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Since the term proteomics was coined by Marc Wilkins in 1994, there has been an explosion in the number of articles reporting the use of the proteomics technique. As the layers of biological organization and their regulation increase, the complexity of living beings increases. Thus, we go from the genome to tissues, cells, cellular compartments, and phenotypes and the complexity of the tools used to study this complexity also increases. Unlike the genome study, in the case of the proteome, we have a more complex panorama. We have a spatial and temporal proteome. Proteomics helps to answer complex biological questions since proteins' function depends on their molecular structure, subcellular localization, and posttranslational modifications. In this protocol, we describe a methodology to extract proteins using different methods, separating proteins by electrophoresis in double-dimensional gels and analyzing the gels using specialized software that allows obtaining information on the number and abundance of the proteins from the gels.

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Two-dimensional gel electrophoresis (2D-GE) is an indispensable technique for the study of proteomes of biological systems, providing an assessment of changes in protein abundance under various experimental conditions. However, due to the complexity of 2D-GE gels, there is no systematic, automatic, and reproducible protocol for image analysis and specific implementations are required for each context. In addition, practically all available solutions are commercial, which implies high cost and little flexibility to modulate the parameters of the algorithms. Using the bacterial strain, Pseudomonas aeruginosaAG1 as a model, we obtained images from 2D-GE of periplasmic protein profiles when the strain was exposed to multiple conditions, including antibiotics. Then, we proceeded to implement and evaluate an image analysis protocol with open-source software, CellProfiler. First, a preprocessing step included a bUnwarpJ-Image pipeline for aligning 2D-GE images. Then, using CellProfiler, we standardized two pipelines for spots identification. Total spots recognition was achieved using segmentation by intensity, whose performance was evaluated when compared with a reference protocol. In a second pipeline with the same program, differential identification of spots was addressed when comparing pairs of protein profiles. Due to the characteristics of the programs used, our workflow can automatically analyze a large number of images and it is parallelizable, which is an advantage with respect to other implementations. Finally, we compared six experimental conditions of bacterial strain in the presence or absence of antibiotics, determining protein profiles relationships by applying clustering algorithms PCA (Principal Components Analysis) and HC (Hierarchical Clustering).

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BACKGROUND: Two-dimensional gel electrophoresis (2-DGE) is a commonly used tool for proteomic analysis. This gel-based technique separates proteins in a sample according to their isoelectric point and molecular weight. 2-DGE images often present anomalies due to the acquisition process, such as: diffuse and overlapping spots, and background noise. This study proposes a joint pre-processing framework that combines the capabilities of nonlinear filtering, background correction and image normalization techniques for pre-processing 2-DGE images. Among the most important, joint nonlinear diffusion filtering, adaptive piecewise histogram equalization and multilevel thresholding were evaluated using both synthetic data and real 2-DGE images. RESULTS: An improvement of up to 46% in spot detection efficiency was achieved for synthetic data using the proposed framework compared to implementing a single technique of either normalization, background correction or filtering. Additionally, the proposed framework increased the detection of low abundance spots by 20% for synthetic data compared to a normalization technique, and increased the background estimation by 67% compared to a background correction technique. In terms of real data, the joint pre-processing framework reduced the false positives up to 93%. CONCLUSIONS: The proposed joint pre-processing framework outperforms results achieved with a single approach. The best structure was obtained with the ordered combination of adaptive piecewise histogram equalization for image normalization, geometric nonlinear diffusion (GNDF) for filtering, and multilevel thresholding for background correction.

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This chapter describes the application of two-dimensional gel electrophoresis (2DGE) combined with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) in the analysis of rat eye lens proteins. The main purpose was to identify proteins that may serve as potential biomarkers in age-related cataract formation. This includes the family of proteins known as the crystallins. Structural proteins and enzymes involved antioxidant activities. In addition, we also analyzed lenses from other species to illustrate the potential of using this technique in clinical and preclinical biomarker studies.

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Von Willebrand disease (VWD) is a common cause of bleeding worldwide. Analysis of von Willebrand factor (VWF) multimer distribution (VWF:MD) is essential to properly classify and treat different types of VWD, and it is performed using a SDS agarose gel electrophoresis followed by Western blotting, a handmade technique that demands days to be completed and requires skillful execution. Aiming both to facilitate gel production and to shorten the preparation time, we developed an uncomplicated technique to provide agility in the analysis of VWF:MD, so that it can be easily accomplished in the routine practice of hemostasis laboratories. On that account, we used a commercial vertical mini-gel electrophoresis system for SDS-PAGE and a semi-dry transfer system, which allowed us to analyze VWF:MD of various samples in a period shorter than 12 h. This technique differentiated VWF:MD in human and animal plasmas under normal, congenital and acquired (experimental envenomation by Bothrops jararaca snake) conditions. This optimized method is cheap, rapid, reproducible, easy to be performed, and uses electrophoresis and Western blotting systems available in most laboratories. All these advantages encourage hemostasis professionals to use it in their routine practices. In order to facilitate the setup and accomplishment of the whole procedure step by step, videos were appended to the article.

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Proteomics studies can be used to identify proteins that affect feed efficiency traits, related to cost and profitability of meat production. We used a proteomic approach based on two-dimensional electrophoresis (2D-PAGE) in combination with mass spectrometry (ESI-MS) to study liver samples of Nellore bulls divergently ranked according to residual feed intake (RFI). The study showed that 71 protein spots were expressed differentially (P < 0.05) among RFI groups and 47 were identified by ESI-MS. In RFI, efficient animals (low RFI) eat less than predictions, based on their weights and growth rate, while inefficient animals (high RFI) that eat more than predicted. Data from 18 animals (9 high vs. 9 low RFI) aged 24-26 months in feedlot finishing were used. Immediately after slaughter, liver samples were collected and protein extracts were separated. The gels of RFI groups were scanned and the images analyzed, whereby we found 279 and 215 liver protein spots in high and low RFI bulls, respectively. The proteins identified were related to the following biological functions: (I) oxygen transport and blood flow; (II) mitochondrial function and energy metabolism; (III) amino acid metabolism, ion transport, and cell survival. The study suggests hemoglobin subunit beta and heat shock protein 71 kDa and as molecular markers to study FE in Nellore cattle. Moreover, proteins such as 3-ketoacyl-CoA thiolase and glutamate dehydrogenase 1 were found in liver from high and low RFI animals, respectively. Such protein expression could be associated with changes in the oxidative capacity of RFI phenotypes.

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In this study, we screened differentially expressed genes in a multidrug-resistant isolate strain of Clostridium perfringens by RNA sequencing. We also separated and identified differentially expressed proteins (DEPs) in the isolate strain by two-dimensional electrophoresis (2-DE) and mass spectrometry (MS). The RNA sequencing results showed that, compared with the control strain, 1128 genes were differentially expressed in the isolate strain, and these included 227 up-regulated genes and 901 down-regulated genes. Bioinformatics analysis identified the following genes and gene categories that are potentially involved in multidrug resistance (MDR) in the isolate strain: drug transport, drug response, hydrolase activity, transmembrane transporter, transferase activity, amidase transmembrane transporter, efflux transmembrane transporter, bacterial chemotaxis, ABC transporter, and others. The results of the 2-DE showed that 70 proteins were differentially expressed in the isolate strain, 45 of which were up-regulated and 25 down-regulated. Twenty-seven DEPs were identified by MS and these included the following protein categories: ribosome, antimicrobial peptide resistance, and ABC transporter, all of which may be involved in MDR in the isolate strain of C. perfringens. The results provide reference data for further investigations on the drug resistant molecular mechanisms of C. perfringens.

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

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Selenium and selenoproteins are important components of living organisms that play a role in different biological processes. Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) is a powerful analytical technique that has been employed to obtain distribution maps of selenium in biological tissues in a direct manner, as well as in selenoproteins, previously separated by their molecular masses and isoelectric points using two-dimensional polyacrylamide gel electrophoresis (2-D PAGE). In this chapter, we present the protocols to perform LA-ICP-MS imaging experiments, allowing the distribution visualization and determination of selenium and/or selenoproteins in biological systems.

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