Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.
MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.
Nat Methods. 2021 Mar;18(3):309-315. doi: 10.1038/s41592-021-01074-x. Epub 2021 Mar 1.
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 μm, 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.
通过光学显微镜对大规模三维 (3D) 样品进行微观可视化,需要克服成像质量和速度方面的挑战,以及大数据采集和管理方面的挑战。我们报告了一种用于对厚组织进行高速、低背景成像的线照明调制 (LiMo) 技术。我们将 LiMo 与薄组织切片相结合,进一步开发了一种高分辨率荧光微光学切片层析成像 (HD-fMOST) 方法,该方法的平均信噪比为 110,从而显著改善了神经元形态重建。我们以 0.32×0.32×1.00μm 的体素分辨率实现了 >30 倍的无损数据压缩,能够将数据无损地在线存储到 USB 驱动器或云端,并实时实现高精度 (95%准确率) 的全脑 3D 细胞计数。这些结果突出了 HD-fMOST 促进全脑高分辨率数据集的大规模采集和分析的潜力。