Logo do repositório
  • Página Inicial(current)
  • Buscar
    Por Data de PublicaçãoPor AutorPor TítuloPor Assunto
  • Tutoriais
  • Documentos
  • Sobre o RI
  • Eventos
    Repositório Institucional da UFRN: 15 anos de conexão com o conhecimento
  • Padrão
  • Amarelo
  • Azul
  • Verde
  • English
  • Português do Brasil
Entrar

SIGAA

  1. Início
  2. Pesquisar por Autor

Navegando por Autor "Miras-Portugal, María Teresa"

Filtrar resultados informando as primeiras letras
Agora exibindo 1 - 1 de 1
  • Resultados por página
  • Opções de Ordenação
  • Carregando...
    Imagem de Miniatura
    Artigo
    Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations
    (2017-12-16) Gómez-Villafuertes, Rosa; Paniagua-Herranz, Lucía; Gascon, Sergio; Agustín-Durán, David de; Ferreras, María de la O; Gil-Redondo, Juan Carlos; Queipo, María José; Menendez-Mendez, Aida; Pérez-Sen, Ráquel; G. Delicado, Esmerilda; Gualix, Javier; Costa, Marcos Romualdo; Schroeder, Timm; Miras-Portugal, María Teresa; Ortega, Felipe
    Understanding the mechanisms that control critical biological events of neural cell populations, such as proliferation, differentiation, or cell fate decisions, will be crucial to design therapeutic strategies for many diseases affecting the nervous system. Current methods to track cell populations rely on their final outcomes in still images and they generally fail to provide sufficient temporal resolution to identify behavioral features in single cells. Moreover, variations in cell death, behavioral heterogeneity within a cell population, dilution, spreading, or the low efficiency of the markers used to analyze cells are all important handicaps that will lead to incomplete or incorrect read-outs of the results. Conversely, performing live imaging and single cell tracking under appropriate conditions represents a powerful tool to monitor each of these events. Here, a time-lapse video-microscopy protocol, followed by post-processing, is described to track neural populations with single cell resolution, employing specific software. The methods described enable researchers to address essential questions regarding the cell biology and lineage progression of distinct neural populations.
Repositório Institucional - UFRN Campus Universitário Lagoa NovaCEP 59078-970 Caixa postal 1524 Natal/RN - BrasilUniversidade Federal do Rio Grande do Norte© Copyright 2025. Todos os direitos reservados.
Contato+55 (84) 3342-2260 - R232Setor de Repositórios Digitaisrepositorio@bczm.ufrn.br
DSpaceIBICT
OasisBR
LAReferencia
Customizado pela CAT - BCZM