Valentin Nägerl  

University of Bordeaux, France

Abstract

Title: Super-resolution imaging of the extracellular space in living brain tissue

The extracellular space (ECS) of the brain has an extremely complex spatial organization, which has defied conventional light microscopy. Consequently, despite a marked interest in the physiological roles of brain ECS, its structure and dynamics remain largely inaccessible for experimenters. We combined 3D-STED microscopy and fluorescent labeling of the extracellular fluid to develop super-resolution shadow imaging (SUSHI) of brain ECS in living organotypic brain slices. SUSHI enables quantitative analysis of ECS structure and reveals dynamics on multiple scales in response to a variety of physiological stimuli. Because SUSHI produces sharp negative images of all cellular structures, it enables unbiased imaging of unlabeled brain cells with respect to their anatomical context. Moreover, the extracellular labeling strategy greatly alleviates problems of photobleaching and phototoxicity associated with traditional imaging approaches. As a straightforward variant of STED microscopy, SUSHI provides unprecedented access to the structure and dynamics of live brain ECS and neuropil.

Biography

Valentin Nägerl received his PhD in neuroscience from UCLA in the lab of Istvan Mody. He then trained with Tobias Bonhoeffer and Arthur Konnerth in Munich and Stefan Hell at the Max Planck Institute in Göttingen, making several crucial observations on activity-dependent structural plasticity of synapses (1,2). Since 2009 Valentin is a full professor of neuroscience and bio-imaging at the University of Bordeaux where he studies the nanoscale mechanisms of neural plasticity using STED microscopy (3,4). In 2016 he received the ‘Equipe FRM’ award and in 2017 became a member of the ‘Institut Universitaire de France’.

References

1 Nägerl et al. (2004) Neuron PMID: 15572108
2 Becker et al. (2008) Neuron PMID: 19038217
3 Tønnesen et al. (2014) Nature Neuroscience PMID: 24657968
4 Chéreau et al. (2017) PNAS PMID: 28115721