Subcellular Resolution Light Sheet Microscope with a Large Field of View

项目摘要 在活体模型生物（如斑马鱼和水果）中捕获神经网络大区域的能力 在亚细胞水平上研究果蝇将进一步推进神经学研究。在这个项目中，我们的目标是提供一个 显微镜平台，能够捕获活体神经系统的286至300微米区域的图像 斑马鱼和水果文件在亚细胞分辨率。我们计划将联合收割机超分辨率技术与光 荧光显微镜来实现这一目标。 Aim 1将超分辨率结构照明显微镜（SR-SIM）与多方向 照明光片荧光显微镜在单物镜配置。我们希望这个系统 应该能够在所有横向方向上实现161 nm的分辨率，以及458 nm和916 nm的轴向分辨率 分别在3D SR-SIM模式和2D SR-SIM模式中。我们期望所提出的方法将提供一个 受精后6至7周斑马鱼整个中脑结构和活动的高度详细图像 幼虫Aim 2将在亚细胞水平上实现各向同性分辨率（横向241 nm，轴向336 nm），同时保持 286到300微米的视野。我们期望所提出的方法将导致图像的能力， 在长时间内（超过1小时）跨越多个肌肉群的突触后丝状伪足的动力学。 目标3将开发一种新的计算机重建算法，以提高有效帧速率并减轻 产生的图像中的伪影。在最终结果中，我们期望看到伪影减少20%， 速度提高了3倍。

PROJECT SUMMARY The ability to capture large regions of the neural network in living model organisms such as zebrafish and fruit flies at a subcellular scale will further advance neurological research. In this project, we aim to provide a microscopy platform that is able to capture images of a 286 to 300 micron area of the nervous system in living zebrafish and fruit files at subcellular resolution. We plan to combine super-resolution techniques with light sheet fluorescence microscopy to accomplish this goal. Aim 1 combines super-resolution structured illumination microscopy (SR-SIM) with multi-direction illumination light sheet fluorescence microscopy in a single objective configuration. We expect that the system should be able to achieve a resolution of 161nm in all lateral directions, and axial resolution of 458nm and 916nm in 3D SR-SIM mode and 2D SR-SIM mode, respectively. We expect that the proposed method will provide a highly detailed image of the entire midbrain structure and activity in 6 to 7 week post-fertilization zebrafish larvae. Aim 2 will achieve isotropic resolution at subcellular level (241nm lateral, 336nm axial) while maintaining a 286 to 300 micron field of view. We expect that the proposed method will result in the capability to image dynamics of postsynaptic filopodia across multiple muscle groups over a long period of time (longer than 1 hour). Aim 3 will develop a novel computer reconstruction algorithm to boost the effective frame rate and alleviate the artifacts in the resulting image. In the final result, we expect to see a 20% decrease in artifacts and an increase in speed by a factor of 3.