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  • Primer
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Tissue clearing

Nature Reviews Methods Primers volume 1, Article number: 84 (2021) Cite this article

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Abstract

Tissue clearing of gross anatomical samples was first described more than a century ago and has only recently found widespread use in the field of microscopy. This renaissance has been driven by the application of modern knowledge of optical physics and chemical engineering to the development of robust and reproducible clearing techniques, the arrival of new microscopes that can image large samples at cellular resolution and computing infrastructure able to store and analyse large volumes of data. Many biological relationships between structure and function require investigation in three dimensions, and tissue clearing therefore has the potential to enable broad discoveries in the biological sciences. Unfortunately, the current literature is complex and could confuse researchers looking to begin a clearing project. The goal of this Primer is to outline a modular approach to tissue clearing that allows a novice researcher to develop a customized clearing pipeline tailored to their tissue of interest. Furthermore, the Primer outlines the required imaging and computational infrastructure needed to perform tissue clearing at scale, gives an overview of current applications, discusses limitations and provides an outlook on future advances in the field.

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Fig. 1: An overview of the components of a tissue clearing experiment.
Fig. 2: Concept of hydrogel embedding.
Fig. 3: Protocols for delipidation.
Fig. 4: Mechanisms of delipidation.
Fig. 5: Examples of the clearing process.
Fig. 6: Whole-organ cell profiling using the latest CUBIC-L/R+ protocol.
Fig. 7: Example of neuronal staining in the mouse brain.
Fig. 8: Representative results of vDISCO panoptic imaging.
Fig. 9: Example of vascular staining in the mouse brain.

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Acknowledgements

The authors thank K. Matsumoto and S. Y. Yoshida for help constructing Figs 5 and 6, respectively, and E. Diel and I. Boothby for preparing samples in Figs 5, 7 and 9. This work was supported by a Japan Science and Technology Corporation (JST) Exploratory Research for Advanced Technology (ERATO) grant (JPMJER2001). H.R.U. was supported by the Science and Technology Platform Program for Advanced Biological Medicine (AMED/MEXT), a Japan Society of the Promotion of Science (JSPS) KAKENHI grant-in-aid for scientific research (JP18H05270), a grant-in-aid from the Human Frontier Science Program and a MEXT Quantum Leap Flagship Program (MEXT QLEAP) grant (JPMXS0120330644). K.M. was supported by a JSPS KAKENHI grant-in-aid for scientific research (20K06885) and a JST Moonshot R&D grant (JPMJMS2023). A.E. was supported by the European Research Council (ERC) Calvaria project, the Vascular Dementia Research Foundation and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy, ID 390857198). K.C. was supported by a Burroughs Wellcome Fund Career Awards at the Scientific Interface, the Searle Scholars Program, the Packard award in Science and Engineering, the NARSAD Young Investigator Award, the McKnight Foundation Technology Award, the JPB Foundation (PIIF and PNDRF), the Institute for Basic Science (IBS-R026-D1) and the NIH grants 1-DP2-ES027992 and U01MH117072. J.W.L. is supported by NIH grants U19NS104653 and P50MH094271. Resources that may help to enable general users to establish the methodology are freely available online at http://www.chunglabresources.org.

Author information

Authors and Affiliations

  1. Harvard Center for Biological Imaging, Harvard University, Cambridge, MA, USA

    Douglas S. Richardson & Jeff W. Lichtman

  2. Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA

    Douglas S. Richardson & Jeff W. Lichtman

  3. Department of Chemical Engineering, MIT, Cambridge, MA, USA

    Webster Guan

  4. Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

    Katsuhiko Matsumoto, Kwanghun Chung & Hiroki R. Ueda

  5. Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, Osaka, Japan

    Katsuhiko Matsumoto & Hiroki R. Ueda

  6. Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, Munich, Germany

    Chenchen Pan & Ali Ertürk

  7. Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig Maximilians University of Munich, Munich, Germany

    Chenchen Pan & Ali Ertürk

  8. Munich Cluster for Systems Neurology (SyNergy), Munich, Germany

    Ali Ertürk

  9. Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA

    Kwanghun Chung

  10. Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Kwanghun Chung

  11. Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA

    Kwanghun Chung

  12. Broad Institute of Harvard University and MIT, Cambridge, MA, USA

    Kwanghun Chung

  13. Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, Republic of Korea

    Kwanghun Chung

  14. Nano Biomedical Engineering (Nano BME) Graduate Program, Yonsei-IBS Institute, Yonsei University, Seoul, Republic of Korea

    Kwanghun Chung

  15. Center for Brain Science, Harvard University, Cambridge, MA, USA

    Jeff W. Lichtman

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  1. Douglas S. Richardson
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  2. Webster Guan
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  4. Chenchen Pan
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Contributions

Introduction (D.S.R. and J.W.L.); Experimentation (D.S.R., W.G., K.M., C.P., K.C., A.E., H.R.U. and J.W.L.); Results (D.S.R., W.G., K.M., C.P., K.C., A.E., H.R.U. and J.W.L.); Applications (D.S.R., W.G., K.M., C.P., K.C., A.E., H.R.U. and J.W.L.); Reproducibility and data deposition (D.S.R., W.G., K.M., C.P., K.C., A.E., H.R.U. and J.W.L.); Limitations and optimizations (D.S.R., W.G., K.M., C.P., K.C., A.E., H.R.U. and J.W.L.); Outlook (D.S.R. and J.W.L.); overview of the Primer (D.S.R. and J.W.L.).

Corresponding author

Correspondence to Douglas S. Richardson.

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Competing interests

H.R.U. is co-founder of CUBICStars, Inc. and a co-inventor on the following 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). K.C. is an inventor for patent applications covering some technologies described in this paper and co-founder of LifeCanvas Technologies. A.E. and C.P. have filed a patent on whole-body clearing and imaging related technologies. The other authors declare no competing interests.

Additional information

Peer review information

Nature Reviews Methods Primers thanks Alan King Lun Liu, Woong Sun, Valery Tuchin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

Aivia: https://www.aivia-software.com/

Arivis Vision4D: https://imaging.arivis.com/en/imaging-science/arivis-vision4d

Cell Image Library: http://www.cellimagelibrary.org/home

ilastik: https://www.ilastik.org/

Image Data Resource: http://idr.openmicroscopy.org/about/

Imaris: https://imaris.oxinst.com/

Intellisis: https://www.zeiss.com/microscopy/us/products/microscope-software/zen-intellesis-image-segmentation-by-deep-learning.html

Napari: https://napari.org/

Online repository for tissue clearing-validated antibodies: https://idisco.info/validated-antibodies/

Trainable Weka Segmentation: https://imagej.net/plugins/tws/

Glossary

Autofluorescence

Fluorescence that arises from endogenous fluorescent molecules contained within a biological specimen. Can also be introduced exogenously (that is, some hydrogels autofluoresce).

Dipping objectives

Objective lenses that are designed to be submerged into a liquid. Dipping objectives are found on upright microscopes, and samples are mounted without a coverslip.

Lattice light sheets

Light sheets that are formed using a specialized interference pattern that results in the projection of thin beams of excitation light into a sample, which are rapidly dithered to form the light sheet.

Steric hindrances

Refers to the inability to mount a sample on a microscope when the working distance of the objective is shorter than the thinnest dimension of the sample.

Optical section

An image of a 2D plane within a 3D object that is derived by optical, rather than mechanical, means.

Isotropic spatial resolution

Refers to specialized light microscopy techniques that produce an identical lateral and axial resolution.

Network switch

A computer network hardware device that allows multiple computers to communicate.

Dispersion

A measure of the change in refractive index relative to the wavelength of light passing through a substance. If a substance has high dispersion, it means blue light and red light will refract differently when passing through it.

Random forests

Machine learning algorithms that comprise many ‘estimators’ that each make a prediction as to which segmentation group a pixel should belong. When many estimators are combined into a ‘forest’, the final prediction is highly accurate.

Tortuosity

Describes the degree of curvature and/or twist in a blood vessel.

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Richardson, D.S., Guan, W., Matsumoto, K. et al. Tissue clearing. Nat Rev Methods Primers 1, 84 (2021). https://doi.org/10.1038/s43586-021-00080-9

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