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在复杂组织深处的定位显微镜中使用稳健的自适应光学。

Robust adaptive optics for localization microscopy deep in complex tissue.

机构信息

Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, Utrecht, the Netherlands.

Department of Axonal Signalling, Netherlands Institute for Neuroscience, Royal Netherlands Academy for Arts and Sciences (KNAW), Amsterdam, the Netherlands.

出版信息

Nat Commun. 2021 Jun 7;12(1):3407. doi: 10.1038/s41467-021-23647-2.

Abstract

Single-Molecule Localization Microscopy (SMLM) provides the ability to determine molecular organizations in cells at nanoscale resolution, but in complex biological tissues, where sample-induced aberrations hamper detection and localization, its application remains a challenge. Various adaptive optics approaches have been proposed to overcome these issues, but the exact performance of these methods has not been consistently established. Here we systematically compare the performance of existing methods using both simulations and experiments with standardized samples and find that they often provide limited correction or even introduce additional errors. Careful analysis of the reasons that underlie this limited success enabled us to develop an improved method, termed REALM (Robust and Effective Adaptive Optics in Localization Microscopy), which corrects aberrations of up to 1 rad RMS using 297 frames of blinking molecules to improve single-molecule localization. After its quantitative validation, we demonstrate that REALM enables to resolve the periodic organization of cytoskeletal spectrin of the axon initial segment even at 50 μm depth in brain tissue.

摘要

单分子定位显微镜 (SMLM) 提供了在纳米尺度分辨率下确定细胞内分子结构的能力,但在复杂的生物组织中,由于样本诱导的像差会阻碍检测和定位,其应用仍然是一个挑战。已经提出了各种自适应光学方法来克服这些问题,但这些方法的精确性能尚未得到一致确立。在这里,我们使用标准化样本通过模拟和实验系统地比较了现有方法的性能,发现它们通常提供有限的校正,甚至会引入额外的错误。对导致这种有限成功的原因进行仔细分析,使我们能够开发出一种改进的方法,称为 REALM(用于定位显微镜的稳健有效的自适应光学),该方法使用 297 帧闪烁分子来校正高达 1 rad RMS 的像差,从而改善单分子定位。在对其进行定量验证后,我们证明 REALM 即使在脑组织中 50μm 的深度也能分辨轴突起始段细胞骨架 spectrin 的周期性结构。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c2c/8184833/9c933920f715/41467_2021_23647_Fig1_HTML.jpg

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