STED microscope

我们申请资金支持购买STED显微镜。STED显微镜是一种光学超分辨率方法，能够产生空间分辨率为几十纳米的生物样品的荧光图像。STED是一种没有计算图像处理元素的真光学超分辨率方法。STED能够通过集成例如自适应光学对厚样品进行3D成像，并允许活细胞成像。我们的研究小组最近通过实施“可交换荧光标记”的概念对STED显微镜做出了贡献，该概念是我们从单分子定位显微镜改编的。通过在成像器缓冲器中保持这些标记可用来实现这种标记与目标的瞬时和重复结合。首先，在任何荧光成像技术中，不断地交换来往于目标的标记可以最大限度地减少光漂白。应用于STED显微镜的其他优点是：易于实现多色成像、3D体积成像和活细胞成像，所有这些都受益于减少光漂白。在这里，我们申请了STED显微镜，通过使用蛋白质标记的新概念来实现多蛋白质成像，从而将该技术进一步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上。其次，我们将开发定量STED显微镜，再次受益于并适应我们在单分子定位显微镜和整合新的蛋白质标记方面的知识。第三，我们将开发计算管道，允许集成最先进的深度学习辅助显微镜工具的各个方面，例如对活体样本进行低光剂量成像的去噪，以延长观察时间。这些工作包需要配备多个激发源的高性能STED显微镜，模块化结构，以及将新型光学或辅助元件集成到显微镜系统中的选项。

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.