Study of proteomic changes associated with healthy and tumoral murine samples in neuroblastoma by principal component analysis and classification methods

Comparative proteomics of whey proteins: New insights into quantitative differences between bovine, goat and camel species

Whey proteins are the leading proteins class in milk and play an essential role in the immune defense of neonatal mammals. The aim of this study was to analyze whey proteins in bovine, goat and camel milk by label free proteomics techniques. Finally, 840 proteins were identified, which considerably increasing the number of whey proteins identified in these species. The results of the PCA revealed significant differences in whey proteome patterns between bovine, goat and camel milk. Proteins such as PAEP, CST3, SERPING1, CTSB and GLG1 play an important role as markers in the classification of bovine, goat and camel milk. Statistical analysis showed that the relative abundances of many whey proteins such as ALB, LALBA, LTF and LPO were significantly different among different species. GO and KEGG functional analysis have shown that while the distribution of biological functions involved in whey proteins was relatively similar across species, they differed in terms of protein quantity. These data shed light on the quantitative differences and potential physiological functions of whey proteins across species, and may point the way to the production of specific functional whey proteins.

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.

Quantitative proteomic analysis of milk fat globule membrane (MFGM) proteins in human and bovine colostrum and mature milk samples through iTRAQ labeling

Milk fat globule membrane (MFGM) proteins have many functions. To explore the different proteomics of human and bovine MFGM, MFGM proteins were separated from human and bovine colostrum and mature milk, and analyzed by the iTRAQ proteomic approach. A total of 411 proteins were recognized and quantified. Among these, 232 kinds of differentially expressed proteins were identified. These differentially expressed proteins were analyzed based on multivariate analysis, gene ontology (GO) annotation and KEGG pathway. Biological processes involved were response to stimulus, localization, establishment of localization, and the immune system process. Cellular components engaged were the extracellular space, extracellular region parts, cell fractions, and vesicles. Molecular functions touched upon were protein binding, nucleotide binding, and enzyme inhibitor activity. The KEGG pathway analysis showed several pathways, including regulation of the actin cytoskeleton, focal adhesion, neurotrophin signaling pathway, leukocyte transendothelial migration, tight junction, complement and coagulation cascades, vascular endothelial growth factor signaling pathway, and adherens junction. These results enhance our understanding of different proteomes of human and bovine MFGM across different lactation phases, which could provide important information and potential directions for the infant milk powder and functional food industries.

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

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

Image Pretreatment Tools II: Normalization Techniques for 2-DE and 2-D DIGE

Gel electrophoresis is usually applied to identify different protein expression profiles in biological samples (e.g., control vs. pathological, control vs. treated). Information about the effect to be investigated (a pathology, a drug, a ripening effect, etc.) is however generally confounded with experimental variability that is quite large in 2-DE and may arise from small variations in the sample preparation, reagents, sample loading, electrophoretic conditions, staining and image acquisition. Obtaining valid quantitative estimates of protein abundances in each map, before the differential analysis, is therefore fundamental to provide robust candidate biomarkers. Normalization procedures are applied to reduce experimental noise and make the images comparable, improving the accuracy of differential analysis. Certainly, they may deeply influence the final results, and to this respect they have to be applied with care. Here, the most widespread normalization procedures are described both for what regards the applications to 2-DE and 2D Difference Gel-electrophoresis (2-D DIGE) maps.

Chemometric Multivariate Tools for Candidate Biomarker Identification: LDA, PLS-DA, SIMCA, Ranking-PCA

2-D gel electrophoresis usually provides complex maps characterized by a low reproducibility: this hampers the use of spot volume data for the identification of reliable biomarkers. Under these circumstances, effective and robust methods for the comparison and classification of 2-D maps are fundamental for the identification of an exhaustive panel of candidate biomarkers. Multivariate methods are the most suitable since they take into consideration the relationships between the variables, i.e., effects of synergy and antagonism between the spots. Here the most common multivariate methods used in spot volume datasets analysis are presented. The methods are applied on a sample dataset to prove their effectiveness.

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

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

Proteomic analysis of cow, yak, buffalo, goat and camel milk whey proteins: quantitative differential expression patterns

To aid in unraveling diverse genetic and biological unknowns, a proteomic approach was used to analyze the whey proteome in cow, yak, buffalo, goat, and camel milk based on the isobaric tag for relative and absolute quantification (iTRAQ) techniques. This analysis is the first to produce proteomic data for the milk from the above-mentioned animal species: 211 proteins have been identified and 113 proteins have been categorized according to molecular function, cellular components, and biological processes based on gene ontology annotation. The results of principal component analysis showed significant differences in proteomic patterns among goat, camel, cow, buffalo, and yak milk. Furthermore, 177 differentially expressed proteins were submitted to advanced hierarchical clustering. The resulting clustering pattern included three major sample clusters: (1) cow, buffalo, and yak milk; (2) goat, cow, buffalo, and yak milk; and (3) camel milk. Certain proteins were chosen as characterization traits for a given species: whey acidic protein and quinone oxidoreductase for camel milk, biglycan for goat milk, uncharacterized protein (Accession Number: F1MK50 ) for yak milk, clusterin for buffalo milk, and primary amine oxidase for cow milk. These results help reveal the quantitative milk whey proteome pattern for analyzed species. This provides information for evaluating adulteration of specific specie milk and may provide potential directions for application of specific milk protein production based on physiological differences among animal species.

Development of a classification and ranking method for the identification of possible biomarkers in two-dimensional gel-electrophoresis based on principal component analysis and variable selection procedures

The identification of biomarkers is one of the leading research areas in proteomics. When biomarkers have to be searched for in spot volume datasets produced by 2D gel-electrophoresis, problems may arise related to the large number of spots present in each map and the small number of samples available in each class (control/pathological). In such cases multivariate methods are usually exploited together with variable selection procedures, to provide a set of possible biomarkers: they are however usually aimed to the selection of the smallest set of variables (spots) providing the best performances in prediction. This approach seems not to be suitable for the identification of potential biomarkers since in this case all the possible candidate biomarkers have to be identified to provide a general picture of the "pathological state": in this case exhaustivity has to be preferred to provide a complete understanding of the mechanisms underlying the pathology. We propose here a ranking and classification method, "Ranking-PCA", based on Principal Component Analysis and variable selection in forward search: the method selects one variable at a time as the one providing the best separation of the two classes investigated in the space given by the relevant PCs. The method was applied to an artificial dataset and a real case-study: Ranking-PCA exhaustively identified the potential biomarkers and provided reliable and robust results.

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.

The principle of exhaustiveness versus the principle of parsimony: a new approach for the identification of biomarkers from proteomic spot volume datasets based on principal component analysis

The field of biomarkers discovery is one of the leading research areas in proteomics. One of the most exploited approaches to this purpose consists of the identification of potential biomarkers from spot volume datasets produced by 2D gel electrophoresis. In this case, problems may arise due to the large number of spots present in each map and the small number of maps available for each class (control/pathological). Multivariate methods are therefore usually applied together with variable selection procedures, to provide a subset of potential candidates. The variable selection procedures available usually pursue the so-called principle of parsimony: the most parsimonious set of spots is selected, providing the best classification performances. This approach is not effective in proteomics since all potential biomarkers must be identified: not only the most discriminating spots, usually related to general responses to inflammatory events, but also the smallest differences and all redundant molecules, i.e. biomarkers showing similar behaviour. The principle of exhaustiveness should be pursued rather than parsimony. To solve this problem, a new ranking and classification method, "Ranking-PCA", based on principal component analysis and variable selection in forward search, is proposed here for the exhaustive identification of all possible biomarkers. The method is successfully applied to three different proteomic datasets to prove its effectiveness.

Evaluation of the variables characterized by significant discriminating power in the application of SIMCA classification method to proteomic studies

SIMCA classification can be applied to 2D-PAGE maps to identify changes occurring in cellular protein contents as a consequence of illnesses or therapies. These data sets are complex to treat due to the large number of proteins detected. A method for identifying relevant proteins from SIMCA discriminating powers is proposed, based on the Box-Cox transformation coupled to probability papers. The method successfully allowed the identification of the relevant spots from 2D maps.

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.

Comparative proteomic expression profile in all-trans retinoic acid differentiated neuroblastoma cell line

Neuroblastoma (NB) is an infant tumor which frequently differentiates into neurons. We used two-dimensional differential in-gel electrophoresis (2D-DIGE) to analyze the cytosolic and nuclear protein expression patterns of LAN-5 cells following neuronal differentiating agent all-trans-retinoic acid treatment. We identified several candidate proteins, from which G beta2 and Prefoldin 3 may have a role on NB development. These results strength the use of proteomics to discover new putative protein targets in cancer.

Application of proteomic technology to neural stem cell science and neurology

There is widespread recognition of the potential that stem cells hold for the treatment and repair of a large number of disorders affecting the human CNS. Therefore, stem cell research will go hand in hand with progress in specific areas of neuroscience. Proteomics has great potential to make important contributions to the basic understanding of neurological processes, and to deliver much needed cellular biomarkers in both of these fields. This review focuses on the importance of proteomic research in neuroscience, in particular the application of biomarker discovery in stem cells and degenerative diseases of the CNS.

Perspectives of proteomics in acute myeloid leukemia

Acute myeloid leukemia (AML) is a frequent hematological malignancy. Despite enormous therapeutic efforts that range from various cytotoxic agents to allogeneic stem cell transplantation, overall survival of patients with AML remains unsatisfying. The poor survival rates are mainly due to therapy-related mortality, failure of induction chemotherapy and early relapses. Therefore, novel therapeutic agents that are more efficient and better tolerated are eagerly sought after. For existing therapeutic strategies, there is a lack of markers that are capable of reliably predicting prognosis or the therapeutic response prior to treatment. There is hope that elucidation of the AML-specific proteome will prompt the discovery of novel therapeutic targets and biomarkers in AML. Modern mass-spectrometry instrumentation has achieved excellent performance in terms of sensitivity, resolution and mass accuracy; however, so far, the contribution of proteomics to the care of patients with AML is virtually zero. This might be partly because mass spectrometry instrumentation and protein fractionation still lack true high-throughput capabilities with highest levels of reproducibility, thus hampering large-scale translational studies with clinical samples. Since mass-spectrometry instruments are very intricate devices, their successful operation will hinge on the willingness and ability of mass-spectrometry experts and clinical researchers to adopt new views, learn from each other and cooperate in order to ultimately benefit the patient suffering from AML. This review highlights some clinical problems circumventing the treatment of patients with AML. Furthermore, it provides a brief overview of the technical background of standard proteomics approaches and describes opportunities, challenges and pitfalls of proteomic studies with regards to AML.

Multivariate statistical tools applied to the characterization of the proteomic profiles of two human lymphoma cell lines by two-dimensional gel electrophoresis

Mantle cell lymphoma (MCL) cell lines have been difficult to generate, since only few have been described so far and even fewer have been thoroughly characterized. Among them, there is only one cell line, called GRANTA-519, which is well established and universally adopted for most lymphoma studies. We succeeded in establishing a new MCL cell line, called MAVER-1, from a leukemic MCL, and performed a thorough phenotypical, cytogenetical and molecular characterization of the cell line. In the present report, the phenotypic expression of GRANTA-519 and MAVER-1 cell lines has been compared and evaluated by a proteomic approach, exploiting 2-D map analysis. By univariate statistical analysis (Student's t-test, as commonly used in most commercial software packages), most of the protein spots were found to be identical between the two cell lines. Thirty spots were found to be unique for the GRANTA-519, whereas another 11 polypeptides appeared to be expressed only by the MAVER-1 cell line. A number of these spots could be identified by MS. These data were confirmed and expanded by multivariate statistical tools (principal component analysis and soft-independent model of class analogy) that allowed identification of a larger number of differently expressed spots. Multivariate statistical tools have the advantage of reducing the risk of false positives and of identifying spots that are significantly altered in terms of correlated expression rather than absolute expression values. It is thus suggested that, in future work in differential proteomic profiling, both univariate and multivariate statistical tools should be adopted.

The expanding role of mass spectrometry in metabolite profiling and characterization

Mass spectrometry has a strong history in drug-metabolite analysis and has recently emerged as the foremost technology in endogenous metabolite research. The advantages of mass spectrometry include a wide dynamic range, the ability to observe a diverse number of molecular species, and reproducible quantitative analysis. These attributes are important in addressing the issue of metabolite profiling, as the dynamic range easily exceeds nine orders of magnitude in biofluids, and the diversity of species ranges from simple amino acids to lipids to complex carbohydrates. The goals of the application of mass spectrometry range from basic biochemistry to clinical biomarker discovery with challenges in generating a comprehensive profile, data analysis, and structurally characterizing physiologically important metabolites. The precedent for this work has already been set in neonatal screening, as blood samples from millions of neonates are tested routinely by mass spectrometry as a diagnostic tool for inborn errors of metabolism. In this review, we will discuss the background from which contemporary metabolite research emerged, the techniques involved in this exciting area, and the current and future applications of this field.

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

In this paper, Legendre moments are calculated to extract the global information from a set of two-dimensional polyacrylamide gel electrophoresis map images. The dataset contains 18 samples belonging to two different cell lines (PACA44 and T3M4) of control (untreated) and drug-treated pancreatic ductal carcinoma cells. The aim of this work was to obtain the correct classification of the 18 samples, using the Legendre moments as discriminant variables. For each image the Legendre moments up to a maximum order of 100 were computed. The stepwise linear discriminant analysis (LDA) was performed in order to select the moments with the highest discriminating power. The results demonstrate that the Legendre moments can be successfully applied for fast classification purposes and similarity analysis.

Spot overlapping in two-dimensional maps: A serious problem ignored for much too long

In the analysis of a neuroblastoma xenograft implanted in mice using two-dimensional maps, some 85 proteins were found to be up- or down-regulated (out of a total of 264 detected by a medium-sensitivity colloidal Coomassie stain). When these spots were eluted and analysed by mass spectrometry in a quadrupole time of flight mass spectrometer, a number of spots were found to be envelopes of different polypeptide chains. Out of a total of 74 proteins identified, 52 (71%) were found to be singlets, 14 (19%) were doublets, 6 (8%) were triplets, 1 was a quadruplet and 1 a quintuplet. Analysis of the DeltapI and DeltaMr of all species contained in a single gel segment eluted helped point out potential errors in protein identification. This was a unique case, in that very minute bioptic sample loads were applied to the gel. In normal cases, where sample loads of ca. 1 mg of total protein are applied and typically at least 1000 spots are visualised, the singlets will be the minority, rarely exceeding 30% of all spots analysed. The experimental data on the abundance of overlapping spots were in excellent agreement with theoretical data calculated on the basis of the statistical theory of spot overlapping, originally proposed by Davis and further developed by some of the authors. Ways and means for minimizing spot overlap and visualising a greater number of spots in a two-dimensional map are discussed.

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

The present review attempts to cover a number of methods that have appeared in the last few years for performing quantitative proteome analysis. However, due to the large number of methods described for both electrophoretic and chromatographic approaches, we have limited this review to conventional two-dimensional (2-D) map analysis which couples orthogonally a charge-based step (isoelectric focusing) to a size-based separation step (sodium dodecyl sulfate-electrophoresis). The first and oldest method applied to 2-D map data reduction is based on statistical analysis performed on sets of gels via powerful software packages, such as Melanie, PDQuest, Z3 and Z4000, Phoretix and Progenesis. This method calls for separately running a number of replicas for control and treated samples. The two sets of data are then merged and compared via a number of software packages which we describe. In addition to commercially-available systems, a number of home made approaches for 2-D map comparison have been recently described and are also reviewed. They are based on fuzzyfication of the digitized 2-D gel image coupled to linear discriminant analysis, three-way principal component analysis or a combination of principal component analysis and soft-independent modeling of class analogy. These statistical tools appear to perform well in differential proteomic studies.