显微镜
计算机科学
直线(几何图形)
光学
体素
材料科学
人工智能
可视化
薄层荧光显微镜
计算机视觉
生物医学工程
调制(音乐)
扫描共焦电子显微镜
物理
几何学
数学
声学
医学
作者
Qiuyuan Zhong,Anan Li,Rui Jin,Dejie Zhang,Xiangning Li,Xueyan Jia,Zhangheng Ding,Pan Luo,Can Zhou,Chenyu Jiang,Zhao Feng,Zhihong Zhang,Hui Gong,Jing Yuan,Qingming Luo
出处
期刊:Nature Methods
[Nature Portfolio]
日期:2021-03-01
卷期号:18 (3): 309-315
被引量:117
标识
DOI:10.1038/s41592-021-01074-x
摘要
The microscopic visualization of large-scale three-dimensional (3D) samples by optical microscopy requires overcoming challenges in imaging quality and speed and in big data acquisition and management. We report a line-illumination modulation (LiMo) technique for imaging thick tissues with high throughput and low background. Combining LiMo with thin tissue sectioning, we further develop a high-definition fluorescent micro-optical sectioning tomography (HD-fMOST) method that features an average signal-to-noise ratio of 110, leading to substantial improvement in neuronal morphology reconstruction. We achieve a >30-fold lossless data compression at a voxel resolution of 0.32 × 0.32 × 1.00 μm3, enabling online data storage to a USB drive or in the cloud, and high-precision (95% accuracy) brain-wide 3D cell counting in real time. These results highlight the potential of HD-fMOST to facilitate large-scale acquisition and analysis of whole-brain high-resolution datasets. HD-fMOST is a microscopy technique for imaging large samples at high throughput and with high definition, which is achieved with a line-illumination modulation approach. The technology is illustrated by imaging fluorescently labeled neurons in whole mouse brains.
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