Accedi

Federal University of Rio Grande do Norte

2 ARTICLES PUBLISHED IN JoVE

Neuroscience

Live Imaging of Primary Cerebral Cortex Cells Using a 2D Culture System

Bruna Soares Landeira¹, Jéssica Alves de Medeiros Araújo¹, Timm Schroeder², Ulrich Müller³, Marcos R. Costa¹

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.

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. Costa⁶, Timm Schroeder⁷, 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.