A wavelet relational fuzzy C-means algorithm for 2D gel image segmentation.

One of the most famous algorithms that appeared in the area of image segmentation is the Fuzzy C-Means (FCM) algorithm. This algorithm has been used in many applications such as data analysis, pattern recognition, and image segmentation. It has the advantages of producing high quality segmentation compared to the other available algorithms. Many modifications have been made to the algorithm to improve its segmentation quality. The proposed segmentation algorithm in this paper is based on the Fuzzy C-Means algorithm adding the relational fuzzy notion and the wavelet transform to it so as to enhance its performance especially in the area of 2D gel images. Both proposed modifications aim to minimize the oversegmentation error incurred by previous algorithms. The experimental results of comparing both the Fuzzy C-Means (FCM) and the Wavelet Fuzzy C-Means (WFCM) to the proposed algorithm on real 2D gel images acquired from human leukemias, HL-60 cell lines, and fetal alcohol syndrome (FAS) demonstrate the improvement achieved by the proposed algorithm in overcoming the segmentation error. In addition, we investigate the effect of denoising on the three algorithms. This investigation proves that denoising the 2D gel image before segmentation can improve (in most of the cases) the quality of the segmentation.

Prognostic value of nuclear matrix protein expression in localized prostate cancer.

PURPOSE: The aim of the study was to correlate nuclear matrix (NM) protein expression profiles with the risk of PSA progression or death in early prostate cancer (PCa). METHODS: High-resolution two-dimensional gel electrophoresis (2D-PAGE) was used to identify tumor-associated NM proteins in the PCa specimens obtained from 94 patients. The association between the expression of each protein and the probability of PSA progression or death was studied through univariate analysis. Unsupervised hierarchical clustering analysis was then used to generate patient clusters showing comparable outcomes by including the proteins that were predictive at univariate analysis. PSA-free and overall survival curves relative to each cluster were constructed by means of the Kaplan-Meier method and curves compared by the log-rank test. Multi-parametric models were constructed according to Cox proportional hazard technique. RESULTS: After a median follow-up of 11.7 years (range, 6.5-16.2), 50 patients progressed and 22 died. Of the eight NM proteins identified through 2D-PAGE, proteins NM-6, NM-7 and NM-8 were confirmed to be individually associated with a higher risk of PSA progression at univariate analysis. Proteins NM-6 and NM-8 were also predictive of survival probability. Hierarchical clustering analysis of these proteins allowed to identify one cluster of tumors co-expressing the three proteins or proteins NM-6 and NM-8, characterized by a very poor outcome, suggesting a specific role for these proteins in PCa progression. The predictive value of this mini-signature in respect to PSA-free survival was confirmed by multivariate analysis. CONCLUSIONS: Changes in NM scaffolding are strongly associated with the clinical outcome of patients following radical prostatectomy.

A new method of comparing 2D-PAGE maps based on the computation of Zernike moments and multivariate statistical tools.

The aim of this work was to obtain the correct classification of a set of two-dimensional polyacrylamide gel electrophoresis map images using the Zernike moments as discriminant variables. For each 2D-PAGE image, the Zernike moments were computed up to a maximum p order of 100. Partial least squares discriminant analysis with variable selection, based on a backward elimination algorithm, was applied to the moments calculated in order to select those that provided the lowest error in cross-validation. The new method was tested on four datasets: (1) samples belonging to neuroblastoma; (2) samples of human lymphoma; (3) samples from pancreatic cancer cells (two cell lines of control and drug-treated cancer cells); (4) samples from colon cancer cells (total lysates and nuclei treated or untreated with a histone deacetylase inhibitor). The results demonstrate that the Zernike moments can be successfully applied for fast classification purposes. The final aim is to build models that can be used to achieve rapid diagnosis of these illnesses.

A new integrated statistical approach to the diagnostic use of two-dimensional maps.

Two-dimensional (2-D) electrophoresis is a very useful technique for the analysis of proteins in biological tissues. The complexity of the 2-D maps obtained causes many difficulties in the comparison of different samples. A new method is proposed for comparing different 2-D maps, based on five steps: (i) the digitalisation of the image; (ii) the transformation of the digitalised map in a fuzzy entity, in order to consider the variability of the 2-D electrophoretic separation; (iii) the calculation of a similarity index for each pair of maps; (iv) the analysis by multidimensional scaling of the previously obtained similarity matrix; (v) the analysis by classification or cluster analysis techniques of the resulting map co-ordinates. The method adopted was first tested on some simulated samples in order to evaluate its sensitivity to small changes in the spots position and size. The optimal setting of the method parameters was also investigated. Finally, the method was successfully applied to a series of real samples corresponding to the electrophoretic bidimensional analysis of sera from normal and nicotine-treated rats. Multidimensional scaling allowed the separation of the two classes of samples without any misclassification.

Evaluation of two-dimensional differential gel electrophoresis for proteomic expression analysis of a model breast cancer cell system.

The technique of fluorescent two-dimensional (2D) difference gel electrophoresis for differential protein expression analysis has been evaluated using a model breast cancer cell system of ErbB-2 overexpression. Labeling of paired cell lysate samples with N-hydroxy succinimidyl ester-derivatives of fluorescent Cy3 and Cy5 dyes for separation on the same 2D gel enabled quantitative, sensitive, and reproducible differential expression analysis of the cell lines. SyproRuby staining was shown to be a highly sensitive and 2D difference gel electrophoresis-compatible method for post-electrophoretic visualization of proteins, which could then be picked and identified by matrix-assisted laser-desorption ionization mass spectroscopy. Indeed, from these experiments, we have identified multiple proteins that are likely to be involved in ErbB-2-mediated transformation. A triple dye labeling methodology was used to identify proteins differentially expressed in the cell system over a time course of growth factor stimulation. A Cy2-labeled pool of samples was used as a standard with all Cy3- and Cy5-labeled sample pairs to facilitate cross-gel quantitative analysis. DeCyder (Amersham Biosciences, Inc.) software was used to distinguish clear statistical differences in protein expression over time and between the cell lines.

Multiwell in-gel protein digestion and microscale sample preparation for protein identification by mass spectrometry.

In-gel peptide digestion has become a widely used technique for characterizing proteins resolved by two-dimensional gel electrophoresis. Peptides generated from gel pieces are frequently contaminated with detergent and salts. Prior to matrix-assisted laser desorption/ionization-time of flight mass spectrometry analysis, these contaminants are removed using micro scale C18 sample preparation columns. In this paper, data are presented to demonstrate the application of a solvent resistant MultiScreen 96-well plate with a low peptide binding membrane and ZipTip micropipette based sample preparation. Recoveries of peptides (m/z of 1000 to 5000 Da) derived from standard protein protease digests, were estimated at various stages of the analytical process. An optimized protocol has been established and all the reagents and consumables have been packaged in a ready to use commercial kit. Data will be presented to show the application of this technology package to accelerate the throughput of protein characterization by protease fragmentation.