Wang Songyue, Qiao Chang, Jiang Amin, Li Di, Li Dong
National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
Biophys Rep. 2021 Aug 31;7(4):304-312. doi: 10.52601/bpr.2021.210017.
Multicolor super-resolution (SR) microscopy plays a critical role in cell biology research and can visualize the interactions between different organelles and the cytoskeleton within a single cell. However, more color channels bring about a heavier budget for imaging and sample preparation, and the use of fluorescent dyes of higher emission wavelengths leads to a worse spatial resolution. Recently, deep convolutional neural networks (CNNs) have shown a compelling capability in cell segmentation, super-resolution reconstruction, image restoration, and many other aspects. Taking advantage of CNN's strong representational ability, we devised a deep CNN-based instant multicolor super-resolution imaging method termed IMC-SR and demonstrated that it could be used to separate different biological components labeled with the same fluorophore, and generate multicolor images from a single super-resolution image . By IMC-SR, we achieved fast three-color live-cell super-resolution imaging with ~100 nm resolution over a long temporal duration, revealing the complicated interactions between multiple organelles and the cytoskeleton in a single COS-7 cell.
多色超分辨率（SR）显微镜在细胞生物学研究中起着关键作用，能够在单个细胞内可视化不同细胞器与细胞骨架之间的相互作用。然而，更多的颜色通道会带来更高的成像和样品制备成本，并且使用发射波长更长的荧光染料会导致空间分辨率变差。近年来，深度卷积神经网络（CNN）在细胞分割、超分辨率重建、图像恢复等诸多方面展现出了强大的能力。利用CNN强大的表征能力，我们设计了一种基于深度CNN的即时多色超分辨率成像方法，称为IMC-SR，并证明它可用于分离用相同荧光团标记的不同生物成分，并从单个超分辨率图像生成多色图像。通过IMC-SR，我们在较长时间内实现了分辨率约为100 nm的快速三色活细胞超分辨率成像，揭示了单个COS-7细胞中多个细胞器与细胞骨架之间的复杂相互作用。
