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.
| Original language | English |
|---|---|
| Article number | 84 |
| Journal | Nature Reviews Methods Primers |
| Volume | 1 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2021 Dec |
Bibliographical note
Funding Information: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 .
Publisher Copyright:
© 2021, Springer Nature Limited.
All Science Journal Classification (ASJC) codes
- Medicine(all)
- Biochemistry, Genetics and Molecular Biology(all)