Pixel-based analysis of multiple images for the identification of changes: a novel approach applied to unravel proteome patterns [corrected] of 2-D electrophoresis gel images

A novel approach for revealing patterns of proteome variation among series of 2-DE gel images is presented. The approach utilises image alignment to ensure that each pixel represents the same information across all gels. Gel images are normalised, and background corrected, followed by unfolding of the images to 1-D pixel vectors and analysing pixel vectors by multivariate data modelling. Information resulting from the data analysis is refolded back to the image domain for visualisation and interpretation. The method is rapid and suitable for automatic routines applied after the gel alignment. The approach is compared with spot volume analysis to illustrate how this approach can solve persistent problems like mismatch of protein spots, erroneous missing values and failure to detect variation in overlapping proteins. The method may also detect variation in the border area of saturated proteins. The approach is given the name pixel-based analysis of multiple images for the identification of changes (PMC). The method can be used for multiple images in general. Effects of pretreatment of the images are discussed.     

Triton X-114 cloud point extraction to subfractionate blood plasma proteins for two-dimensional gel electrophoresis

A simple and reproducible procedure for enrichment of a plasma protein subfraction suitable for two-dimensional polyacrylamide gel electrophoresis (2DE) was developed, using a Triton X-114-based cloud point extraction (CPE). Appropriate conditions for such a CPE procedure were found by SDS-PAGE to be a plasma protein concentration of about 10 mg/ml in 3% (w/v) Triton X-114. 2DE of proteins obtained by CPE of 400 μl of human plasma revealed about 200 spots constituting a spot pattern very different from the pattern of total plasma. The CPE procedure only had a limited contribution to the technical variation. Identification of about 60 spots, representing only 22 proteins, revealed that several proteins in the obtained subfraction were present in more isoforms or modifications. Among these were apolipoproteins (A-1, D, E, L1, and M), haptoglobin-related protein, phosphatidylcholine-sterol acyltransferase, serum amyloid A, and serum paraoxonase/arylesterase 1, which are proteins of a hydrophobic nature, as in plasma they relate to lipoprotein particles. Thus, Triton X-114-based CPE is a simple plasma prefractionation tool, attractive for detailed 2DE studies of hydrophobic plasma proteins and their isoforms or modifications.     

The QUEST system for quantitative analysis of two-dimensional gels

The strategies and methods used by the QUEST system for two-dimensional gel analysis are described, and the performance of the system is evaluated. Radiolabeled proteins, resolved on two-dimensional gels and detected using calibrated exposures to film, are quantified in units of disintegrations per minute or as a fraction of the total protein radioactivity applied to the gel. Spot quantitation and resolution of overlapping spots is performed by two-dimensional gaussian fitting. Pattern matching is carried out for groups of gels called matchsets, and within each matchset every gel is matched to every other gel. During the matching process, spots are automatically added to each pattern at positions where unmatched spots were detected in other patterns. This results in enhanced accuracy for both spot detection and for matching. The spot fitting procedure is repeated after matching. Tests show that up to 97% of spots in each pattern can be matched and that fewer than 1% of the spots are matched inconsistently. Approximately 2000 proteins are detected from typical gels. Of these 1600 are high quality spots. Tests to measure the coefficient of variation of spot quantitation versus spot quality show that the average coefficient of variation for high quality spots is 21%. The intensities of the detected proteins range from 4 to 20,000 ppm of total protein synthesis. The QUEST analysis system has been used to build a quantitative database for the proteins of normal and transformed REF52 cells, as presented in the accompanying reports (Garrels, J., and Franza, B. R., Jr. (1989) J. Biol. Chem. 264, 5283-5298, 5299-5312).     

Quantitative 2-D gel electrophoresis-based expression proteomics of albumin and IgG immunodepleted plasma

Proteomic analysis of plasma is challenging because of its large dynamic range, which prevents the detection of low abundance proteins. Immunodepletion of high abundance proteins, such as albumin and IgG, has emerged as a favored technology to overcome this problem; however its suitability in quantitative expression proteomics has not yet been adequately addressed. In this study, albumin and IgG immunodepletion was evaluated by ELISAs and the reproducibility of depletion was tested with 2-DGE. Depletion of plasma resulted in removal of 62+/-1.2% of the total protein, 93+/-1.4% of the albumin (0.43 microg/microL, residual), and 94+/-1.5% of the IgG (0.21 microg/microL, residual). These results were confirmed by immunoblotting. Computerized image analysis of 2-D gels using Progenesis SameSpots software revealed an enhancement in the number of visible spots (675-1325), with 10+/-6% inter-gel variability in spot density. LC-ESI-MS/MS identification of newly resolved protein spots further validated the procedure. An innovative application of the software employed led to identification of 11 proteins lost non-specifically during depletion. This study demonstrates the effectiveness of immunodepletion of albumin and IgG in quantitative 2-DGE-based differential analysis of plasma proteins.     

A probabilistic treatment of the missing spot problem in 2D gel electrophoresis experiments

Two-dimensional SDS-PAGE gel electrophoresis using post-run staining is widely used to measure the abundances of thousands of protein spots simultaneously. Usually, the protein abundances of two or more biological groups are compared using biological and technical replicates. After gel separation and staining, the spots are detected, spot volumes are quantified, and spots are matched across gels. There are almost always many missing values in the resulting data set. The missing values arise either because the corresponding proteins have very low abundances (or are absent) or because of experimental errors such as incomplete/over focusing in the first dimension or varying run times in the second dimension as well as faulty spot detection and matching. In this study, we show that the probability for a spot to be missing can be modeled by a logistic regression function of the logarithm of the volume. Furthermore, we present an algorithm that takes a set of gels with technical and biological replicates as input and estimates the average protein abundances in the biological groups from the number of missing spots and measured volumes of the present spots using a maximum likelihood approach. Confidence intervals for abundances and p-values for differential expression between two groups are calculated using bootstrap sampling. The algorithm is compared to two standard approaches, one that discards missing values and one that sets all missing values to zero. We have evaluated this approach in two different gel data sets of different biological origin. An R-program, implementing the algorithm, is freely available at http://bioinfo.thep .lu.se/MissingValues2Dgels.html.     

Flicker image comparison of 2-D gel images for putative protein identification using the 2DWG meta-database

With the availability of two-dimensional (2-D) gel electrophoresis databases that have many characterized proteins, it may be possible to compare a researcher’s gel images with those in relevant databases. This may lead to the putative identification of unknown protein spots in a researcher’s gel with those characterized in a given database, saving the researcher time and money by suggesting monoclonal antibodies to try in confirming these identifications. We have developed two tools to help with this comparison: (1) Flicker, http://www.lecb.ncifcrf.gov/flicker/, a Java applet program running in the researcher’s Web browser, to visually compare their gels against gels on the Internet; and (2) the 2DWG meta-database, http://www.lecb.ncifcrf.gov/2dwgDB/, a searchable database of locations of 2-D electrophoretic gel images found on the Internet. Recent additions to Flicker allow users to click on a protein spot in a gel that is linked to a federated 2D gel database, such as SWISS-2DPAGE, and have it retrieve a report from that Web database for that protein.     

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.     

慢性阻塞性肺疾病与非慢性阻塞性肺疾病吸烟者肺组织蛋白质组学比较分析

吸烟是导致慢性阻塞性肺疾病（COPD）发生发展的主要原因之一.但吸烟者是否发生COPD存在个体差异,其机制尚未完全阐明.蛋白质组学研究能够高效率发现疾病相关蛋白并为深入研究疾病的发病机制提供线索.本研究运用二维凝胶电泳（2-DE）和基质辅助激光解吸电离飞行时间质谱(MALDI-TOF-MS)蛋白质组学研究方法结合生物信息学数据,对吸烟COPD患者和非COPD吸烟者肺组织表达的差异蛋白进行筛选和鉴定,共鉴定出24种差异蛋白,涉及基本代谢酶类、氧化应激相关酶类、凝血/纤溶相关蛋白、蛋白降解相关酶以及细胞生长分化相关蛋白等.本研究结果为进一步探索COPD的发病机制提供了新的线索.     

Sputum proteomics identifies elevated PIGR levels in smokers and mild-to-moderate COPD

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of morbidity and mortality around the world. However, the exact mechanisms leading to COPD and its progression are still poorly understood. In this study, induced sputum was analyzed by cysteine-specific two-dimensional difference gel electrophoresis (2D-DIGE) coupled with mass spectrometry to identify proteins involved in COPD pathogenesis. The comparison of nonsmokers, smokers, and smokers with moderate COPD revealed 15 changed proteins with the majority, including polymeric immunoglobulin receptor (PIGR), being elevated in smokers and subjects with COPD. PIGR, which is involved in specific immune defense and inflammation, was further studied in sputum, lung tissue, and plasma by Western blot, immunohistochemistry/image analysis, and/or ELISA. Sputum PIGR was characterized as glycosylated secretory component (SC). Lung PIGR was significantly elevated in the bronchial and alveolar epithelium of smokers and further increased in the alveolar area in mild to moderate COPD. Plasma PIGR was elevated in smokers and smokers with COPD compared to nonsmokers with significant correlation to obstruction. In conclusion, new proteins in smoking-related chronic inflammation and COPD could be identified, with SC/PIGR being one of the most prominent not only in the lung but also in circulating blood.     

Hierarchical grid transformation for image warping in the analysis of two-dimensional electrophoresis gels

Two-dimensional electrophoresis is a widely used method for separating a large number of proteins from complex protein mixtures and for revealing differential patterns of protein expressions. In the computer-assisted proteome research, the comparison of protein separation profiles involves several heuristic steps, ranging from protein spot detection to matching of unknown spots. An important prerequisite for efficient protein spot matching is the image warping step, where the geometric relationship between the gel profiles is modeled on the basis of a given set of known corresponding spots, so-called landmarks, and the locations of unknown spots are predicted using the optimized model. Traditionally, polynomial functions together with least squares optimization has been used, even though this approach is known to be incapable of modeling all the complex distortions inherent in electrophoretic data. To satisfy the need of more flexible gel distortion correction, a hierarchical grid transformation method with stochastic optimization is presented. The method provides an adaptive multiresolution model between the gels, and good correction performance in the practical cross-validation tests suggests that automatic warping of gel images could be based on this approach. We believe that the proposed model also has significance in the ultimate comparison of corresponding protein spots since the matching process should benefit from the closeness of the true spot pairs.     

2-D differential membrane proteome analysis of scarce protein samples

Proteome studies with small sample amounts are difficult to perform, especially when membrane proteins are the focus of interest. In our study a new method for the analysis of scarce membrane protein samples combining large gel 2-D-CTAB/SDS-PAGE with fluorescence dye saturation labelling (satDIGE) was developed, allowing a highly sensitive differential analysis of different cell states. After Triton X-114 phase partitioning, enriched membrane protein samples of T cells were labelled at cysteine residues using fluorescence dyes and separated by large gel 2D-CTAB/SDS-PAGE. For a differential analysis 3 mug protein was found to be sufficient to detect proteins in a widespread well-separated diagonal spot pattern.     

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 Universit?t 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.     

Using standard positions and image fusion to create proteome maps from collections of two-dimensional gel electrophoresis images

Databases for two-dimensional protein gels pose new challenges in extracting meaningful information from large numbers of experiments. In order to create expression profiles, positions of corresponding protein spots across all gel images have to be established. In larger gel sets errors may accumulate rapidly during this spot matching process, effectively limiting the number of samples available for data mining. Here we present a novel approach for organizing spot data based on the concept of a standard position for a protein species. Standard positions are meaningful average positions that are determined using all occurrences of a protein species. They can be extended to spots that are not annotated via interpolation. The standard position of a spot can serve as a unifying index across all gels in a database, thus allowing creation and analysis of expression profiles that span the whole collection. The standard position gives a much more accurate estimation of a spot's position on a gel than can be obtained using theoretical isoelectric point and molecular weight. Positional indexing is a complement to a priori identifications (e.g. by mass spectrometry or Edman degradation). Moreover it can be used in advance to select spots that are worth identifying because they show relevant expression profiles. Furthermore, we show how to combine all spots that occur on any of the gels into one synthetic but nevertheless realistic-looking image. This composite image is produced such that all spots have their standard positions. It can serve as a proteome reference map for an organism. As an application, we have computed a reference map from 23 gel images of Bacillus subtilis, using an enhanced prerelease version of the gel analysis software Delta2D (DECODON, Greifswald, Germany).     

Interindividual Variation in the Proteome of Human Peripheral Blood Mononuclear Cells

Peripheral blood mononuclear cells (PBMCs) are main actors in inflammatory processes and linked to many diseases, including rheumatoid arthritis, atherosclerosis, asthma, HIV and cancer. Moreover, they seem an interesting ‘surrogate tissue’ that can be used in biomarker discovery. In order to get a good experimental design for quantitative expression studies, the knowledge of the interindividual variation is an essential part. Therefore, PBMCs were isolated from 24 healthy volunteers (15 males, 9 females, ages 63–86) with no clinical signs of inflammation. The extracted proteins were separated using the two dimensional difference in gel electrophoresis technology (2D-DIGE), and the gel images were processed with the DeCyder 2D software. Protein spots present in at least 22 out of 24 healthy volunteers were selected for further statistical analysis. Determination of the coefficient of variation (CV) of the normalized spot volume values of these proteins, reveals that the total variation of the PBMC proteome varies between 12,99% to 148,45%, with a mean value of 28%. A supplemental look at the causes of technical variation showed that the isolation of PBMCs from whole blood is the factor which influences the experimental variance the most. This isolation should be handled with extra care and an additional washing step would be beneficial. Knowing the extent of variation, we show that at least 10 independent samples per group are needed to obtain statistical powerful data. This study demonstrates the importance of considering variance of a human population for a good experimental design for future protein profiling or biomarker studies.     

Proteome approaches to characterize seed storage proteins related to ditelocentric chromosomes in common wheat (Triticum aestivum L.)

Changes in protein composition of wheat endosperm proteome were investigated in 39 ditelocentric chromosome lines of common wheat (Triticum aestivum L.) cv. Chinese Spring. Two-dimensional gel electrophoresis followed by Coomassie Brilliant Blue staining has resolved a total of 105 protein spots in a gel. Quantitative image analysis of protein spots was performed by PDQuest. Variations in protein spots between the euploid and the 39 ditelocentric lines were evaluated by spot number, appearance, disappearance and intensity. A specific spot present in all gels was taken as an internal standard, and the intensity of all other spots was calculated as the ratio of the internal standard. Out of the 1755 major spots detected in 39 ditelocentric lines, 1372 (78%) spots were found variable in different spot parameters: 147 (11%) disappeared, 978 (71%) up-regulated and 247 (18%) down-regulated. Correlation studies in changes in protein intensities among 24 protein spots across the ditelocentric lines were performed. High correlations in changes of protein intensities were observed among the proteins encoded by genes located in the homoeologous arms. Locations of structural genes controlling 26 spots were identified in 10 chromosomal arms. Multiple regulators of the same protein located at various chromosomal arms were also noticed. Identification of structural genes for most of the proteins was found difficult due to multiple regulators encoding the same protein. Two novel subunits (1B(Z,) 1BDz), the structure of which are very similar to the high molecular weight glutenin subunit 12, were identified, and the chromosome arm locations of these subunits were assigned.     

Proteomic methodological recommendations for studies involving human plasma, platelets, and peripheral blood mononuclear cells

This study was designed to develop, optimize and validate protocols for blood processing prior to proteomic analysis of plasma, platelets and peripheral blood mononuclear cells (PBMC) and to determine analytical variation of a single sample of depleted plasma, platelet and PBMC proteins within and between four laboratories each using their own standard operating protocols for 2D gel electrophoresis. Plasma depleted either using the Beckman Coulter IgY-12 proteome partitioning kit or the Amersham albumin and IgG depletion columns gave good quality gels, but reproducibility appeared better with the single-use immuno-affinity column. The use of the Millipore Filter Device for protein concentration gave a 16% ( p < 0.005) higher recovery of protein in flow-through sample compared with acetone precipitation. The use of OptiPrep gave the lowest level of platelet contamination (1:0.8) during the isolation of PBMC from blood. Several proteins (among which are alpha-tropomyosin, fibrinogen and coagulation factor XIII A) were identified that may be used as biomarkers of platelet contamination in future studies. When identifying preselected spots, at least three out of the four centers found similar identities for 10 out of the 10 plasma proteins, 8 out of the 10 platelet proteins and 8 out of the 10 PBMC proteins. The discrepancy in spot identifications has been described before and may be explained by the mis-selection of spots due to laboratory-to-laboratory variation in gel formats, low scores on the peptide analysis leading to no or only tentative identifications, or incomplete resolution of different proteins in what appears as a single abundant spot. The average within-laboratory coefficient of variation (CV) for each of the matched spots after automatic matching using either PDQuest or ProteomWeaver software ranged between 18 and 69% for depleted plasma proteins, between 21 and 55% for platelet proteins, and between 22 and 38% for PBMC proteins. Subsequent manual matching improved the CV with on average between 1 and 16%. The average between laboratory CV for each of the matched spots after automatic matching ranged between 4 and 54% for depleted plasma proteins, between 5 and 60% for platelet proteins, and between 18 and 70% for PBMC proteins. This variation must be considered when designing sufficiently powered studies that use proteomics tools for biomarker discovery. The use of tricine in the running buffer for the second dimension appears to enhance the resolution of proteins especially in the high molecular weight range.     

小鼠垂体双向凝胶电泳图谱的计算机分析

以小鼠垂体蛋白质为材料,用固相ｐＨ梯度等电聚焦和ＳＤＳ－ＰＡＧＥ获得蛋白质组双向凝胶电泳 图谱。以已知等电点和相对分子质量的外标和内标做为参照物,经过ＰＤＱＵＥＳＴ软件对凝胶图像进行计算机分析,获得了小鼠垂体蛋白质组的等电点、相对分子质量和相对含量等参数。     

2-D DIGE analysis of the budding yeast pH 6-11 proteome in meiosis

Meiosis is the cell division that generates haploid gametes from diploid precursors. To provide insight into the functional proteome of budding yeast during meiosis, a 2-D DIGE kinetic approach was used to study proteins in the pH 6-11 range. Nearly 600 protein spots were visualised and 79 spots exhibited statistically significant changes in abundance as cells progressed through meiosis. Expression changes of up to 41-fold were detected and protein sequence information was obtained for 48 spots. Single protein identifications were obtained for 21 spots including different gel mobility forms of 5 proteins. A large number of post-translational events are suggested for these proteins, including processing, modification and import. The data are incorporated into an online 2-DE map of meiotic proteins in budding yeast, which extends our initial DIGE investigation of proteins in the pH 4-7 range. Together, the analyses provide peptide sequence data for 84 protein spots, including 50 single-protein identifications and gel mobility isoforms of 8 proteins. The largest classes of identified proteins include carbon metabolism, protein catabolism, protein folding, protein synthesis and the oxidative stress response. A number of the corresponding genes are required for yeast meiosis and recent studies have identified similar classes of proteins expressed during mammalian meiosis. This proteomic investigation and the resulting protein reference map make an important contribution towards a more detailed molecular view of yeast meiosis.     

Effect of glycosylation on the protein pattern in 2-D-gel electrophoresis

Single proteins, when analyzed with 2-D-PAGE, often show multiple spots due to PTMs. In gels of human body fluids, the spot patterns facilitate the assignment and identification of the proteins. We analyzed serums from patients with congenital disorders of glycosylation (CDG) in which glycoproteins are strongly impacted and exhibit highly distinguishable spot patterns compared to healthy controls. We detected a typical protein pattern for alpha1-acid glycoprotein (AGP) and transferrin (Trf) that are markers for CDG. AGP contains five glycosylation sites which results in a complex microheterogeneity of the glycoprotein. On the other hand, in Trf, a glycoprotein with only two glycosylation sites, mainly biantennary complex-type-N-linked glycans are bound. We used 2-D-PAGE, MALDI-TOF-MS, and ESI-MS for the analysis of these glycoproteins and their corresponding glycans. In AGP, the heterogenic glycosylation of the different glycosylation sites is responsible for the complex spot pattern. In contrast to AGP, the protein spots of Trf cannot be explained by glycosylation. We found strong evidence that oxidation of cysteine is responsible for the spot pattern. This study contradicts the commonly accepted assumption that the multiple protein spots of Trf observed in 2-D-PAGE are due, as in AGP, to the glycosylation of the protein.     

Preprocessing of 2-Dimensional Gel Electrophoresis Images Applied to Proteomic Analysis: A Review

Various methods and specialized software programs are available for processing twodimensional gel electrophoresis(2-DGE)images.However,due to the anomalies present in these images,a reliable,automated,and highly reproducible system for 2-DGE image analysis has still not been achieved.The most common anomalies found in 2-DGE images include vertical and horizontal streaking,fuzzy spots,and background noise,which greatly complicate computational analysis.In this paper,we review the preprocessing techniques applied to 2-DGE images for noise reduction,intensity normalization,and background correction.We also present a quantitative comparison of non-linear?ltering techniques applied to synthetic gel images,through analyzing the performance of the?lters under speci?c conditions.Synthetic proteins were modeled into a two-dimensional Gaussian distribution with adjustable parameters for changing the size,intensity,and degradation.Three types of noise were added to the images:Gaussian,Rayleigh,and exponential,with signal-to-noise ratios(SNRs)ranging 8–20 decibels(d B).We compared the performanceof wavelet,contourlet,total variation(TV),and wavelet-total variation(WTTV)techniques using parameters SNR and spot ef?ciency.In terms of spot ef?ciency,contourlet and TV were more sensitive to noise than wavelet and WTTV.Wavelet worked the best for images with SNR ranging 10–20 d B,whereas WTTV performed better with high noise levels.Wavelet also presented the best performance with any level of Gaussian noise and low levels(20–14 d B)of Rayleigh and exponential noise in terms of SNR.Finally,the performance of the non-linear?ltering techniques was evaluated using a real 2-DGE image with previously identi?ed proteins marked.Wavelet achieved the best detection rate for the real image.