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Advanced CUBIC tissue clearing for whole-organ cell profiling

Nature Protocols volume 14, pages 3506–3537 (2019)Cite this article

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Abstract

Tissue-clearing techniques are powerful tools for biological research and pathological diagnosis. Here, we describe advanced clear, unobstructed brain imaging cocktails and computational analysis (CUBIC) procedures that can be applied to biomedical research. This protocol enables preparation of high-transparency organs that retain fluorescent protein signals within 7–21 d by immersion in CUBIC reagents. A transparent mouse organ can then be imaged by a high-speed imaging system (>0.5 TB/h/color). In addition, to improve the understanding and simplify handling of the data, the positions of all detected cells in an organ (3–12 GB) can be extracted from a large image dataset (2.5–14 TB) within 3–12 h. As an example of how the protocol can be used, we counted the number of cells in an adult whole mouse brain and other distinct anatomical regions and determined the number of cells transduced with mCherry following whole-brain infection with adeno-associated virus (AAV)-PHP.eB. The improved throughput offered by this protocol allows analysis of numerous samples (e.g., >100 mouse brains per study), providing a platform for next-generation biomedical research.

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Fig. 1: Overview of the advanced CUBIC pipeline in whole-organ cell profiling.
Fig. 2: Procedures for tissue clearing and gel embedding.
Fig. 3: Whole-organ imaging with a customized LSFM.
Fig. 4: MOVIE system for high-speed and high-quality volumetric imaging.
Fig. 5: Details of MOVIE-focus.
Fig. 6: Details of MOVIE-skip.
Fig. 7: Details of cell-nucleus-detection algorithm.
Fig. 8: Cell-nucleus detection and alignment.

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Data availability

CUBIC-Atlas v.1.2 reference data are available from http://cubic-atlas.riken.jp. Other datasets generated or analyzed during the current study are available from the corresponding author upon reasonable request.

Code availability

All source code and a brief guide for data analysis are available on https://github.com/lsb-riken/CUBIC-informatics. Source code for the microscope and MOVIE system is available from the corresponding author upon reasonable request.

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Acknowledgements

We thank the lab members at RIKEN BDR and The University of Tokyo, in particular M. Kuroda, for his kind advice on sample gel embedding; D. Shigeta, for his kind help in 3D image preparation; C. Shimizu for support with swelling experiments; and E. A. Susaki, K. Tainaka and A. Kuno for help with developing CUBIC reagents. We thank K. Wilkins for proofreading the manuscript. T.T.M. was supported by the Osaka University Medical Doctor Scientist Training Program. This research was partially supported by the program for Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) from the Japan Agency for Medical Research and Development, an AMED grant (JP19dm0207057 to H.H.), HFSP (RGP0019/2018 to H.R.U) and JSPS KAKENHI grants 18H02521 (to H.H.) and 19K06899 (to A.K.).

Author information

Author notes

  1. These authors contributed equally: Katsuhiko Matsumoto, Tomoki T. Mitani, Shuhei A. Horiguchi.

Authors and Affiliations

  1. Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, Osaka, Japan

    Katsuhiko Matsumoto, Tomoki T. Mitani, Shuhei A. Horiguchi, Hiroshi Fujishima & Hiroki R. Ueda

  2. Faculty of Medicine, Osaka University, Osaka, Japan

    Tomoki T. Mitani

  3. Osaka University Hospital, Osaka, Japan

    Tomoki T. Mitani

  4. Department of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan

    Shuhei A. Horiguchi

  5. Laboratory for Comprehensive Bioimaging, RIKEN Center for Biosystems Dynamics Research, Osaka, Japan

    Junichi Kaneshiro & Tomonobu M. Watanabe

  6. Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

    Tatsuya C. Murakami & Hiroki R. Ueda

  7. Department of Information Physics and Computing, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan

    Tomoyuki Mano & Hiroki R. Ueda

  8. International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Tokyo, Japan

    Tomoyuki Mano & Hiroki R. Ueda

  9. Department of Neurophysiology & Neural Repair, Gunma University Graduate School of Medicine, Maebashi, Japan

    Ayumu Konno & Hirokazu Hirai

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Contributions

H.R.U., K.M., T.T.M. and S.A.H. designed the study. T.C.M. developed the CUBIC-X protocols. J.K., T.T.M., T.M. and T.M.W. designed and constructed a customized LSFM. K.M. and T.T.M. developed the MOVIE system. T.M., T.C.M. and S.A.H. prepared the CUBIC-Atlas and analysis programs. H.F. and T.C.M. performed tissue clearing. A.K. and H.H. prepared AAV-PHP.eB (NSE-H2B-mCherry) and the infected mouse. K.M., T.T.M., S.A.H., T.C.M., T.M. and H.R.U. wrote the manuscript. All authors discussed the results and commented on the manuscript text.

Corresponding author

Correspondence to Hiroki R. Ueda.

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Competing interests

H.R.U. and T.C.M. have filed a patent application (PCT/JP2014/070618, 2013-168705) for the CUBIC protocol.

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Peer review information Nature Protocols thanks Ludovic Silvestri and other anonymous reviewer(s) for their contribution to the peer review of this work.

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Related links

Key references using this protocol

Murakami, T. C. et al. Nat. Neurosci. 21, 625–637 (2018): https://doi.org/10.1038/s41593-018-0109-1

Tainaka, K. et al. Cell Rep. 24, 2196–2210.e9 (2018): https://doi.org/10.1016/j.celrep.2018.07.056

Kubota, S. I. et al. Cell Rep. 20, 236–250 (2017): https://doi.org/10.1016/j.celrep.2017.06.010

Integrated supplementary information

Supplementary Fig. 1 3D and cross-section images of CUBIC-L/R+ treated brain.

Volume-rendered and cross-section images of CUBIC-L/R+ treated whole mouse brain labelled with H2B-mCherry (green) and RD2 (red). Overlapped signals are shown in yellow. Scale bar, 1mm. All experiments followed governmental and institutional guidelines for the animal experiments.

Supplementary Fig. 2 Parameter optimization for MOVIE-focus.

(a) Optimized six parameters of MOVIE-focus is shown in this table by comprehensive autofocus simulation by using 400,000 images datasets (400 of different light-sheet positions at every 0.35 μm) which was acquired by our LSFM at the 5 μm step size covering 5000 μm depth, using a PI stained mouse brain. Several parts of parameter optimization are shown as an example in (b)-(i). In these panels, only one or two parameters from six parameters are changed. (b)The relationship between block size for image analysis and mean error from ideal focus positions. Error bars show standard deviation of error. (c) The relationship among the block size for image analysis of MOVIE-focus and minimum calculation time for 80% focus accuracy. The focus accuracy was calculated from the ideal focus position, which is determined by the light-sheet position with the highest DCTS score. Although block size 128×128 and 64×64 exhibit similar error in panel (b), the minimum calculation time of block size 128×128 is less than 10 ms per image and faster than that of block size 64×64. (d) The relationship between analyzed area and mean error from ideal focus positions. Error bars show standard deviation of error. (e) The relationship between amplitude of light-sheet oscillation and mean error from ideal focus positions. Error bars show standard deviation of error. (f) The relationship between focus value threshold and mean error from ideal focus positions. Error bars show standard deviation of error. (g) The relationship between focus value threshold and tracking ability. (h) The relationship between block selection order and mean error from ideal focus positions. “Descending” and “Ascending” indicate block selections from top or bottom, respectively. Error bars show standard deviation of error. (i) The relationship between block selection distance (distance function used in block selection; Mean, L1-norm and L2-norm) and mean error from ideal focus positions. Error bars show standard deviation of error.

Supplementary Fig. 3 Result of autofocus simulation for MOVIE performance.

MOVIE-focus was simulated by 400,000 images data sets (400 of different light-sheet positions with 0.35 μm span) which was acquired by our LSFM at the 5 μm step size covering 5000 μm depth, using a PI stained mouse brain. MOVIE (−) used the constant light-sheet position throughout. MOVIE (+) used parameters of Supplementary Fig. 2a. Scale bar, 100 μm.

Supplementary Fig. 4 Result of autofocus simulation for four algorithms.

Result of autofocus simulation. 400,000 images data sets (400 of different light-sheet positions with 0.35 μm span) which was acquired by our LSFM at the 5 μm step size covering 5000 μm depth were used. MOVIE used parameters optimized in Supplementary Fig. 2a. “Without autofocus” means the constant light-sheet position throughout. “Conventional Autofocus” was stop and exposure autofocus mode updated every 1000 μm. An ideal focus position was determined by the light-sheet position with the highest DCTS score of 400 images at each z position. rDCTS (relative DCTS) was calculated with division by DCTS of ideal focus positions. Scale bar, 10 μm.

Supplementary information

Supplementary Information

Supplementary Figures 1–4

Reporting Summary

Supplementary Video 1. Volume-rendered 3D images of a brain transfected with AAV-PHP.eB: NSE-H2B-mCherry.

Volume-rendered 3D images of a brain transfected with AAV-PHP.eB: NSE-H2B-mCherry (green) counterstained by RD2 (red). The brain is cleared by CUBIC-L/R+ protocol. Overlapped signals are shown in yellow.

Supplementary Video 2. The effect of the MOVIE-focus. Image quality with or without MOVIE-focus.

The left image was acquired with MOVIE-focus, the right was acquired with the constant light-sheet position throughout.

Supplementary Video 3. The reduction of imaging area with MOVIE-skip.

The region surrounded by the yellow frame shows the imaging area without MOVIE-skip. The volume of the image was decreased by more than 50%.

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Matsumoto, K., Mitani, T.T., Horiguchi, S.A. et al. Advanced CUBIC tissue clearing for whole-organ cell profiling. Nat Protoc 14, 3506–3537 (2019). https://doi.org/10.1038/s41596-019-0240-9

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