Quantitative comparison and evaluation of two commercially available, two-dimensional electrophoresis image analysis software packages, Z3 and Melanie

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.

The impact of growth hormone on proteomic profiles: a review of mouse and adult human studies

Growth hormone (GH) is a protein that is known to stimulate postnatal growth, counter regulate insulin's action and induce expression of insulin-like growth factor-1. GH exerts anabolic or catabolic effects depending upon on the targeted tissue. For instance, GH increases skeletal muscle and decreases adipose tissue mass. Our laboratory has spent the past two decades studying these effects, including the effects of GH excess and depletion, on the proteome of several mouse and human tissues. This review first discusses proteomic techniques that are commonly used for these types of studies. We then examine the proteomic differences found in mice with excess circulating GH (bGH mice) or mice with disruption of the GH receptor gene (GHR^-/-). We also describe the effects of increased and decreased GH action on the proteome of adult patients with either acromegaly, GH deficiency or patients after short-term GH treatment. Finally, we explain how these proteomic studies resulted in the discovery of potential biomarkers for GH action, particularly those related with the effects of GH on aging, glucose metabolism and body composition.

Mixture Modeling of 2-D Gel Electrophoresis Spots Enhances the Performance of Spot Detection

2-D gel electrophoresis is the most commonly used method in biomedicine to separate even thousands of proteins in a complex sample on a single gel. Even though the technique is quite known, there is still a need to find an efficient and reliable method for detection of protein spots on gel image. In this paper, a three-step algorithm based on mixture of 2-D normal distribution functions is introduced to improve the efficiency of spot detection performed by the existing algorithms, namely Pinnacle software and watershed segmentation method. Comparison of methods is based on using simulated and real data sets with known true spot positions and different number of spots. Fitting a mixture of components to gel image allows for achieving higher sensitivity in detecting spots, regardless the method used to find initial conditions for the model parameters, and it leads to better overall performance of spot detection. By using mixture model, location of spot centers can be estimated with higher accuracy than using the Pinnacle method. An application of spot shape modeling gives higher sensitivity of obtaining low-intensity spots than the watershed method, which is crucial in the discovery of novel biomarkers.

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

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

Biomarkers of systemic inflammation in farmers with musculoskeletal disorders; a plasma proteomic study

Background

Farmers have an increased risk for musculoskeletal disorders (MSD) such as osteoarthritis of the hip, low back pain, and neck and upper limb complaints. The underlying mechanisms are not fully understood. Work-related exposures and inflammatory responses might be involved. Our objective was to identify plasma proteins that differentiated farmers with MSD from rural referents.

Methods

Plasma samples from 13 farmers with MSD and rural referents were included in the investigation. Gel based proteomics was used for protein analysis and proteins that differed significantly between the groups were identified by mass spectrometry.

Results

In total, 15 proteins differed significantly between the groups. The levels of leucine-rich alpha-2-glycoprotein, haptoglobin, complement factor B, serotransferrin, one isoform of kininogen, one isoform of alpha-1-antitrypsin, and two isoforms of hemopexin were higher in farmers with MSD than in referents. On the other hand, the levels of alpha-2-HS-glycoprotein, alpha-1B-glycoprotein, vitamin D- binding protein, apolipoprotein A1, antithrombin, one isoform of kininogen, and one isoform of alpha-1-antitrypsin were lower in farmers than in referents. Many of the identified proteins are known to be involved in inflammation.

Conclusions

Farmers with MSD had altered plasma levels of protein biomarkers compared to the referents, indicating that farmers with MSD may be subject to a more systemic inflammation. It is possible that the identified differences of proteins may give clues to the biochemical changes occurring during the development and progression of MSD in farmers, and that one or several of these protein biomarkers might eventually be used to identify and prevent work-related MSD.

Electronic supplementary material

The online version of this article (doi:10.1186/s12891-016-1059-y) contains supplementary material, which is available to authorized users.

Comparative Evaluation of Software Features and Performances

Analysis of two-dimensional gel images is a crucial step for the determination of changes in the protein expression, but at present, it still represents one of the bottlenecks in 2-DE studies. Over the years, different commercial and academic software packages have been developed for the analysis of 2-DE images. Each of these shows different advantageous characteristics in terms of quality of analysis. In this chapter, the characteristics of the different commercial software packages are compared in order to evaluate their main features and performances.

Algorithms for Warping of 2-D PAGE Maps

Software-based image analysis of 2-D PAGE maps is an important step for the investigation of proteome. Warping algorithms, which are employed to register spots among gels, are able to overcome the difficulties due to the low reproducibility of this analytical technique. Over the years, the research of new matching and warping mathematical methods has allowed the development of several routine applications of easy-to-use software. This chapter describes common and basic spatial transformations used for the alignment of protein spots present in different gel maps; some recently new approaches are also presented.

Three-dimensional electrophoresis for quantitative profiling of complex proteomes

Quantitative 2D-gel-dependent proteomics became feasible with 2D fluorescence difference gel electrophoresis (2D-DIGE), and this technique has gained wide acceptance because it has eliminated the gel to gel variations and greatly facilitated the quantitative comparisons across gels for many different experimental conditions. However, the co-migration of several proteins in the same spot is still a major limitation which detracts from the accuracy of comparative quantification and prevents unambiguous post-translational modifications (PTMs) detection.A protocol based on traditional polyacrylamide gel IEF sample fractionation, and followed by two consecutive SDS-PAGE electrophoreses alleviates co-migration limitations. The use of two different buffer systems for SDS-PAGE is central to the proposed approach.

Spot quantification in two dimensional gel electrophoresis image analysis: comparison of different approaches and presentation of a novel compound fitting algorithm

Background

Various computer-based methods exist for the detection and quantification of protein spots in two dimensional gel electrophoresis images. Area-based methods are commonly used for spot quantification: an area is assigned to each spot and the sum of the pixel intensities in that area, the so-called volume, is used a measure for spot signal. Other methods use the optical density, i.e. the intensity of the most intense pixel of a spot, or calculate the volume from the parameters of a fitted function.

Results

In this study we compare the performance of different spot quantification methods using synthetic and real data. We propose a ready-to-use algorithm for spot detection and quantification that uses fitting of two dimensional Gaussian function curves for the extraction of data from two dimensional gel electrophoresis (2-DE) images. The algorithm implements fitting using logical compounds and is computationally efficient. The applicability of the compound fitting algorithm was evaluated for various simulated data and compared with other quantification approaches. We provide evidence that even if an incorrect bell-shaped function is used, the fitting method is superior to other approaches, especially when spots overlap. Finally, we validated the method with experimental data of urea-based 2-DE of Aβ peptides andre-analyzed published data sets. Our methods showed higher precision and accuracy than other approaches when applied to exposure time series and standard gels.

Conclusion

Compound fitting as a quantification method for 2-DE spots shows several advantages over other approaches and could be combined with various spot detection methods.

The algorithm was scripted in MATLAB (Mathworks) and is available as a supplemental file.

Two-dimensional gel electrophoresis image registration using block-matching techniques and deformation models

Block-matching techniques have been widely used in the task of estimating displacement in medical images, and they represent the best approach in scenes with deformable structures such as tissues, fluids, and gels. In this article, a new iterative block-matching technique-based on successive deformation, search, fitting, filtering, and interpolation stages-is proposed to measure elastic displacements in two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) images. The proposed technique uses different deformation models in the task of correlating proteins in real 2D electrophoresis gel images, obtaining an accuracy of 96.6% and improving the results obtained with other techniques. This technique represents a general solution, being easy to adapt to different 2D deformable cases and providing an experimental reference for block-matching algorithms.

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.

A streamlined approach to high-throughput proteomics

Proteomics has rapidly become an important tool for life science research, allowing the integrated analysis of global protein expression from a single experiment. To accommodate the complexity and dynamic nature of any proteome, researchers must use a combination of disparate protein biochemistry techniques, often a highly involved and time-consuming process. Whilst highly sophisticated, individual technologies for each step in studying a proteome are available, true high-throughput proteomics that provides a high degree of reproducibility and sensitivity has been difficult to achieve. The development of high-throughput proteomic platforms, encompassing all aspects of proteome analysis and integrated with genomics and bioinformatics technology, therefore represents a crucial step for the advancement of proteomics research. ProteomIQ (Proteome Systems) is the first fully integrated, start-to-finish proteomics platform to enter the market. Sample preparation and tracking, centralized data acquisition and instrument control, and direct interfacing with genomics and bioinformatics databases are combined into a single suite of integrated hardware and software tools, facilitating high reproducibility and rapid turnaround times. This review will highlight some features of ProteomIQ, with particular emphasis on the analysis of proteins separated by 2D polyacrylamide gel electrophoresis.

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

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

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

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

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.

Plasma Protein Biomarkers Correlated with the Development of Diet-Induced Type 2 Diabetes in Mice

INTRODUCTION: Early detection, assessment of disease progression, and application of an appropriate therapeutic intervention are all important for the care of patients with type 2 diabetes. Currently, however, there is no simple test for early detection of type 2 diabetes. Established diagnostic tests for the disease including oral glucose tolerance, fasting blood glucose, and hemoglobin A1c are relatively late markers where the disease has already progressed. Since blood is in direct contact with many tissues, we hypothesized that pathological tissue changes are likely to be reflected in proteomic profiles of plasma. METHODS: Mice were reared either on regular chow or a high-fat diet at weaning and several physiological responses (i.e., weight, fasting plasma glucose and insulin, and glucose tolerance) were monitored at regular time intervals. Plasma was collected at regular intervals for proteomic analysis by two-dimensional gel electrophoresis and subsequent mass spectrometry. RESULTS: Onset of hyperinsulinemia with corresponding glucose intolerance was observed in 2 weeks and fasting blood glucose levels rose significantly after 4 weeks on the high-fat diet. Many proteins were found to exist in multiple forms (isoforms). Levels of some isoforms including plasma retinol binding protein, transthyretin, Apolipoprotein A1, and kininogen showed significant changes as early as 4 weeks which coincided with the very early development of glucose intolerance. CONCLUSIONS: These results show that a proteomic approach to study the development of type 2 diabetes may uncover unknown early post-translationally modified diagnostic and/or therapeutic protein targets.

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.

Ocular proteomics with emphasis on two-dimensional gel electrophoresis and mass spectrometry

The intention of this review is to provide an overview of current methodologies employed in the rapidly developing field of ocular proteomics with emphasis on sample preparation, two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and mass spectrometry (MS). Appropriate sample preparation for the diverse range of cells and tissues of the eye is essential to ensure reliable results. Current methods of protein staining for 2D-PAGE, protein labelling for two-dimensional difference gel electrophoresis, gel-based expression analysis and protein identification by MS are summarised. The uses of gel-free MS-based strategies (MuDPIT, iTRAQ, ICAT and SILAC) are also discussed. Proteomic technologies promise to shed new light onto ocular disease processes that could lead to the discovery of strong novel biomarkers and therapeutic targets useful in many ophthalmic conditions.

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.

Proteomic analysis of striatal neuronal cell cultures after morphine administration

Using primary neuronal cell culture assays, combined with 2-D gel electrophoresis and capillary LC-MS, we identified differences in proteomes between control and morphine-treated cells. Statistically significant differences were observed among 26 proteins. Nineteen of them were up-regulated, while seven were down-regulated in morphine-treated cell populations. The identified proteins belong to classes involved in energy metabolism, associated with oxidative stress, linked with protein biosynthesis, cytoskeletal ones, and chaperones. The detected proteins demand further detailed studies of their biological roles in morphine addiction. It is crucial to confirm observed processes in vivo in order to reveal the nature and importance of the biological effect of proteome changes after morphine administration. Further investigations may lead to the discovery of new proteome-based effects of morphine on living organisms.

Two-dimensional gel electrophoresis-based proteomics of mycobacteria

Two-dimensional gel electrophoresis (2-DE) in combination with mass spectrometry (MS) is the classic proteomics approach used to monitor the dynamics of protein abundance and posttranslational modifications in biological systems. In this chapter, we provide detailed protocols for 2-DE-based proteomics of mycobacteria. Adequate standard operating procedures for mycobacterial culture, subcellular fractionation, and selective enrichment of proteins are indispensable prerequisites for targeted proteome analyses. Therefore, we also provide approved protocols for selective and efficient extraction of cytosolic, secreted, and hydrophobic plasma membrane proteins of mycobacteria, as well as for isolation of mycobacteria from infected macrophages.

The myth of automated, high-throughput two-dimensional gel analysis

Many software packages have been developed to process and analyze 2-D gel images. Some programs have been touted as automated, high-throughput solutions. We tested five commercially available programs using 18 replicate gels of a rat brain protein extract. We determined computer processing time, approximate spot editing time, time required to correct spot mismatches, as well as total processing time. We also determined the number of spots automatically detected, number of spots kept after manual editing, and the percentage of automatically generated correct matches. We also determined the effect of increasing the number of replicate gels on spot matching efficiency for two of the programs. We found that for all programs tested, less than 3% of the total processing time was automated. The remainder of the time was spent in manual, subjective editing of detected spots and computer generated matches. Total processing time for 18 gels varied from 22 to 84 h. The percentage of correct matches generated automatically varied from 1 to 62%. Increasing the number of gels in an experiment dramatically reduced the percentage of automatically generated correct matches. Our results demonstrate that these 2-D gel analysis programs are not automatic or rapid, and also suggest that matching accuracy decreases as experiment size increases.

An image analysis suite for spot detection and spot matching in two-dimensional electrophoresis gels

We propose a suite of novel algorithms for image analysis of protein expression images obtained from 2-D electrophoresis. These algorithms are a segmentation algorithm for protein spot identification, and an algorithm for matching protein spots from two corresponding images for differential expression study. The proposed segmentation algorithm employs the watershed transformation, k-means analysis, and distance transform to locate the centroids and to extract the regions of the proteins spots. The proposed spot matching algorithm is an integration of the hierarchical-based and optimization-based methods. The hierarchical method is first used to find corresponding pairs of protein spots satisfying the local cross-correlation and overlapping constraints. The matching energy function based on local structure similarity, image similarity, and spatial constraints is then formulated and optimized. Our new algorithm suite has been extensively tested on synthetic and actual 2-D gel images from various biological experiments, and in quantitative comparisons with ImageMaster2D Platinum the proposed algorithms exhibit better spot detection and spot matching.

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.

Neuroproteomics and its applications in research on nicotine and other drugs of abuse

The rapidly growing field of neuroproteomics is able to track changes in protein expression and protein modifications underlying various physiological conditions, including the neural diseases related to drug addiction. Thus, it presents great promise in characterizing protein function, biochemical pathways, and networks to understand the mechanisms underlying drug dependence. In this article, we first provide an overview of proteomics technologies and bioinformatics tools available to analyze proteomics data. Then we summarize the recent applications of proteomics to profile the protein expression pattern in animal or human brain tissues after the administration of nicotine, alcohol, amphetamine, butorphanol, cocaine, and morphine. By comparing the protein expression profiles in response to chronic nicotine exposure with those appearing in response to treatment with other drugs of abuse, we identified three biological processes that appears to be regulated by multiple drugs of abuse: energy metabolism, oxidative stress response, and protein degradation and modification. Such similarity indicates that despite the obvious differences among their chemical properties and the receptors with which they interact, different substances of abuse may cause some similar changes in cellular activities and biological processes in neurons.

Start-to-end processing of two-dimensional gel electrophoretic images

Gel electrophoresis serves as a basic analytical tool in the proteomic studies. However, processing of gel electrophoretic images is still the main bottleneck of data analysis, and there is an increasing need for the fully automated approaches. The proposed start-to-end strategy of analyzing the gel images consists of chemometric tools, which allow their effective preprocessing, automatic warping, and data modeling. The image preprocessing techniques: denoising in the wavelet domain and the penalized asymmetric least squares approach for the background estimation are proposed. Matching of images is based on fuzzy warping of features, extracted from the gel images. For the classification or calibration purpose, multivariate approaches such, as partial least squares (PLS) or kernel-PLS methods are used. Performance of the proposed strategy is demonstrated on the real set of the two-dimensional gel images.

Proteome map of Aspergillus nidulans during osmoadaptation

The model filamentous fungus Aspergillus nidulans, when grown in a moderate level of osmolyte (+0.6M KCl), was previously found to have a significantly reduced cell wall elasticity (Biotech Prog, 21:292, 2005). In this study, comparative proteomic analysis via two-dimensional gel electrophoresis (2de) and matrix-assisted laser desorption ionization/time-of-flight (MALDI-TOF) mass spectrometry was used to assess molecular level events associated with this phenomenon. Thirty of 90 differentially expressed proteins were identified. Sequence homology and conserved domains were used to assign probable function to twenty-one proteins currently annotated as "hypothetical." In osmoadapted cells, there was an increased expression of glyceraldehyde-3-phosphate dehydrogenase and aldehyde dehydrogenase, as well as a decreased expression of enolase, suggesting an increased glycerol biosynthesis and decreased use of the TCA cycle. There also was an increased expression of heat shock proteins and Shp1-like protein degradation protein, implicating increased protein turnover. Five novel osmoadaptation proteins of unknown functions were also identified.

The pitfalls of proteomics experiments without the correct use of bioinformatics tools

The elucidation of the entire genomic sequence of various organisms, from viruses to complex metazoans, most recently man, is undoubtedly the greatest triumph of molecular biology since the discovery of the DNA double helix. Over the past two decades, the focus of molecular biology has gradually moved from genomes to proteomes, the intention being to discover the functions of the genes themselves. The postgenomic era stimulated the development of new techniques (e.g. 2-DE and MS) and bioinformatics tools to identify the functions, reactions, interactions and location of the gene products in tissues and/or cells of living organisms. Both 2-DE and MS have been very successfully employed to identify proteins involved in biological phenomena (e.g. immunity, cancer, host-parasite interactions, etc.), although recently, several papers have emphasised the pitfalls of 2-DE experiments, especially in relation to experimental design, poor statistical treatment and the high rate of 'false positive' results with regard to protein identification. In the light of these perceived problems, we review the advantages and misuses of bioinformatics tools - from realisation of 2-DE gels to the identification of candidate protein spots - and suggest some useful avenues to improve the quality of 2-DE experiments. In addition, we present key steps which, in our view, need to be to taken into consideration during such analyses. Lastly, we present novel biological entities named 'interactomes', and the bioinformatics tools developed to analyse the large protein-protein interaction networks they form, along with several new perspectives of the field.

Proteome informatics I: Bioinformatics tools for processing experimental data

Bioinformatics tools for proteomics, also called proteome informatics tools, span today a large panel of very diverse applications ranging from simple tools to compare protein amino acid compositions to sophisticated software for large-scale protein structure determination. This review considers the available and ready to use tools that can help end-users to interpret, validate and generate biological information from their experimental data. It concentrates on bioinformatics tools for 2-DE analysis, for LC followed by MS analysis, for protein identification by PMF, by peptide fragment fingerprinting and by de novo sequencing and for data quantitation with MS data. It also discloses initiatives that propose to automate the processes of MS analysis and enhance the quality of the obtained results.

Predominant expression of diploid mandarin leaf proteome in two citrus mandarin-derived somatic allotetraploid hybrids

Fusion of citrus diploid parental protoplasts generates allotetraploid hybrids which do not retain their parental traits with regard to leaf aroma compound biosynthesis. The aim of this study was thus to examine hybrid leaf proteomes in comparison with their parents. Leaf soluble proteins from two citrus allotetraploid hybrids (mandarin + lime and mandarin + kumquat) and their diploid parents (mandarin, lime, and kumquat) were submitted to 2-D gel electrophoresis. Leaf proteome maps of the tetraploid hybrids were compared with those of their parents on the basis of the presence/absence of spots and of their spot relative volumes. The two allotetraploid hybrid maps were found closer to that of their mandarin parent than to those of their nonmandarin parents in terms of the presence/absence of spots as well as at a quantitative level. This approach has to be related to the already observed dominance of mandarin in allotetraploids with regard to volatile compound biosynthesis in leaves.

Proteomics, Part II: The Emerging Role of Proteomics Over Genomics in Spontaneous Preterm Labor/Birth

Conventional wisdom holds that complications of immature organ systems such as respiratory distress syndrome, intraventricular hemorrhage, necrotizing enterocolitis, and bronchopulmonary dysplasia are the primary causes of the high neonatal morbidity and mortality attendant preterm delivery. However, recent evidence suggests that a major cause of prematurity-associated neonatal pathology is the fetal and neonatal response to inflammation/infection. Although functional genomics offered the promise of providing answers to many of these questions, the identification of the genes intrinsic to human parturition proved to be a difficult task. Proteomic profiling of the amniotic fluid (AF) provides a precise means for detection of inflammation by revealing the presence of 4 biomarkers (defensins-2 and -1, calgranulin-C, and calgranulin-A) that are highly predictive of intrauterine inflammation (MR score). The MR score is especially useful as it presents a gradient of disease activity progressing from "absent" to "mild" to "severe" inflammation. Thus, it provides the ability to identify patients who may benefit from interventions in utero in a modern diagnostic-therapeutic framework.

TARGET AUDIENCE

Obstetricians & Gynecologists, Family Physicians.

LEARNING OBJECTIVES

After completion of this article, the reader should be able to explain that the cause or causes of preterm delivery are still unknown, recall that functional genomics has not given the answer to these causes, and state that proteomic profiling of amniotic fluid, through mass-restricted (MR) scoring, may be predictive of intrauterine inflammation and allow for potential diagnosis and potential therapy.

Dependence on saturation of average minimum resolution in two-dimensional statistical-overlap theory: peak overlap in saturated two-dimensional separations

A theory is proposed for the dependence on saturation of the average minimum resolution R(*) in point-process statistical-overlap theory for two-dimensional separations. Peak maxima are modelled by clusters of overlapping circles in hexagonal arrangements similar to close-packed layers. Such clusters exist only for specific circle numbers, but equations are derived that facilitate prediction of equivalent cluster properties for any number of circles. A metric is proposed for the average minimum resolution that separates two such clusters into two maxima. From this metric, the average minimum resolution of the two nearest-neighbor single-component peaks (SCPs)--one in each cluster--is calculated. Its value varies with the number of SCPs in both clusters. These resolutions are weighted by the probability that the two clusters contain the postulated numbers of SCPs and summed to give R(*), which decreases with increasing saturation. The dependence of R(*) on saturation is combined with a theory correcting the probability of overlap in a reduced square for boundary effects. The numbers of maxima in simulations of 75, 150, and 300 randomly distributed bi-Gaussians having exponential heights and aspect ratios of 1, 30, and 60 are compared to predictions. Excellent agreement between maxima numbers and theory is found at low and high saturation. Good estimates of the numbers of bi-Gaussians in simulations are calculated by fitting theory to numbers of maxima using least-squares regression. The theory is applied to mimicked GC x GCs of 93 compounds having many correlated retention times, with predictions that agree fairly well with maxima numbers.

Proteomics: Recent Applications and New Technologies

Interest in proteomics as a tool for drug development and a myriad of other applications continues to expand at a rapid rate. Proteomic analyses have recently been conducted on tissues, biofluids, subcellular components and enzymatic pathways as well as various disease and toxicological states, in both animal models and man. In addition, several recent studies have attempted to integrate proteomics data with genomics and/or metabonomics data in a systems biology approach. The translation of proteomic technology and bioinformatics tools to clinical samples, such as in the areas of disease and toxicity biomarkers, represents one of the major opportunities and challenges facing this field. An ongoing challenge in proteomics continues to be the analysis of the serum proteome due to the vast number and complexity of proteins estimated to be present in this biofluid. Aside from the removal of the most abundant proteins, a number of interesting approaches have recently been suggested that may help reduce the overall complexity of serum analysis. In keeping with the increasing interest in applications of proteomics, the tools available for proteomic analyses continue to improve and expand. For example, enhanced tools (such as software and labeling procedures) continue to be developed for the analysis of 2D gels and protein quantification. In addition, activity-based probes are now being used to tag, enrich and isolate distinct sets of proteins based on enzymatic activity. One of the most active areas of development involves microarrays. Antibody-based microarrays have recently been released as commercial products while numerous additional capture agents (e.g. aptamers) and many additional types of microarrays are being explored.

Contour area filtering of two-dimensional electrophoresis images

We describe an algorithm, Contour Area Filtering, for separating background from foreground in gray scale images. The algorithm is based on the area contained within gray scale contour lines. It can be viewed as a form of local thresholding, or as a seed growing algorithm, or as a type of watershed segmentation. The most important feature of the algorithm is that it uses object area to determine the segmentation. Thus, it is relatively impervious to brightness and contrast variations across an image or between different images. Contour Area Filtering was designed specifically for image analysis of 2D electrophoresis gels, although it can be applied to other gray scale images. A typical gel image is an electrophoretogram or a phosphor image of 1000-2500 spots representing protein or DNA restriction fragments. The images are quantitative with spot intensities reflective of the number of proteins or the DNA fragment copy number. The background intensity can vary widely across the image caused both by variation in spot density and by the physical laboratory process of creating a gel. Analyzing and comparing gel images entails extracting and segmenting spots, registering images and matching spots, and measuring differences between spots. We present experimental results which show that Contour Area Filtering is a quick, efficient method for separating electrophoresis gel background from foreground with extremely high accuracy.

New Data Analysis and Mining Approaches Identify Unique Proteome and Transcriptome Markers of Susceptibility to Autoimmune Diabetes

Non-obese diabetic (NOD) mice spontaneously develop autoimmunity to the insulin producing beta cells leading to insulin-dependent diabetes. In this study we developed and used new data analysis and mining approaches on combined proteome and transcriptome (molecular phenotype) data to define pathways affected by abnormalities in peripheral leukocytes of young NOD female mice. Cells were collected before mice show signs of autoimmunity (age, 2-4 weeks). We extracted both protein and RNA from NOD and C57BL/6 control mice to conduct both proteome analysis by two-dimensional gel electrophoresis and transcriptome analysis on Affymetrix expression arrays. We developed a new approach to analyze the two-dimensional gel proteome data that included two-way analysis of variance, cluster analysis, and principal component analysis. Lists of differentially expressed proteins and transcripts were subjected to pathway analysis using a commercial service. From the list of 24 proteins differentially expressed between strains we identified two highly significant and interconnected networks centered around oncogenes (Myc and Mycn) and apoptosis-related genes (Bcl2 and Casp3). The 273 genes with significant strain differences in RNA expression levels created six interconnected networks with a significant over-representation of genes related to cancer, cell cycle, and cell death. They contained many of the same genes found in the proteome networks (including Myc and Mycn). The combination of the eight, highly significant networks created one large network of 272 genes of which 82 had differential expression between strains either at the protein or the RNA level. We conclude that new proteome data analysis strategies and combined information from proteome and transcriptome can enhance the insights gained from either type of data alone. The overall systems biology of prediabetic NOD mice points toward abnormalities in regulation of the opposing processes of cell renewal and cell death even before there are any clear signatures of immune system activation.

Comparative evaluation of two two-dimensional gel electrophoresis image analysis software applications using synovial fluids from patients with joint disease

The proteomic composition of synovial fluid (SF) may hold clues to understanding the molecular basis of arthritis. However, the highly viscous nature and proteomic complexity of SF present a challenge when analyzing results obtained by two-dimensional gel electrophoresis (2D-GE). Several software applications are available for analyzing 2D-GE images. Despite inherent strengths and weaknesses, no comparison between these applications has been reported using SF or any human fluid specimens. We evaluated two common software packages--PDQuest and Progenesis Workstation--for spot detection, matching, and quantitation of 2D-GE images of SF from four patients with arthritic disease. Initially, whole 2D-gel images were analyzed for spot detection, which suggested that PDQuest is more consistent than Progenesis; however, PDQuest appeared to require more user intervention than Progenesis. Subsequently, two small areas (spots well resolved and spots not well resolved) were selected from each gel image, which were analyzed by the software for spot detection, matching, volume, and resolution. These analyses suggest that both tools can quantify well-resolved spots relatively consistently when compared with manual spot detection (the "gold standard"). The "3D viewer" option offered by both tools enables correct spot identification and matching. The strengths and weaknesses of these computer tools can provide guidance in the choice of a particular workstation for identifying biomarkers of arthritis.

Software-induced variance in two-dimensional gel electrophoresis image analysis

Experimental variability in 2-DE is well documented, but little attention has been paid to variability arising from postexperimental quantitative analyses using various 2-DE software packages. The performance of two 2-DE analysis software programs, Phoretix 2D Expression v2004 (Expression) and PDQuest 7.2 (PDQuest), was evaluated in this study. All available background subtraction and smoothing algorithms were tested using both data generated from one single 2-DE gel image, thus excluding experimental variance, and with authentic sets of replicate gels (n = 5). A slight shift of the image boundaries (the "cropping area") caused both programs to induce variance in protein spot quantification of otherwise identical gel images. The resulting variance for PDQuest (CV(mean) = 8%) was approximately twice that for Expression (CV(mean) = 4%). In authentic sets of replicate 2-DE gels (n = 5), the experimental variance confounded the software-induced variance to some extent. However, Expression still outperformed PDQuest, which exhibited software-induced variance as high as 25% of the total observed variance. Surprisingly, the complete omission of background subtraction algorithms resulted in the least amount of software-based variance. These data indicate that 2-DE gel analysis software constitutes a significant source of the variance observed in quantitative proteomics, and that the use of background subtraction algorithms can further increase the variance.

“Plasmo2D”: An Ancillary Proteomic Tool to Aid Identification of Proteins from Plasmodium falciparum

Bioinformatics tools to aid gene and protein sequence analysis have become an integral part of biology in the post-genomic era. Release of the Plasmodium falciparum genome sequence has allowed biologists to define the gene and the predicted protein content as well as their sequences in the parasite. Using pI and molecular weight as characteristics unique to each protein, we have developed a bioinformatics tool to aid identification of proteins from Plasmodium falciparum. The tool makes use of a Virtual 2-DE generated by plotting all of the proteins from the Plasmodium database on a pI versus molecular weight scale. Proteins are identified by comparing the position of migration of desired protein spots from an experimental 2-DE and that on a virtual 2-DE. The procedure has been automated in the form of user-friendly software called "Plasmo2D". The tool can be downloaded from http://144.16.89.25/Plasmo2D.zip.

Optimal replication and the importance of experimental design for gel-based quantitative proteomics

Quantitative proteomic studies, based on two-dimensional gel electrophoresis, are commonly used to find proteins that are differentially expressed between samples or groups of samples. These proteins are of interest as potential diagnostic or prognostic biomarkers, or as proteins associated with a trait. The complexity of proteomic data poses many challenges, so while experiments may reveal proteins that are differentially expressed, these are often not significant when subjected to rigorous statistical analysis. However, this can be addressed through appropriate experimental design. A good experimental design considers the impact of different sources of variation, both analytical and biological, on the statistical importance of the results. The design should address the number of samples that must be analyzed and the number of replicate gels per sample, in the context of a particular minimum difference that one is seeking to achieve. In this study, we explore the ways to improve the quality of protein expression data from 2-DE gels, and describe an approach for defining the number of samples required and the number of gels per sample. It has been developed for the simplest of situations, two groups of samples with variation at two levels: between samples and between gels. This approach will also be useful as a guide for more complex designs involving more than two groups of samples. We describe some Internet-accessible tools that can assist in the design of proteomic studies.