Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advanced CUBIC tissue clearing for whole-organ cell profiling

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

Subjects

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.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

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.

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.

References

  1. Voie, A. H., Burns, D. H. & Spelman, F. A. Orthogonal-plane fluorescence optical sectioning: three-dimensional imaging of macroscopic biological specimens. J. Micros. 170, 229–236 (1993).

    Article  CAS  Google Scholar 

  2. Keller, P. J., Schmidt, A. D., Wittbrodt, J. & Stelzer, E. H. Reconstruction of zebrafish early embryonic development by scanned light sheet microscopy. Science 322, 1065–1069 (2008).

    Article  CAS  PubMed  Google Scholar 

  3. Huisken, J., Swoger, J., Del Bene, F., Wittbrodt, J. & Stelzer, E. H. K. Optical sectioning deep inside live embryos by selective plane illumination microscopy. Science 305, 1007–1009 (2004).

    Article  CAS  PubMed  Google Scholar 

  4. Dodt, H. U. et al. Ultramicroscopy: three-dimensional visualization of neuronal networks in the whole mouse brain. Nat. Methods 4, 331–336 (2007).

    Article  CAS  PubMed  Google Scholar 

  5. Kim, Y. et al. Mapping social behavior-induced brain activation at cellular resolution in the mouse. Cell Rep. 10, 292–305 (2015).

    Article  CAS  PubMed  Google Scholar 

  6. Tomer, R. et al. SPED light sheet microscopy: fast mapping of biological system structure and function. Cell 163, 1796–1806 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Kubota, S. I. et al. Whole-body profiling of cancer metastasis with single-cell resolution. Cell Rep. 20, 236–250 (2017).

    Article  CAS  PubMed  Google Scholar 

  8. Nojima, S. et al. CUBIC pathology: three-dimensional imaging for pathological diagnosis. Sci. Rep. 7, 9269 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Glaser, A. K. et al. Light-sheet microscopy for slide-free non-destructive pathology of large clinical specimens. Nat. Biomed. Eng. 1, 0084 (2017).

  10. Tainaka, K. et al. Chemical landscape for tissue clearing based on hydrophilic reagents. Cell Rep. 24, 2196–2210.e9 (2018).

    Article  CAS  PubMed  Google Scholar 

  11. Murakami, T. C. et al. A three-dimensional single-cell-resolution whole-brain atlas using CUBIC-X expansion microscopy and tissue clearing. Nat. Neurosci. 21, 625–637 (2018).

    Article  CAS  PubMed  Google Scholar 

  12. Spalteholz, W. Über das Durchsichtigmachen von menschlichen und tierischen Präparaten. (S. Hirzel, 1914).

  13. Erturk, A. et al. Three-dimensional imaging of the unsectioned adult spinal cord to assess axon regeneration and glial responses after injury. Nat. Med. 18, 166–171 (2011).

    Article  PubMed  CAS  Google Scholar 

  14. Erturk, A. et al. Three-dimensional imaging of solvent-cleared organs using 3DISCO. Nat. Protoc. 7, 1983–1995 (2012).

    Article  CAS  PubMed  Google Scholar 

  15. Pan, C. et al. Shrinkage-mediated imaging of entire organs and organisms using uDISCO. Nat. Methods 13, 859–867 (2016).

    Article  CAS  PubMed  Google Scholar 

  16. Chung, K. et al. Structural and molecular interrogation of intact biological systems. Nature 497, 332–337 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Hogervorst, J. G., Schouten, L. J., Konings, E. J., Goldbohm, R. A. & van den Brandt, P. A. A prospective study of dietary acrylamide intake and the risk of endometrial, ovarian, and breast cancer. Cancer Epidemiol. Biomark. Prev. 16, 2304–2313 (2007).

    Article  CAS  Google Scholar 

  18. Tomer, R., Ye, L., Hsueh, B. & Deisseroth, K. Advanced CLARITY for rapid and high-resolution imaging of intact tissues. Nat. Protoc. 9, 1682–1697 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Yang, B. et al. Single-cell phenotyping within transparent intact tissue through whole-body clearing. Cell 158, 945–958 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Park, Y.-G. et al. Protection of tissue physicochemical properties using polyfunctional crosslinkers. Nat. Biotechnol. 37, 73 (2018).

    Article  CAS  Google Scholar 

  21. Bakutkin, V. V., Maksimova, I. L., Semyonova, T. N., Tuchin, V. V. & Kon., I. L. Controlling of optical properties of sclera. Proc. SPIE 2393, 137–141 (1995).

    Article  Google Scholar 

  22. Tuchin, V. V. et al. Light propagation in tissues with controlled optical properties. J. Biomed. Opt. 2, 401–417 (1997).

    Article  CAS  PubMed  Google Scholar 

  23. Tuchin, V. V. Light scattering of tissues. Physic 40, 495–515 (1997).

    Google Scholar 

  24. Tuchin, V. V. Optical clearing of tissues and blood using the immersion method. J. Phys. D. Appl. Phys. 38, 2497–2518 (2005).

    Article  CAS  Google Scholar 

  25. Hama, H. et al. Scale: a chemical approach for fluorescence imaging and reconstruction of transparent mouse brain. Nat. Neurosci. 14, 1481–1488 (2011).

    Article  CAS  PubMed  Google Scholar 

  26. Ke, M. T., Fujimoto, S. & Imai, T. SeeDB: a simple and morphology-preserving optical clearing agent for neuronal circuit reconstruction. Nat. Neurosci. 16, 1154–1161 (2013).

    Article  CAS  PubMed  Google Scholar 

  27. Susaki, E. A. et al. Whole-brain imaging with single-cell resolution using chemical cocktails and computational analysis. Cell 157, 726–739 (2014).

    Article  CAS  PubMed  Google Scholar 

  28. Hama, H. et al. ScaleS: an optical clearing palette for biological imaging. Nat. Neurosci. 18, 1518–1529 (2015).

    Article  CAS  PubMed  Google Scholar 

  29. Perbellini, F. et al. Free-of-acrylamide SDS-based tissue clearing (FASTClear) for three dimensional visualization of myocardial tissue. Sci. Rep. 7, 5188 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Tainaka, K. et al. Whole-body imaging with single-cell resolution by tissue decolorization. Cell 159, 911–924 (2014).

    Article  CAS  PubMed  Google Scholar 

  31. Liu, Y. C. & Chiang, A. S. High-resolution confocal imaging and three-dimensional rendering. Methods 30, 86–93 (2003).

    Article  CAS  PubMed  Google Scholar 

  32. Helmchen, F. & Denk, W. Deep tissue two-photon microscopy. Nat. Methods 2, 932–940 (2005).

    Article  CAS  PubMed  Google Scholar 

  33. Silvestri, L., Bria, A., Sacconi, L., Iannello, G. & Pavone, F. S. Confocal light sheet microscopy: micron-scale neuroanatomy of the entire mouse brain. Opt. Express 20, 20582–20598 (2012).

    Article  CAS  PubMed  Google Scholar 

  34. Seiriki, K. et al. High-speed and scalable whole-brain imaging in rodents and primates. Neuron 94, 1085–1100.e6 (2017).

    Article  CAS  PubMed  Google Scholar 

  35. Keller, P. J. & Ahrens, M. B. Visualizing whole-brain activity and development at the single-cell level using light-sheet microscopy. Neuron 85, 462–483 (2015).

    Article  CAS  PubMed  Google Scholar 

  36. Helmstaedter, M. & Mitra, P. P. Computational methods and challenges for large-scale circuit mapping. Curr. Opin. Neurobiol. 22, 162–169 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Ahrens, M. B., Orger, M. B., Robson, D. N., Li, J. M. & Keller, P. J. Whole-brain functional imaging at cellular resolution using light-sheet microscopy. Nat. Methods 10, 413–420 (2013).

    Article  CAS  PubMed  Google Scholar 

  38. Portugues, R., Feierstein, C. E., Engert, F. & Orger, M. B. Whole-brain activity maps reveal stereotyped, distributed networks for visuomotor behavior. Neuron 81, 1328–1343 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Renier, N. et al. iDISCO: a simple, rapid method to immunolabel large tissue samples for volume imaging. Cell 159, 896–910 (2014).

    Article  CAS  PubMed  Google Scholar 

  40. Amat, F. et al. Efficient processing and analysis of large-scale light-sheet microscopy data. Nat. Protoc. 10, 1679–1696 (2015).

    Article  CAS  PubMed  Google Scholar 

  41. Menegas, W. et al. Dopamine neurons projecting to the posterior striatum form an anatomically distinct subclass. eLife 4, e10032 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Susaki, E. A. & Ueda, H. R. Whole-body and whole-organ clearing and imaging techniques with single-cell resolution: toward organism-level systems biology in mammals. Cell Chem. Biol. 23, 137–157 (2016).

    Article  CAS  PubMed  Google Scholar 

  43. Tsai, P. S. et al. Correlations of neuronal and microvascular densities in murine cortex revealed by direct counting and colocalization of nuclei and vessels. J. Neurosci. 29, 14553–14570 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Quan, T. et al. NeuroGPS: automated localization of neurons for brain circuits using L1 minimization model. Sci. Rep. 3, 1414 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Latorre, A., Alonso-Nanclares, L., Muelas, S., Peña, J.-M. & Defelipe, J. 3D segmentations of neuronal nuclei from confocal microscope image stacks. Front. Neuroanat. 7, 49 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Frasconi, P. et al. Large-scale automated identification of mouse brain cells in confocal light sheet microscopy images. Bioinformatics 30, i587–i593 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Amat, F. et al. Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data. Nat. Methods 11, 951–958 (2014).

    Article  CAS  PubMed  Google Scholar 

  48. Wu, J. et al. 3D BrainCV: simultaneous visualization and analysis of cells and capillaries in a whole mouse brain with one-micron voxel resolution. NeuroImage 87, 199–208 (2014).

    Article  PubMed  Google Scholar 

  49. Quan, T. et al. Digital reconstruction of the cell body in dense neural circuits using a spherical-coordinated variational model. Sci. Rep. 4, 4970 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Silvestri, L. et al. Quantitative neuroanatomy of all Purkinje cells with light sheet microscopy and high-throughput image analysis. Front. Neuroanat. 9, 68 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Kim, Y. et al. Brain-wide maps reveal stereotyped cell-type-based cortical architecture and subcortical sexual dimorphism. Cell 171, 456–469.e22 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Furth, D. et al. An interactive framework for whole-brain maps at cellular resolution. Nat. Neurosci. 21, 139–149 (2018).

    Article  CAS  PubMed  Google Scholar 

  53. Xing, F. & Yang, L. Robust nucleus/cell detection and segmentation in digital pathology and microscopy images: a comprehensive review. IEEE Rev. Biomed. Eng. 9, 234–263 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Zhang, M., Wu, T. & Bennett, K. M. Small blob identification in medical images using regional features from optimum scale. IEEE Trans. Biomed. Eng. 62, 1051–1062 (2015).

    Article  PubMed  Google Scholar 

  55. Theer, P., Mongis, C. & Knop, M. PSFj: know your fluorescence microscope. Nat. Methods 11, 981–982 (2014).

    Article  CAS  PubMed  Google Scholar 

  56. Kanda, T., Sullivan, K. F. & Wahl, G. M. Histone-GFP fusion protein enables sensitive analysis of chromosome dynamics in living mammalian cells. Curr. Biol. 8, 377–385 (1998).

    Article  CAS  PubMed  Google Scholar 

  57. Chan, K. Y. et al. Engineered AAVs for efficient noninvasive gene delivery to the central and peripheral nervous systems. Nat. Neurosci. 20, 1172–1179 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Shinohara, Y. et al. Effects of neutralizing antibody production on AAV-PHP.B-mediated transduction of the mouse central nervous system. Mol. Neurobiol. 56, 4203–4214 (2018).

    Article  PubMed  CAS  Google Scholar 

  59. Sun, Y., Duthaler, S. & Nelson, B. J. Autofocusing in computer microscopy: selecting the optimal focus algorithm. Microsc. Res. Tech. 65, 139–149 (2004).

    Article  PubMed  Google Scholar 

  60. Royer, L. A. et al. Adaptive light-sheet microscopy for long-term, high-resolution imaging in living organisms. Nat. Biotechnol. 34, 1267–1278 (2016).

    Article  CAS  PubMed  Google Scholar 

  61. Ryan, D. P. et al. Automatic and adaptive heterogeneous refractive index compensation for light-sheet microscopy. Nat. Commun. 8, 612 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Silvestri, L. et al. RAPID: real-time image-based autofocus for all wide-field optical microscopy systems. Preprint at bioRxiv, https://www.biorxiv.org/content/10.1101/170555v1 (2017).

  63. Royer, L. A., Lemon, W. C., Chhetri, R. K. & Keller, P. J. A practical guide to adaptive light-sheet microscopy. Nat. Protoc. 13, 2462–2500 (2018).

    Article  CAS  PubMed  Google Scholar 

  64. Lindeberg, T. Feature detection with automatic scale selection. Int. J. Comput. Vis. 30, 79–116 (1998).

    Article  Google Scholar 

  65. Hawick, K. A., Leist, A. & Playne, D. P. Parallel graph component labelling with GPUs and CUDA. Parallel Comput. 36, 655–678 (2010).

    Article  Google Scholar 

  66. Soh, Y., Ashraf, H., Hae, Y. & Kim, I. Fast parallel connected component labeling algorithms using CUDA based on 8-directional label selection. Int. J. Latest Res. Sci. Technol. 3, 187–190 (2014).

    Google Scholar 

  67. Frangi, A. F., Niessen, W. J., Vincken, K. L. & Viergever, M. A. Multiscale vessel enhancement filtering. In Medical Image Computing and Computer-Assisted Intervention — MICCAI’98, 130-137 (Springer, 1998).

  68. Avants, B. B. et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage 54, 2033–2044 (2011).

    Article  PubMed  Google Scholar 

  69. Pende, M. et al. High-resolution ultramicroscopy of the developing and adult nervous system in optically cleared Drosophila melanogaster. Nat. Commun. 9, 4731 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Konno, A. & Okazaki, S. Aqueous-based tissue clearing in crustaceans. Zool. Lett. 4, 13 (2018).

    Article  Google Scholar 

  71. Kurihara, D., Mizuta, Y., Sato, Y. & Higashiyama, T. ClearSee: a rapid optical clearing reagent for whole-plant fluorescence imaging. Development 142, 4168–4179 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Watanave, M. et al. Pharmacological enhancement of retinoid-related orphan receptor alpha function mitigates spinocerebellar ataxia type 3 pathology. Neurobiol. Dis. 121, 263–273 (2019).

    Article  CAS  PubMed  Google Scholar 

  73. Huisken, J. & Stainier, D. Y. Even fluorescence excitation by multidirectional selective plane illumination microscopy (mSPIM). Opt. Lett. 32, 2608–2610 (2007).

    Article  PubMed  Google Scholar 

Download references

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

Authors
  1. Katsuhiko Matsumoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tomoki T. Mitani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuhei A. Horiguchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Junichi Kaneshiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tatsuya C. Murakami
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tomoyuki Mano
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hiroshi Fujishima
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ayumu Konno
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Tomonobu M. Watanabe
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Hirokazu Hirai
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Hiroki R. Ueda
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Ethics declarations

Competing interests

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

Additional information

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.

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%.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-019-0240-9

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing