Chemical Landscape for Tissue Clearing Based on Hydrophilic Reagents

• Tainaka, Kazuki

• Murakami, Tatsuya C.

• Susaki, Etsuo A.

• Shimizu, Chika

• Saito, Rie

• Takahashi, Kei

• Hayashi-Takagi, Akiko

• Sekiya, Hiroshi

• Arima, Yasunobu

• Nojima, Satoshi

• Ikemura, Masako

• Ushiku, Tetsuo

• Shimizu, Yoshihiro

• Murakami, Masaaki

• Tanaka, Kenji F.

• Iino, Masamitsu

• Kasai, Haruo

• Sasaoka, Toshikuni

• Kobayashi, Kazuto

• Miyazono, Kohei

• Morii, Eiichi

• Isa, Tadashi

• Fukayama, Masashi

• Kakita, Akiyoshi

• Ueda, Hiroki R.

Abstract

We describe a strategy for developing hydrophilic chemical cocktails for tissue delipidation, decoloring, refractive index (RI) matching, and decalcification, based on comprehensive chemical profiling. More than 1, 600 chemicals were screened by a high-throughput evaluation system for each chemical process. The chemical profiling revealed important chemical factors: salt-free amine with high octanol/water partition-coefficient (logP) for delipidation, N-alkylimidazole for decoloring, aromatic amide for RI matching, and protonation of phosphate ion for decalcification. The strategic integration of optimal chemical cocktails provided a series of CUBIC (clear, unobstructed brain/body imaging cocktails and computational analysis) protocols, which efficiently clear mouse organs, mouse body including bone, and even large primate and human tissues. The updated CUBIC protocols are scalable and reproducible, and they enable three-dimensional imaging of the mammalian body and large primate and human tissues. This strategy represents a future paradigm for the rational design of hydrophilic clearing cocktails that can be used for large tissues.

Journal

• Cell reports

  Cell reports 24 (8), 2196-2210.e9, 2018-08-21

  Elsevier BV

Keywords

• chemistry in tissue clearing
• hydrophilic reagents
• high-throughput chemical screening
• comprehensive chemical profiling
• three-dimensional imaging
• virtual multiplex imaging

Details

• CRID: 1050564288162490880
• NII Article ID: 120006542018
• ISSN: 22111247
• DOI: 10.1016/j.celrep.2018.07.056
• HANDLE: 2433/235513
• Web Site: https://api.elsevier.com/content/article/PII:S2211124718311598?httpAccept=text/xml
• Web Site: https://api.elsevier.com/content/article/PII:S2211124718311598?httpAccept=text/plain
• Text Lang: en
• Article Type: journal article
• Data Source:
  • IRDB
  • Crossref
  • CiNii Articles
  • KAKEN