Refinement of two-dimensional electrophoresis for vitreous proteome profiling using an artificial neural network
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.6/9211 |
Resumo: | Despite technological advances, two-dimensional electrophoresis (2DE) of biological fluids, such as vitreous, remains a major challenge. In this study, artificial neural network was applied to optimize the recovery of vitreous proteins and its detection by 2DE analysis through the combination of several solubilizing agents (CHAPS, Genapol, DTT, IPG buffer), temperature, and total voltage. The highest protein recovery (94.9% ± 4.5) was achieved using 4% (w/v) CHAPS, 0.1% (v/v) Genapol, 20 mM DTT, and 2% (v/v) IPG buffer. Two iterations were required to achieve an optimized response (580 spots) using 4% (w/v) CHAPS, 0.2% (v/v) Genapol, 60 mM DTT, and 0.5% (v/v) IPG buffer at 35 kVh and 25 °C, representing a 2.4-fold improvement over the standard initial conditions of the experimental design. The analysis of depleted vitreous using the optimized protocol resulted in an additional 1.3-fold increment in protein detection over the optimal output, with an average of 761 spots detected in vitreous from different vitreoretinopathies. Our results clearly indicate the importance of combining the appropriate amount of solubilizing agents with a suitable control of the temperature and voltage to obtain high-quality gels. The high-throughput of this model provides an effective starting point for the optimization of 2DE protocols. This experimental design can be adapted to other types of matrices. Graphical abstract. |
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Refinement of two-dimensional electrophoresis for vitreous proteome profiling using an artificial neural networkTwo-Dimensional gel electrophoresisGel-based proteomicsArtificial Neural NetworksOcular pathologiesVitreousDespite technological advances, two-dimensional electrophoresis (2DE) of biological fluids, such as vitreous, remains a major challenge. In this study, artificial neural network was applied to optimize the recovery of vitreous proteins and its detection by 2DE analysis through the combination of several solubilizing agents (CHAPS, Genapol, DTT, IPG buffer), temperature, and total voltage. The highest protein recovery (94.9% ± 4.5) was achieved using 4% (w/v) CHAPS, 0.1% (v/v) Genapol, 20 mM DTT, and 2% (v/v) IPG buffer. Two iterations were required to achieve an optimized response (580 spots) using 4% (w/v) CHAPS, 0.2% (v/v) Genapol, 60 mM DTT, and 0.5% (v/v) IPG buffer at 35 kVh and 25 °C, representing a 2.4-fold improvement over the standard initial conditions of the experimental design. The analysis of depleted vitreous using the optimized protocol resulted in an additional 1.3-fold increment in protein detection over the optimal output, with an average of 761 spots detected in vitreous from different vitreoretinopathies. Our results clearly indicate the importance of combining the appropriate amount of solubilizing agents with a suitable control of the temperature and voltage to obtain high-quality gels. The high-throughput of this model provides an effective starting point for the optimization of 2DE protocols. This experimental design can be adapted to other types of matrices. Graphical abstract.CENTRO-07-ST24-FEDER-002014; CNB-CSIC is supported by grant PT13/0001, of the PE I +D+i 2013–2016, funded by ISCIII and FEDER.SpringeruBibliorumSantos, Fátima Raquel Milhano dosAlbuquerque, Tânia GonçalvesGaspar, Leonor M.Dias, Joao MLSousa, João Paulo Castro DeParadela, AlbertoTomaz, C. T.Passarinha, LA2022-01-01T01:30:10Z20192019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.6/9211eng10.1007/s00216-019-01887-yinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-12-15T09:50:04Zoai:ubibliorum.ubi.pt:10400.6/9211Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:49:24.318274Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Refinement of two-dimensional electrophoresis for vitreous proteome profiling using an artificial neural network |
title |
Refinement of two-dimensional electrophoresis for vitreous proteome profiling using an artificial neural network |
spellingShingle |
Refinement of two-dimensional electrophoresis for vitreous proteome profiling using an artificial neural network Santos, Fátima Raquel Milhano dos Two-Dimensional gel electrophoresis Gel-based proteomics Artificial Neural Networks Ocular pathologies Vitreous |
title_short |
Refinement of two-dimensional electrophoresis for vitreous proteome profiling using an artificial neural network |
title_full |
Refinement of two-dimensional electrophoresis for vitreous proteome profiling using an artificial neural network |
title_fullStr |
Refinement of two-dimensional electrophoresis for vitreous proteome profiling using an artificial neural network |
title_full_unstemmed |
Refinement of two-dimensional electrophoresis for vitreous proteome profiling using an artificial neural network |
title_sort |
Refinement of two-dimensional electrophoresis for vitreous proteome profiling using an artificial neural network |
author |
Santos, Fátima Raquel Milhano dos |
author_facet |
Santos, Fátima Raquel Milhano dos Albuquerque, Tânia Gonçalves Gaspar, Leonor M. Dias, Joao ML Sousa, João Paulo Castro De Paradela, Alberto Tomaz, C. T. Passarinha, LA |
author_role |
author |
author2 |
Albuquerque, Tânia Gonçalves Gaspar, Leonor M. Dias, Joao ML Sousa, João Paulo Castro De Paradela, Alberto Tomaz, C. T. Passarinha, LA |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
uBibliorum |
dc.contributor.author.fl_str_mv |
Santos, Fátima Raquel Milhano dos Albuquerque, Tânia Gonçalves Gaspar, Leonor M. Dias, Joao ML Sousa, João Paulo Castro De Paradela, Alberto Tomaz, C. T. Passarinha, LA |
dc.subject.por.fl_str_mv |
Two-Dimensional gel electrophoresis Gel-based proteomics Artificial Neural Networks Ocular pathologies Vitreous |
topic |
Two-Dimensional gel electrophoresis Gel-based proteomics Artificial Neural Networks Ocular pathologies Vitreous |
description |
Despite technological advances, two-dimensional electrophoresis (2DE) of biological fluids, such as vitreous, remains a major challenge. In this study, artificial neural network was applied to optimize the recovery of vitreous proteins and its detection by 2DE analysis through the combination of several solubilizing agents (CHAPS, Genapol, DTT, IPG buffer), temperature, and total voltage. The highest protein recovery (94.9% ± 4.5) was achieved using 4% (w/v) CHAPS, 0.1% (v/v) Genapol, 20 mM DTT, and 2% (v/v) IPG buffer. Two iterations were required to achieve an optimized response (580 spots) using 4% (w/v) CHAPS, 0.2% (v/v) Genapol, 60 mM DTT, and 0.5% (v/v) IPG buffer at 35 kVh and 25 °C, representing a 2.4-fold improvement over the standard initial conditions of the experimental design. The analysis of depleted vitreous using the optimized protocol resulted in an additional 1.3-fold increment in protein detection over the optimal output, with an average of 761 spots detected in vitreous from different vitreoretinopathies. Our results clearly indicate the importance of combining the appropriate amount of solubilizing agents with a suitable control of the temperature and voltage to obtain high-quality gels. The high-throughput of this model provides an effective starting point for the optimization of 2DE protocols. This experimental design can be adapted to other types of matrices. Graphical abstract. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019 2019-01-01T00:00:00Z 2022-01-01T01:30:10Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.6/9211 |
url |
http://hdl.handle.net/10400.6/9211 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1007/s00216-019-01887-y |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799136386409824256 |