Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/49096
Título: | Employing an open-source tool to assess astrocyte tridimensional structure |
Autor(es): | Tavares, Gabriela Pires Martins, Manuella Mendes Correia, Joana Sofia Silva Sardinha, Vanessa Alexandra Morais Gomes, Sónia Isabel Nunes Guerra Neves, Sofia Pereira Marques, Fernanda Sousa, Nuno Oliveira, João F. |
Palavras-chave: | Astrocyte Structure Reconstruction GFAP Immunofluorescence |
Data: | 1-Mai-2017 |
Editora: | Springer Verlag |
Revista: | Brain Structure and Function |
Citação: | Tavares, G., Martins, M., Correia, J. S., Sardinha, V. M., Guerra-Gomes, S., das Neves, S. P., ... & Oliveira, J. F. (2017). Employing an open-source tool to assess astrocyte tridimensional structure. Brain Structure and Function, 222(4), 1989-1999 |
Resumo(s): | Astrocytes display important features that allow them to maintain a close dialog with neurons, ultimately impacting brain function. The complex morphological structure of astrocytes is crucial to the role of astrocytes in brain networks. Therefore, assessing morphologic features of astrocytes will help provide insights into their physiological relevance in healthy and pathological conditions. Currently available tools that allow the tridimensional reconstruction of astrocytes present a number of disadvantages, including the need for advanced computational skills and powerful hardware, and are either time-consuming or costly. In this study, we optimized and validated the FIJI-ImageJ, Simple Neurite Tracer (SNT) plugin, an open-source software that aids in the reconstruction of GFAP-stained structure of astrocytes. We describe (1) the loading of confocal microscopy Z-stacks, (2) the selection criteria, (3) the reconstruction process, and (4) the post-reconstruction analysis of morphological features (process length, number, thickness, and arbor complexity). SNT allows the quantification of astrocyte morphometric parameters in a simple, efficient, and semi-automated manner. While SNT is simple to learn, and does not require advanced computational skills, it provides reproducible results, in different brain regions or pathophysiological states. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/49096 |
DOI: | 10.1007/s00429-016-1316-8 |
ISSN: | 1863-2653 |
e-ISSN: | 1863-2661 |
Versão da editora: | https://link.springer.com/article/10.1007/s00429-016-1316-8 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | ICVS - Artigos em revistas internacionais / Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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2017tavaresg_.pdf | 2,53 MB | Adobe PDF | Ver/Abrir |