model underscores the need for a system-level understanding for biomarker research and personalized medicine.

Minimizing Variability in Two-dimensional Electrophoresis Gel Image Analysis

For reliable protein identification and quantitation, it is important to minimize the variability associated with two-dimensional electrophoresis (2-DE) analysis. Since experimental factors contribute largely to the variability observed in 2-DE, most studies have focused on reducing this variability with modest concern to the variability associated with post-experimental analyses. Although often ignored, software analyses of 2-DE gel images present a considerable source of variability in the analysis of proteins. In particular, cropping of gel images prior to quantitative 2-DE analysis has been shown to contribute a significant amount of variability in image analysis. To address this problem, we propose a simple, reliable, and objective method of cropping 2-DE gel images to consequently minimize the variability in 2-DE analysis.

Development of an integrated approach for evaluation of 2-D gel image analysis: Impact of multiple proteins in single spots on comparative proteomics in conventional 2-D gel/MALDI workflow

With 2-D gel mapping, it is often observed that essentially identical proteins migrate to different positions in the gel, while some seemingly well-resolved protein spots consist of multiple proteins. These observations can undermine the validity of gel-based comparative proteomic studies. Through a comparison of protein identifications using direct MALDI-TOF/TOF and LC-ESI-MS/MS analyses of 2-D gel separated proteins from cauliflower florets, we have developed an integrated approach to improve the accuracy and reliability of comparative 2-D electrophoresis. From 46 spots of interest, we identified 51 proteins by MALDI-TOF/TOF analysis and 108 proteins by LC-ESI-MS/MS. The results indicate that 75% of the analyzed spots contained multiple proteins. A comparison of hit rank for protein identifications showed that 37 out of 43 spots identified by MALDI matched the top-ranked hit from the ESI-MS/MS. By using the exponentially modified protein abundance index (emPAI) to determine the abundance of the individual component proteins for the spots containing multiple proteins, we found that the top-hit proteins from 40 out of 43 spots identified by MALDI matched the most abundant proteins determined by LC-MS/MS. Furthermore, our 2-D-GeLC-MS/MS results show that the top-hit proteins in 44 identified spots contributed on average 81% of the spots' staining intensity. This is the first quantitative measurement of the average rate of false assignment for direct MALDI analysis of 2-D gel spots using a new integrated workflow (2-D gel imaging, "2-D GeLC-MS/MS", and emPAI analysis). Here, the new approach is proposed as an alternative to traditional gel-based quantitative proteomics studies.

Improvements in the search for potential biomarkers by proteomics: Application of principal component and discriminant analyses for two-dimensional maps evaluation

In this study, we evaluated if the application of multivariate analysis on the data obtained from two-dimensional protein maps could mean an improvement in the search for protein markers. First, we performed a classical proteomic study of the differential expression of serum N-glycoproteins in colorectal cancer patients. Then, applying principal component analysis (PCA) we assessed the utility of the 2-D protein pattern and certain subsets of spots as a tool to distinguish control and case samples, and tested the accuracy of the classification model by linear discriminant analysis (LDA). On the other hand we looked for altered spots by univariate statistics and then analysed them as a cluster by PCA and LDA. We found that those proteins combined presented a theoretical sensitivity and specificity of 100%. Finally, the spots with known protein identity were analysed by multivariate methods, finding a subgroup that behaved as the most obvious candidates for further validation trials.

Improved comparative proteome analysis based on two-dimensional gel electrophoresis

The purpose of this study was to test the extent to which differences in spot intensity can be reliably recognized between two groups of two-dimensional electrophoresis gels (pH 4-7, visualized with ruthenium fluorescent stain) each loaded with different amounts of protein from rat brain (power analysis). Initial experiments yielded only unsatisfactory results: 546 spots were matched from two groups of 6 gels each loaded with 200 microg and 250 microg protein, respectively. Only 72 spots were higher (p<0.05), while 58 spots were significantly lower in the 250-microg group. The construction of new apparatuses that allowed the simultaneous processing of 24 gels throughout all steps between rehydration and staining procedure considerably lowered the between-gel variation. This resulted in the detection of significant differences in spot intensities in 77-90% of all matched spots on gel groups with a 25% difference in protein load. This applied both when protein from 24 biological replicates was loaded onto two groups of 12 gels and when two pooled tissue samples were each loaded onto 6 gels. At a difference of 50% in protein load, more than 90% of all spots differed significantly between two experimental groups.