小柯机器人

以色列理工学院Lokey生命科学和工程中心Yoav Shechtman团队取得一项新突破。他们开发的DeepSTORM3D可通过深度学习进行密集的3D定位显微镜和点扩散函数(PSF)设计。相关论文于2020年6月15日发表于《自然-方法学》杂志。

研究人员建立了一个神经网络，以在较大的轴向范围内将密集重叠的Tetrapod扩展函数（PSF）定位在多个发射器上。然后，研究人员使用该网络为多发射器设计最佳PSF。通过线粒体的超分辨率重建和荧光标记端粒在细胞中的体积成像实验，研究人员证明了该方法的可用性。DeepSTORM3D方法使研究全细胞生物学过程的时间尺度在定位显微镜中成为可能，之前该可能性鲜有探索。

研究人员表示，单分子定位显微镜面临的一个巨大挑战是在密集标记的样品中如何实现各个点发射器在三维中的精确定位。一种建立三维单分子定位的方法是PSF，利用额外光学元件将PSF设计为随发射器深度而显著变化。然而，由于发射器PSF的横向重叠，对于提高时间分辨率而言，预期密集发射器的图像对工程化PSF的算法定位提出了挑战。

附：英文原文

Title: DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning

Author: Elias Nehme, Daniel Freedman, Racheli Gordon, Boris Ferdman, Lucien E. Weiss, Onit Alalouf, Tal Naor, Reut Orange, Tomer Michaeli, Yoav Shechtman

Issue&Volume: 2020-06-15

Abstract: An outstanding challenge in single-molecule localization microscopy is the accurate and precise localization of individual point emitters in three dimensions in densely labeled samples. One established approach for three-dimensional single-molecule localization is point-spread-function (PSF) engineering, in which the PSF is engineered to vary distinctively with emitter depth using additional optical elements. However, images of dense emitters, which are desirable for improving temporal resolution, pose a challenge for algorithmic localization of engineered PSFs, due to lateral overlap of the emitter PSFs. Here we train a neural network to localize multiple emitters with densely overlapping Tetrapod PSFs over a large axial range. We then use the network to design the optimal PSF for the multi-emitter case. We demonstrate our approach experimentally with super-resolution reconstructions of mitochondria and volumetric imaging of fluorescently labeled telomeres in cells. Our approach, DeepSTORM3D, enables the study of biological processes in whole cells at timescales that are rarely explored in localization microscopy.

DOI: 10.1038/s41592-020-0853-5

Source: https://www.nature.com/articles/s41592-020-0853-5

Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex