Extraction methods of red blood cell membrane proteins for Multidimensional Protein Identification Technology (MudPIT) analysis |
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Authors: | Antonella De Palma Antonella Roveri Mattia Zaccarin Louise Benazzi Simone Daminelli Giorgia Pantano Mauro Buttarello Fulvio Ursini Massimo Gion Pier Luigi Mauri |
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Affiliation: | 1. Proteomics and Metabolomics Unit, Institute for Biomedical Technologies (ITB) – CNR, Via Fratelli Cervi 93, I-20090 Segrate, Milan, Italy;2. Department of Biological Chemistry, University of Padua, I-35131 Padua, Italy;3. Istituto Oncologico Veneto (IOV), IRCCS, I-35131 Padua, Italy;4. Department of Laboratory Medicine, Azienda Ospedaliera, University of Padua, I-35131 Padua, Italy;5. Department of Laboratory Medicine, Azienda Ospedaliera, ULSS16, University of Padua, I-35131 Padua, Italy;6. Centre for the Study of Biological Markers of Malignancy, Consortium Istituto Oncologico Veneto IRCCS, Regional Hospital, AULSS 12, Venice, Italy |
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Abstract: | Since red blood cells (RBCs) lack nuclei and organelles, cell membrane is their main load-bearing component and, according to a dynamic interaction with the cytoskeleton compartment, plays a pivotal role in their functioning. Even if erythrocyte membranes are available in large quantities, the low abundance and the hydrophobic nature of cell membrane proteins complicate their purification and detection by conventional 2D gel-based proteomic approaches. So, in order to increase the efficiency of RBC membrane proteome identification, here we took advantage of a simple and reproducible membrane sub-fractionation method coupled to Multidimensional Protein Identification Technology (MudPIT). In addition, the adoption of a stringent RBC filtration strategy from the whole blood, permitted to remove exhaustively contaminants, such as platelets and white blood cells, and to identify a total of 275 proteins in the three RBC membrane fractions collected and analysed. Finally, by means of software for the elaboration of the great quantity of data obtained and programs for statistical analysis and protein classification, it was possible to determine the validity of the entire system workflow and to assign the proper sub-cellular localization and function for the greatest number of the identified proteins. |
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Keywords: | 2DC-MS/MS, two-dimensional chromatography coupled to tandem mass spectrometry MudPIT, Multidimensional Protein Identification Technology RBC, red blood cell WBC, white blood cell 2DE, bidimensional electrophoresis ROD, Repair Or Destroy |
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