Geometrical distortions in two-dimensional gels: applicable correction methods

Two-dimensional electrophoresis (2-DE) provides a rapid means for separating thousands of proteins from cell and tissue samples in one run. Although this powerful research tool has been enthusiastically applied in many fields of biomedical research, accurate analysis and interpretation of the data have provided many challenges. Several analysis steps are needed to convert the large amount of noisy data obtained with 2-DE into reliable and interpretable biological information. The goals of such analysis steps include accurate protein detection and quantification, as well as the identification of differentially expressed proteins between samples run on different gels. To achieve these goals, systematic errors such as geometric distortions between the gels must be corrected by using computer-assisted methods. A wide range of computer software has been developed, but no general consensus exists as standard for 2-DE data analysis protocol. The choice of analysis approach is an important element depending both on the data and on the goals of the experiment. Therefore, basic understanding of the algorithms behind the software is required for optimal results. This review highlights some of the common themes in 2-DE data analysis, including protein spot detection and geometric image warping using both spot- and pixel-based approaches. Several computational strategies are overviewed and their relative merits and potential pitfalls discussed. Finally, we offer our own personal view of future trends and developments in large-scale proteome research.     

A novel experimental design for comparative two-dimensional gel analysis: two-dimensional difference gel electrophoresis incorporating a pooled internal standard

The comparison of two-dimensional (2-D) gel images from different samples is an established method used to study differences in protein expression. Conventional methods rely on comparing images from at least 2 different gels. Due to the high variation between gels, detection and quantification of protein differences can be problematic. Two-dimensional difference gel electrophoresis (Ettan trade mark DIGE) is an emerging technique for comparative proteomics, which improves the reproducibility and reliability of differential protein expression analysis between samples. In the application of DIGE different samples are labelled with mass and charge matched spectrally resolvable fluorescent dyes and are then separated on the same 2-D gel. Using an Escherichia coli lysate "spiked" with varying amounts of four different known proteins, we have tested a novel experimental design that exploits the sample multiplexing capabilities of DIGE, by including a standard sample in each gel. The standard sample comprises equal amounts of each sample to be compared and was found to improve the accuracy of protein quantification between samples from different gels allowing accurate detection of small differences in protein levels between samples.     

Comparison of PDQuest and Progenesis software packages in the analysis of two-dimensional electrophoresis gels

Efficient analysis of protein expression by using two-dimensional electrophoresis (2-DE) data relies on the use of automated image processing techniques. The overall success of this research depends critically on the accuracy and the reliability of the analysis software. In addition, the software has a profound effect on the interpretation of the results obtained, and the amount of user intervention demanded during the analysis. The choice of analysis software that best meets specific needs is therefore of interest to the research laboratory. In this paper we compare two advanced analysis software packages, PDQuest and Progenesis. Their evaluation is based on quantitative tests at three different levels of standard 2-DE analysis: spot detection, gel matching and spot quantitation. As test materials we use three gel sets previously used in a similar comparison of Z3 and Melanie, and three sets of gels from our own research. It was observed that the quality of the test gels critically influences the spot detection and gel matching results. Both packages were sensitive to the parameter or filter settings with respect to the tendency of finding true positive and false positive spots. Quantitation results were very accurate for both analysis software packages.     

Protein spot detection and quantification in 2-DE gel images using machine-learning methods

Two-dimensional gel electrophoresis (2-DE) is the most established protein separation method used in expression proteomics. Despite the existence of sophisticated software tools, 2-DE gel image analysis still remains a serious bottleneck. The low accuracies of commercial software packages and the extensive manual calibration that they often require for acceptable results show that we are far from achieving the goal of a fully automated and reliable, high-throughput gel processing system. We present a novel spot detection and quantification methodology which draws heavily from unsupervised machine-learning methods. Using the proposed hierarchical machine learning-based segmentation methodology reduces both the number of faint spots missed (improves sensitivity) and the number of extraneous spots introduced (improves precision). The detection and quantification performance has been thoroughly evaluated and is shown to compare favorably (higher F-measure) to a commercially available software package (PDQuest). The whole image analysis pipeline that we have developed is fully automated and can be used for high-throughput proteomics analysis since it does not require any manual intervention for recalibration every time a new 2-DE gel image is to be analyzed. Furthermore, it can be easily parallelized for high performance and also applied without any modification to prealigned group average gels.     

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%.     

Comparative 2-DE protein analysis in a 3-D geometry gel

Two-dimensional gel electrophoresis (2-DE) separation has not been considered suitable for large-scale comparative protein expression studies due to its limited throughput. We present a high-throughput analysis method based on three-dimensional (3-D) geometry gel electrophoresis. Following conventional isoelectric focusing (IEF), up to 36 immobilized pH gradient (IPG) strips are arrayed on the top surface of a 3-D gel body, and the samples transferred electrokinetically to the gel. A specific thermal management ensures that sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) occurs under identical electrophoretic and thermal conditions, avoiding gel-to-gel variations and thereby providing immediate comparability of the separation patterns. Proteins are Cy3-labeled for online detection of laser-induced fluorescence (LIF). Images are acquired by a digital camera and recorded as a 3-D image stack during electrophoresis. Image processing software decomposes the 3-D image stack into vertical sections representing conventional 2-DE slab gels, making results immediately accessible without further gel processing. The large number of simultaneously analyzed samples (n = 36) allows treating the sample index as a quasi-continuous experimental parameter (e.g., concentration, time, dose). The method offers a wide range of applications in molecular discovery, clinical diagnosis, pharmacology, and toxicology, like protein monitoring during disease development and screening of drug candidates for their effect on protein expression.     

Comparing SILAC and two-dimensional gel electrophoresis image analysis for profiling urokinase plasminogen activator signaling in ovarian cancer cells

Two-dimensional gel electrophoresis (2-DE) image analysis is conventionally used for comparative proteomics. However, there are a number of technical difficulties associated with 2-DE protein separation that limit the depth of proteome coverage, and the image analysis steps are typically labor-intensive and low-throughput. Recently, mass spectrometry-based quantitation strategies have been described as alternative differential proteome analysis techniques. In this study, we investigated changes in protein expression using an ovarian cancer cell line, OVMZ6, 24 h post-stimulation with the relatively weak agonist, urokinase-type plasminogen activator (uPA). Quantitative protein profiles were obtained by MALDI-TOF/TOF from stable isotope-labeled cells in culture (SILAC), and these results were compared to the quantitative ratios obtained using 2-DE gel image analysis. MALDI-TOF/TOF mass spectrometry showed that differential quantitation using SILAC was highly reproducible (approximately 8% coefficient of variation (CV)), and this variance was considerably lower than that achieved using automated 2-DE image analysis strategies (CV approximately 25%). Both techniques revealed subtle alterations in cellular protein expression following uPA stimulation. However, due to the lower variances associated with the SILAC technique, smaller changes in expression of uPA-inducible proteins could be found with greater certainty.     

蛋白负染方法在蛋白质组学中的应用评价

为评价蛋白质负染方法在蛋白质组学分析中的应用,采用负染和考马斯亮蓝染色两种方法对同一样品的双向电泳胶进行染色,取相对应的8对蛋白点,并进行胶内酶解及MALDI-TOF/TOF分析,比较两种方法与质谱的兼容性。图像分析显示,负染方法展示出的蛋白点更多,但三维峰图不如考染明晰;质谱结果显示,8个负染蛋白点中有7个鉴定结果有效,8个考染蛋白点鉴定结果均有效。因此可以得出以下结论:负染的灵敏度高于考染,与质谱的兼容性良好,适用于建立双向电泳参考图谱的研究;但负染后的胶图不适于进行蛋白点丰度对比分析。     

蛋白质双向电泳图像分析

随着人类基因组计划的接近完成,蛋白质组（proteome）研究成为新的热点.其中高分辨率的双向电泳（two-dimensional gel electrophoresis, 2-DE）技术使对组织或细胞的整个蛋白质组的综合分析成为可能.近年来这一技术有了很大的改进和提高,特别是图像分析系统,算法更为先进,功能日益强大,操作也更简便,为大规模研究提供了良好的工具.使用新一代的2D图像分析系统,对离体培养的雪旺氏细胞的蛋白质样品双向电泳结果进行了初步分析,探讨了在图像扫描、点检测、背景消除、匹配、结果报告和数据分析各步中的技术问题,并报告了进行2D图像分析的体会.     

A novel approach to spot detection for two-dimensional gel electrophoresis images using pixel value collection

Separation of complex mixtures of proteins by two-dimensional gel electrophoresis (2-DE) is a fundamental component of current proteomic technology. Quantitative analysis of the images generated by digitization of such gels is critical for the identification of alterations in protein expression within a given biological system. Despite the availability of several commercially available software packages designed for this purpose, image analysis is extremely resource intensive, subjective and remains a major bottleneck. In addition to reducing throughput, the requirement for manual intervention results in the introduction of operator subjectivity, which can limit the statistical significance of the numerical data generated. A key requirement of image analysis is the accurate definition of protein spot boundaries using a suitable method of image segmentation. We describe a method of spot detection applicable to 2-DE image files using a segmentation method involving pixel value collection via serial analysis of the image through its range of density levels. This algorithm is reproducible, sensitive, accurate and primarily designed to be automatic, removing operator subjectivity. Furthermore, it is believed that this method may offer the potential for improved spot detection over currently available software.     

Comparative Proteomics Analysis of Cerebrospinal Fluid of Patients with Guillain–Barré Syndrome

To better understand the pathophysiologic mechanisms underlying Guillain-Barré syndrome (GBS), Comparative proteomic analysis of cerebrospinal fluid (CSF) between patients with GBS (the experiment group) and control subjects suffering from other neurological disorders (the control group) was carried out using two-dimensional gel electrophoresis (2-DE) technique, in combination with matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS) and database searching to determine abnormal CSF proteins in GBS patients. Image analysis of 2-DE gels silver stained revealed that 10 protein spots showed significant differential expression between the two groups of CSF samples. The expression of cystatin C, transthyretin, apolipoprotein E and heat shock protein 70 were decreased. However, haptoglobin, alpha-1-antitrypsin, apolipoprotein A-IV and neurofilaments were elevated. The subsequent ELISA measured the concentration of cystatin C and confirmed the result of the proteomic analysis. These identified proteins may be involved in the pathophysiological process of GBS and call for further studying the role of these proteins in the pathogenesis of the disease.     

Normalization and analysis of residual variation in two-dimensional gel electrophoresis for quantitative differential proteomics

Although two-dimensional gel electrophoresis (2-DE) has long been a favorite experimental method to screen proteomes, its reproducibility is seldom analyzed with the assistance of quantitative error models. The lack of models of residual distributions that can be used to assign likelihood to differential expression reflects the difficulty in tackling the combined effect of variability in spot intensity and uncertain recognition of the same spot in different gels. In this report we have analyzed a series of four triplicate two-dimensional gels of chicken embryo heart samples at two distinct development stages to produce such a model of residual distribution. In order to achieve this reference error model, a nonparametric procedure for consistent spot intensity normalization had to be established, and is also reported here. In addition to variability in normalized intensity due to various sources, the residual variation between replicates was observed to be compounded by failure to identify the spot itself (gel alignment). The mixed effect is reflected by variably skewed bimodal density distributions of residuals. The extraction of a global error model that accommodated such distribution was achieved empirically by machine learning, specifically by bootstrapped artificial neural networks. The model described is being used to assign confidence values to observed variations in arbitrary 2-DE gels in order to quantify the degree of over-expression and under-expression of protein spots.     

Proposal for a standard representation of two-dimensional gel electrophoresis data

The global analysis of proteins is now feasible due to improvements in techniques such as two-dimensional gel electrophoresis (2-DE), mass spectrometry, yeast two-hybrid systems and the development of bioinformatics applications. The experiments form the basis of proteomics, and present significant challenges in data analysis, storage and querying. We argue that a standard format for proteome data is required to enable the storage, exchange and subsequent re-analysis of large datasets. We describe the criteria that must be met for the development of a standard for proteomics. We have developed a model to represent data from 2-DE experiments, including difference gel electrophoresis along with image analysis and statistical analysis across multiple gels. This part of proteomics analysis is not represented in current proposals for proteomics standards. We are working with the Proteomics Standards Initiative to develop a model encompassing biological sample origin, experimental protocols, a number of separation techniques and mass spectrometry. The standard format will facilitate the development of central repositories of data, enabling results to be verified or re-analysed, and the correlation of results produced by different research groups using a variety of laboratory techniques.     

Separation of human erythrocyte membrane associated proteins with one-dimensional and two-dimensional gel electrophoresis followed by identification with matrix-assisted laser desorption/ionization-time of flight mass spectrometry

A classical proteomic analysis was used to establish a reference map of proteins associated with healthy human erythrocyte ghosts. Following osmotic lysis and differential centrifugation, ghost proteins were separated by either one-dimensional gel electrophoresis (1-DE) or two-dimensional gel electrophoresis (2-DE). Selected protein bands or spots were excised and trypsinized before mass spectrometric analyses and data mining was performed using the SWISS-PROT and NCBI nonredundant databases. A total of 102 protein spots from a 2-D gel were successfully identified. These corresponded to 59 distinct polypeptides with the remaining 43 being isoforms. As for the 1-D gel, 44 polypeptides were identified, of which 19 were also found on the 2-D gel. Most of the 19 common polypeptides were membrane cytoskeletal proteins that are often referred to as the "band" proteins. The remaining 25 polypeptides that were found exclusively on 1-D gels were proteins with high hydrophobicity (e.g., sorbitol dehydrogenase and glucose transporter) and high molecular mass (e.g., Kell blood group glycoprotein and Janus-kinase 2). A higher number of signaling proteins was also identified on 1-D gels compared to 2-D gels. These included Ras, cAMP dependent protein kinase and TGF-beta receptor type 1 precursor.     

Statistical analysis of the experimental variation in the proteomic characterization of human plasma by two-dimensional difference gel electrophoresis

The complexity of human plasma presents a number of challenges to the efficient and reproducible proteomic analysis of differential expression in response to disease. Before individual variation and disease-specific protein biomarkers can be identified from human plasma, the experimental variability inherent in the protein separation and detection techniques must be quantified. We report on the variation found in two-dimensional difference gel electrophoresis (2-D DIGE) analysis of human plasma. Eight aliquots of a human plasma sample were subjected to top-6 highest abundant protein depletion and were subsequently analyzed in triplicate for a total of 24 DIGE samples on 12 gels. Spot-wise standard deviation estimates indicated that fold changes greater than 2 can be detected with a manageable number of replicates in simple ANOVA experiments with human plasma. Mixed-effects statistical modeling quantified the effect of the dyes, and segregated the spot-wise variance into components of sample preparation, gel-to-gel differences, and random error. The gel-to-gel component was found to be the largest source of variation, followed by the sample preparation step. An improved protocol for the depletion of the top-6 high-abundance proteins is suggested, which, along with the use of statistical modeling and future improvements in gel quality and image processing, can further reduce the variation and increase the efficiency of 2-D DIGE proteomic analysis of human plasma.     

High performance two-dimensional gel electrophoresis using a wetting agent Tergitol NP7

Two-dimensional electrophoresis is an efficient method for the analysis of a broad range of complex protein samples. Current two-dimensional gel techniques are not suited for analysis of the small amount of proteins from tissue samples in the presence of high concentration of salts. Here we describe an improved two-dimensional gel electrophoresis procedure based on the use of a nonionic wetting agent, Tergitol NP7, in rehydration solution combined with the application of a linear potential sweep during isoelectrofocusing. This experimental approach yields a dramatic increase in the resolution and focusing of proteins visualized on two-dimensional gels. This technique is less time-consuming and laborious than the current techniques and can be used for a variety of two-dimensional electrophoresis applications, including proteome analysis.     

Protein/RNA coextraction and small two-dimensional polyacrylamide gel electrophoresis for proteomic/gene expression analysis of renal cancer biopsies

A small amount of bioptic tissue ( approximately 5-10mg of fresh tissue) usually does not contain enough material to extract protein and RNA separately, to obtain preparative two-dimensional polyacrylamide gel electrophoresis (2-DE), and to identify a large number of separated proteins by MS. We tested a method, on small renal cancer specimens, for the coextraction of protein and RNA coupled with 2-DE and matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) or quadrupole time-of-flight (Q-TOF) analysis. We coextracted 0.28+/-0.05mg of proteins and 2.5+/-0.33microg of RNA for each 10mg of renal carcinoma tissue. Small and large 2-DE gels were compared: they showed a similar number of spots, and it was possible to match each other; using small format gels, one-fifth of the protein amount was required to identify, by Q-TOF analysis, the same number of proteins identifiable in large-format gel using MALDI-TOF analysis. Quality of RNA coextracted with the proteins was tested by real-time PCR on a set of housekeeping genes. They were quantified with high amplification efficiency and specificity. In conclusion, using 5 to 10mg of fresh tissue, it was possible to perform comprehensive parallel proteomic and genomic analysis by high-resolution, small-format 2-DE gels, allowing approximately 300 proteins identification and 1000 genes expression analysis.     

Challenges related to analysis of protein spot volumes from two-dimensional gel electrophoresis as revealed by replicate gels

Assumptions that need to be considered prior to statistical analysis of protein spot volumes from two-dimensional gel electrophoresis (2-DE) data are studied using replicate gels of the same sample. The most important observation is that the data tables of protein spot volumes from 2-DE images contain a large number of missing values, which are not consistent with the presence or absence of the proteins. This implies both loss of information and problems for the subsequent statistical analysis. Challenges with 2-DE protein spot volumes are viewed in light of multiple gel comparisons and multivariate data analysis.     

nm23-H1基因转染前后人高转移大细胞肺癌细胞株双向凝胶电泳图谱分析

为了更全面地了解nm23-H1在肺癌中发挥转移抑制的机理,用双向凝胶电泳技术比较人高转移大细胞肺癌细胞株(L9981)和转染nm23-H1基因的人大细胞肺癌细胞株(L9981-nm23-H1)间蛋白表达的差异.利用固相pH梯度双向凝胶电泳分离人高转移大细胞肺癌细胞株(L9981)和转染nm23-H1基因的人大细胞肺癌细胞株(L9981-nm23-H1)的总蛋白,用图像分析软件比较分析以识别细胞间的差异表达蛋白质.结果成功地获得了两株细胞蛋白组分辨率高、重复性好的双向凝胶电泳图谱.软件分析两种细胞的凝胶电泳图谱后发现,在相同分析条件下识别的蛋白质斑点数L9981为902±169个、L9981-nm23-H1为1160±212个.比较L9981和L9981-nm23-H1人大细胞肺癌细胞株的双向凝胶电泳蛋白质图谱后发现6个蛋白质点仅在L9981中有表达,17个蛋白质点仅在L9981-nm23-H1中有表达.此外,发现13个在两种细胞株中均存在,但表达量差异在2倍以上的蛋白质点(P<0.05).结果提示,nm23-H1基因转染引起人高转移大细胞肺癌细胞株蛋白质表达谱的变化,可能是其逆转肺癌侵袭转移的生物学基础.