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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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.
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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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.).
H.R.U. and T.C.M. have filed a patent application (PCT/JP2014/070618, 2013-168705) for the CUBIC protocol.
Peer review information Nature Protocols thanks Ludovic Silvestri and other anonymous reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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
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.
(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.
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.
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 Figures 1–4
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.
The left image was acquired with MOVIE-focus, the right was acquired with the constant light-sheet position throughout.
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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