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Federal University of Rio Grande do Norte

2 ARTICLES PUBLISHED IN JoVE

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Neuroscience

Live Imaging of Primary Cerebral Cortex Cells Using a 2D Culture System
Bruna Soares Landeira 1, Jéssica Alves de Medeiros Araújo 1, Timm Schroeder 2, Ulrich Müller 3, Marcos R. Costa 1
1Brain Institute, Federal University of Rio Grande do Norte, 2Department of Biosystems Science and Engineering, ETH Zurich, 3The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University

Live imaging is a powerful tool to study cellular behaviors in real time. Here, we describe a protocol for time-lapse video-microscopy of primary cerebral cortex cells that allows a detailed examination of the phases enacted during the lineage progression from primary neural stem cells to differentiated neurons and glia.

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Neuroscience

Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations
Rosa Gómez-Villafuertes *1,2,3, Lucía Paniagua-Herranz *1,2,3, Sergio Gascon *4,5, David de Agustín-Durán 1,2,3, María de la O Ferreras 1,2,3, Juan Carlos Gil-Redondo 1,2,3, María José Queipo 1,2,3, Aida Menendez-Mendez 1,2,3, Ráquel Pérez-Sen 1,2,3, Esmerilda G. Delicado 1,2,3, Javier Gualix 1,2,3, Marcos R. Costa 6, Timm Schroeder 7, María Teresa Miras-Portugal 1,2,3, Felipe Ortega 1,2,3
1Biochemistry and Molecular Biology Department, Faculty of Veterinary medicine, Complutense University, 2University Institute for Neurochemistry Research (IUIN), 3Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), 4Institute of Stem Cell Research, Helmholtz Center Munich, Neuherberg/Munich, Germany Physiological Genomics, Biomedical Center, Ludwig-Maximilians University Munich, 5Toxicology and Pharmacology Department, Faculty of Veterinary medicine, Complutense University, 6Brain Institute, Federal University of Rio Grande do Norte, 7Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule (ETH) Zurich

A robust protocol to monitor neural populations by time-lapse video-microscopy followed by software-based post-processing is described. This method represents a powerful tool to identify biological events in a selected population during live imaging experiments.

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