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High-definition imaging using line-illumination modulation microscopy

Nature Methods volume 18, pages 309–315 (2021)Cite this article

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Abstract

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.

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Fig. 1: LiMo microscopy.
Fig. 2: Whole-brain imaging of the anterograde projections of AAV-YFP-labeled neurons in the motor cortex.
Fig. 3: Neuronal morphology reconstruction.
Fig. 4: Automatic signal segmentation on a raw data block with 11,200 × 5,200 × 3,200 voxels.

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Data availability

The imaging data reported in the paper are available at http://atlas.brainsmatics.org/a/zhong2019. Source data are provided with this paper.

Code availability

The code for data acquisition in this paper is available at http://atlas.brainsmatics.org/a/zhong2019.

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Acknowledgements

We thank the MOST group members of the Britton Chance Center for Biomedical Photonics for assistance with experiments and comments on the manuscript. We are grateful to X. Yang and J. Chen for experimental help, T. Quan, M. Liao, K. Ning, N. Wang, J. Zhou, S. Bao, M. Tian and B. Lu for morphology reconstruction and analysis, and H. Li, H. Dong, Z. J. Huang, M. Luo, T. Xu, Y. Han, X. Chen and Y. Sun for constructive comments. This work was supported by National Key Research and Development Program of China (grant no. 2017YFA0700402 to J.Y.), Science Fund for Creative Research Group of China (grant no. 61721092 to Q.L.), National Natural Science Foundation of China (grant nos. 81827901 to Q.L. and A.L., 91749209 to Q.L., 61890950 to H.G., J.Y. and A.L., 61890953 to H.G., J.Y. and X.L., 61890954 to A.L.), and Fundamental Research Funds for the Central Universities (HUST: grant nos. 2019kfyXMBZ011, 2019kfyXMBZ039, 2018KFYXMPT018, 2018KFYXKJC030 to Q.L., H.G. and Z.Z.). We also thank Analytical and Testing Center (HUST) and Optical Bioimaging Core Facility of WNLO-HUST for the support in data acquisition, and the director fund of the WNLO.

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Author notes

  1. These authors contributed equally: Qiuyuan Zhong, Anan Li.

Authors and Affiliations

  1. Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China

    Qiuyuan Zhong, Anan Li, Rui Jin, Dejie Zhang, Xiangning Li, Zhangheng Ding, Pan Luo, Can Zhou, Chenyu Jiang, Zhao Feng, Zhihong Zhang, Hui Gong, Jing Yuan & Qingming Luo

  2. MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China

    Qiuyuan Zhong, Anan Li, Rui Jin, Dejie Zhang, Xiangning Li, Zhangheng Ding, Pan Luo, Can Zhou, Chenyu Jiang, Zhao Feng, Zhihong Zhang, Hui Gong, Jing Yuan & Qingming Luo

  3. HUST-Suzhou Institute for Brainsmatics, Suzhou, China

    Qiuyuan Zhong, Anan Li, Xiangning Li, Xueyan Jia, Zhangheng Ding, Hui Gong, Jing Yuan & Qingming Luo

  4. CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Science, Shanghai, China

    Anan Li & Hui Gong

  5. School of Biomedical Engineering, Hainan University, Haikou, China

    Qingming Luo

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  1. Qiuyuan Zhong
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Contributions

J.Y., Q.L., Z.Q. and H.G. conceived and designed the study and wrote the manuscript. Q.Z., R.J., D.Z., C.J., Z.D. and J.Y. constructed the microscopes and performed data acquisition. X.L., P.L., C.Z., S.J., Z.Z. and H.G. designed the tissue preparation and built the virus tracing samples. J.Y., A.L., Q.Z., X.J., Z.F., H.G. and Q.L. performed image processing and visualization and analyzed the data. J.Y., Q.Z., A.L., H.G. and Q.L. modified the manuscript.

Corresponding authors

Correspondence to Jing Yuan or Qingming Luo.

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Competing interests

Q.L., J.Y., Q.Z., R.J. and H.G. have filed patent applications based on this work.

Additional information

Peer review information Nature Methods thanks Lamiae Abdeladim and the other, anonymous, reviewer for their contribution to the peer review of this work. Nina Vogt was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Notes 1 and 2, Figs. 1–20 and Tables 1–7.

Reporting Summary

Supplementary Video 1

Background inhibition using LiMo, point-confocal, 2PM and line-confocal methods.

Supplementary Video 2

Whole-brain MIP of the sparsely labeled mouse brain.

Supplementary Video 3

3D rendering of the injection site of the same dataset with Supplementary Video 2.

Supplementary Video 4

3D rendering of the reconstructed neurons.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

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Zhong, Q., Li, A., Jin, R. et al. High-definition imaging using line-illumination modulation microscopy. Nat Methods 18, 309–315 (2021). https://doi.org/10.1038/s41592-021-01074-x

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