HERMeS: A second generation approach to the automatic analysis of two-dimensional electrophoresis gels. Part V: Data analysis

A proteomic view of cellular responses of macrophages to copper when added as ion or as copper-polyacrylate complex

Copper is an essential metal for life, but is toxic at high concentrations. In mammalian cells, two copper transporters are known, CTR1 and CTR2. In order to gain insights on the possible influence of the import pathway on cellular responses to copper, two copper challenges were compared: one with copper ion, which is likely to use preferentially CTR1, and one with a copper-polyacrylate complex, which will be internalized via the endosomal pathway and is likely to use preferentially CTR2. A model system consisting in the J774A1 mouse macrophage system, with a strong endosomal/lysosomal pathway, was used. In order to gain wide insights into the cellular responses to copper, a proteomic approach was used. The proteomic results were validated by targeted experiments, and showed differential effects of the import mode on cellular physiology parameters. While the mitochondrial transmembrane potential was kept constant, a depletion in the free glutahione content was observed with copper (ion and polylacrylate complex). Both copper-polyacrylate and polyacrylate induced perturbations in the cytoskeleton and in phagocytosis. Inflammatory responses were also differently altered by copper ion and copper-polyacrylate. Copper-polyacrylate also perturbed several metabolic enzymes. Lastly, enzymes were used as a test set to assess the predictive value of proteomics. SIGNIFICANCE: Proteomic profiling provides an in depth analysis of the alterations induced on cells by copper under two different exposure modes to this metal, namely as the free ion or as a complex with polyacrylate. The cellular responses were substantially different between the two exposure modes, although some cellular effects are shared, such as the depletion in free glutathione. Targeted experiments were used to confirm the proteomic results. Some metabolic enzymes showed altered activities after exposure to the copper-polyacrylate complex. The basal inflammatory responses were different for copper ion and for the copper-polyacrylate complex, while the two forms of copper inhibited lipopolysaccharide-induced inflammatory responses.

The Whereabouts of 2D Gels in Quantitative Proteomics

Two-dimensional gel electrophoresis has been instrumental in the development of proteomics. Although it is no longer the exclusive scheme used for proteomics, its unique features make it a still highly valuable tool, especially when multiple quantitative comparisons of samples must be made, and even for large samples series. However, quantitative proteomics using two-dimensional gels is critically dependent on the performances of the protein detection methods used after the electrophoretic separations. This chapter therefore examines critically the various detection methods, (radioactivity, dyes, fluorescence, and silver) as well as the data analysis issues that must be taken into account when quantitative comparative analysis of two-dimensional gels is performed.

The latest advancements in proteomic two-dimensional gel electrophoresis analysis applied to biological samples

Two-dimensional gel electrophoresis (2DE) is one of the fundamental approaches in proteomics for the separation and visualization of complex protein mixtures. Proteins can be analyzed by 2DE using isoelectric focusing (IEF) in the first dimension, combined to sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) in the second dimension, gel staining (silver and Coomassie), image analysis, and 2DE gel database. High-resolution 2DE can resolve up to 5,000 different proteins simultaneously (∼2,000 proteins routinely), and detect and quantify <1 ng of protein per spot. Here, we describe the latest developments for a more complete analysis of biological fluids.

The whereabouts of 2D gels in quantitative proteomics

Two-dimensional gel electrophoresis has been instrumental in the development of proteomics. Although it is no longer the exclusive scheme used for proteomics, its unique features make it a still highly valuable tool, especially when multiple quantitative comparisons of samples must be made, and even for large samples series. However, quantitative proteomics using 2D gels is critically dependent on the performances of the protein detection methods used after the electrophoretic separations. This chapter therefore examines critically the various detection methods (radioactivity, dyes, fluorescence, and silver) as well as the data analysis issues that must be taken into account when quantitative comparative analysis of 2D gels is performed.

Two-dimensional gel electrophoresis in proteomics: a tutorial

Two-dimensional electrophoresis of proteins has preceded, and accompanied, the birth of proteomics. Although it is no longer the only experimental scheme used in modern proteomics, it still has distinct features and advantages. The purpose of this tutorial paper is to guide the reader through the history of the field, then through the main steps of the process, from sample preparation to in-gel detection of proteins, commenting the constraints and caveats of the technique. Then the limitations and positive features of two-dimensional electrophoresis are discussed (e.g. its unique ability to separate complete proteins and its easy interfacing with immunoblotting techniques), so that the optimal type of applications of this technique in current and future proteomics can be perceived. This is illustrated by a detailed example taken from the literature and commented in detail. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP 2).

Two-dimensional gel electrophoresis in proteomics: Past, present and future

Two-dimensional gel electrophoresis has been instrumental in the birth and developments of proteomics, although it is no longer the exclusive separation tool used in the field of proteomics. In this review, a historical perspective is made, starting from the days where two-dimensional gels were used and the word proteomics did not even exist. The events that have led to the birth of proteomics are also recalled, ending with a description of the now well-known limitations of two-dimensional gels in proteomics. However, the often-underestimated advantages of two-dimensional gels are also underlined, leading to a description of how and when to use two-dimensional gels for the best in a proteomics approach. Taking support of these advantages (robustness, resolution, and ability to separate entire, intact proteins), possible future applications of this technique in proteomics are also mentioned.

Power and limitations of electrophoretic separations in proteomics strategies

Proteomics can be defined as the large-scale analysis of proteins. Due to the complexity of biological systems, it is required to concatenate various separation techniques prior to mass spectrometry. These techniques, dealing with proteins or peptides, can rely on chromatography or electrophoresis. In this review, the electrophoretic techniques are under scrutiny. Their principles are recalled, and their applications for peptide and protein separations are presented and critically discussed. In addition, the features that are specific to gel electrophoresis and that interplay with mass spectrometry (i.e., protein detection after electrophoresis, and the process leading from a gel piece to a solution of peptides) are also discussed.

Evaluation of the variables characterized by significant discriminating power in the application of SIMCA classification method to proteomic studies

SIMCA classification can be applied to 2D-PAGE maps to identify changes occurring in cellular protein contents as a consequence of illnesses or therapies. These data sets are complex to treat due to the large number of proteins detected. A method for identifying relevant proteins from SIMCA discriminating powers is proposed, based on the Box-Cox transformation coupled to probability papers. The method successfully allowed the identification of the relevant spots from 2D maps.

Application of partial least squares discriminant analysis and variable selection procedures: a 2D-PAGE proteomic study

2D gel electrophoresis is a tool for measuring protein regulation, involving image analysis by dedicated software (PDQuest, Melanie, etc.). Here, partial least squares discriminant analysis was applied to improve the results obtained by classic image analysis and to identify the significant spots responsible for the differences between two datasets. A human colon cancer HCT116 cell line was analyzed, treated and not treated with a new histone deacetylase inhibitor, RC307. The proteins regulated by RC307 were detected by analyzing the total lysates and nuclear proteome profiles. Some of the regulated spots were identified by tandem mass spectrometry. The preliminary data are encouraging and the protein modulation reported is consistent with the antitumoral effect of RC307 on the HCT116 cell line. Partial least squares discriminant analysis coupled with backward elimination variable selection allowed the identification of a larger number of spots than classic PDQuest analysis. Moreover, it allows the achievement of the best performances of the model in terms of prediction and provides therefore more robust and reliable results. From this point of view, the multivariate procedure applied can be considered a good alternative to standard differential analysis, also taking into account the interdependencies existing among the variables.

2D-PAGE maps analysis

Due to the low reproducibility affecting 2D gel-electrophoresis and the complex maps provided by this technique, the use of effective and robust methods for the comparison and classification of 2D maps is a fundamental tool for the development of automated diagnostic methods. A review of classical and recently developed methods for the comparison of 2D maps is presented here. The methods proposed regard both the analysis of spot volume datasets through multivariate statistical tools (pattern recognition methods, cluster analysis, and classification methods) and the analysis of 2D map images through fuzzy logic, three-way PCA, and the use of moment functions. The theoretical basis of each procedure is briefly introduced, together with a review of the most interesting applications present in recent literature.

Multivariate statistical tools applied to the characterization of the proteomic profiles of two human lymphoma cell lines by two-dimensional gel electrophoresis

Mantle cell lymphoma (MCL) cell lines have been difficult to generate, since only few have been described so far and even fewer have been thoroughly characterized. Among them, there is only one cell line, called GRANTA-519, which is well established and universally adopted for most lymphoma studies. We succeeded in establishing a new MCL cell line, called MAVER-1, from a leukemic MCL, and performed a thorough phenotypical, cytogenetical and molecular characterization of the cell line. In the present report, the phenotypic expression of GRANTA-519 and MAVER-1 cell lines has been compared and evaluated by a proteomic approach, exploiting 2-D map analysis. By univariate statistical analysis (Student's t-test, as commonly used in most commercial software packages), most of the protein spots were found to be identical between the two cell lines. Thirty spots were found to be unique for the GRANTA-519, whereas another 11 polypeptides appeared to be expressed only by the MAVER-1 cell line. A number of these spots could be identified by MS. These data were confirmed and expanded by multivariate statistical tools (principal component analysis and soft-independent model of class analogy) that allowed identification of a larger number of differently expressed spots. Multivariate statistical tools have the advantage of reducing the risk of false positives and of identifying spots that are significantly altered in terms of correlated expression rather than absolute expression values. It is thus suggested that, in future work in differential proteomic profiling, both univariate and multivariate statistical tools should be adopted.

Multivariate approaches in plant science

The objective of proteomics is to get an overview of the proteins expressed at a given point in time in a given tissue and to identify the connection to the biochemical status of that tissue. Therefore sample throughput and analysis time are important issues in proteomics. The concept of proteomics is to encircle the identity of proteins of interest. However, the overall relation between proteins must also be explained. Classical proteomics consist of separation and characterization, based on two-dimensional electrophoresis, trypsin digestion, mass spectrometry and database searching. Characterization includes labor intensive work in order to manage, handle and analyze data. The field of classical proteomics should therefore be extended to also include handling of large datasets in an objective way. The separation obtained by two-dimensional electrophoresis and mass spectrometry gives rise to huge amount of data. We present a multivariate approach to the handling of data in proteomics with the advantage that protein patterns can be spotted at an early stage and consequently the proteins selected for sequencing can be selected intelligently. These methods can also be applied to other data generating protein analysis methods like mass spectrometry and near infrared spectroscopy and examples of application to these techniques are also presented. Multivariate data analysis can unravel complicated data structures and may thereby relieve the characterization phase in classical proteomics. Traditionally statistical methods are not suitable for analysis of the huge amounts of data, where the number of variables exceed the number of objects. Multivariate data analysis, on the other hand, may uncover the hidden structures present in these data. This study takes its starting point in the field of classical proteomics and shows how multivariate data analysis can lead to faster ways of finding interesting proteins. Multivariate analysis has shown interesting results as a supplement to classical proteomics and added a new dimension to the field of proteomics.

A mass spectrometric journey into protein and proteome research

It is a frequently debated question whether technology drives biology or whether biology drives the development of new technologies. This issue is discussed in this manuscript as an account that covers approximately a decade during which mass spectrometry and protein biochemistry have intersected. It is shown that the capabilities of the mass spectrometric methods, initially developed to address the specific need to identify proteins reliably and at high sensitivity soon transcended the intended task. The rapid development of mass spectrometric technologies applied to protein research has catalyzed entirely new experimental approaches and opened up new types of biological questions to experimentation, culminating in the field of proteomics. Some conclusions from this case study relating to technological research and the environment in which it is carried out are also discussed.

Proteomics: the first decade and beyond

Proteomics is the systematic study of the many and diverse properties of proteins in a parallel manner with the aim of providing detailed descriptions of the structure, function and control of biological systems in health and disease. Advances in methods and technologies have catalyzed an expansion of the scope of biological studies from the reductionist biochemical analysis of single proteins to proteome-wide measurements. Proteomics and other complementary analysis methods are essential components of the emerging 'systems biology' approach that seeks to comprehensively describe biological systems through integration of diverse types of data and, in the future, to ultimately allow computational simulations of complex biological systems.

Quantitative and reproducible two-dimensional gel analysis using Phoretix 2D Full

Quantitative two-dimensional polyacrylamide gel electrophoresis (2-D PAGE) is used to determine changes in individual protein levels in complex protein mixtures. To provide reliable data, the software used for 2-D gel image analysis must provide a linear response over a wide dynamic range of data output. Here, we show that Phoretix 2D Full analysis of 2-D gels stained with colloidal Coomassie Brilliant Blue G-250 can provide a linear measure of changes in protein quantity. We show using a complex mixture of Arabidopsis thaliana proteins, that this is true for essentially all focused proteins, in a data output range greater than three orders of magnitude. An analysis of the factors that affect errors in the results demonstrated that reproducibility of the data is significantly improved by user seeding, whereas it is reduced by use of the background subtraction algorithms.

Apolipoprotein E, transthyretin and actin in the CSF of Alzheimer's patients: relation with the senile plaques and cytoskeleton biochemistry

We measured the levels of two beta-amyloid (Abeta)-sequestering proteins, apolipoprotein (Apo) E and transthyretin (TTR), in ventricular human cerebrospinal fluid (CSF) of Alzheimer's disease (AD) patients and controls in relation to brain histological findings. We also studied actin levels in CSF as a marker of the biochemical role of these two proteins in the cytoskeleton. We show that TTR levels in CSF were significantly decreased in AD patients compared to controls and negatively correlated with the senile plaque (SP) abundance. Moreover, actin levels were positively linked to TTR levels and increased in CSF samples of patients homozygous for the ApoE epsilon4-allele. We propose that TTR and ApoE4 may have competition in the aggregation of Abeta and its deposition in the SP of AD brain. The relationships between ApoE, TTR and actin could suggest a metabolic implication of ApoE genetics and TTR levels in cytoskeletal biochemistry which may be relevant to the pathogenesis of AD.

Adaptive classification of two-dimensional gel electrophoretic spot patterns by neural networks and cluster analysis

The interpretation of two-dimensional gel electrophoresis spot profiles can be facilitated by statistical and machine learning programs. Two different approaches to classification of spot profiles - cluster analysis and neural networks - are discussed. Neural networks for two different model patterns were designed and an algorithm for training of the net for the classification was developed. It was shown that the performance of neural networks is higher compared to cluster and principal component analysis. The possibility of combining both approaches into one process can increase reliability and speed of classification. Artificially created training sets with added random noise can be used for network training. The analysis was applied on the Streptomyces coelicolor developmental two-dimensional (2-D) gel database.

Fine resolution of histones by two-dimensional polyacrylamide gel electrophoresis: developmental implications

A two-dimensional electrophoresis for fine separation of histones is described in detail. The method is relatively simple and gives very reproducible results. In the first dimension the histones are separated by their charge in acid-urea gels, while in the second dimension the separation is based on both the charge and the differential affinity of histones to Triton in acid-urea-Triton gels. In this electrophoretic system, the linker histones are resolved on the gel diagonal, while the core histones are separated above the diagonal. The electrophoresis is very sensitive to charge effect, and thus it is very well suited to resolving histone-modified forms. The application of the two-dimensional electrophoresis in Xenopus developmental studies is illustrated.

Computational method to predict mitochondrially imported proteins and their targeting sequences

Most of the proteins that are used in mitochondria are imported through the double membrane of the organelle. The information that guides the protein to mitochondria is contained in its sequence and structure, although no direct evidence can be obtained. In this article, discriminant analysis has been performed with 47 parameters and a large set of mitochondrial proteins extracted from the SwissProt database. A computational method that facilitates the analysis and objective prediction of mitochondrially imported proteins has been developed. If only the amino acid sequence is considered, 75-97% of the mitochondrial proteins studied have been predicted to be imported into mitochondria. Moreover, the existence of mitochondrial-targeting sequences is predicted in 76-94% of the analyzed mitochondrial precursor proteins. As a practical application, the number of unknown yeast open reading frames that might be mitochondrial proteins has been predicted, which revealed that many of them are clustered.

Evolution of biological regulation networks under complex environmental constraints

This work investigates the influence of environmental inducers on the organization of cell regulation networks, using a connectionist approach. Protein interactions are modeled by an asymmetrical recurrent network, the units of which take continuous values. In contrast to classical models, we explicitly introduce a genome to encode the architecture of the system. This feature enables us to introduce an evolution model, in which a genetic algorithm that mimics the effects of evolution on proteins mutual interactions is used. We assume an efficient system to respond to persistent environmental stimuli, independently of their amplitude. Results are presented that show a structuration of the network with the emergence of specialized hierarchal structures. These structures seem to drive the system at the edge of chaos, so that it can present adapted responses to significant environmental changes.

Two-dimensional electrophoresis of proteins: an updated protocol and implications for a functional analysis of the genome

The two-dimensional electrophoresis (2-DE) technique developed by Klose in 1975 (Humangenetik 1975, 26, 211-234), independently of the technique developed by O'Farrell (J. Biol. Chem. 1975, 250, 4007-4021), has been revised in our laboratory and an updated protocol is presented. This protocol is the result of our experience in using this method since its introduction. Many modifications and suggestions found in the literature were also tested and then integrated into our original method if advantageous. Gel and buffer composition, size of gels, use of stacking gels or not, necessity of isoelectric focusing (IEF) gel incubation, freezing of IEF gels or immediate use, carrier ampholytes versus Immobilines, regulation of electric current, conditions for staining and drying the gels - these and other problems were the subject of our concern. Among the technical details and special equipment which constitute our 2-DE method presented here, a few features are of particular significance: (i) sample loading onto the acid side of the IEF gel with the result that both acidic and basic proteins are well resolved in the same gel; (ii) use of large (46 x 30 cm) gels to achieve high resolution, but without the need of unusually large, flat gel equipment; (iii) preparation of ready-made gel solutions which can be stored frozen, a prerequisite, among others, for high reproducibility. Using the 2-DE method described we demonstrate that protein patterns revealing more than 10 000 polypeptide spots can be obtained from mouse tissues. This is by far the highest resolution so far reported in the literature for 2-DE of complex protein mixtures. The 2-DE patterns were of high quality with regard to spot shape and background. The reproducibility of the protein patterns is demonstrated and shown to be thoroughly satisfactory. An example is given to show how effectively 2-DE of high resolution and reproducibility can be used to study the genetic variability of proteins in an interspecific mouse backcross (Mus musculus x Mus spretus) established by the European Backcross Collaborative Group for mapping the mouse genome. We outline our opinion that the structural analysis of the human genome, currently pursued most intensively on a worldwide scale, should be accompanied by a functional analysis of the genome that starts from the proteins of the organism.

Uses of digital image analysis in electrophoresis

The information in electrophoretic gels can be extracted visually, or by using specialised computer hardware and software which treat the gels as digital images. We discuss issues which arise in applying digital image analysis to electrophoresis, and comment on the range of computer packages available. We also illustrate how image analysis can make gel interpretation easier and more reliable. Methods covered include gel registration and warping, mathematical morphology and deconvolution to sharpen images.

Analysis of scaling methods to minimize experimental variations in two-dimensional electrophoresis quantitative data: application to the comparison of maize inbred lines

The analysis of two-dimensional (2-D) electrophoresis quantitative data from a design involving 21 maize genotypes revealed a significant experimental variation. In order to minimize this variation, we investigated the possible causes and found that it was essentially due to global effects, affecting all the spots in a gel in a similar way, and occurring during the 2-D run/staining procedure. Three scaling methods to discard these experimental variations were analyzed: the linear scaling method, a method based on principal component analysis, and a combined method that unites the advantages of both of the former. Comparing these three methods, we found that they led to consistent results with regard to the factor under study, i.e. the genetic factor in our case. However, the combined scaling method was the most efficient in reducing experimental variations.

Superimposing two-dimensional gels to study genetic variation in malaria parasites

Two-dimensional polyacrylamide gel electrophoresis is a valuable tool for studying genetic variation in the human malaria parasite, Plasmodium falciparum. It involves examining the position of protein spots in gel produced from different isolates. Some spots have been seen to vary, while others have had a constant position in all isolates so far examined. These invariant spots provide a reference frame to compare variations in other spots. This paper discusses the usefulness of digital image handling, warping and superimposition in a personal computer environment. Rather than produce a fully automatic interpretation system, we show how the computer may be used as a tool for manipulating gel images, although interpretation of the gels' features remains with the human expert. Autoradiographs are scanned on a desktop scanner, and the images in digital form can be displayed on a monitor attached to a personal computer. The coordinates of the invariant spots on each of several gels are identified by the user. Each of the gels is then warped so that the invariant spots of all the gels coincide as closely as possible. The variable spots are then examined. We have used both affine warping transformations, which match the invariant spots as closely as possible, and thin plate spline transformations, which match them exactly. Colour superimposition proved a useful way of examining the gels.

Classification of crossed immunoelectrophoretic patterns using digital image processing and artificial neural networks

A method is presented which makes it possible to present crossed immunoelectrophoretic patterns to an artificial neural network. The electrophoretic patterns are presented for the artificial neural network as three-dimensional vectors and it is shown that it is possible with this representation to train the network to learn the patterns and classify them. It was found that the ability to generalize was substantially increased by the addition of noise to the input patterns during training. Furthermore, the addition of noise decreased the number of presentations needed to reach the predetermined error level. The trained neural network was able to classify all distorted patterns correctly within an error range of 1%.

Global changes in gene expression related to antibiotic synthesis in Streptomyces hygroscopicus

Two-dimensional gel electrophoresis was used to follow changes in gene expression associated with antibiotic (bialaphos) biosynthesis in Streptomyces hygroscopicus. Cultures were pulse-labelled with [35S]-methionine before, during, and after the switch from primary to secondary metabolism in order to compare kinetic profiles of bialaphos (antibiotic) production (bap) genes during this metabolic transition. Separation of gene products on two-dimensional gels revealed that 27 were dependent on brpA for optimal expression and were activated as the culture approached stationary phase. Genes which encoded 10 brpA-dependent proteins were mapped to a 10 kb SstI fragment of the 35 kb bap gene cluster by expressing them in Streptomyces lividans using the thiostrepton-inducible tipA promoter. N-terminal amino acid sequences of two brpA-dependent proteins, obtained by direct microsequencing of protein spots excised from two-dimensional gels, identified them as gene products mapping to the same region and involved in secondary metabolic conversions of the bap pathway. The kinetics of synthesis of 16 brpA-dependent gene products were characterized using QUEST computer software. Cluster analysis performed on the kinetics of synthesis of 346 of the most highly expressed gene products of HP5-29, including 16 brpA-dependent ones, identified 75 families having distinct patterns of expression. Many brpA-dependent proteins were clustered together; 10 were found in one kinetic family. These kinetic families also included brpA-independent gene products perhaps subject to similar regulatory mechanisms and thus possibly involved in bialaphos biosynthesis. The activation/derepression of bap expression took place as cultures approached stationary phase and was temporally related to synthesis of ppGpp.

Use of principal components analysis for mutation detection with two-dimensional electrophoresis protein separations

The application of two-dimensional electrophoresis (2-DE) to mutation detection requires the capability to monitor each protein in a 2-DE pattern for significant changes in abundance indicative of a mutation event. Previously, mutation searches were done using a univariate outlier detection method in which each protein spot was considered independently in a classical outlier search. An alternative approach to analysis of 2-DE patterns for quantitative changes is a multivariate procedure which takes advantage of the observation that protein spots in a 2-DE pattern often represent correlated rather than independent measurements. We have compared the efficiency of univariate and multivariate procedures for mutation detection using data from the Argonne National Laboratory 2-DE database of mouse liver proteins. Analyses involving a total of over 1500 gels were performed to compare the performance of a multivariate method based on principal components analysis (PCA) with the univariate method. Up to 279 spots from each pattern were used for PCA. First, a simulation was performed to assess the detection efficiency of PCA for single protein spots decreased in abundance by 50%. Then, the ability to detect actual mutations was tested using eight confirmed mutations. Results show that, compared to a univariate approach to analysis of data from the mouse model system, the multivariate method increases the number of protein spots on each 2-DE pattern that can be monitored for quantitative changes indicative of mutations by compensating for variables that contribute to the background quantitative variability of protein spots.

The MELANIE project: from a biopsy to automatic protein map interpretation by computer

The goals of the MELANIE project are to determine if disease-associated patterns can be detected in high resolution two-dimensional polyacrylamide gel electrophoresis (HR 2D-PAGE) images and if a diagnosis can be established automatically by computer. The ELSIE/MELANIE system is a set of computer programs which automatically detect, quantify, and compare protein spots shown on HR 2D-PAGE images. Classification programs help the physician to find disease-associated patterns from a given set of two-dimensional gel electrophoresis images and to form diagnostic rules. Prototype expert systems that use these rules to establish a diagnosis from new HR 2D-PAGE images have been developed. They successfully diagnosed cirrhosis of the liver and were able to distinguish a variety of cancer types from biopsies known to be cancerous.

Analysis of two-dimensional electrophoretic patterns of proteins obtained from the sera of normal and tumor-bearing nude rats

To classify two-dimensional electrophoresis (2-DE) patterns of normal and abnormal samples, a new parameter, named shift value, is introduced. The shift value is defined by the product of three indices; differences in density, differences in area, and the Euclid distance between peaks of matched glycoprotein spots in the 2-DE patterns. Shift values obtained from the differences between the 2-DE patterns of tumor-bearing and normal sera (control) were always found to be larger than those obtained from two control patterns. The shift value obtained from glycoprotein spots could be more effectively used to distinguish normal and abnormal patterns than that obtained from simple proteins. In our earlier attempts to compare serum proteins of normal and tumor patients, we could not discriminate differences caused by genetic background. In order to circumvent this difficulty, we employed an inbred strain of nude rats into which various types of human cancer had been implanted. Reliability of the analysis is enhanced by using a polyacrylamide gel backed with a silanized glass support and an automatic 2-DE apparatus.

Quantitative exploration of the REF52 protein database: cluster analysis reveals the major protein expression profiles in responses to growth regulation, serum stimulation, and viral transformation

Quantitative protein databases reveal the response of cells to experimental variables, such as exposure to growth factors or transfection with a transforming gene. The nature of the response depends on the type of cell and its internal state at the time of the stimulus. By constructing a protein database to study a given cell line, we can better understand the differentiated state of the cell, the growth regulatory mechanisms it employs, the particular mechanisms it uses to cope with its environment, and the ways these mechanisms may have been compromised through mutation or transformation. The REF52 database is a quantitative database designed to study growth control and transformation in a well-defined family of normal and transformed rat cell lines. The database, which has been described and analyzed elsewhere (J. I. Garrels and B. R. Franza, J. Biol. Chem. 1989, 264, 5283-5298 and J. I. Garrels and B. R. Franza, J. Biol. Chem. 1989, 264, 5299-5312) is further explored here using cluster analysis. This method reveals the most common protein expression profiles for each series of two-dimensional gels without requiring any prior hypothesis or queries on the part of the investigator. This study reveals, for each experiment, large and small clusters of protein expression profiles, most of which have readily apparent biological meaning. For example, large clusters of proteins induced or repressed during growth to confluence have been revealed, and several clusters of transformation-sensitive proteins reveal differential effects of transformation by DNA- and RNA-tumor viruses. This analysis extends our earlier quantitative explorations of the REF52 protein database and helps to show how such a database can be used to provide context and guidance for molecular studies of regulation in a given cell system.

Image analysis techniques for automatic evaluation of two-dimensional electrophoresis

Techniques for automatic analysis of two-dimensional electrophoresis gels by computer-aided image analysis are described. Original gels or photographic films are scanned using a laser scanner and the files are transferred to a microcomputer. The program package first performs a compression and preevaluation of the files. Spot identification and quantification is performed by the chain code algorithm after appropriate zooming and cutting. Labeling facilitates spot identification and quantification in numerical and graphical (pseudocolor) representation on peripheral devices for camera ready output. Interpolation between measured basepoints is performed by cubic spline algorithms which are automatically switched on and off, depending on the need by the program. High speed analysis and graphic representation is achieved using fast Assembler language routines rather than high level languages. One-dimensional gels can be analyzed using the same software. Spot matching between parallel two-dimensional gels has not yet been implemented.

Two-dimensional electrophoresis aided by personal computer analysis for screening of mutant proteins in inherited diseases

A rapid and reproducible method of two-dimensional electrophoresis was developed for screening of abnormal proteins expressed in fibroblasts from patients with inherited diseases. After silver staining, the electrophoresis gel was subjected to semiautomatic digitizer-personal computer analysis: scanning with an image sensor video camera connected to a digitizer, followed by quantitative determination and statistical analysis with a personal computer. The protein spots analyzed by this method showed quantitative variations of various degrees, particularly in 2 of 247 spots examined. Seven spots were not always detected in control and pathological cells in this study. Slight variations in molecular weight were observed in 3 different spots.

Database and search techniques for two-dimensional gel protein data: a comparison of paradigms for exploratory data analysis and prospects for biological modeling

Two-dimensional (2-D) polyacrylamide gel electrophoresis can detect thousands of polypeptides, separating them by apparent molecular weight (Mr) and isoelectric point (pI). Thus it provides a more realistic and global view of cellular genetic expression than any other technique. This technique has been useful for finding sets of key proteins of biological significance. However, a typical experiment with more than a few gels often results in an unwiedly data management problem. In this paper, the GELLAB-II system is discussed with respect to how data reduction and exploratory data analysis can be aided by computer data management and statistical search techniques. By encoding the gel patterns in a "three-dimensional" (3-D) database, an exploratory data analysis can be carried out in an environment that might be called a "spread sheet for 2-D gel protein data". From such databases, complex parametric network models of protein expression during events such as differentiation might be constructed. For this, 2-D gel databases must be able to include data from other domains external to the gel itself. Because of the increasing complexity of such databases, new tools are required to help manage this complexity. Two such tools, object-oriented databases and expert-system rule-based analysis, are discussed in this context. Comparisons are made between GELLAB and other 2-D gel database analysis systems to illustrate some of the analysis paradigms common to these systems and where this technology may be heading.

Automatic classification of two-dimensional gel electrophoresis pictures by heuristic clustering analysis: a step toward machine learning

The interpretation of two-dimensional gel electrophoresis (2-DGE) profiles can be facilitated by artificial intelligence and machine learning programs. We have incorporated into our 2-DGE computer analysis system (termed MELANIE-Medical Electrophoresis Analysis Interactive Expert system) a program which automatically classifies 2-DGE patterns using heuristic clustering analysis. This program is a step toward machine learning. In this publication, we describe the classification method and the preliminary results obtained with liver biopsy electrophoretograms. Heuristic clustering is also compared to other classification techniques.

Specific modulation by ethanol of the protein synthesis pattern in the C2 rat hepatoma cell line

The effect of ethanol on protein synthesis in the C2 rat hepatoma cell line was analyzed by two-dimensional gel electrophoresis after the labeling with [35S]methionine of cells that were untreated or had been treated with 180 mM ethanol. In this cell line, this concentration of ethanol is known to induce gamma-glutamyl transpeptidase, a marker of alcoholism in man (Barouki et al., Hepatology 1983; 3: 323-329). In the present work we demonstrate that ethanol, besides causing a slight decrease in overall protein synthesis (less than 25%), primarily regulates the expression of two unique proteins among 1500 labeled products that were analyzed: one of these was induced and did not correspond to gamma-glutamyl transpeptidase, and one was repressed after 20 h of ethanol treatment. We conclude that the set of hepatic proteins altered by ethanol is likely to be very limited in number, which reflects the specificity of alcohol action on protein synthesis in the C2 cell line.

Two-dimensional electrophoresis computerized processing

This paper describes various methods suitable for implementation of two-dimensional processing software. The different steps leading to a complete processing are described, from the digitalization of the image to the processing of the resulting data. The characteristics of a convenient digitalization system are discussed. The different software devoted to spot detection is reviewed with respect to the presence or otherwise of a spot model and its characteristics. The major techniques for gel matching are compared as are designs for database structures suitable for tabulation of measurements. Finally, the need for a sophisticated system of data processing is stressed and its main requirements are described.