New algorithmic approaches to protein spot detection and pattern matching in two-dimensional electrophoresis gel databases

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.

Spot Matching of 2-DE Images Using Distance, Intensity, and Pattern Information

The analysis of a large number of two-dimensional gel electrophoresis (2-DE) images requires developing automatic methods. In such analyses, spot matching plays a fundamental role, in particular for the identification of proteins. We describe a simple and accurate method which allows to automatically and accurately match spots in 2-DE images. The method consists of simultaneously exploiting the distance between the spots, their intensity, and the pattern formed by their spatial configuration.

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.

Mass spectrometry-coupled techniques for viral-related disease biomarker identification

The advent of high-resolution mass spectrometers coupled with proteomic techniques has facilitated the discovery and characterization of novel viral proteins and the detection of virus-induced changes in the cellular proteome. These advances have enabled a more comprehensive characterization of viral interactions involved in infection and pathogenesis, and allowed the discovery of viral biomarkers. This article focuses on the role of mass spectrometry proteomic techniques to identify and characterize both prospective and verified viral biomarkers, and their implications on the diagnosis of disease.

Proteomic analysis of stromal cells derived from the dental pulp of human exfoliated deciduous teeth

Human dental pulp derived from exfoliated deciduous teeth has been described as a promising alternative source of multipotent stem cells. While these cells share certain similarities with mesenchymal stem-like cells (MSC) isolated from other tissues, basically they are still poorly characterized. In this study, for the first time, a proteomic map of abundantly expressed proteins in stromal cells derived from the dental pulp of human exfoliated deciduous teeth (SHED) was established. We also analyzed proteomic signatures of 2 clonal strains derived from SHEDs by single-cell cloning. The SHEDs were established from enzyme-disaggregated deciduous dental pulp from 6-year-old children. They had typical fibroblastoid morphology and high colony-forming efficiency index (16.4%). Cloning was performed at the second passage using limiting dilution in a 96-well plate (0.3 cell/well). Differentiation assessment revealed strong osteogenic but no adipogenic potential of the SHEDs in either clonal strain. The cells expressed characteristic antigens of MSC-like cells, including CD73, CD90, CD105, CD146, and did not express hematopoietic markers CD14, CD34, and CD45, as assessed with FACS analysis. For proteomic studies, cytosolic and nuclear proteins were analyzed with 2-dimensional gel electrophoresis (2-DE) and identified using matrix-assisted laser desorption/ionization (MALDI)-time of fl ight (TOF)-mass spectrometry (MS). All proteins were identified with high level of confidence (the lowest sequence coverage was 27%). Identification of highly expressed proteins in SHEDs revealed proteomic profiles very similar to that of MSC-like cells derived from other tissues. We also found a high degree of similarity between proteomic signatures of primary SHEDs and clonal cell strains. Thus, our data confirm a close resemblance between SHEDs and MSC-like cells from other tissues and may serve as starting point for creating-comprehensive proteomic maps.

A meta-analysis of two-dimensional electrophoresis pattern of the Parkinson's disease-related protein DJ-1

MOTIVATION

The two-dimensional electrophoresis (2-DE) pattern of proteins is thought to be specifically related to the physiological or pathological condition at the moment of sample preparation. On this ground, most proteomic studies move to identify specific hallmarks for a number of different conditions. However, the information arising from these investigations is often incomplete due to inherent limitations of the technique, to extensive protein post-translational modifications and sometimes to the paucity of available samples. The meta-analysis of proteomic data can provide valuable information pertinent to various biological processes that otherwise remains hidden.

RESULTS

Here, we show a meta-analysis of the PD protein DJ-1 in heterogeneous 2-DE experiments. The protein was shown to segregate into specific clusters associated with defined conditions. Interestingly, the DJ-1 pool from neural tissues displayed a specific and characteristic molecular weight and isoelectric point pattern. Moreover, changes in this pattern have been related to neurodegenerative processes and aging. These results were experimentally validated on human brain specimens from control subjects and PD patients.

AVAILABILITY

ImageJ is a public domain image processing program developed by the National Institutes of Health and is freely available at http://rsbweb.nih.gov/ij. All the ImageJ macros used in this study are available as supplementary material and upon request at info@biodigitalvalley.com. XLSTAT can be purchased online at http://www.xlstat.com/en/home/ at a current cost of approximately 300 EUR.

Computational methods for analysis of two-dimensional gels

Two-dimensional gel electrophoresis (2D gels) is an essential quantitative proteomics technique that is frequently used to study differences between samples of clinical relevance. Although considered to have a low throughput, 2D gels can separate thousands of proteins in one gel, making it a good complementary method to MS-based protein quantification. The main drawback of the technique is the tendency of large and hydrophobic proteins such as membrane proteins to precipitate in the isoelectric focusing step. Furthermore, tests using different programs with distinct algorithms for 2D-gel analysis have shown inconsistent ratio values. The aim here is therefore to provide a discussion of algorithms described for the analysis of 2D gels.

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.

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.

Alignment of two-dimensional electrophoresis gels

Two-dimensional electrophoresis is a major separating technique for proteins in proteomics. Alignment of gel images is critical for intra-laboratory or even more difficult inter-laboratory gel comparisons. In the paper, we propose a novel iterative closest point (ICP) method for 2D-gel electrophoresis image alignment. The paper seeks to introduce an information theoretic measure as one part of distance metric to gel image alignment. We combine intensity information of spots with geometric information of landmarks by applying information potential idea. The proposed method has been applied to both synthetic and real gel images accessible in public 2D-electrophoresis gel protein databases. The high accuracy and robustness of the algorithm indicate that it is promising for gel image alignment.

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.

Analysing proteomic data

The rapid growth of proteomics has been made possible by the development of reproducible 2D gels and biological mass spectrometry. However, despite technical improvements 2D gels are still less than perfectly reproducible and gels have to be aligned so spots for identical proteins appear in the same place. Gels can be warped by a variety of techniques to make them concordant. When gels are manipulated to improve registration, information is lost, so direct methods for gel registration which make use of all available data for spot matching are preferable to indirect ones. In order to identify proteins from gel spots a property or combination of properties that are unique to that protein are required. These can then be used to search databases for possible matches. Molecular mass, pI, amino acid composition and short sequence tags can all be used in database searches. Currently the method of choice for protein identification is mass spectrometry. Proteins are eluted from the gels and cleaved with specific endoproteases to produce a series of peptides of different molecular mass. In peptide mass fingerprinting, the peptide profile of the unknown protein is compared with theoretical peptide libraries generated from sequences in the different databases. Tandem mass spectroscopy (MS/MS) generates short amino acid sequence tags for the individual peptides. These partial sequences combined with the original peptide masses are then used for database searching, greatly improving specificity. Increasingly protein identification from MS/MS data is being fully or partially automated. When working with organisms, which do not have sequenced genomes (the case with most helminths), protein identification by database searching becomes problematical. A number of approaches to cross species protein identification have been suggested, but if the organism being studied is only distantly related to any organism with a sequenced genome then the likelihood of protein identification remains small. The dynamic nature of the proteome means that there really is no such thing as a single representative proteome and a complete set of metadata (data about the data) is going to be required if the full potential of database mining is to be realised in the future.

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.

Multivariate approaches in plant science

The objective of proteomics is to get an overview of the proteins expressed at a given point in time in a given tissue and to identify the connection to the biochemical status of that tissue. Therefore sample throughput and analysis time are important issues in proteomics. The concept of proteomics is to encircle the identity of proteins of interest. However, the overall relation between proteins must also be explained. Classical proteomics consist of separation and characterization, based on two-dimensional electrophoresis, trypsin digestion, mass spectrometry and database searching. Characterization includes labor intensive work in order to manage, handle and analyze data. The field of classical proteomics should therefore be extended to also include handling of large datasets in an objective way. The separation obtained by two-dimensional electrophoresis and mass spectrometry gives rise to huge amount of data. We present a multivariate approach to the handling of data in proteomics with the advantage that protein patterns can be spotted at an early stage and consequently the proteins selected for sequencing can be selected intelligently. These methods can also be applied to other data generating protein analysis methods like mass spectrometry and near infrared spectroscopy and examples of application to these techniques are also presented. Multivariate data analysis can unravel complicated data structures and may thereby relieve the characterization phase in classical proteomics. Traditionally statistical methods are not suitable for analysis of the huge amounts of data, where the number of variables exceed the number of objects. Multivariate data analysis, on the other hand, may uncover the hidden structures present in these data. This study takes its starting point in the field of classical proteomics and shows how multivariate data analysis can lead to faster ways of finding interesting proteins. Multivariate analysis has shown interesting results as a supplement to classical proteomics and added a new dimension to the field of proteomics.

Hierarchical, model-based merging of multiple fragments for improved three-dimensional segmentation of nuclei

BACKGROUND

Automated segmentation of fluorescently labeled cell nuclei in three-dimensional confocal images is essential for numerous studies, e.g., spatiotemporal fluorescence in situ hybridization quantification of immediate early gene transcription. High accuracy and automation levels are required in high-throughput and large-scale studies. Common sources of segmentation error include tight clustering and fragmentation of nuclei. Previous region-based methods are limited because they perform merging of two nuclear fragments at a time. To achieve higher accuracy without sacrificing scale, more sophisticated yet computationally efficient algorithms are needed.

METHODS

A recursive tree-based algorithm that can consider multiple object fragments simultaneously is described. Starting with oversegmented data, it searches efficiently for the optimal merging pattern guided by a quantitative scoring criterion based on object modeling. Computation is bounded by limiting the depth of the merging tree.

RESULTS

The proposed method was found to perform consistently better, achieving merging accuracy in the range of 92% to 100% compared with our previous algorithm, which varied in the range of 75% to 97%, even with a modest merging tree depth of 3. The overall average accuracy improved from 90% to 96%, with roughly the same computational cost for a set of representative images drawn from the CA1, CA3, and parietal cortex regions of the rat hippocampus.

CONCLUSION

Hierarchical tree model-based algorithms significantly improve the accuracy of automated nuclear segmentation without sacrificing speed.

Geometric algorithms for the analysis of 2D-electrophoresis gels

In proteomics, two-dimensional gel electrophoresis (2-DE) is a separation technique for proteins. The resulting protein spots can be identified either by using picking robots and subsequent mass spectrometry or by visual cross inspection of a new gel image with an already analyzed master gel. Difficulties especially arise from inherent noise and irregular geometric distortions in 2-DE images. Aiming at the automated analysis of large series of 2-DE images, or at the even more difficult interlaboratory gel comparisons, the bottleneck is to solve the two most basic algorithmic problems with high quality: Identifying protein spots and computing a matching between two images. For the development of the analysis software CAROl at Freie Universität Berlin, we have reconsidered these two problems and obtained new solutions which rely on methods from computational geometry. Their novelties are: 1. Spot detection is also possible for complex regions formed by several "merged" (usually saturated) spots; 2. User-defined landmarks are not necessary for the matching. Furthermore, images for comparison are allowed to represent different parts of the entire protein pattern, which only partially "overlap." The implementation is done in a client server architecture to allow queries via the internet. We also discuss and point at related theoretical questions in computational geometry.

Human proteomic databases: a powerful resource for functional genomics in health and disease

Decoding of the genome information in terms of regulation and function will be the next great challenge in the life sciences in this millennium and indeed, today we are experiencing a rapid explosion of technology for the high throughput expression analysis of genes and their products (functional genomics). In particular, the field of proteomics is booming as proteins are often the functional molecules and represent important targets for the pharmaceutical industry. The proteomic technology is complex, and comprises a plethora of state-of-the-art techniques to resolve, identify and detect their interacting partners, as well as to store and communicate protein information in comprehensive two-dimensional polyacrylamide gel electrophoresis (2D PAGE) databases. Besides annotating the genome, these databases will offer a global approach to the study of gene expression both in health and disease. Here, we review the current status of human 2D PAGE databases that we are systematically constructing for the study of bladder cancer and skin ageing.

Physical markers for landmarking fluorescently stained gels that facilitate automated spot-picking

The quantitative comparison of spot patterns relies heavily on protein stains that do provide an appropriate dynamic range. Unfortunately most spot picking robot devices are still limited to nonfluorescent protein stains and the appropriate equipment is still quite expensive. These problems are solved by the application of a newly developed "GelMarker" that combines a spot picking robot device and a UV scanner. The "GelMarkers" are detectable in both the visible and UV range of light and permit the comparison of gel pictures taken by such different devices. The application of these "GelMarkers" together with the transformation of spot coordinates by using a "spot matching" procedure allows the automated excision of selected protein spots. The obtained picking accuracies are as good as those obtainable from visible stained gels due to the shape stability of the gels even over a longer time period.

Automatic registration for images of two-dimensional protein gels

Two-dimensional polyacrylamide gel electrophoresis 1 (2-D PAGE 1) is currently the method of choice for separating complex mixtures of cellular proteins. Despite its usefulness, 2-D PAGE is not being applied to its full potential because of difficulties with both the method and analysis of the results. One of the key problems is the difficulty and slowness of image analysis, especially registration (image alignment), which is laborious and the results unsatisfactory. We have developed a novel system for analysis of 2-D PAGE images, called Z3, that performs the analysis faster and more precisely. The Z3 system employs novel approaches to image registration, image display, computation of differential expression, and the design and analysis of 2-D gel experiments. This paper describes in detail the registration algorithm, and briefly discusses the merits of complementary pseudocolor display. The registration algorithm is novel in that for the first time raw-image-based registration technology is applied to 2-D gel analysis.

2D or not 2D. Two-dimensional gel electrophoresis

2D gel electrophoresis is the technology that everyone loves to hate-it requires manual dexterity and precision to reproduce precisely and is thus not well-suited as a high-throughput technology. Although almost everyone would like to replace it, the resolution and sensitivity it offers are exquisite and unsurpassed if one wants a global view of cellular activity. There have been several recent developments, for example, the detection of low abundance proteins, and the resolution possible with narrow-range IPG gels.

An alternative approach to deal with geometric uncertainties in computer analysis of two-dimensional electrophoresis gels

With the growing importance of proteomics in biomedical and pharmaceutical sciences a need has emerged for computing tools that are capable of digitally visualizing and analyzing protein spot patterns within two-dimensional electrophoresis (2-DE) gel. Matching programs need to meet requirements such as interlaboratory comparison and the comparison of samples from different origins. For such research purposes, we have developed the CAROL system that implements new algorithms for spot detection and matching, which enable researchers to take a different approach to protein spot identification and comparison. The present short communication discusses how the system deals with uncertain geometric spot information that arises from streaks and complex spot regions and how this can be amplified for the matching procedure.

Exploratory data analysis groupware for qualitative and quantitative electrophoretic gel analysis over the Internet-WebGel

Many scientists use quantitative measurements to compare the presence and amount, of various proteins and nucleotides among series of one- and two-dimensional (1-D and 2-D) electrophoretic gels. These gels are often scanned into digital image files. Gel spots are then quantified using stand-alone analysis software. However, as more research collaborations take place over the Internet, it has become useful to share intermediate quantitative data between researchers. This allows research group members to investigate their data and share their work in progress. We developed a World Wide Web group-accessible software system, WebGel, for interactively exploring qualitative and quantitative differences between electrophoretic gels. Such Internet databases are useful for publishing quantitative data and allow other researchers to explore the data with respect to their own research. Because intermediate results of one user may be shared with their collaborators using WebGel, this form of active data-sharing constitutes a groupware method for enhancing collaborative research. Quantitative and image gel data from a stand-alone gel image processing system are copied to a database accessible on the WebGel Web server. These data are then available for analysis by the WebGel database program residing on that server. Visualization is critical for better understanding of the data. WebGel helps organize labeled gel images into montages of corresponding spots as seen in these different gels. Various views of multiple gel images, including sets of spots, normalization spots, labeled spots, segmented gels, etc. may also be displayed. These displays are active and may be used for performing database operations directly on individual protein spots by simply clicking on them. Corresponding regions between sets of gels may be visually analyzed using Flicker-comparison (Electrophoresis 1997, 18, 122-140) as one of the WebGel methods for qualitative analysis. Quantitative exploratory data analysis can be performed by comparing protein concentration values between corresponding spots for multiple samples run in separate gels. These data are then used to generate reports on statistical differences between sets of gels (e.g., between different disease states such as benign or metastatic cancers, etc.). Using combined visual and quantitative methods, WebGel can help bridge the analysis of dissimilar gels which are difficult to analyze with stand-alone systems and can serve as a collaborative Internet tool in a groupware setting.