小柯机器人

南方科技大学Yiming Li团队近期取得重要工作进展，他们研究利用场相关深度学习实现高通量全细胞3D超分辨率成像。相关研究成果2023年2月23日在线发表于《自然—方法学》杂志上。

据介绍，单分子定位显微镜在典型的宽场设置中被广泛用于研究具有超分辨率的亚细胞结构，然而，场相关像差将视场（FOV）限制在几十微米。

研究人员提出了一种深度学习方法，用于在覆盖现代sCMOS相机全芯片的大型FOV上精确定位空间变化点发射器（FD DeepLoc）。使用基于图形处理单元的矢量点扩散函数（PSF）拟合器，研究人员可以快速准确地建模整个FOV中高数值孔径物镜的空间变化PSF。

最后，结合基于变形镜的最佳PSF工程，研究人员在~180×180×5 μm³的体积上展示了高精度的三维单分子定位显微镜，在单个成像周期内对整个细胞的线粒体和核孔复合体进行成像，而无需硬件扫描，与最先进的技术相比，吞吐量增加了100倍。

附：英文原文

Title: Field-dependent deep learning enables high-throughput whole-cell 3D super-resolution imaging

Author: Fu, Shuang, Shi, Wei, Luo, Tingdan, He, Yingchuan, Zhou, Lulu, Yang, Jie, Yang, Zhichao, Liu, Jiadong, Liu, Xiaotian, Guo, Zhiyong, Yang, Chengyu, Liu, Chao, Huang, Zhen-li, Ries, Jonas, Zhang, Mingjie, Xi, Peng, Jin, Dayong, Li, Yiming

Issue&Volume: 2023-02-23

Abstract: Single-molecule localization microscopy in a typical wide-field setup has been widely used for investigating subcellular structures with super resolution; however, field-dependent aberrations restrict the field of view (FOV) to only tens of micrometers. Here, we present a deep-learning method for precise localization of spatially variant point emitters (FD-DeepLoc) over a large FOV covering the full chip of a modern sCMOS camera. Using a graphic processing unit-based vectorial point spread function (PSF) fitter, we can fast and accurately model the spatially variant PSF of a high numerical aperture objective in the entire FOV. Combined with deformable mirror-based optimal PSF engineering, we demonstrate high-accuracy three-dimensional single-molecule localization microscopy over a volume of ~180×180×5μm3, allowing us to image mitochondria and nuclear pore complexes in entire cells in a single imaging cycle without hardware scanning; a 100-fold increase in throughput compared to the state of the art.

DOI: 10.1038/s41592-023-01775-5

Source: https://www.nature.com/articles/s41592-023-01775-5

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