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Tissue clearing and its applications in neuroscience

A Publisher Correction to this article was published on 09 March 2020

This article has been updated


State-of-the-art tissue-clearing methods provide subcellular-level optical access to intact tissues from individual organs and even to some entire mammals. When combined with light-sheet microscopy and automated approaches to image analysis, existing tissue-clearing methods can speed up and may reduce the cost of conventional histology by several orders of magnitude. In addition, tissue-clearing chemistry allows whole-organ antibody labelling, which can be applied even to thick human tissues. By combining the most powerful labelling, clearing, imaging and data-analysis tools, scientists are extracting structural and functional cellular and subcellular information on complex mammalian bodies and large human specimens at an accelerated pace. The rapid generation of terabyte-scale imaging data furthermore creates a high demand for efficient computational approaches that tackle challenges in large-scale data analysis and management. In this Review, we discuss how tissue-clearing methods could provide an unbiased, system-level view of mammalian bodies and human specimens and discuss future opportunities for the use of these methods in human neuroscience.

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Fig. 1: Major tissue-clearing methods and their key features.
Fig. 2: Whole-brain single-cell-resolution imaging and analysis.
Fig. 3: The SHIELD–MAP and AAV-based labelling system.
Fig. 4: Towards a 3D developmental human cell atlas.
Fig. 5: Resolution and speed of custom and commercial light-sheet microscopes.

Change history

  • 09 March 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.


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The authors thank E. A. Susaki for help in compiling Table 1 and Supplementary Table 1 for current tissue-clearing protocols and reagents, K. Matsumoto and Y. Shinohara for drawing the chemical structures in the supplementary information, T. Mano for contributing to the CUBIC figure, R. Cai and C. Pan for contributing to the uDISCO figure, S. R. Kumar, G. M. Coughlin, R. Challis and C. Challis for contributing to the viral-assisted spectral tracing figure and Y.-G. Park, C. H. Sohn, T. Ku, V. Lilascharoen and B. K. Lim for contributing to the SHIELD figure. The authors also gratefully acknowledge grant support from Brain/MINDS, the Basic Science and Platform Technology Program for Innovative Biological Medicine (AMED/MEXT), the Japan Society for the Promotion of Science (Grant-in-Aid for Scientific Research (S)) and the Human Frontier Science Program Research Grant Program (HFSP RGP0019/2018) (H.R.U.), the Munich Cluster for Systems Neurology (SyNergy), the Fritz Thyssen Stiftung and the Deutsche Forschungsgemeinschaft (A.E.), the David and Lucile Packard Foundation (Packard Fellowship), the McKnight Foundation, the US National Institutes of Health (NIH) (1-DP2-ES027992; U01MH117072), the NCSOFT Cultural Foundation and the Koreaan Institute for Basic Science (IBS-R026-D1) (K.C.), the NIH BRAIN Initiative, the NIH Office of the Director and the US National Science Foundation (NeuroNex) (V.G.), LABEX LIFESENSES (reference ANR-10-LABX-65) managed by the French Agence National de la Recherche within the Investissements d’Avenir programme under reference ANR-11-IDEX-0004-02 (A.C.), the European Regional Development Fund in the framework of the Czech IT4Innovations National Supercomputing Center path to exascale project, project number CZ.02.1.01/0.0/0.0/16_013/0001791, within the Czech Research, Development and Education Operational Programme (P.T.) and the Howard Hughes Medical Institute (P.J.K.).

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H.R.U., A.E., K.C., V.G., A.C. and P.J.K. researched data for the article. All the authors contributed to substantial discussion of its content, wrote the article and reviewed and edited the manuscript before submission.

Corresponding author

Correspondence to Hiroki R. Ueda.

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Competing interests

H.R.U. is a co-inventor on a patent applications covering the CUBIC reagents (PCT/JP2014/070618 (pending), patent applicant is RIKEN, other co-inventors are E. A. Susaki and K. Tainaka; PCT/JP2017/016410 (pending), patent applicant is RIKEN, other co-inventors are K. Tainaka and T. Murakami) and a co-founder of CUBICStars Inc. A.E. is the applicant and the inventor on a patent application for technologies relating to vDISCO clearing (PCT/EP2018/063098 (pending)). K.C. is the inventor or a co-inventor on patents and patent applications for CLARITY (PCT/US2013/031066 (active), patent applicant is Stanford University, co-inventor is K. A. Deisseroth), stochastic electrotransport (PCT/US2015/024297 (active), patent applicant is MIT), SHIELD (PCT/US2016/064538 (pending), applicant is Massachusetts Institute of Technology (MIT), other co-inventors are E. Murray and J. H. Cho), SWITCH (PCT/US2016/064538 (pending), applicant is MIT, other co-inventors are E. Murray and J. H. Cho) and MAP (PCT/US2017/030285 (pending), applicant is MIT, other co-inventors are T. Ku, J. M. Swaney and J. Y. Park) and a co-founder of LifeCanvas Technologies. V.G. is a co-inventor on patent applications covering PACT and PARS (PCT/US2014/048985 (active), applicant is California Institute of Technology, other co-inventors are V. Gradinaru and B. Yang) and adeno-associated virus (US14/485,024 (active), applicant is California Institute of Technology, other co-inventors are B. E. Deverman, P. H. Patterson and V. Gradinaru) technologies. P.J.K. is an inventor or co-inventor on patents and patent applications covering multiview imaging (US14/049,470 (active), applicant is Howard Hughes Medical Institute) and adaptive light-sheet microscopy (PCT/US2017/038970 (pending), applicant is Howard Hughes Medical Institute, other co-inventors are R. K. Chhetri and L. A. Royer). P.T. and A.C. declare no competing interests.

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Supplementary Information


Tissue clearing

A method to make a biological specimen transparent by minimization of light scattering and light absorption by the biological specimen.

Hydrophobic tissue clearing

One of three major tissue-clearing methods; it uses hydrophobic (water-immiscible) reagents. It is also referred to as ‘solvent tissue clearing’.

Hydrophilic tissue clearing

One of three major tissue-clearing methods; it uses hydrophilic (water-miscible) reagents. It is also referred to as ‘aqueous tissue clearing’ but does not always involve the use of water.

Refractive index

(RI). The ratio of the speed of light in a vacuum to its speed in a specified medium. The RI of a vacuum is 1 by definition, whereas the RI of water is ~1.33.

Light-sheet microscopy

A technique that allows fast, high-resolution imaging of large biological specimens with low light exposure by rapidly acquiring images of thin optical sections illuminated by laser light sheets.

Passive CLARITY technique

(PACT). A technique that allows flexible hydrogel formulation and clearing without the need to use electrophoresis.

Refractive index-matching solution

A solution that is compatible with the passive CLARITY technique, perfusion-assisted agent release in situ and CLARITY. It provides high-resolution imaging at depth by further reducing light scattering in both cleared and uncleared samples.

Perfusion-assisted agent release in situ

(PARS). A method that allows whole-rodent clearing and labelling. It uses the intrinsic circulatory system (the vasculature) to deliver clearing agents and labels instead of relying on passive diffusion, which can be prohibitively slow for large organs or whole organisms.

Hydrogel-based tissue clearing

One of three major tissue-clearing methods; it crosslinks biological specimens to make a synthetic hydrogel.

sCMOS detectors

Scientific-grade CMOS-based cameras that offer a large sensor area, high pixel count, low noise, high frame rate, high dynamic range and high quantum yield, all of which are highly desirable properties for detectors used in widefield fluorescence light microscopy.

Hierarchical data format version 5

(HDF5). A hierarchical data format and versatile data model to manage and represent extremely large and complex data objects.

Compute unified device architecture

(CUDA). A parallel computing platform and programming model developed by Nvidia for general computing on its own graphics processing units.

Adaptive particle representation

A content-adaptive representation of fluorescence microscopy images that overcomes storage and processing bottlenecks in big microscopy image data.

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Ueda, H.R., Ertürk, A., Chung, K. et al. Tissue clearing and its applications in neuroscience. Nat Rev Neurosci 21, 61–79 (2020).

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