2D-PAGE maps analysis

Two-Dimensional Gel Electrophoresis Image Analysis

Gel-based proteomics is still quite widespread due to its high-resolution power; the experimental approach is based on differential analysis, where groups of samples (e.g., control vs diseased) are compared to identify panels of potential biomarkers. However, the reliability of the result of the differential analysis is deeply influenced by 2D-PAGE maps image analysis procedures. The analysis of 2D-PAGE images consists of several steps, such as image preprocessing, spot detection and quantitation, image warping and alignment, spot matching. Several approaches are present in literature, and classical or last-generation commercial software packages exploit different algorithms for each step of the analysis. Here, the most widespread approaches and a comparison of the different strategies are presented.

2D-DIGE and Fluorescence Image Analysis

2D-DIGE is still a very widespread technique in proteomics for the identification of panels of biomarkers, allowing to tackle with some important drawback of classical two-dimensional gel-electrophoresis. However, once 2D-gels are obtained, they must undergo a quite articulated multistep image analysis procedure before the final differential analysis via statistical mono- and multivariate methods. Here, the main steps of image analysis software are described and the most recent procedures reported in the literature are briefly presented.

Activation of Human Peripheral Blood Eosinophils by Cytokines in a Comparative Time-Course Proteomic/Phosphoproteomic Study

Activated eosinophils contribute to airway dysfunction and tissue remodeling in asthma and thus are considered to be important factors in asthma pathology. We report here comparative proteomic and phosphoproteomic changes upon activation of eosinophils using eight cytokines individually and in selected cytokine combinations in time-course reactions. Differential protein and phosphoprotein expressions were determined by mass spectrometry after 2-dimensional gel electrophoresis (2DGE) and by LC-MS/MS. We found that each cytokine-stimulation produced significantly different changes in the eosinophil proteome and phosphoproteome, with phosphoproteomic changes being more pronounced and having an earlier onset. Furthermore, we observed that IL-5, GM-CSF, and IL-3 showed the greatest change in protein expression and phosphorylation, and this expression differed markedly from those of the other five cytokines evaluated. Comprehensive univariate and multivariate statistical analyses were employed to evaluate the comparative results. We also monitored eosinophil activation using flow cytometry (FC) analysis of CD69. In agreement with our proteomic studies, FC indicated that IL-5, GM-CSF, and IL-3 were more effective than the other five cytokines studied in stimulating a cell surface CD69 increase indicative of eosinophil activation. Moreover, selected combinations of cytokines revealed proteomic patterns with many proteins in common with single cytokine expression patterns but also showed a greater effect of the two cytokines employed, indicating a more complex signaling pathway that was reflective of a more typical inflammatory pathology.

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.

Unraveling Mesenchymal Stem Cells' Dynamic Secretome Through Nontargeted Proteomics Profiling

The modulatory and regenerative potential shown by the use of MSC secretomes has emphasized the importance of their proteomics profiling. Proteomic analysis, initially focused on the targeted analysis of some candidate proteins or the identification of the secreted proteins, has been changing to an untargeted profiling also based on the quantitative evaluation of the secreted proteins.The study of the secretome can be accomplished through several different proteomics-based approaches; however this analysis must overcome one key challenge of secretome analysis: the low amount of secreted proteins and usually their high dilution.In this chapter, a general workflow for the untargeted proteomic profile of MSC's secretome is presented, in combination with a comprehensive description of the major techniques/procedures that can be used. Special focus is given to the main procedures to obtain the secreted proteins, from secretome concentration by ultrafiltration to protein precipitation. Lastly, different proteomics-based approaches are presented, emphasizing alternative digestion techniques and available mass spectrometry-based quantitative methods.

Bioinformatics for spermatogenesis: annotation of male reproduction based on proteomics

Proteomics strategies have been widely used in the field of male reproduction, both in basic and clinical research. Bioinformatics methods are indispensable in proteomics-based studies and are used for data presentation, database construction and functional annotation. In the present review, we focus on the functional annotation of gene lists obtained through qualitative or quantitative methods, summarizing the common and male reproduction specialized proteomics databases. We introduce several integrated tools used to find the hidden biological significance from the data obtained. We further describe in detail the information on male reproduction derived from Gene Ontology analyses, pathway analyses and biomedical analyses. We provide an overview of bioinformatics annotations in spermatogenesis, from gene function to biological function and from biological function to clinical application. On the basis of recently published proteomics studies and associated data, we show that bioinformatics methods help us to discover drug targets for sperm motility and to scan for cancer-testis genes. In addition, we summarize the online resources relevant to male reproduction research for the exploration of the regulation of spermatogenesis.

A method for processing multivariate data in medical studies

Traditional displays of principal component analyses lack readability to discriminate between putative clusters of variables or cases. Here, the author proposes a method that clusterizes and visualizes variables or cases through principal component analyses thus facilitating their analysis. The method displays pre-determined clusters of variables or cases as urchins that each has a soma (the average point) and spines (the individual variables or cases). Through three examples in the field of neuropsychology, the author illustrates how urchins help examine the modularity of cognitive tasks on the one hand and identify groups of healthy versus brain-damaged participants on the other hand. Some of the data used in this article were obtained from the Alzheimer's Disease Neuroimaging Initiative database. The urchin method was implemented in MATLAB, and the source code is available in the Supporting information. Urchins can be useful in biomedical studies to identify distinct phenomena at first glance, each having several measures (clusters of variables) or distinct groups of participants (clusters of cases).

Biomarker discovery from the top down: Protein biomarkers for efficient virus transmission by insects (Homoptera: Aphididae) discovered by coupling genetics and 2-D DIGE

Yellow dwarf viruses cause the most economically important virus diseases of cereal crops worldwide and are vectored by aphids. The identification of vector proteins mediating virus transmission is critical to develop sustainable virus management practices and to understand viral strategies for circulative movement in all insect vectors. Previously, we applied 2-D DIGE to an aphid filial generation 2 population to identify proteins correlated with the transmission phenotype that were stably inherited and expressed in the absence of the virus. In the present study, we examined the expression of the DIGE candidates in previously unstudied, field-collected aphid populations. We hypothesized that the expression of proteins involved in virus transmission could be clinically validated in unrelated, virus transmission-competent, field-collected aphid populations. All putative biomarkers were expressed in the field-collected biotypes, and the expression of nine of these aligned with the virus transmission-competent phenotype. The strong conservation of the expression of the biomarkers in multiple field-collected populations facilitates new and testable hypotheses concerning the genetics and biochemistry of virus transmission. Integration of these biomarkers into current aphid-scouting methodologies will enable rational strategies for vector control aimed at judicious use and development of precision pest control methods that reduce plant virus infection.

Proteins involved in biotic and abiotic stress responses as the most significant biomarkers in the ripening of Pinot Noir skins

We propose an integrated approach, obtained by the combination of multivariate statistics and proteomics, useful to isolate candidate biomarkers for the evaluation of grape ripening. We carried out a comparative 2-DE analysis of grape skins collected in three moments of ripening and analyzed the spot volume dataset through the application of principal component analysis followed by forward stepwise-linear discriminant analysis. This technique allowed to discriminate véraison, quite mature and mature samples, and to sort the matched spots according to their significance. We identified 36 spots showing high discriminating coefficients through liquid chromatography - electrospray ionization - tandem mass spectrometry (LC-ESI-MS/MS). Most of them were involved in biotic and abiotic stress responses indicating these enzymes as good candidate markers of berry ripening. These evidences hint at a likely developmental role of these proteins, in addition to their reported activity in stress events. Restricting the same statistical analysis to the samples belonging to the two last stages, it was indicated that this approach can clearly distinguish these close and similar phases of berry development. Taken all together, these results bear out that the employment of the combination of 2-DE and multivariate statistics is a reliable tool in the identification of new protein markers for describing the ripening phases and to assess the overall quality of the fruit.

A red line not to cross: evaluating the limitation and properness of gel image tuning procedures

Currently, results of gel electrophoresis are commonly documented in digital formats by image acquisition instruments. In this study, gel images tuned by a common image processing software package, Photoshop, were assessed to understand the transforming algorithms and their impacts on quantitative analysis. TotalLab 100, an electrophoresis gel image analysis software package, was applied for image quantitation and evaluation. The three most frequently used image tuning functions-adjustments of the brightness, contrast, and grayscale span (level) of images-were investigated using both data generated from a standard grayscale tablet and an actual electrophoresis gel image. The influences of these procedures were analyzed for the grayscale transformation between the input and output images. Although all three procedures differentially improved the visualization of the input image, adjusting the contrast of images disrupted the quantitative information because of its nonlinear transforming algorithm. Under certain conditions, adjusting the brightness or the level of images could preserve the quantitative information because of the linear transforming algorithms. It was found that when the minimum and maximum grayscales of a gel image were recognized, using a commercial software package to maximally stretch the level may significantly improve the quality of a gel image without jeopardizing quantitative analysis.

Cross species proteomics

Proteomics has advanced in leaps and bounds over the past couple of decades. However, the continuing dependency of mass spectrometry-based protein identification on the searching of spectra against protein sequence databases limits many proteomics experiments. If there is no sequenced genome for a given species, then cross species proteomics is required, attempting to identify proteins across the species boundary, typically using the sequenced genome of a closely related species. Unlike sequence searching for homologues, the proteomics equivalent is confounded by small differences in amino acid sequences, leading to large differences in peptide masses; this renders mass matching of peptides and their product ions difficult. Therefore, the phylogenetic distance between the two species and the attendant level of conservation between the homologous proteins play a huge part in determining the extent of protein identification that is possible across the species boundary. In this chapter, we review the cross species challenge itself, as well as various approaches taken to deal with it and the success met with in past studies. This is followed by recommendations of best practice and suggestions to researchers facing this challenge as well as a final section predicting developments, which may help improve cross species proteomics in the future.

KF-1 Ubiquitin Ligase: An Anxiety Suppressor

Anxiety is an instinct that may have developed to promote adaptive survival by evading unnecessary danger. However, excessive anxiety is disruptive and can be a basic disorder of other psychiatric diseases such as depression. The KF-1, a ubiquitin ligase located on the endoplasmic reticulum (ER), may prevent excessive anxiety; kf-1^−/− mice exhibit selectively elevated anxiety-like behavior against light or heights. It is surmised that KF-1 degrades some target proteins, responsible for promoting anxiety, through the ER-associated degradation pathway, similar to Parkin in Parkinson's disease (PD). Parkin, another ER-ubiquitin ligase, prevents the degeneration of dopaminergic neurons by degrading the target proteins responsible for PD. Molecular phylogenetic studies have revealed that the prototype of kf-1 appeared in the very early phase of animal evolution but was lost, unlike parkin, in the lineage leading up to Drosophila. Therefore, kf-1^−/− mice may be a powerful tool for elucidating the molecular mechanisms involved in emotional regulation, and for screening novel anxiolytic/antidepressant compounds.