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.     

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.     

The state of the art in the analysis of two-dimensional gel electrophoresis images

Software-based image analysis is a crucial step in the biological interpretation of two-dimensional gel electrophoresis experiments. Recent significant advances in image processing methods combined with powerful computing hardware have enabled the routine analysis of large experiments. We cover the process starting with the imaging of 2-D gels, quantitation of spots, creation of expression profiles to statistical expression analysis followed by the presentation of results. Challenges for analysis software as well as good practices are highlighted. We emphasize image warping and related methods that are able to overcome the difficulties that are due to varying migration positions of spots between gels. Spot detection, quantitation, normalization, and the creation of expression profiles are described in detail. The recent development of consensus spot patterns and complete expression profiles enables one to take full advantage of statistical methods for expression analysis that are well established for the analysis of DNA microarray experiments. We close with an overview of visualization and presentation methods (proteome maps) and current challenges in the field. An erratum to this article can be found at     

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).     

Sources of Variability among Replicate Samples Separated by Two-Dimensional Gel Electrophoresis

Two-dimensional gel electrophoresis (2DE) offers high-resolution separation for intact proteins. However, variability in the appearance of spots can limit the ability to identify true differences between conditions. Variability can occur at a number of levels. Individual samples can differ because of biological variability. Technical variability can occur during protein extraction, processing, or storage. Another potential source of variability occurs during analysis of the gels and is not a result of any of the causes of variability named above. We performed a study designed to focus only on the variability caused by analysis. We separated three aliquots of rat left ventricle and analyzed differences in protein abundance on the replicate 2D gels. As the samples loaded on each gel were identical, differences in protein abundance are caused by variability in separation or interpretation of the gels. Protein spots were compared across gels by quantile values to determine differences. Fourteen percent of spots had a maximum difference in intensity of 0.4 quantile values or more between replicates. We then looked individually at the spots to determine the cause of differences between the measured intensities. Reasons for differences were: failure to identify a spot (59%), differences in spot boundaries (13%), difference in the peak height (6%), and a combination of these factors (21). This study demonstrates that spot identification and characterization make major contributions to variability seen with 2DE. Methods to highlight why measured protein spot abundance is different could reduce these errors.     

Elsie 4: quantitative computer analysis of sets of two-dimensional gel electrophoretograms

We have developed and refined a system for quantitative computer analysis of two-dimensional polyacrylamide gel electrophoretograms. The system, named Elsie 4, is based on one described by Vo et al. (Anal. Biochem. 112, 258 (1981]. It is highly automated. Elsie 4 can find, and measure the intensity of, almost any spot resolvable on two-dimensional gels, including spots visible only as shoulders off larger spots and spots so close together that there is no "valley" between them. It can automatically match the spot patterns of different gels, potentially without the need for a user to provide landmark matches. The matches between paired gels let us follow the synthesis of any spot through a set of gels. Information about a group of matched spots can be obtained by referring to any spot in the group. There is generally no need for a standard or reference gel. Data for two experiments can be combined and compared by matching any gel in one experiment with any gel in the other. There are ways to automatically find possible mismatches in sets of gels. Scans and the results of the analysis can be shown on an image displayer. The programs use function libraries; this helps ensure consistency and increase portability. The programs and functions can be linked together in many ways; this lets users build custom programs for analysis of specific experiments.     

Pinnacle: a fast, automatic and accurate method for detecting and quantifying protein spots in 2-dimensional gel electrophoresis data

MOTIVATION: One of the key limitations for proteomic studies using 2-dimensional gel electrophoresis (2DE) is the lack of rapid, robust and reproducible methods for detecting, matching and quantifying protein spots. The most commonly used approaches involve first detecting spots and drawing spot boundaries on individual gels, then matching spots across gels and finally quantifying each spot by calculating normalized spot volumes. This approach is time consuming, error-prone and frequently requires extensive manual editing, which can unintentionally introduce bias into the results. RESULTS: We introduce a new method for spot detection and quantification called Pinnacle that is automatic, quick, sensitive and specific and yields spot quantifications that are reliable and precise. This method incorporates a spot definition that is based on simple, straightforward criteria rather than complex arbitrary definitions, and results in no missing data. Using dilution series for validation, we demonstrate Pinnacle outperformed two well-established 2DE analysis packages, proving to be more accurate and yielding smaller coefficiant of variations (CVs). More accurate quantifications may lead to increased power for detecting differentially expressed spots, an idea supported by the results of our group comparison experiment. Our fast, automatic analysis method makes it feasible to conduct very large 2DE-based proteomic studies that are adequately powered to find important protein expression differences. AVAILABILITY: Matlab code to implement Pinnacle is available from the authors upon request for non-commercial use.     

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.     

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.     

Improved comparative proteome analysis based on two-dimensional gel electrophoresis

The purpose of this study was to test the extent to which differences in spot intensity can be reliably recognized between two groups of two-dimensional electrophoresis gels (pH 4-7, visualized with ruthenium fluorescent stain) each loaded with different amounts of protein from rat brain (power analysis). Initial experiments yielded only unsatisfactory results: 546 spots were matched from two groups of 6 gels each loaded with 200 microg and 250 microg protein, respectively. Only 72 spots were higher (p<0.05), while 58 spots were significantly lower in the 250-microg group. The construction of new apparatuses that allowed the simultaneous processing of 24 gels throughout all steps between rehydration and staining procedure considerably lowered the between-gel variation. This resulted in the detection of significant differences in spot intensities in 77-90% of all matched spots on gel groups with a 25% difference in protein load. This applied both when protein from 24 biological replicates was loaded onto two groups of 12 gels and when two pooled tissue samples were each loaded onto 6 gels. At a difference of 50% in protein load, more than 90% of all spots differed significantly between two experimental groups.     

Evaluation of individual protein errors in silver-stained two-dimensional gels

The relationship between spot volume and variation for all protein spots observed on large format 2D gels when utilising silver stain technology and a model system based on mammalian NSO cell extracts is reported. By running multiple gels we have shown that the reproducibility of data generated in this way is dependent on individual protein spot volumes, which in turn are directly correlated with the coefficient of variation. The coefficients of variation across all observed protein spots were highest for low abundant proteins which are the primary contributors to process error, and lowest for more abundant proteins. Using the relationship between spot volume and coefficient of variation we show it is necessary to calculate variation for individual protein spot volumes. The inherent limitations of silver staining therefore mean that errors in individual protein spot volumes must be considered when assessing significant changes in protein spot volume and not global error.     

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

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

Proteomic analysis of individual variation in normal livers of human beings using difference gel electrophoresis

Normal Chinese Liver Proteome Expression Profile is one of the major parts of Human Liver Proteome Project. Before starting the studies, it is necessary to examine the interindividual variation of normal liver proteome and evaluate the minimal size of samples for proteomic analysis. In this study, normal liver samples from ten individual volunteers were collected and the proteome profiles of these samples were analyzed using 2-D difference gel electrophoresis (DIGE) combined with MALDI-TOF/TOF MS. The individual liver tissue lysates were labeled with Cy3 and Cy5 while the pooled sample was labeled with Cy2 as an internal standard, which minimized gel-to-gel variation. After analysis by the DeCyder software, up to 2056 protein spots were detected on the master gel. The CV of standardized abundance was calculated for the protein spots that were matched across all ten gels. The CV values of these protein spots ranged from 6.4 to 108.5% and the median CV was approximately 19%, which demonstrated that the protein expression of normal liver among different individuals was relatively stable. The eight proteins with CV values over 50% were identified which would be a caveat when considering these proteins as potential disease-related markers. Moreover, the one-way ANOVA feature showed a correlation between sample size and individual variations. The results showed that when the sample size exceeded 7, the individual variations were not significant to the whole pool. Our results are an important basis for liver protein expression profiles and comparative proteomics of liver disease.     

小麦幼穗蛋白质双向电泳条件的优化

本研究以温光敏小麦为试材,用TCA/丙酮和酚提取法提取小麦幼穗蛋白样品,进行了双向电泳优化分析,并对双向电泳过程中出现的问题进行了讨论。结果表明,用TCA/丙酮法提取小麦幼穗蛋白质其产率(浓度)高于酚提取法。SDS-PAGE电泳显示,用TCA/丙酮提取法提取的蛋白质能获得较清晰条带,分辨率较高,而酚提取法提取的蛋白质其条带模糊,分辨率低。对蛋白质纯化除盐可以提高分辨率,减少横竖纹,获得背景清晰的圆形蛋白点。通过ImageMasterTM 2D Platinum5.0软件分析凝胶图谱,结果显示纯化后可降低噪点,纯化后蛋白点数可从未纯化蛋白点数的216增加到583。显然,采用TCA/丙酮法可获得高浓度高质量的蛋白质,而进一步纯化、除盐离子可进一步获得背景清晰可高重复性的电泳图谱。在双向电泳实验过程中,观察到一些异常缺陷胶的出现,如双向电泳图谱中蛋白点扩散,蛋白聚集形成斑点串,没有点或点很少,出现纵纹横纹及图谱扭曲等影响图谱质量的严重问题,本研究对这些问题做了分析并提出了解决方案。     

Preliminary analysis of the cauliflower mitochondrial proteome

Purified cauliflower (Brassica oleracea var. botrytis) mitochondrial proteins fractionated into soluble, membrane, integral membrane and peripheral membrane samples were analyzed by 2D- PAGE (isoelectric focusing/ SDS polyacrylamide gel electrophoresis). 2D gels patterns were compared using the Imager Master 2D Elite software. 561 silver stained protein spots were resolved after electrophoresis under standard conditions of a whole protein extract. In the soluble fraction a prevalent number of more intense protein spots was observed. The cauliflower protein 2D patterns resembled Arabidopsis thaliana 2D patterns. The two protein spots selected which occupied a similar isoelectric point positions on both gels represented the same proteins as revealed by ESI-MS analysis of cauliflower proteins. The third selected spot belongs to unidentified proteins. The comparative analysis of mitochondrial suborganellar fractions proved the usefulness of this approach.     

Mass spectrometry-based label free quantification of gel separated proteins

Co-migrating proteins in 2D PAGE spots cause difficulties in the assignment of quantitative data obtained from the staining density of a gel spot. We present an application of a label free LC-MS quantitative method that can overcome problems like this. Protein mixtures were prepared with varying compositions, and were run on either 1D or 2D PAGE. Relative and absolute quantitative evaluation was carried out using a simple but reliable method based on integrated MS signals of the three most intensive peptides of each protein. The efficacy of digestion and peptide extraction is, however, influenced by different factors in gel from those in solution, hence the method had to be validated via a quantitative assessment of proteins from 1D or 2D gels. Our findings suggest that a reliable relative quantification is viable using peptide ion intensities when protein levels in two gels have to be compared. In the case of 2D gels, label free MS quantification provides more precise results on changes of protein expression levels than gel spot intensity-based measurements, especially in the case of overlapping proteins. Absolute amounts of different proteins in 1D or 2D gels can be evaluated to a reasonable precision.     

How many spots with missing values can be tolerated in quantitative two-dimensional gel electrophoresis when applying univariate statistics?

Quantitative proteomic comparisons require a sufficient number of samples to reach an acceptable level of significance. But 2D gel electrophoresis commonly results in incomplete data sets due to spots with missing values reducing thereby the number of parallel measurements for individual proteins. Here we investigated how many missing values per spot can be tolerated. The number of spots in common between all gels was found to decrease with the number of parallel gels in a non-linear fashion. Increasing numbers of missing values were associated with a moderate increase in the quantitative variation of spot volumes. Based on the missing value pattern in 20 gels we performed an analysis of the multiple testing power for the hypothetical scenario of a comparative 2DE study with six or twelve parallel gels. The calculation considered the statistical power of the individual spot as well as the number of spots included in the analysis. The power increased with inclusion of spots with higher number of missing values and showed an optimum at a specific minimum number of spot replicates. The results suggest that proteins with missing values can be included in a univariate analysis as long as a sufficient number of parallel gels are made.     

Identification of PSME3 as a novel serum tumor marker for colorectal cancer by combining two-dimensional polyacrylamide gel electrophoresis with a strictly mass spectrometry-based approach for data analysis

The purpose of this study was to identify and validate novel serological protein biomarkers of human colorectal cancer (CRC). Proteins from matched CRC and adjacent normal tissue samples were resolved by two-dimensional gel electrophoresis. From each gel all spots were excised, and enveloped proteins were identified by MS. By comparison of the resulting protein profiles, dysregulated proteins can be identified. A list of all identified proteins and validation of five exemplarily selected proteins, elevated in CRC was reported previously (Roessler, M., Rollinger, W., Palme, S., Hagmann, M. L., Berndt, P., Engel, A. M., Schneidinger, B., Pfeffer, M., Andres, H., Karl, J., Bodenmuller, H., Ruschoff, J., Henkel, T., Rohr, G., Rossol, S., Rosch, W., Langen, H., Zolg, W., and Tacke, M. (2005) Identification of nicotinamide N-methyltransferase as a novel serum tumor marker for colorectal cancer. Clin. Cancer Res. 11, 6550-6557). Here we describe identification and initial validation of another potential marker protein for CRC. Comparison of tissue protein profiles revealed strong elevation of proteasome activator complex subunit 3 (PSME3) expression in CRC tissue. This dysregulation was not detectable based on the spot pattern. The PSME3-containing spot on tumor gels showed no visible difference to the corresponding spot on matched control gels. MS analysis revealed the presence of two proteins, PSME3 and annexin 4 (ANXA4) in one and the same spot on tumor gels, whereas the matched spot contained only one protein, ANXA4, on control gels. Therefore, dysregulation of PSME3 was masked by ANXA4 and could only be recognized by MS-based analysis but not by image analysis. To validate this finding, antibody to PSME3 was developed, and up-regulation in CRC was confirmed by Western blot analysis and immunohistochemistry. Finally by developing a highly sensitive immunoassay, PSME3 could be detected in human sera and was significantly elevated in CRC patients compared with healthy donors and patients with benign bowel disease. We propose that PSME3 be considered a novel serum tumor marker for CRC that may have significance in the detection and in the management of patients with this disease. Further studies are needed to fully assess the potential clinical value of this marker candidate.     

Determination of protein markers in human serum: Analysis of protein expression in toxic oil syndrome studies

Toxic oil syndrome (TOS) is a disease that appeared in Spain in 1981. It affected more than 20 000 people and produced over 300 deaths in the first 2 years. In this paper, a prospective study on the differences in gene expression in sera between a control versus a TOS-affected population, both originally exposed to the toxic oil, is presented. Differential protein expression was analyzed by two-dimensional electrophoresis (2-DE). Several problems related with serum analysis by 2-DE were addressed in order to improve protein detection in the gel images. Three new commercial systems for albumin depletion were tested to optimize the detection of minor proteins that can be obscured by the presence of a few families of high abundance proteins (albumin, immunoglobulins). Other factors, such as the use of nonionic reductants or the presence of thiourea in the gels, were also tested. From these optimized images, a group of 329 major gel spots was located, matched and compared in serum samples. Thirty-five of these protein spots were found to be under- or overexpressed in TOS patients (> three-fold increase or decrease). Proteins in the differential spots were identified by matrix-assisted laser desorption/ionization-time of flight peptide map fingerprinting and database search. Several haptoglobin isoforms were found to be differentially expressed, showing expression phenotypes that could be related with TOS affection. Haptoglobin phenotypes have been previously reported to have important biological and clinical consequences and have been described as risk factors for several diseases.     

Largest contour segmentation: a tool for the localization of spots in confocal images

An accurate determination of the 3-D positions of multiple spots in images obtained by confocal microscopy is essential for the investigation of the spatial distribution of specific components or processes in biological specimens. The position of the centroid, as an estimator for the position of a spot, can be calculated on the basis of all voxels that belong to the domain of the spot. For this calculation a domain that defines which voxels belong to the spot must be delimited. To create a boundary for a domain we developed a 3-D image segmentation procedure: the largest contour segmentation (LCS). This procedure is based on an iterative region-growing procedure around each local maximum of intensity. By means of this procedure the position of each spot was determined accurately and automatically. Qualities of the procedure were evaluated by means of simulated test-images as well as 3-D images of real biological specimens.