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.     

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).     

Computer analysis of two-dimensional gels

New methods and computer programs are described which enable one to analyze autoradiograms produced by two-dimensional gel electrophoresis. These programs are completely automatic with respect to finding spots resolved by such gels and quantitating the radioactivity in them. Semiautomatic programs have also been developed to match the spot patterns of different autoradiograms, and to follow the synthesis of any individual polypeptide through a series of gels.     

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).     

A proteomics study of in vitro cyst germination and appressoria formation in Phytophthora infestans

A proteomics study using two-dimensional gel electrophoresis (2-DE) and mass spectrometry was performed on Phytophthora infestans. Proteins from cysts, germinated cysts and appressoria grown in vitro were isolated and separated by 2-DE. Statistical quantitative analysis of the protein spots from five independent experiments of each developmental stage revealed significant up-regulation of ten spots on gels from germinated cysts compared to cysts. Five spots were significantly up-regulated on gels from appressoria compared to germinated cysts and one of these up-regulated spots was not detectable on gels from cysts. In addition, one spot was significantly down-regulated and another spot not detectable on the gels from appressoria. The corresponding proteins to 13 of these spots were identified with high confidence using tandem mass spectrometry and database searches. The functions of the proteins that were up-regulated in germinated cysts and appressoria can be grouped into the following categories: protein synthesis (e.g. a DEAD box RNA helicase), amino acid metabolism, energy metabolism and reactive oxygen species scavenging. The spot not detected in appressoria was identified as the P. infestans crinkling- and necrosis-inducing protein CRN2. The identified proteins are most likely involved in the establishment of the infection of the host plant.     

The role of bioinformatics in two-dimensional gel electrophoresis

Over the last two decades, two-dimensional electrophoresis (2-DE) gel has established itself as the de facto approach to separating proteins from cell and tissue samples. Due to the sheer volume of data and its experimental geometric and expression uncertainties, quantitative analysis of these data with image processing and modelling has become an actively pursued research topic. The results of these analyses include accurate protein quantification, isoelectric point and relative molecular mass estimation, and the detection of differential expression between samples run on different gels. Systematic errors such as current leakage and regional expression inhomogeneities are corrected for, followed by each protein spot in the gel being segmented and modelled for quantification. To assess differential expression of protein spots in different samples run on a series of two-dimensional gels, a number of image registration techniques for correcting geometric distortion have been proposed. This paper provides a comprehensive review of the computation techniques used in the analysis of 2-DE gels, together with a discussion of current and future trends in large scale analysis. We examine the pitfalls of existing techniques and highlight some of the key areas that need to be developed in the coming years, especially those related to statistical approaches based on multiple gel runs and image mining techniques through the use of parallel processing based on cluster computing and the grid technology.     

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     

Analyses of genetic variants of l-glycerol-3-phosphate dehydrogenase in Drosophila melanogaster by two-dimensional gel electrophoresis and immunoelectrophoresis

A protein spot corresponding to l-glycerol-3-phosphate dehydrogenase (α-GPDH, E.C. 1.1.1.8, NAD⁺ oxidoreductase) has been identified on a two-dimensional gel (isoelectric focusing-SDS gel) containing up to 150 stained protein spots from a crude Drosophila homogenate. Preliminary identification of the α-GPDH spot was made by including a suitable amount of purified Drosophila α-GPDH in crude fly homogenates prior to electrophoresis and observing an intensity enhancement of the corresponding protein spot on the gels. When three purified electrophoretic variants (slow, fast, and ultrafast) were mixed and analyzed by two-dimensional gel electrophoresis, horizontal displacements of the three protein spots were observed. Immunoprecipitation of the enzyme prior to electrophoresis and gene mapping further confirmed the identity of the α-GPDH protein spot. The α-GPDH spot can also be detected by autoradiography of a two-dimensional gel from a single fly extract, where it has been estimated to constitute 0.5–1% of the total soluble protein. Mutants which express no apparent α-GPDH activity were analyzed by two-dimensional gels and immunoelectrophoresis in an attempt to identify and characterize the inactive proteins. It is suggested that these techniques provide a powerful tool for the analysis of CRM⁺-null activity mutants of a specific gene-enzyme system.     

Computer analysis of double-labeled two-dimensional electrophoresis gels

A computerized process for the automatic analysis of double-label autoradiography after two-dimensional polyacrylamide gel electrophoresis has been developed. Matching fluorographs and autoradiographs produced from gels containing 3H- and 14C-labeled proteins are digitized by a rotating drum densitometer and analyzed by the Man-computer Interactive Data Analysis System III. This system locates corresponding protein spots in the films with edge-detection algorithms, converts spot density readings to isotopic disintegrations by reference to standard curves, and computes a 3H:14C ratio for each spot in the gels. On the average, calculated ratios are accurate to approximately 9% for test strips of polyacrylamide gel containing uniform mixtures of 3H and 14C. Values obtained for two-dimensional gels containing n protein spots with a known 3H:14C ratio of 8.6 +/- 0.1 are as follows: 8.1 +/- 1.4 (n = 268), 8.8 +/- 2.1 (n = 278), 9.1 +/- 1.7 (n = 245), and 8.8 +/- 2.2 (n = 223). The computer process greatly reduces the time required to precisely compare two complex protein mixtures and has sufficient precision to detect a doubling in the biosynthesis of any individual protein.     

A multiple high-resolution mini two-dimensional polyacrylamide gel electrophoresis system: imaging two-dimensional gels using a cooled charge-coupled device after staining with silver or labeling with fluorophore.

A multiple mini two-dimensional electrophoretic method which results in three two-dimensional protein spot patterns being positioned side by side in an individual gel has been developed. Preparation time has been minimized by employing disposable capillary tubes for the isoelectric focusing gels and reducing the number of second-dimensional gels required. Commercially available vertical slab units were used for the second-dimensional electrophoresis. The protein spot patterns were visualized either by staining the second-dimensional gel with silver or fluorescently labeling the focused proteins while present in the isoelectric focusing gel and subsequently electrophoresing them into the second-dimensional gel. The fluorescently labeled second-dimensional gel was imaged while still present in the glass mold immediately following electrophoresis. Two fluorophores were compared: 2-methoxy-2,4-diphenyl-3(2H)-furanone and 5-(4,6-dichlorotriazin-2-yl)aminofluorescein hydrochloride. A rapid imaging system based on a cooled charge-coupled device was used to view both the silver-stained and fluorescently labeled two-dimensional spot patterns. The sensitivity of detection of protein spots in the mini two-dimensional gels was similar for the two types of fluorescently labeled gels and the silver-stained gels.     

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.     

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.     

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.     

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

In image analysis of two-dimensional electrophoresis gels, individual spots need to be identified and quantified. Two classes of algorithms are commonly applied to this task. Parametric methods rely on a model, making strong assumptions about spot appearance, but are often insufficiently flexible to adequately represent all spots that may be present in a gel. Nonparametric methods make no assumptions about spot appearance and consequently impose few constraints on spot detection, allowing more flexibility but reducing robustness when image data is complex. We describe a parametric representation of spot shape that is both general enough to represent unusual spots, and specific enough to introduce constraints on the interpretation of complex images. Our method uses a model of shape based on the statistics of an annotated training set. The model allows new spot shapes, belonging to the same statistical distribution as the training set, to be generated. To represent spot appearance we use the statistically derived shape convolved with a Gaussian kernel, simulating the diffusion process in spot formation. We show that the statistical model of spot appearance and shape is able to fit to image data more closely than the commonly used spot parameterizations based solely on Gaussian and diffusion models. We show that improvements in model fitting are gained without degrading the specificity of the representation.     

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.     

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.     

Polar electrophoresis: shape of two-dimensional maps is as important as size

The performance of two-dimensional electrophoresis in conventional gels in Cartesian coordinates (2-DE) vs. polar coordinates (2-PE) is here evaluated. Although 2-DE is performed in much longer Immobiline gels in the first dimension (17 cm) vs. barely 7-cm in 2-PE, an equivalent resolving power is found. Moreover, due to the possibility of running up to seven Immobiline strips in the radial gel format, the reproducibility of spot position is seen to be higher, this resulting in a 20% higher matching efficiency. As an extra bonus, strings of "isobaric" spots (i.e. polypeptides of identical mass with different pI values) are more resolved in the radial gel format, especially in the 10 to 30 kDa region, where the gel area fans out leaving extra space for spot resolution. In conclusion, this novel gel format in the second dimension of 2D gels is seen as an important improvement of this technique, still one of the most popular in proteome 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.     

Computer analysis of two-dimensional gels.

This work identifies statistical algorithms which need to be included in analysis of two-dimensional gels for accurate determination of differential changes. Two-dimensional electrophoresis is a powerful tool for determining differential protein expression in complex mixtures, but the methodology, to date, is not producing expected results due to the degree of gel variability. The new DIGE procedure, comparing two samples in the same gel, does eliminate some of the variability introduced with gel-to-gel comparison, but still has variability due to differences in dye binding, charge, and fluorescence. Introducing quality-assurance statistical algorithms is necessary to extract meaningful data from the gels. A quality-control analysis of replicate gels needs to be performed prior to using the set in the final analysis. Increasing replicates to five from the usual three can only add greater variability. A statistical "replicate quality" gel test needs to be done on the computer gel scans, and replicates with greater than 20-30% variability should not be used. In addition, since spot intensity data are not normally distributed, spot differential analysis cannot be a t-test. The Studentized Range has been suggested as a more accurate method for calculating significant difference.     

A Novel Gaussian Extrapolation Approach for 2D Gel Electrophoresis Saturated Protein Spots

Analysis of images obtained from two-dimensional gel electrophoresis (2D-GE) is a topic of utmost importance in bioinformatics research, since commercial and academic software available currently has proven to be neither completely effective nor fully automatic, often requiring manual revision and refinement of computer generated matches. In this work, we present an effective technique for the detection and the reconstruction of over-saturated protein spots. Firstly, the algorithm reveals overexposed areas, where spots may be truncated, and plateau regions caused by smeared and overlapping spots. Next, it reconstructs the correct distribution of pixel values in these overexposed areas and plateau regions, using a two-dimensional least-squares fitting based on a generalized Gaussian distribution. Pixel correction in saturated and smeared spots allows more accurate quantification, providing more reliable image analysis results. The method is validated for processing highly exposed 2D-GE images, comparing reconstructed spots with the corresponding non-saturated image, demonstrating that the algorithm enables correct spot quantification.