无监督学习可实现多色同步荧光显微镜观察,且无细胞结构串扰

IF 5.4 1区 物理与天体物理 Q1 OPTICS
APL Photonics Pub Date : 2024-05-30 DOI:10.1063/5.0202622
Bolin Lu, Zhangheng Ding, Kefu Ning, Xiaoyu Zhang, Xiangning Li, Jiangjiang Zhao, Ruiheng Xie, Dan Shen, Jiahong Hu, Tao Jiang, Jianwei Chen, Hui Gong, Jing Yuan
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引用次数: 0

摘要

在多色荧光显微镜中,根据细胞结构图像的精确解剖注释,以单细胞分辨率确定生物结构的方向至关重要。然而,在同步多色成像过程中,由于光谱混合,4′,6-二脒基-2-苯基吲哚(DAPI)染色的细胞结构图像的蓝色信号串扰到绿色波段,阻碍了绿色信号的可视化和识别。在此,我们提出了一个基于深度学习的框架,名为 "串扰消除和细胞结构增强管道(CECEP)",用于在多色荧光成像过程中同时获取绿色通道中的无串扰信号和高对比度的 DAPI 染色细胞结构图像。针对 CECEP 网络,我们提出了一种名为 "细胞结构增强网络(CENet)"的无监督学习算法,该算法能在 25 Hz 的重建速度下将 1800 × 1800 像素图像的细胞结构信噪比(SBR)从 1.5 提高到 15.0。CECEP 网络广泛适用于不同质量的图像、不同类型的组织和不同的多色荧光显微镜。此外,CECEP 网络还能促进各种下游分析任务,如细胞识别、结构张量计算和脑区分割。利用 CECEP 网络,我们同时获取了两个特定的荧光标记神经元分布及其共定位的高 SBR 细胞结构图像,整个大脑中没有出现串扰。实验结果表明,我们的方法有可能促进多色荧光成像在生物学中的应用,如揭示和可视化具有精确位置和方向的不同类型的生物结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unsupervised learning enables multicolor synchronous fluorescence microscopy without cytoarchitecture crosstalk
In multicolor fluorescence microscopy, it is crucial to orient biological structures at a single-cell resolution based on precise anatomical annotations of cytoarchitecture images. However, during synchronous multicolor imaging, due to spectral mixing, the crosstalk from the blue signals of 4′,6-diamidino-2-phenylindole (DAPI)-stained cytoarchitecture images to the green waveband hinders the visualization and identification of green signals. Here, we proposed a deep learning-based framework named the crosstalk elimination and cytoarchitecture enhancement pipeline (CECEP) to simultaneously acquire crosstalk-free signals in the green channel and high-contrast DAPI-stained cytoarchitecture images during multicolor fluorescence imaging. For the CECEP network, we proposed an unsupervised learning algorithm named the cytoarchitecture enhancement network (CENet), which increased the signal-to-background ratio (SBR) of the cytoarchitecture images from 1.5 to 15.0 at a reconstruction speed of 25 Hz for 1800 × 1800 pixel images. The CECEP network is widely applicable to images of different quality, different types of tissues, and different multicolor fluorescence microscopy. In addition, the CECEP network can also facilitate various downstream analysis tasks, such as cell recognition, structure tensor calculation, and brain region segmentation. With the CECEP network, we simultaneously acquired two specific fluorescence-labeled neuronal distributions and their colocated high-SBR cytoarchitecture images without crosstalk throughout the brain. Experimental results demonstrate that our method could potentially facilitate multicolor fluorescence imaging applications in biology, such as revealing and visualizing different types of biological structures with precise locations and orientations.
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来源期刊
APL Photonics
APL Photonics Physics and Astronomy-Atomic and Molecular Physics, and Optics
CiteScore
10.30
自引率
3.60%
发文量
107
审稿时长
19 weeks
期刊介绍: APL Photonics is the new dedicated home for open access multidisciplinary research from and for the photonics community. The journal publishes fundamental and applied results that significantly advance the knowledge in photonics across physics, chemistry, biology and materials science.
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