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Beaurepaire, E.

Publications and source records attributed to Beaurepaire, E..

3 recordsLinked to original sources

Dynamic spatiotemporal coordination of neural stem cell fate decisions through local feedback in the adult vertebrate brain

Neural stem cell (NSC) populations persist in the adult vertebrate brain over a life time, and their homeostasis is controlled at the population level. The nature and properties of these coordination mechanisms remain unknown. Here we combine dynamic imaging of entire NSC populations in their in vivo niche over weeks, pharmacological manipulations, mathematical modeling and spatial statistics, and demonstrate that NSCs use spatiotemporally resolved local feedbacks to coordinate their decision to divide. These involve a Notch-mediated inhibition from transient neural progenitors, and a dispersion effect from dividing NSCs themselves, exerted with a delay of 9-12 days. Simulations from a stochastic NSC lattice model capturing these interactions demonstrate that they are linked by lineage progression and control the spatiotemporal distribution of output neurons. These results highlight how local and temporally delayed interactions occurring between brain germinal cells generate self-propagating dynamics that maintain NSC population homeostasis with specific spatiotemporal correlations.

neuroscience

Balancing signal and photoperturbation in multiphoton light-sheet microscopy by optimizing laser pulse frequency

Improving the imaging speed of multiphoton microscopy is an active research field. Among recent strategies, light-sheet illumination holds distinctive advantages for achieving fast imaging in vivo. However, photoperturbation in multiphoton light-sheet microscopy remains poorly investigated. We show here that the heart beat rate of zebrafish embryos is a sensitive probe of linear and nonlinear photoperturbations. By analyzing its behavior with respect to laser power, pulse frequency and wavelength, we derive guidelines to balance signal and photoperturbation. We then demonstrate one order-of-magnitude signal enhancement over previous implementations by optimizing the laser pulse frequency. These results open new opportunities for fast live tissue imaging.

biophysics

A scale-space approach for 3D neuronal traces analysis

AO_SCPLOWBSTRACTC_SCPLOWTridimensional microscopy and algorithms for automated segmentation and tracing are revolutionizing neuroscience through the generation of growing libraries of neuron reconstructions. Innovative computational methods are needed to analyze these neural traces. In particular, means to analyse the geometric properties of traced neurites along their trajectory have been lacking. Here, we propose a local tridimensional (3D) scale metric derived from differential geometry, which is the distance in micrometers along which a curve is fully 3D as opposed to being embedded in a 2D plane or 1D line. We apply this metric to various neuronal traces ranging from single neurons to whole brain data. By providing a local readout of the geometric complexity, it offers a new mean of describing and comparing axonal and dendritic arbors from individual neurons or the behavior of axonal projections in different brain regions. This broadly applicable approach termed nAdder is available through the GeNePy3D open-source Python quantitative geometry library.

neuroscience