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.

  • Protocol
  • Published:

Advanced CUBIC protocols for whole-brain and whole-body clearing and imaging

Nature Protocols volume 10, pages 1709–1727 (2015)Cite this article

Subjects

Abstract

Here we describe a protocol for advanced CUBIC (Clear, Unobstructed Brain/Body Imaging Cocktails and Computational analysis). The CUBIC protocol enables simple and efficient organ clearing, rapid imaging by light-sheet microscopy and quantitative imaging analysis of multiple samples. The organ or body is cleared by immersion for 1–14 d, with the exact time required dependent on the sample type and the experimental purposes. A single imaging set can be completed in 30–60 min. Image processing and analysis can take <1 d, but it is dependent on the number of samples in the data set. The CUBIC clearing protocol can process multiple samples simultaneously. We previously used CUBIC to image whole-brain neural activities at single-cell resolution using Arc-dVenus transgenic (Tg) mice. CUBIC informatics calculated the Venus signal subtraction, comparing different brains at a whole-organ scale. These protocols provide a platform for organism-level systems biology by comprehensively detecting cells in a whole organ or body.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Figure 1: Overview of the CUBIC pipeline.
Figure 2: Procedure of the simple immersion protocol.
Figure 3: Procedure of the CB-perfusion protocol.
Figure 4: Whole-organ imaging with LSFM.
Figure 5: Preprocessing of acquired 3D image for comparison analysis.
Figure 6: Calculation of signal subtraction.

Similar content being viewed by others

References

  1. 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  Google Scholar 

  2. Osten, P. & Margrie, T.W. Mapping brain circuitry with a light microscope. Nat. Methods 10, 515–523 (2013).

    Article  CAS  Google Scholar 

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

  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  Google Scholar 

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

    Article  Google Scholar 

  6. Becker, K., Jährling, N., Saghafi, S., Weiler, R. & Dodt, H.U. Chemical clearing and dehydration of GFP expressing mouse brains. PLoS ONE 7, e33916 (2012).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  8. Schwarz, M.K. et al. Fluorescent-protein stabilization and high-resolution imaging of cleared, intact mouse brains. PLoS ONE 10, e0124650 (2015).

    Article  Google Scholar 

  9. 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  Google Scholar 

  10. 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  Google Scholar 

  11. Kuwajima, T. et al. ClearT: a detergent- and solvent-free clearing method for neuronal and non-neuronal tissue. Development 140, 1364–1368 (2013).

    Article  CAS  Google Scholar 

  12. Aoyagi, Y., Kawakami, R., Osanai, H., Hibi, T. & Nemoto, T. A rapid optical clearing protocol using 2,2′-thiodiethanol for microscopic observation of fixed mouse brain. PLoS ONE 10, e0116280 (2015).

    Article  Google Scholar 

  13. Costantini, I. et al. A versatile clearing agent for multi-modal brain imaging. Sci. Rep. 5, 9808 (2015).

    Article  CAS  Google Scholar 

  14. Hou, B. et al. Scalable and DiI-compatible optical clearance of the mammalian brain. Front. Neuroanat. 9, 19 (2015).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  16. 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  Google Scholar 

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

    Article  CAS  Google Scholar 

  18. 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  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  21. Keller, P.J. et al. Fast, high-contrast imaging of animal development with scanned light sheet-based structured-illumination microscopy. Nat. Methods 7, 637–642 (2010).

    Article  CAS  Google Scholar 

  22. 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  Google Scholar 

  23. Panier, T. et al. Fast functional imaging of multiple brain regions in intact zebrafish larvae using selective plane illumination microscopy. Front. Neural Circuits 7, 65 (2013).

    Article  Google Scholar 

  24. Murphy, K. et al. Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge. IEEE Trans. Med. Imaging 30, 1901–1920 (2011).

    Article  Google Scholar 

  25. Yushkevich, P.A. et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31, 1116–1128 (2006).

    Article  Google Scholar 

  26. Eguchi, M. & Yamaguchi, S. In vivo and in vitro visualization of gene expression dynamics over extensive areas of the brain. Neuroimage 44, 1274–1283 (2009).

    Article  Google Scholar 

  27. Prewitt, J.M.S. Object enhancement and extraction. In Picture Processing and Psychopictorics (eds. Lipkin, B.S. & Rosenfeld, A.) 75–149 (Academic Press, 1970).

  28. Jenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W. & Smith, S.M. Fsl. Neuroimage 62, 782–790 (2012).

    Article  Google Scholar 

  29. Lein, E.S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007).

    Article  CAS  Google Scholar 

  30. Alnuami, A.A., Zeedi, B., Qadri, S.M. & Ashraf, S.S. Oxyradical-induced GFP damage and loss of fluorescence. Int. J. Biol. Macromol. 43, 182–186 (2008).

    Article  CAS  Google Scholar 

  31. Steinke, H. & Wolff, W. A modified Spalteholz technique with preservation of the histology. Ann. Anat. 183, 91–95 (2001).

    Article  CAS  Google Scholar 

  32. Faisal, A.A., White, J.A. & Laughlin, S.B. Ion-channel noise places limits on the miniaturization of the brain's wiring. Curr. Biol. 15, 1143–1149 (2005).

    Article  CAS  Google Scholar 

  33. Richards, K.L. et al. Hippocampal volume and cell density changes in a mouse model of human genetic epilepsy. Neurology 80, 1240–1246 (2013).

    Article  Google Scholar 

  34. Feng, G.P. et al. Imaging neuronal subsets in transgenic mice expressing multiple spectral variants of GFP. Neuron 28, 41–51 (2000).

    Article  CAS  Google Scholar 

  35. Okabe, M., Ikawa, M., Kominami, K., Nakanishi, T. & Nishimune, Y. 'Green mice' as a source of ubiquitous green cells. FEBS Lett. 407, 313–319 (1997).

    Article  CAS  Google Scholar 

  36. Ertürk, 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 (2012).

    Article  Google Scholar 

  37. Soderblom, C. et al. 3D imaging of axons in transparent spinal cords from rodents and nonhuman primates. eNeuro 2 doi:10.1523/ENEURO.0001-15.2015 (2015).

  38. Weber, T.G. et al. Apoptosis imaging for monitoring DR5 antibody accumulation and pharmacodynamics in brain tumors noninvasively. Cancer Res. 74, 1913–1923 (2014).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank the lab members at RIKEN QBiC and The University of Tokyo, in particular S.I. Kubota for his kind help in preparing the materials; A. Millius for his critical reading and editing of the manuscript; and T. Mano for his kind contributions and suggestions to discuss image resolution. This work was supported by the Program for Innovative Cell Biology by Innovative Technology and the Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan; a Grant-in-Aid for Scientific Research (S) (grant No. 25221004), for Scientific Research on Innovative Areas (grant no. 23115006) and for Young Scientists (A) (grant no. 15H05650) from MEXT/Japan Society for the Promotion of Science (JSPS); by the strategic programs for R&D (President's discretionary fund) of RIKEN; by an intramural Grant-in-Aid from the RIKEN QBiC; by a grant from AMED-CREST; by the RIKEN Special Postdoctoral Research Program; by the RIKEN Foreign Postdoctoral Researcher Program; by a Grant-in-Aid from the Japan Foundation for Applied Enzymology; by the Brain Sciences Project of the Center for Novel Science Initiatives of the National Institutes of Natural Sciences (grant nos. BS261004 and BS271005); by the Tokyo Society of Medical Science; and by the Shimabara Science Promotion Foundation.

Author information

Author notes

  1. Etsuo A Susaki, Kazuki Tainaka and Dimitri Perrin: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Systems Pharmacology, The University of Tokyo, Tokyo, Japan

    Etsuo A Susaki, Kazuki Tainaka, Akihiro Kuno & Hiroki R Ueda

  2. Japan Agency for Medical Research and Development (AMED) – Core Research for Evolutionary Science and Technology (CREST), AMED, Tokyo, Japan

    Etsuo A Susaki, Kazuki Tainaka, Akihiro Kuno & Hiroki R Ueda

  3. Laboratory for Synthetic Biology, RIKEN Quantitative Biology Center (QBiC), Osaka, Japan

    Etsuo A Susaki, Kazuki Tainaka, Hiroko Yukinaga & Hiroki R Ueda

  4. School of Electrical Engineering and Computer Science, Science and Engineering Faculty, Queensland University of Technology (QUT), Brisbane, Queensland, Australia

    Dimitri Perrin

  5. Department of Anatomy and Embryology, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan

    Akihiro Kuno

  6. PhD Program in Human Biology, School of Integrative and Global Majors, University of Tsukuba, Ibaraki, Japan

    Akihiro Kuno

Authors
  1. Etsuo A Susaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kazuki Tainaka
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dimitri Perrin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hiroko Yukinaga
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Akihiro Kuno
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hiroki R Ueda
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

H.R.U., E.A.S., K.T. and D.P. designed the study. E.A.S., H.Y. and A.K. performed most of the immersion protocol. K.T. performed most of the CB-perfusion protocol. D.P. performed most of the computational image analysis. A.K. developed the improved immersion protocol. All authors discussed the results and commented on the manuscript text.

Corresponding author

Correspondence to Hiroki R Ueda.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Data

Scripts and guide for software described. (ZIP 49 kb)

Spot-counting analysis of hippocampal neurons in Thy1-YFP-H Tg mouse brain shown in Fig. 4b-f.

Almost all cells are roughly detected as single spots in such a relatively dense region. The analysis was performed with Imaris software. Some of parameters are manually adjusted. (MP4 26325 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Susaki, E., Tainaka, K., Perrin, D. et al. Advanced CUBIC protocols for whole-brain and whole-body clearing and imaging. Nat Protoc 10, 1709–1727 (2015). https://doi.org/10.1038/nprot.2015.085

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nprot.2015.085

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