STED microscope

我们申请资助购买一台STED显微镜。STED显微镜是一种光学超分辨率方法，能够产生生物样品的荧光图像，其空间分辨率为几十纳米。STED是一种不含计算图像处理元素的真光学超分辨方法。通过集成自适应光学，STED能够对厚样品进行三维成像，并允许活细胞成像。我们的研究小组最近通过实施“可交换荧光团标签”的概念，为STED显微镜做出了贡献，这是我们从单分子定位显微镜中改编的。这样的标签会短暂地和重复地绑定到目标上，这是通过在成像仪缓冲区中保持这些标签可用来实现的。首先，在任何荧光成像技术中，标签与目标之间的连续交换可以最大限度地减少光漂白。应用于STED显微镜，额外的好处是：易于实施的多色成像，3D体积成像和活细胞成像，这些都受益于减少光漂白。在这里，我们申请STED显微镜，通过使用新的蛋白质标记概念实现多蛋白质成像，进一步将该技术发展为结构细胞生物学显微镜方法。这项工作将集中在单细胞，多细胞组织和组织切片。其次，我们将开发定量STED显微镜，再次利用和调整我们在单分子定位显微镜和整合新的蛋白质标签方面的知识。第三，我们将开发计算管道，允许整合最先进的深度学习辅助显微镜工具的各个方面，例如，为长时间观察活标本的低光剂量成像去噪。这些工作包需要一个高性能STED显微镜配备多个激励源，一个模块化的结构和集成新的光学或辅助元件到显微镜系统的选择。

We apply for financial support to purchase a STED microscope. STED microscopy is an optical super-resolution method that is capable of generating fluorescence images of biological samples with a spatial resolution of a few tens of nanometers. STED is a true-optical super-resolution method without elements of computational image processing. STED is capable of 3D imaging of thick samples through the integration of e.g. adaptive optics and allows for live-cell imaging. Our research group has recently contributed to STED microscopy by implementing the concept of “exchangeable fluorophore labels”, which we adapted from single-molecule localization microscopy. Such labels transiently and repeatedly bind to a target, which is achieved by keeping these labels available in the imager buffer. At the first place, a continuous exchange of labels to and from their target minimizes photobleaching in any fluorescence imaging technique. Applied to STED microscopy, additional benefits are: easy-to-implement multi-color imaging, 3D volume imaging and live cell imaging, which all profit from reduced photobleaching. Here, we apply for a STED microscope to further develop the technology into a structural cell biology microscopy method by enabling multi-protein imaging by using novel concepts of protein labeling. This work will focus on single cells, multi-cellular organisations and tissue sections. Second, we will develop quantitative STED microscopy, again profiting from and adapting our knowledge in single-molecule localization microscopy and integrating new protein labels. Third, we will develop computational pipelines that allow integrating various aspects of state-of-the-art deep-learning assisted microscopy tools, e.g. denoising for low light dose imaging of live specimens for a prolonged observation time. These work packages demand for a high-performance STED microscope equipped with multiple excitation sources, a modular construction and the option of integrating novel optical or auxiliary elements to the microscope system.