Subcellular Resolution Light Sheet Microscope with a Large Field of View

项目总结 在斑马鱼和水果等活的模式生物中捕获神经网络大区域的能力 亚细胞尺度的苍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 能够捕捉活体神经系统286到300微米区域的图像的显微镜平台 亚细胞分辨率的斑马鱼和水果文件。我们计划将超分辨率技术与光相结合 实现这一目标的平板荧光显微镜。 AIM 1结合了超分辨率结构照明显微镜(SR-SIM)和多方向 单物镜配置的照明光片荧光显微镜。我们预计，该系统 应能达到所有横向161 nm的分辨率，轴向分辨率458 nm和916 nm 分别在3D SR-SIM模式和2D SR-SIM模式下。我们期望拟议的方法将提供一个 斑马鱼受精后6至7周内整个中脑结构和活动的高细节图像 幼虫。目标2将在亚细胞水平(横向241 nm，轴向336 nm)实现各向同性分辨率，同时保持 286到300微米的视场。我们期望所提出的方法将产生成像的能力 长时间(超过1小时)跨多个肌群的突触后丝状足的动态变化。 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缓解 结果图像中的瑕疵。在最终结果中，我们希望看到人工制品减少20%，而增加 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.