Tissue clearing has become a powerful technique for studying anatomy and morphology at scales ranging from entire organisms to subcellular features. With the recent proliferation of tissue-clearing methods and imaging options, it can be challenging to determine the best clearing protocol for a particular tissue and experimental question. The fact that so many clearing protocols exist suggests there is no one-size-fits-all approach to tissue clearing and imaging. Even in cases where a basic level of clearing has been achieved, there are many factors to consider, including signal retention, staining (labeling), uniformity of transparency, image acquisition and analysis. Despite reviews citing features of clearing protocols, it is often unknown a priori whether a protocol will work for a given experiment, and thus some optimization is required by the end user. In addition, the capabilities of available imaging setups often dictate how the sample needs to be prepared. After imaging, careful evaluation of volumetric image data is required for each combination of clearing protocol, tissue type, biological marker, imaging modality and biological question. Rather than providing a direct comparison of the many clearing methods and applications available, in this tutorial we address common pitfalls and provide guidelines for designing, optimizing and imaging in a successful tissue-clearing experiment with a focus on light-sheet fluorescence microscopy (LSFM).
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We thank J. Liu, A. Glaser, P. Ariel, D. Richardson, F. Helmchen, R. Power and the Huisken lab for their comments and discussion on the manuscript. Illustrations for Figs. 1 and 3 were done by M. Neufeld (madyrose.com). The Circos133 data visualization tool was used to generate Fig. 2. Blender (www.blender.org) was used for rendering Fig. 4. Brain tissue samples displayed in Fig. 3 were provided by L. Egolf, D. Kirschenbaum, F. Catto, P. Perin, K. Le Corf and A. Frick. We also thank the UW-Madison tissue clearing group and all those who provided inspiration, insight and samples: M. Taylor, R. Sulllivan, E. Dent, R. Taylor, K. Chan, R. Salamon and N. Iyer. We acknowledge funding by the Morgridge Institute for Research and the NIH (R01OD026219).
The authors declare no competing interests.
Peer review information Nature Protocols thanks Hans-Ulrich Dodt and the 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.
Key references using this protocol
Tainaka, K. et al. Annu. Rev. Cell Dev. Biol. 32, 713–714 (2016) (https://www.annualreviews.org/doi/abs/10.1146/annurev-cellbio-111315-125001) introduces the key strategies for clearing tissue and provides an overview of protocols developed up to 2016.
Gradinaru, V. et al. Annu. Rev. Biophys. 45, 355–376 (2018) (https://www.annualreviews.org/doi/10.1146/annurev-biophys-070317-032905) provides an introduction into the principles underlying hydrogel-based tissue processing techniques.
The Mesoscale Selective Plane Illumination Initiative (mesoSPIM) (http://www.mesospim.org) is an open-hardware project aimed at making instructions and software to set up and operate versatile light-sheet microscopes for large cleared samples more accessible.
Jonkman, J. et al. Nat. Protoc. 15, 1585–1611 (2020) (https://www.nature.com/articles/s41596-020-0313-9) is an excellent resource for optimizing fluorescent imaging experiments relevant to many imaging modalities.
The Confocal Microscopy List (http://confocal-microscopy-list.588098.n2.nabble.com/) has, since the early 1990s, been a go-to resource for microscopy and imaging-related questions.
Microform (https://forum.microlist.org/) was recently established by Jennifer Waters and Tally Lambert. Currently, most questions are on hardware, fluorophores and other practical aspects of microscopy.
TeraStitcher (https://abria.github.io/TeraStitcher/) is a stitching tool for large microscopy datasets developed by Alessandro Bria, Giulio Iannello and Roberto Valenti.
Napari (https://github.com/napari/napari) is a rapidly evolving multidimensional image viewer especially well suited for microscopy datasets written in Python. The development is spearheaded by the Chan-Zuckerberg Biohub.
BigStitcher (https://imagej.net/BigStitcher) is a stitching toolbox integrated into the ImageJ/Fiji ecosystem capable of stitching and fusing large multiview lightsheet datasets. The BigStitcher software was developed in the laboratory of Stephan Preibisch.
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Weiss, K.R., Voigt, F.F., Shepherd, D.P. et al. Tutorial: practical considerations for tissue clearing and imaging. Nat Protoc 16, 2732–2748 (2021). https://doi.org/10.1038/s41596-021-00502-8