Tissue clearing and its applications in neuroscience

Hiroki R. Ueda, Ali Ertürk, Kwanghun Chung, Viviana Gradinaru, Alain Chédotal, Pavel Tomancak, Philipp J. Keller

Research output: Contribution to journalReview articlepeer-review

55 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)61-79
Number of pages19
JournalNature Reviews Neuroscience
Volume21
Issue number2
DOIs
Publication statusPublished - 2020 Feb 1

Bibliographical note

Funding Information:
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.).

Publisher Copyright:
© 2020, Springer Nature Limited.

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)

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