Statistical models of shape for the analysis of protein spots in two-dimensional electrophoresis gel images

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.

Mining biomedical images towards valuable information retrieval in biomedical and life sciences

Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries.

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.

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.

Mining images in biomedical publications: Detection and analysis of gel diagrams

Authors of biomedical publications use gel images to report experimental results such as protein-protein interactions or protein expressions under different conditions. Gel images offer a concise way to communicate such findings, not all of which need to be explicitly discussed in the article text. This fact together with the abundance of gel images and their shared common patterns makes them prime candidates for automated image mining and parsing. We introduce an approach for the detection of gel images, and present a workflow to analyze them. We are able to detect gel segments and panels at high accuracy, and present preliminary results for the identification of gene names in these images. While we cannot provide a complete solution at this point, we present evidence that this kind of image mining is feasible.

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.

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.

Image analysis tools and emerging algorithms for expression proteomics

Since their origins in academic endeavours in the 1970s, computational analysis tools have matured into a number of established commercial packages that underpin research in expression proteomics. In this paper we describe the image analysis pipeline for the established 2-DE technique of protein separation, and by first covering signal analysis for MS, we also explain the current image analysis workflow for the emerging high-throughput 'shotgun' proteomics platform of LC coupled to MS (LC/MS). The bioinformatics challenges for both methods are illustrated and compared, whereas existing commercial and academic packages and their workflows are described from both a user's and a technical perspective. Attention is given to the importance of sound statistical treatment of the resultant quantifications in the search for differential expression. Despite wide availability of proteomics software, a number of challenges have yet to be overcome regarding algorithm accuracy, objectivity and automation, generally due to deterministic spot-centric approaches that discard information early in the pipeline, propagating errors. We review recent advances in signal and image analysis algorithms in 2-DE, MS, LC/MS and Imaging MS. Particular attention is given to wavelet techniques, automated image-based alignment and differential analysis in 2-DE, Bayesian peak mixture models, and functional mixed modelling in MS, and group-wise consensus alignment methods for LC/MS.

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.

Improved dynamic range of protein quantification in silver-stained gels by modelling gel images over time

Silver staining is a commonly used protein stain to visualise proteins separated by 2-DE. Despite this, the technique suffers from a limited dynamic range, making the simultaneous quantification of high- and low-abundant proteins difficult. In this paper we take advantage of the fact that silver staining is not an end-point stain by photographing the gels during development. This procedure provides information about the change in measured absorbance for each pixel in the protein spots on the gel. The maximum rate of change was found to be correlated with the amount of applied protein, providing a new way of estimating protein amount in 2-DE gels. We observed an improvement in the dynamic range of silver staining by up to two orders of magnitude.

Local pixel value collection algorithm for spot segmentation in two-dimensional gel electrophoresis research

Two-dimensional gel-electrophoresis (2-DE) images show the expression levels of several hundreds of proteins where each protein is represented as a blob-shaped spot of grey level values. The spot detection, that is, the segmentation process has to be efficient as it is the first step in the gel processing. Such extraction of information is a very complex task. In this paper, we propose a novel spot detector that is basically a morphology-based method with the use of a seeded region growing as a central paradigm and which relies on the spot correlation information. The method is tested on our synthetic as well as on real gels with human samples from SWISS-2DPAGE (two-dimensional polyacrylamide gel electrophoresis) database. A comparison of results is done with a method called pixel value collection (PVC). Since our algorithm efficiently uses local spot information, segments the spot by collecting pixel values and its affinity with PVC, we named it local pixel value collection (LPVC). The results show that LPVC achieves similar segmentation results as PVC, but is much faster than PVC.

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.

Statistics for proteomics: Experimental design and 2-DE differential analysis

Proteomics relies on the separation of complex protein mixtures using bidimensional electrophoresis. This approach is largely used to detect the expression variations of proteins prepared from two or more samples. Recently, attention was drawn on the reliability of the results published in literature. Among the critical points identified were experimental design, differential analysis and the problem of missing data, all problems where statistics can be of help. Using examples and terms understandable by biologists, we describe how a collaboration between biologists and statisticians can improve reliability of results and confidence in conclusions.

Examination of 2-DE in the Human Proteome Organisation Brain Proteome Project pilot studies with the new RAIN gel matching technique

The Human Proteome Organisation (HUPO) Brain Proteome Project (BPP) pilot studies have generated over 200 2-D gels from eight participating laboratories. This data includes 67 single-channel and 60 DIGE gels comparing 30 whole frozen C57/BL6 female mouse brains, ten each at embryonic day 16, postnatal day 7 (juvenile) and postnatal day 54-56 (adult); and ten single-channel and three DIGE gels comparing human epilepsy surgery of the temporal front lobe with a corresponding post-mortem specimen. The samples were generated centrally and distributed to the participating laboratories, but otherwise no restrictions were placed on sample preparation, running and staining protocols, nor on the 2-D gel analysis packages used. Spots were characterised by MS and the annotated gel images published on a ProteinScape web server. In order to examine the resultant differential expression and protein identifications, we have reprocessed a large subset of the gels using the newly developed RAIN (Robust Automated Image Normalisation) 2-D gel matching algorithm. Traditional approaches use symbolic representation of spots at the very early stages of the analysis, which introduces persistent errors due to inaccuracies in spot modelling and matching. With RAIN, image intensity distributions, rather than selected features, are used, where smooth geometric deformation and expression bias are modelled using multi-resolution image registration and bias-field correction. The method includes a new approach of volume-invariant warping which ensures the volume of protein expression under transformation is preserved. An image-based statistical expression analysis phase is then proposed, where small insignificant expression changes over one gel pair can be revealed when reinforced by the same consistent changes in others. Results of the proposed method as applied to the HUPO BPP data show significant intra-laboratory improvements in matching accuracy over a previous state-of-the-art technique, Multi-resolution Image Registration (MIR), and the commercial Progenesis PG240 package.

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.

Quantitation in two-dimensional fluorescence difference gel electrophoresis: effect of protein fixation

Analyzing a large number of unfixed gels in a 2-D fluorescence difference gel electrophoresis (2-DIGE) experiment presents a challenge of avoiding variable protein diffusion within and across the comparison groups. The characteristics of protein detection and quantitation in a 2-D differential in gel fluorescence experiment were compared for gels with and without protein fixation. The current study tests and concludes that when dealing with a large sample size with variable protein diffusion across the 2-D gel over a period of 2-4 days, it is a preferred choice to fix the gel without affecting the protein quantitation.

ProteomeGRID: towards a high-throughput proteomics pipeline through opportunistic cluster image computing for two-dimensional gel electrophoresis

The quest for high-throughput proteomics has revealed a number of critical issues. Whilst improved two-dimensional gel electrophoresis (2-DE) sample preparation, staining and imaging issues are being actively pursued by industry, reliable high-throughput spot matching and quantification remains a significant bottleneck in the bioinformatics pipeline, thus restricting the flow of data to mass spectrometry through robotic spot excision and protein digestion. To this end, it is important to establish a full multi-site Grid infrastructure for the processing, archival, standardisation and retrieval of proteomic data and metadata. Particular emphasis needs to be placed on large-scale image mining and statistical cross-validation for reliable, fully automated differential expression analysis, and the development of a statistical 2-DE object model and ontology that underpins the emerging HUPO PSI GPS (Human Proteome Organization Proteomics Standards Initiative General Proteomics Standards). The first step towards this goal is to overcome the computational and communications burden entailed by the image analysis of 2-DE gels with Grid enabled cluster computing. This paper presents the proTurbo framework as part of the ProteomeGRID, which utilises Condor cluster management combined with CORBA communications and JPEG-LS lossless image compression for task farming. A novel probabilistic eager scheduler has been developed to minimise make-span, where tasks are duplicated in response to the likelihood of the Condor machines' owners evicting them. A 60 gel experiment was pair-wise image registered (3540 tasks) on a 40 machine Linux cluster. Real-world performance and network overhead was gauged, and Poisson distributed worker evictions were simulated. Our results show a 4:1 lossless and 9:1 near lossless image compression ratio and so network overhead did not affect other users. With 40 workers a 32x speed-up was seen (80% resource efficiency), and the eager scheduler reduced the impact of evictions by 58%.

Description of an ultrathin multiwire proportional chamber-based detector and application to the characterization of theSpraguea lophii(Microspora) two-dimensional genome fingerprint

Multiwire proportional chamber is a useful technology to build detectors that supersede the lack of interactivity of autoradiography in molecular biology experiments. Some drawbacks still limited the diffusion of existing instruments in biological laboratories. The major competitors are storage phosphor imaging systems. The simplified description of a radio-chromato-imager prototype (RCI) based on an original ultrathin multiwire proportional chamber is presented. It combines the advantage of the different existing technologies to present competitive properties in terms of efficiency, spatial resolution, robustness, manipulation easiness and production cost. Application of the RCI detector to molecular biology was performed by the analysis of karyotype and restriction display two-dimensional pulsed-field gel electrophoresis (KARD 2-D PFGE) data which are used to describe small eukaryotic genome structures. The comparative analysis with autoradiography was performed with the PDQuest software on Spraguea lophii (Microspora) genome fingerprints. The spot detection procedure applied to the different images leads to a similar conclusion considering the genome structure of S. lophii which appeared to be composed of 15 chromosomes for 13 karyotypic bands (200-880 kbp).