Abstract: Light-sheet microscopy has become the preferred method for long-term imaging of large living samples because of its low photo-invasiveness and good optical sectioning capabilities. Unfortunately, refraction and scattering often pose obstacles to light-sheet propagation and limit imaging depth. This is typically addressed by imaging multiple complementary views to obtain high and uniform image quality throughout the sample. However, multi-view imaging often requires complex multi-objective configurations that complicate sample mounting, or sample rotation that decreases imaging speed. Recent developments in single-objective light-sheet microscopy have shown that it is possible to achieve high spatio-temporal resolution with a single objective for both illumination and detection. Here we describe a single-objective light-sheet microscope that achieves: high-resolution, large field-of-view, simpler design, better sample ergonomics, and fast volumetric imaging. Finally, we demonstrate the speed, field of view and resolution of our novel instrument by imaging zebrafish tail development.
Biography: Dr. Royer first studied engineering, math, and physics in his native France. He then obtained a master’s degree in artificial intelligence, specializing in cognitive robotics, followed by a Ph.D. in bioinformatics from the Dresden University of Technology in Germany. As a member of Gene Myers’ lab, first at HHMI’s Janelia Research Campus and then at the Max Planck Institute of Molecular Cell Biology and Genetics, he developed the first “self-driving” multi-view light-sheet microscope. Royer is fascinated by a seemingly simple but quite complex question: How do organisms develop from a single cell into a fully functional body with billions of self-organizing cells that form tissues and have different functions? He believes that solving this question will require expertise across computer science, advanced microscopy, and biology. To that end, Royer’s pluridisciplinary team designs and builds novel state-of-the-art light-sheet microscopes, develops deep learning-based image processing and analysis algorithms, and is using these technologies to build a time-resolved and multimodal atlas of vertebrate development, using zebrafish as model organisms.