PN-ImTLSM有助于在细胞深处进行高通量低背景单分子定位显微镜。

Boxin Xue, Caiwei Zhou, Yizhi Qin, Yongzheng Li, Yuao Sun, Lei Chang, Shipeng Shao, Yongliang Li, Mengling Zhang, Chaoying Sun, Renxi He, Qian Peter Su, Yujie Sun
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引用次数: 0

摘要

当对细胞核结构进行成像时,失焦荧光作为背景,阻碍了对弱信号的检测。光片荧光显微镜(LSFM)是一种具有去除背景和成像速度优点的宽视场成像方法。然而,由于空间位阻的存在,通常采用的正交激发/检测方案难以应用于单细胞成像。对于具有高时空单细胞成像能力的lsfm,复杂的仪器设计和操作在很大程度上限制了它们的数据采集吞吐量。在这里,我们提出了一种利用浸入式倾斜光片显微镜(ImTLSM)对单细胞进行高通量无背景荧光成像的方法。ImTLSM是基于一个光片投影的光轴上的一个水浸物镜。ImTLSM将照明物镜与检测物镜相对放置,可以在保持单分子检测灵敏度和分辨率的同时,对多个细胞进行快速巡视和光学切片。此外,ImTLSM在操作和维护方面的简单性和鲁棒性使高通量图像采集能够建立用于深度学习的背景去除数据集。利用深度学习模型训练从外照照图像到ImTLSM照明图像的映射,即PN-ImTLSM,我们演示了跨模态荧光成像,将外照照图像转化为接近ImTLSM获得的背景去除性能。我们证明了PN-ImTLSM可以推广到大视场均匀光照成像,从而进一步提高了成像吞吐量。此外,与常用的背景去除方法相比,PN-ImTLSM在背景强度在空间上变化剧烈的区域表现出更好的性能,便于高密度的单分子定位显微镜。总之,PN-ImTLSM为普通倒置显微镜的无背景荧光成像铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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PN-ImTLSM facilitates high-throughput low background single-molecule localization microscopy deep in the cell.

When imaging the nucleus structure of a cell, the out-of-focus fluorescence acts as background and hinders the detection of weak signals. Light-sheet fluorescence microscopy (LSFM) is a wide-field imaging approach which has the best of both background removal and imaging speed. However, the commonly adopted orthogonal excitation/detection scheme is hard to be applied to single-cell imaging due to steric hindrance. For LSFMs capable of high spatiotemporal single-cell imaging, the complex instrument design and operation largely limit their throughput of data collection. Here, we propose an approach for high-throughput background-free fluorescence imaging of single cells facilitated by the Immersion Tilted Light Sheet Microscopy (ImTLSM). ImTLSM is based on a light-sheet projected off the optical axis of a water immersion objective. With the illumination objective and the detection objective placed opposingly, ImTLSM can rapidly patrol and optically section multiple individual cells while maintaining single-molecule detection sensitivity and resolution. Further, the simplicity and robustness of ImTLSM in operation and maintenance enables high-throughput image collection to establish background removal datasets for deep learning. Using a deep learning model to train the mapping from epi-illumination images to ImTLSM illumination images, namely PN-ImTLSM, we demonstrated cross-modality fluorescence imaging, transforming the epi-illumination image to approach the background removal performance obtained with ImTLSM. We demonstrated that PN-ImTLSM can be generalized to large-field homogeneous illumination imaging, thereby further improving the imaging throughput. In addition, compared to commonly used background removal methods, PN-ImTLSM showed much better performance for areas where the background intensity changes sharply in space, facilitating high-density single-molecule localization microscopy. In summary, PN-ImTLSM paves the way for background-free fluorescence imaging on ordinary inverted microscopes.

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