Suppr超能文献

关于样本,举例来说:优化单分子定位显微镜。

About samples, giving examples: Optimized Single Molecule Localization Microscopy.

机构信息

Aix Marseille Université, CNRS, INP UMR7051, NeuroCyto, Marseille, France.

Aix Marseille Université, CNRS, INP UMR7051, NeuroCyto, Marseille, France; Abbelight, Paris, France.

出版信息

Methods. 2020 Mar 1;174:100-114. doi: 10.1016/j.ymeth.2019.05.008. Epub 2019 May 10.

Abstract

Super-resolution microscopy has profoundly transformed how we study the architecture of cells, revealing unknown structures and refining our view of cellular assemblies. Among the various techniques, the resolution of Single Molecule Localization Microscopy (SMLM) can reach the size of macromolecular complexes and offer key insights on their nanoscale arrangement in situ. SMLM is thus a demanding technique and taking advantage of its full potential requires specifically optimized procedures. Here we describe how we perform the successive steps of an SMLM workflow, focusing on single-color Stochastic Optical Reconstruction Microscopy (STORM) as well as multicolor DNA Points Accumulation for imaging in Nanoscale Topography (DNA-PAINT) of fixed samples. We provide detailed procedures for careful sample fixation and immunostaining of typical cellular structures: cytoskeleton, clathrin-coated pits, and organelles. We then offer guidelines for optimal imaging and processing of SMLM data in order to optimize reconstruction quality and avoid the generation of artifacts. We hope that the tips and tricks we discovered over the years and detail here will be useful for researchers looking to make the best possible SMLM images, a pre-requisite for meaningful biological discovery.

摘要

超分辨率显微镜极大地改变了我们研究细胞结构的方式,揭示了未知的结构,并完善了我们对细胞组装体的认识。在各种技术中,单分子定位显微镜(SMLM)的分辨率可以达到大分子复合物的大小,并提供关于其在原位纳米尺度排列的关键见解。因此,SMLM 是一项要求很高的技术,要充分发挥其潜力,需要特别优化的程序。在这里,我们描述了如何执行 SMLM 工作流程的连续步骤,重点介绍了单色彩虹光重构显微镜(STORM)以及固定样本的多色 DNA 点积累用于纳米形貌成像(DNA-PAINT)。我们提供了详细的程序,用于仔细固定和免疫标记典型的细胞结构:细胞骨架、网格蛋白包被的凹陷和细胞器。然后,我们提供了优化 SMLM 数据成像和处理的指南,以优化重建质量并避免产生伪影。我们希望多年来发现的技巧和窍门,并在这里详细介绍,将对希望获得最佳 SMLM 图像的研究人员有用,这是进行有意义的生物学发现的前提。

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