S2P: A software tool to quickly carry out reproducible biomedical research projects involving 2D-gel and MALDI-TOF MS protein data

Background and objective 2D-gel electrophoresis is widely used in combination with MALDI-TOF mass spectrometry in order to analyze the proteome of biological samples. For instance, it can be used to discover proteins that are differentially expressed between two groups (e.g. two disease conditions, case vs. control, etc.) thus obtaining a set of potential biomarkers. This procedure requires a great deal of data processing in order to prepare data for analysis or to merge and integrate data from different sources. This kind of work is usually done manually (e.g. copying and pasting data into spreadsheet files), which is highly time consuming and distracts the researcher from other important, core tasks. Moreover, engaging in a repetitive process in a non-automated, handling-based manner is prone to error, thus threatening reliability and reproducibility. The objective of this paper is to present S2P, an open source software to overcome these drawbacks. Methods S2P is implemented in Java on top of the AIBench framework, and relies on well-established open source libraries to accomplish different tasks. Results S2P is an AIBench based desktop multiplatform application, specifically aimed to process 2D-gel and MALDI-mass spectrometry protein identification-based data in a computer-aided, reproducible manner. Different case studies are presented in order to show the usefulness of S2P. Conclusions S2P is open source and free to all users at http://www.sing-group.org/s2p. Through its user-friendly GUI interface, S2P dramatically reduces the time that researchers need to invest in order to prepare data for analysis.Ministerio de Economía y Competitividad | Ref. TIN2013-47153-C3-3-RXunta de GaliciaFundação para a Ciência e a Tecnologia | Ref. SFRH/BD/109201/2015Fundação para a Ciência e a Tecnologia | Ref. SFRH/BD/120537/201

Analysis of 2D-gel images for detection of protein spots using a novel non-separable wavelet based method

2D-gel electrophoresis (2DGE) is an important technique in proteomics for analyzing the protein expressions. However, the analysis of 2DGE images is still a cumbersome and tedious task. One of the main reasons is the presence of a large amount of the inhomogeneities in the foreground and the background intensities. In this paper, we have proposed a novel approach of segmentation of the protein spots in the non separable wavelet domain. It utilizes the inter-scale relationship among enhanced wavelet coefficients, which can easily distinguish the different features of the image the interior region of spots, the edges and the background. This technique is based on a single threshold and is independent of the gray value of the image. It copes with the inhomogeneities in the 2DGE images up to a great extent, which is helpful for finding the protein spots accurately. The artifacts are further removed using a non-threshold based method comprising a weighted Gaussian energy distribution model. Experimental results show that our method outperforms the available commercial software and previously reported works. (C) 2015 Elsevier Ltd. All rights reserved

Performance and Fouling during Bioreactor Harvesting

Tangential flow filtration has many advantages for bioreactor harvesting as the permeate could be introduced directly to the subsequent capture step, the process is easy to scale up, and fouling of the filter is limited by the cross flow. However, membrane fouling has limited its widespread use. This is particularly problematic given the high cell densities encountered today. Here a reverse asymmetric commercial membrane, BioOptimal™ MF-SL (Asahi Kasei), where the more open surface faces the feed stream, and the tighter barrier layer faces the permeate stream, has been investigated for bioreactor harvesting. The open surface contains pores up to 40 µm in diameter, while the tighter barrier layer has an average pore size of 0.4 µm. The filtration performance, including fouling analysis conducted in this dissertation, involves using different feed streams, comparison of the filter performance with other filters possessing different membrane structures, mathematical modeling to predict the flux and fouling, fouling visualization using confocal laser scanning microscopy, and fouling identification using liquid chromatography-mass spectrometry. For the feed streams studies, filtration of yeast suspensions and Chinese hamster ovary cell culture has been conducted under various conditions. The yeast cells are trapped in the open pore structure, while CHO cells are more externally deposited. The membrane stabilizes an internal porous cake that acts as a depth filter. This stabilized cake layer removes particulate matter that fouls the barrier layer, protecting the fine pores from the large aggregates. As filtration continues, a cake layer forms on the membrane surface. Resistance-in-series model has been developed to describe the permeate flux during tangential flow filtration. The model contains three fitted parameters, which can easily be determined from constant pressure normal flow filtration experiments and total recycle constant flux tangential flow filtration experiments. The model can be used to estimate the filter\u27s capacity for a given feed stream. Our results suggest that using a reverse asymmetric membrane could avoid severe flux decline associated with fouling of the barrier during bioreactor harvesting. Laser scanning confocal microscopy is used to observe the location of particle entrapment. The throughput of the reverse asymmetric membrane is significantly greater than the symmetric membranes. The membrane stabilizes an internal high permeability cake that acts as a depth filter. Confocal imaging helps visualize the secondary membrane directly by staining the DNA and membrane proteins using fluorescent dyes. Host cell proteins are the most challenging impurities for downstream purification processes. In order to investigate the fouling during cell clarification, HCPs in the bioreactor, harvest, and backwash are identified and quantified using different methods. A dataset is established using the identified HCPs and used to train the deep learning model. The model predicts unknown HCPs on fouling potential with an accuracy of 76%. The dataset of identified HCPs in this study provides insights into the characterization of membrane fouling, membrane selection, and process development. This approach could be used to screen cell lines or hosts to select those with reduced HCP profiles or identify HCPs that are problematic and difficult to remove

Metodología para la caracterización de la apitoxina desde imágenes de electroforesis bidimensional en gel usando descriptores espaciales

La electroforesis bidimensional, una de las técnicas más empleadas para el análisis proteómico, permite separar cientos o miles de proteínas en un único gel, mostrando un patrón característico. En el análisis de estas imágenes es muy importante una correcta detección de las proteínas, ya que cualquier error en esta etapa puede llevar a la detección de falsas proteínas, o a obviar proteínas importantes, pero de baja abundancia, lo cual afectaría los resultados del análisis. Técnicas de segmentación son empleadas para separar las proteínas del fondo y encontrar anomalías. Los métodos empleados para la segmentación de imágenes de electroforesis bidimensional en gel (2DGE) se pueden clasificar como: métodos basados en detección de bordes, métodos morfológicos, umbralización, multiumbralización y métodos basados en regiones. Adicional a la detección de proteínas en imágenes 2DGE, en muchos estudios proteómicos se hace necesario la fusión o registro de imágenes para la identificación y comparación de patrones de varias muestras diferentes. Para este proceso de fusión se pueden usar las imágenes originales o los resultados de la segmentación. A pesar de los avances significativos en el campo de procesamiento de imágenes de 2DGE, no se encuentran en la literatura métodos completamente automatizados. Las herramientas comerciales disponibles para el análisis y procesamiento de imágenes 2DGE requieren que el usuario seleccione adecuadamente ciertos parámetros, de los cuales dependen los resultados arrojados por el software. Este proyecto propone una metodología de procesamiento de imágenes 2DGE que incluye la fase de segmentación y fusión. Se realiza una comparación de técnicas de segmentación usando 24 imágenes 2DGE obtenidas del veneno de apitoxina. A partir de esta comparación, los mejores resultados fueron obtenidos con la técnica de multiumbralización automática en 16 y 8 ventanas. Por su parte, la fusión de imágenes se obtiene con base en el promedio de valores de pixeles relacionados en cada par de imágenes comparadas. A partir de la metodología propuesta se logró caracterizar la apitoxina para abejas de interior y exterior, con una identificación automática de 79 de las 115 proteínas conocidas en el patrón, equivalente al 68.7%.Magister en Automatización y Contro