Resource

Chemical Landscape for Tissue Clearing Based on
Hydrophilic Reagents
Graphical Abstract

Authors
Kazuki Tainaka, Tatsuya C. Murakami,
Etsuo A. Susaki, ..., Masashi Fukayama,
Akiyoshi Kakita, Hiroki R. Ueda

Correspondence
uedah-tky@umin.ac.jp

In Brief
Tainaka et al. describe a strategy fordeveloping hydrophilic tissue-clearing
reagents based on comprehensive
chemical profiling. The strategic
integration of optimal chemical cocktails
provided a series of CUBIC protocols,
which enable the 3D imaging of the
mammalian body and large primate andhuman tissues.

HighlightsdComprehensive chemical profiling of hydrophilic chemicals
for tissue clearingdStrategic integration of optimal chemical cocktails for rational
clearing protocolsdLarge-tissue clearing and cell-resolution imaging of tissues
by CUBIC protocolsdVirtual multiplex imaging of fluorescent protein-expressing
mouse brains

Tainaka et al., 2018, Cell Reports 24, 2196–2210
August 21, 2018 ª 2018 The Author(s).
https://doi.org/10.1016/j.celrep.2018.07.056

Cell Reports

Resource
Chemical Landscape for Tissue Clearing
Based on Hydrophilic Reagents
Kazuki Tainaka,1,2,3,20 Tatsuya C. Murakami,1,20 Etsuo A. Susaki,1,2,4 Chika Shimizu,2 Rie Saito,5 Kei Takahashi,6
Akiko Hayashi-Takagi,4,7 Hiroshi Sekiya,8 Yasunobu Arima,9 Satoshi Nojima,10 Masako Ikemura,11 Tetsuo Ushiku,11
Yoshihiro Shimizu,12 Masaaki Murakami,9 Kenji F. Tanaka,13 Masamitsu Iino,8,14 Haruo Kasai,15,19 Toshikuni Sasaoka,16Kazuto Kobayashi,17 Kohei Miyazono,6 Eiichi Morii,10 Tadashi Isa,18 Masashi Fukayama,11 Akiyoshi Kakita,5and Hiroki R. Ueda1,2,19,21,*
1Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku,
Tokyo 113-0033, Japan
2Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, 1-3 Yamadaoka, Suita, Osaka 565-5241, Japan
3Department of System Pathology for Neurological Disorders, Brain Research Institute, Niigata University, 1-757 Asahimachidori, Chuo-ku,
Niigata 951-8585, Japan
4PRESTO, Japan Science and Technology Agency (JST), 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
5Department of Pathology, Brain Research Institute, Niigata University, 1-757 Asahimachidori, Chuo-ku, Niigata 951-8585, Japan
6Department of Molecular Pathology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku,
Tokyo 113-0033, Japan
7Laboratory of Medical Neuroscience, Institute for Molecular and Cellular Regulation, Gunma University, 3-39-15 Showacho, Maebashi,
Gunma 371-8512, Japan
8Department of Pharmacology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
9Division of Psychoimmunology, Institute of Genetic Medicine, Graduate School of Medicine, Hokkaido University, Kita-15, Nishi-7, Kita-ku,
Sapporo, Hokkaido 060-0815, Japan
10Department of Pathology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan
11Department of Pathology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
12Laboratory for Cell-Free Protein Synthesis, RIKEN Center for Biosystems Dynamics Research, 6-2-3, Furuedai, Suita,
Osaka 565-0874, Japan
13Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan
14Division of Cellular and Molecular Pharmacology, Nihon University School of Medicine, 30-1 Oyaguchikami-cho, Itabashi-ku, Tokyo
173-8610, Japan
15Laboratory of Structural Physiology, Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University ofTokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
16Department of Comparative and Experimental Medicine, Brain Research Institute, Niigata University, 1-757 Asahimachidori, Chuo-ku,
Niigata 951-8585, Japan
17Department of Molecular Genetics, Institute of Biomedical Sciences, Fukushima Medical University School of Medicine, 1 Hikarigaoka,
Fukushima, Fukushima 960-1295, Japan
18Department of Neuroscience, Graduate School of Medicine and Faculty of Medicine, Kyoto University, Yoshida-konoe-cho, Sakyo-ku,
Kyoto 606-8501, Japan
19International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Tokyo, Japan
20These authors contributed equally
21Lead Contact*Correspondence: uedah-tky@umin.ac.jp
https://doi.org/10.1016/j.celrep.2018.07.056

SUMMARY

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 ahigh-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 andreproducible, and they enable three-dimensional imaging of the mammalian body and large primate andhuman tissues. This strategy represents a future
paradigm for the rational design of hydrophilicclearing cocktails that can be used for large tissues.
INTRODUCTION
To examine an entire mammalian body as functional assemblies
of individual cells is an important goal in biology and medicine.

2196 Cell Reports 24, 2196–2210, August 21, 2018 ª 2018 The Author(s).
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

The systematic identification of cellular properties in their physiological context helps us understand the complex cellular networks in the body. To this end, three-dimensional (3D) imaging
techniques that use tissue clearing and are compatible withvarious labeling techniques are promising approaches. The tissue-clearing step is critical for determining the quality of the subsequent acquired image and the feasibility of image processing.
Since tissue-clearing methods were first reported more than acentury ago (Spalteholz, 1914), the field has expanded dramatically (Susaki and Ueda, 2016; Tainaka et al., 2016). Several scalable tissue-clearing protocols with high clearing performancethat can be used with 3D-imaging procedures were recently
developed. The perfusion-assisted agent release in situ-passive
clarity technique (PARS-PACT) protocol enables whole adultmouse body clearing (Treweek et al., 2015; Yang et al., 2014).
The clear, unobstructed brain/body imaging cocktails andcomputational analysis (CUBIC) protocol enables whole organ
and body imaging with single-cell resolution (Kubota et al.,2017; Susaki et al., 2015; Tainaka et al., 2014). The systemwide control of interaction time and kinetics of chemicals(SWITCH) protocol achieves large (>1 cm3) tissue clearing,
including that of whole adult primate brain and large human tissue samples (Murray et al., 2015). The ultimate DISCO (uDISCO)
protocol enables whole adult rat body imaging and the visualization of high-resolution images of bone marrow in the entire
mouse body without chemical decalcification (Pan et al., 2016).
These protocols and related technologies have greatly advanced
the applicability of 3D imaging (Ando et al., 2014; Belle et al.,2014, 2017; Doerr et al., 2017; Liebmann et al., 2016; Renier
et al., 2014, 2016). However, many clearing methods are still
empirical protocols based on serendipitously discovered effective chemicals and ad hoc chemical combinations. In contrast,
to achieve improved tissue-clearing techniques, it is importantto understand the effective functional groups and chemical
properties of each clearing step. Such comprehensive chemical
profiling would lead to rationally designed clearing cocktails.
Although organic solvents were systematically screened by
Sparteholz and his successors (Becker et al., 2012; Pan et al.,2016), clearing cocktails based on hydrophilic chemicals are still
under development.
In this study, we applied a strategy for designing hydrophilic
chemical cocktails optimized for individual chemical parameters. To achieve the scalable clearing of large primate and humantissue samples and of mammalian bodies including bone tissue
in aqueous media, we considered a series of chemical processes, including delipidation, decoloring, refractive index (RI)
matching, and decalcification. Using high-throughput evaluation
systems suitable for each chemical process, we comprehensively screened >1,600 hydrophilic chemicals. Chemical
profiling of the screened chemicals revealed important chemicalfactors: salt-free amine with high logP for delipidation, N-alkylimidazole for decoloring, aromatic amide for RI matching, andprotonation of phosphate ion for decalcification. Finally, by strategically integrating optimal chemical cocktails, we obtained aseries of updated CUBIC protocols that efficiently clearedmouse organs, mouse body including bone, and large primate
tissue samples, including marmoset brain and human tissue
samples >10 cm3. The CUBIC-treated tissues were transparent

enough that almost all of the cells could be detected at thewhole-organ scale. In addition, a fluorescent protein (FP)compatible CUBIC protocol enabled the visualization of various
fluorescent labels.
RESULTS
Rational Chemical Profiles for Tissue Clearing Obtainedby Comprehensively Screening >1,600 CUBIC
Chemicals
To comprehensively screen chemicals for hydrophilic chemicalbased clearing methods, >1,600 commercially available chemical candidates were chosen based on their potential solubility
in aqueous media (Figure 1A; Table S1). If a chemical was sufficiently soluble in water, then a 10% (w/w) stock solution was prepared in deionized water. These chemical solutions were thensubjected to a series of easy and high-throughput CUBIC assays(for pH, fluorescent quenching of FPs, lipid solubility, decoloring,
RI matching, and decalcification) (Figures 1B–1F; Table S2).
After the first chemical screening, we narrowed down the candidates by a second evaluation using real tissue (Table S2). The pHof each stock solution was estimated by the colorimetric analysis
of pH-indicator paper (Figures S1A–S1C). To evaluate thequenching effect of each chemical, recombinant EGFP and
Sirius were used as described previously (Figures 1F, S1D, andS1E) (Susaki et al., 2014).
Delipidation and Decoloring
Qualitatively, the lipid solubility of a chemical is inversely
correlated with the turbidity of a fixed brain suspension treatedwith the chemical (Susaki et al., 2014). Thus, we obtained lipid
solubility scores from the optical density 600 (OD600) of a
mixture of brain suspension and each 10% (w/w) chemicalsolution (Figure 1B). We classified the chemicals into threegroups: detergents, salts, and other non-detergent and saltfree chemicals. Anionic and non-ionic detergents with asuitable logP (octanol-water partition coefficient) value of thehydrophobic group exhibited significantly higher lipid solubility, while organic salts were less effective (Figures S2A–
S2C). The other water-soluble chemicals were subjected tofurther functional chemical profiling. Aliphatic amines, amino
alcohols, and amino ethers were found to be the most effective functional groups for lipid solubility (Figure 2A). A basic
condition (pH >10) was significantly superior to an acidic
one (pH < 6) (Figure 2B). To determine the effective chemicalparameters for delipidation, we used a multivariate linear
regression model. We prepared a series of parameters consisting of three classes, related to functional groups, solubility(calculated by Hansen’s solubility parameters [Hansen, 2007]),and structure and topology (Figure S1F). Figure S1G shows
examples of how we counted the functional groups. Ourmodel revealed logP to be the most influential property (Figure 2C). In actuality, the lipid solubility score of less polaramines (logP R 2.0) was linearly correlated with their logP
(Figure 2D). Therefore, water-soluble, relatively hydrophobic,
and uncharged amine derivatives had the potential for highlipid solubility. Next, we measured the residual phospholipid
and cholesterol contents of mouse brains treated with the

Cell Reports 24, 2196–2210, August 21, 2018 2197

A

C

B

D

E

F

Figure 1. Strategy for Screening >1,600 CUBIC Chemicals(A) Experimental overview of the screening of >1,600 CUBIC chemicals. In the first in vitro screening, six chemical properties of potentially water-soluble CUBIC
chemicals were quantitatively evaluated. In the second evaluation, using real tissue samples, the candidate chemicals were narrowed down.
(B–F) Experimental scheme for the high-throughput chemical screening of delipidation (B), decoloring (C), RI measurement (D), decalcification (E), and FP
fluorescence measurement (F). A popular FP, EGFP, and a pH-insensitive FP, Sirius, were used. PFA, paraformaldehyde.
See also Figures S1 and S8 and Table S1.

chemicals and evaluated the relative transmittance of thesebrains after RI matching with the modified ScaleCUBIC-2(Figures 2E and S2D). The delipidation efficiency, especiallyfor the phospholipid content, was correlated with the final
tissue transparency, indicating that delipidation is essential
for thorough tissue clearing. Aliphatic amines exhibited amuch better delipidation efficiency than did amino alcohols

2198 Cell Reports 24, 2196–2210, August 21, 2018or detergents (Figure 2F). More important, the logP value ofthese chemicals could predict the final transparency of thechemically treated brain (Figure S2E). Therefore, the variability
within aliphatic amines and N-alcohols could be explained bytheir logP value. It is likely that detergents were inferior toaliphatic amines in tissue permeability despite their highpotential lipophilicity.

A

D

G

B

C

E

F

H

I

Figure 2. Comprehensive CUBIC Chemical Profiling for Delipidation and Decoloring(A and B) Dependency of the lipid solubility score on the chemical functional group (A) and pH (B), compared with the alcohol group and pH 7–8, respectively.
(C) The variable importance projection (VIP) scores of two major partial least-squares (PLS) components for each variance.
(D) Scatterplot of the logP and lipid solubility score for water-soluble CUBIC chemicals. The logP of less polar amines (including aliphatic amines, N-alcohols, andN-ethers with logP R 2.0) was correlated with their lipid solubility score.
(E) Scatterplot of the relative transmittance and phospholipid content of brain hemispheres of adult mice treated with highly lipid-soluble chemicals. Data aremeans ± SDs (n = 2).
(F) Delipidation efficiency of the aliphatic amines, amino alcohol (N-alcohol), detergents, and others in (E).
(G) Dependency of the decoloring score on chemical functional groups compared with the alcohol group.
(H) Decoloring efficiency for adult mouse spleen of N-alkylimidazole (N-Alk-Imd), C-alkylimidazole (C-Alk-Imd), amine, pyridine, and cationic detergent (Cat Det).
(I) Representative chemical candidates for delipidation and/or decoloring. Box and whisker plots showing the 25th–75th percentile (boxes), 10th–90th percentile(whiskers), median (vertical lines), and mean (cross). *p < 0.05, **p < 0.01, and ***p < 0.001.
See also Figures S1 and S2, Table S2, and the STAR Methods.

We established a high-throughput screening system for thedecoloring ability of chemicals by measuring the OD420 of the supernatant obtained from a fixed blood suspension treated withthe chemical (Tainaka et al., 2014) (Figure 1C). Cationic detergents tended to elute heme more efficiently, while organic salts
were significantly less active (Figures S2F and S2G). The pH dependency of the decoloring was also similar to that of the lipid
solubility (Figure S2H). The functional chemical profiling of theother water-soluble chemicals revealed chemical groups similar
to those identified in the lipid solubility assay (Figure 2G). We
noted that N-alkylimidazoles (1-ethylimidazole [CU#0938] and1-(3-aminopropyl)imidazole [CU#0484]) were the most effective
decoloring chemicals. Next, we investigated how well thesechemicals decolored heme-rich mouse spleen by soaking
this tissue in the chemical solution and measuring the OD400 of
the supernatant. N-Alkylimidazole was found to be the best

Cell Reports 24, 2196–2210, August 21, 2018 2199

A

B 1.360

C

***

***
**

OD600 of lung tissue

***

1.350
1.345
1.340

***
***

1.34

1.35

1.2

0.06

0.07

0.08

0.09

Histodenz
CU#1283
1.48

0.10

1.49

OD400

1.51

O

OD600 of lung tissue

**

1.5

1.0

0.5

n.s., p = 0.10

2.0

N

1.5

CU#0788
Amidine

1.0

N
0.5

N

Histodenz

F

Amide
n=5

G
***
***
OD600 of HAp suspension

***
***

2.0

4.0

6.0

1.5

1.0

0.5

0

8.0

2

4

LogK
8

High

Low

HOOC
HOOC
N

COOH
COOH

HOOC
HOOC
N

1.5
1.0
0.5
0.0

6

8

10

12

14

Hetcyc amine
w/o 1º Aminen = 29

J

COOH
COOH

N

HOOC
HOOC

N

N

COOH

DOTA

O

PDTA (CU#0624)

COOH

N

O

1º Aminen = 55

RI match chemical

O

N

N
H

N

COOH
N

2º-4º Aminen = 42

N

6
HOOC

***

CU#1352OCyDTA (CU#0065)

4

2.0

pH

N

***
*

2.5

titrated with CU#0414
or NaOH

Relative decalcifiaction scoreI

3.0

CU#0414-EDTA
CU#0414-HCl
EDTA-CU#0414
EDTA-NaOH

0.0

0.0

Other
n = 13H
titrated with EDTA
or HCl2.0***

CU#0587

Amidinen=4

Relative decalcification score

pH 0-1 (n = 12)
pH 1-2 (n = 60)
pH 2-3 (n = 70)
pH 3-4 (n = 42)
pH 4-5 (n = 56)
pH 5-6 (n = 207)
pH 6-7 (n = 202)
pH 7-8 (n = 83)
pH 8-9 (n = 58)
pH 9-10 (n = 50)
pH 10-11 (n = 45)
pH 11-12 (n = 41)
pH 12-13 (n = 106)
pH 13-14 (n = 51)

N

COOH

N

HOOC

CU#0640
Antipyrine

EGTA

CU#1283
N-methylnicotinamide

Decalcification chemical

2

COOH
0

DOTA
CyDTA
PDTA
EGTA
DTPA
EDTA
E0393
U0014
HEDTA
N0474
G0229
TMDTA
PMIDA
TTHA
MGDA
BAPTA
DPTA-OH
NTA
C0821
N0408
H0489
P0554
CU#0680
ADA
462314
MIDA
B1363
IDA
320-56101
EDTA-2Na
EDTA-2NH3
PBS

Relative decalcification score

CU#1283

0.0

Monoaromatic
Mutiaromatic
Aliphatic
Inorganic salt
Amide
AmidineHydroxyl
1º-3º amine
4º amine
LogP
LogS
dD
dP
dH
dHD
dHA
pH
RI
MWt
MVol
MDens
PSA
CSASA
CMSA
CSEV
OvalityTopoφ
Bindex
MTindex

0.0

1.52

Amide

2.5

Component 1
Component 2

2.0

1.50

Refractive index

E

2.5

1.4

1.0

0.0
0.05

Refractive index

VIP score

0.0
1.0

0.5

1.330
0.04

1.36

PBS

1.0

1.5

1.335

***

2.0

Plot
Linear fit

2.0

1.33

D

R2 = 0.005
p = 0.65

2.5
1.355

Refractive index

All (n = 1025)
Alkene, Alkyn (n = 61)
Phenyl (n = 140)
Alcohol (n = 287)
Sugar (n = 45)
Ether (n = 100)
Ketone, Ester (n = 74)
Carboxylate (n = 153)
Aldehyde (n = 16)
Amine (n = 361)
Amide, Nitro (n = 118)
Pyridine (n = 74)
Azole (n = 21)
Sulfur (n = 82)
Phosphate (n = 19)
Halogen (n = 74)

HOOC

pH adjusted with imidazole

N

N

COOH

N

NH

HOOC
EDTA

CU#1352
Imidazole

Figure 3. Comprehensive CUBIC Chemical Profiling of RI Matching and Decalcification(A) Dependency of RI on functional groups compared with the alcohol group.
(B) Scatterplot of 400-nm absorbance and RI. Less colored (OD400 <0.07) and high RI (RI >1.350) CUBIC chemicals were chosen for further screening.

(legend continued on next page)

2200 Cell Reports 24, 2196–2210, August 21, 2018decoloring chemical among several chemical categories (Figure 2H). However, since N-alkylimidazole showed a low delipidation efficiency, we further analyzed decoloring chemicals that
had a high delipidation efficiency. We applied the multivariate
linear regression model to amine groups, and the model revealed
the chain length to be the most influential parameter (Figure S2I).
Multiple polar groups (e.g., –NH2 and –OH) and a longer chain
length between the polar groups (C-Clength R3) were associatedwith efficient decoloring ability (Figure S2J).
Among the screened chemicals, we highlight the following fourchemicals: N-butyldiethanolamine and 1,3-bis(aminomethyl)
cyclohexane are a cost-effective, high-performance amino
alcohol and an aliphatic amine, respectively; 1,2-hexanediol is
an exceptional aliphatic diol that acts as a neutral delipidation
chemical; and 1-methylimidazole is especially effective for tissue
decoloring (Figure 2I).
RI Matching
To clarify whether the RI value of the medium itself is important
for the final transparency or whether specific chemical groups
are important for RI homogenization, we used a non-biased
approach to determine the best RI-matched chemicals. To
develop an alternative high-RI medium based on hydrophilic
chemicals, we selected candidate chemicals that had high watersolubility and a high RI value per unit weight, because the RI
value of aqueous medium increases according to the solute concentration. Chemical profiling of the RI values of 10% (w/w)
chemical solutions with an Abbe refractometer (Figure 1D) revealed that aromatic groups (phenyl, pyridine, and azole groups)
exhibited higher RI values among the chemical groups (Figure 3A). We then chose candidate chemicals with high RI values(R1.350) and low absorbance (OD400 <0.07) to avoid incoming
light absorption by the medium itself (Figure 3B). We chose 21
extremely water-soluble RI-matching candidates, including
Histodenz (soluble in water at 60–70 wt%; Sigma-Aldrich),
whose RIs ranged from 1.47 to 1.52. To examine the clearing
performance of these chemicals qualitatively, the OD600 of small
pieces of chemically treated mouse lung was measured(Figure 3C). The RI values of the mounting medium showed nocorrelation with the tissue transparency, suggesting that theclearing performance does not depend solely on the RI valueof the medium but does reflect some chemical properties of
the constituent. We further applied the multivariate linear regres-sion model to this result. The amide group showed a significantly
higher clearing performance than the other chemical groups(Figures 3D, 3E, and S2K). Finally, we introduced antipyrine
and CU#1283 (N-methylnicotinamide) as RI-matching chemicals(Figure 3J, top). N-Methylnicotinamide had the highest clearing
performance among the candidates. In addition to its highclearing performance, antipyrine avoided tissue shrinkage,even with a dense solution, because this chemical causes tissueswelling (Murakami et al., 2018).
Decalcification
Inorganic bone mineral mostly consists of carbonated hydroxyapatite (HAp). Thus, we developed an HAp-based screening system to identify potent decalcification chemicals by measuring
the OD600 of a chemically treated HAp suspension (Figure 1E).
The OD600 of HAp suspensions was linearly correlated with the
HAp concentration below 10 mg/mL (Figure S2L). Decalcification was strongly promoted by an acidic condition, especiallybelow pH 2.0 (Figure 3F). Because phosphate ion has a pKa1
value of 2.12 (Weast, 1983), the facilitated decalcification underacidic conditions may have been associated with an equilibrium
shift in the phosphate ion. Since acid-based decalcification wasincompatible with many of the other tissue-clearing steps, wesought other decalcifying chemicals that would act in neutral
medium. We found that the decalcification efficiency of a 10 wt% EDTA-Na solution markedly decreased over pH 8.0 (Figure 3G). Although this result apparently contradicts the pH dependency of the stability constant of EDTA for the calcium ion(Harris, 2011), an unexpected pH dependency was already reported for both decalcification and demineralization by EDTA (Kiviranta et al., 1980; Serper and Calt, 2002). This may also reflect
the equilibrium shift of the phosphate ion because of its pKa2
value of 7.21. It is also noteworthy that a 10 wt% EDTA solution
neutralized with N-butyldiethanolamine exhibited significantly
higher decalcification efficiency at pH 8.0 than did EDTA-Na (Figure 3G), implying that decalcification would be greatly facilitatedby neutralization with an organic base instead of an inorganic
base (NaOH). Therefore, we screened EDTA-related acids andN-butyldiethanolamine-related organic bases among the listed
chemicals. While EDTA-related calcium chelators including
CU#0065 (trans-1,2-diaminocyclohexane-N,N,N0 ,N0 -tetraacetic
acid monohydrate [CyDTA]) and CU#0624 (1,2-diaminopropane-N,N,N’,N’-tetraacetic acid [PDTA]) were the most effective(C) Scatterplot of the RI value of concentrated solutions of 22 candidate chemicals and the OD600 of small pieces of mouse lung treated with those chemicals.
Data are means ± SDs (n = 2).
(D) VIP scores of two major components for each variance.
(E) The 22 chemicals in (C) were divided into three groups: amides, amidines, and others, and the OD600 of lungs treated with these groups were compared. Amideand amidine groups are shown in red dashed-line boxes at right.
(F) Dependency of relative decalcification score on pH compared with pH 7–8.
(G) pH dependence of OD600 of chemically treated HAp suspension. Data are means ± SDs (n = 3). N-butyldiethanolamine (CU#0414) 10 wt% titrated with EDTA
or HCl and EDTA 10 wt% titrated with CU#0414 or NaOH were used.
(H) Relative decalcification scores of EDTA-based cocktails (pH 7–8) neutralized with various counter-bases including heterocyclic amines without primary amine(Hetcyc amine w/o 1 amine), secondary and tertiary amines and quaternary ammoniums (2 –4 amine), and other amines including primary amine (1 amine).
(I) Relative decalcification score of EDTA derivatives. Data are means ± SDs (n = 3). Chemicals are presented in descending order of logK. Chemical ID is a typicalabbreviation or vendor catalog number. The top three chemicals shared a common EDTA structure (red).
(J) Chemical candidates for high RI matching medium and decalcification cocktails consisting of EDTA and imidazole (CU#1352). Box and whisker plots show
25th–75th percentiles (boxes), 10th–90th percentiles (whiskers), median (horizontal lines), and mean (cross). *p < 0.05, **p < 0.01, and ***p < 0.001.
See also Figures S1 and S2, Table S2, and the STAR Methods.

Cell Reports 24, 2196–2210, August 21, 2018 2201

1.51

1.50

1.49

1.52

C

U

#0

07

0

C
C U#
U 00
#0 7
63 0
1

C
SD U#
S 007
0

S

mCherryPBS
CUBIC-P
CUBIC-L

1.5

CUBIC-B
CUBIC-R
CUBIC-RA

1.0
0.5
0

Name

Role
Delipidation and rapid decolorization

CUBIC-L

Delipidation and decolorization

10 wt% N-butyldiethanolamine (CU#0414), 10 wt% Triton X-100

CUBIC-HL

Rapid delipidation and rapid decolorization

10 wt% 1,3-bis(aminomethyl)cyclohexane (CU#0070), 10 wt% sodium dodecylbenzenesulfonate(CU#0631), pH 12.0 adjusted by p-toluenesulfonic acid

CUBIC-B

Decalcification

10 wt% EDTA, 15 wt% imidazole (CU#1352)

CUBIC-R

RI matching, more cost effective than CUBIC-RA

45 wt% antipyrine (CU#0640), 30 wt% nicotinamide (CU#0855), optionally pH 8-9
adjusted by N-butyldiethanolamine (CU#0414)

CUBIC-RA

RI matching, kinder to FP than CUBIC-R

45 wt% antipyrine (CU#0640), 30 wt% N-methylnicotinamide (CU#1283), optionally pH 8-9
adjusted by N-butyldiethanolamine (CU#0414)

Components and concentrations
5 wt% 1-methylimidazole, 10 wt% N-butyldiethanolamine (CU#0414), 5 wt% Triton X-100

PurposeProtocolMouse organ &body clearing

3 - 7 days, 37°C

2 days

CUBIC-L

CUBIC-R/RA
50% 100%

CUBIC-P (optional)
3 - 7 days, 37°C
CUBIC protocol II

Tissues includingbone clearing

CUBIC protocol III

Aggressive
organ clearing

CUBIC protocol IV

Human brain
clearing

CUBIC-L2 - 4 days, 37°C

2 days

CUBIC-B

CUBIC-L

CUBIC-R/RA
50% 100%

PBS

PBS

protocol II

protocol II

K

Mouse leg

protocol IIM

2 daysN
protocol III
Human heart

BeforeBeforeAfter

Mouse spine

After

Magnified view

L

Mouse body

BeforeMouse headM, S3I, S6L, S6N

CUBIC-R
50% 100%

CUBIC-L

J, K, L, S3D

2 days
CUBIC-R
50% 100%

CUBIC-HL

After

J

PBS

5 - 7 days, 37°C

7 - 14 days, 45°C

protocol I

I, S3C
PBS

PBS

7 - 14 days, 37°C or 45°C

Mouse brain

ResultN

Human kidney

protocol IV
Human brain

BeforeProtocol Name

20wt% CU#0070

Triton X-100

10wt% CU#0070

CU#1460

CU#1151

CU#1455

CU#0631

CU#0865

SDS

ScaleCUBIC-1

PBS

2.0

day 0
day 2
day 4

45 wt%
CU#0640+30 wt%
CU#1283

with 10 wt% CU#0070

F

day 0
day 2
day 4

45 wt%
CU#0788+
30 wt%
CU#1283

N/A

0

day 0
day 2
day 4
day 1445 wt%
CU#1102+30 wt%
CU#1283

5

day 0
day 2
day 4
day 1445 wt%
Histodenz+
30 wt%
CU#1283

CU#063110

CUBIC-P

CUBIC protocol I

I

20wt% CU#0414

Triton X-100

CU#1151

with 10 wt% CU#0414

10wt% CU#0414

CU#1455

CU#1460

CU#0631

CU#0865

SDS

0

S
O O

15

Human brain

5

NaO

20

day 0
day 2

1.49

5

25

day 0
day 2
day 4
day 14

1.49

10

Mouse
lung

1.50

70 wt%
CU#1283

15

Phospholipid content (%)

Humankidney45 wt%
CU#0587+
30 wt%
CU#1283

20

Relativefluorescence intensity

70 wt%
Histodenz

PBS

20wt% CU#0414

Triton X-100

with 10 wt% CU#0414

10wt% CU#0414

CU#1511

CU#1460

CU#1455

CU#0865

CU#0631

SDS

ScaleCUBIC-1

PBS

0.0

Human brain

ScaleCUBIC-1

0.5

D

25

PBS

Phospholipid content (%)

Mouse brain
Mouse kidney

1.0

E

H

PB
Human
brain

PB
Human
brain

Mouse
brain
Mouse
kidneyRelative
phospholipid content

B

G

C

S
C cal
U e
BI
C
-1

C
Tr U #
ito 04
n 14
X10
0
C
U
#0
41
4

S

S
C cal
U e
BI
C
-1

C
SD U#
S 041

4

Tr U #
ito 04
n 14
X10
0
C
U
#0
41
4

4

C

C
SD U#
S 041

S
C cal
U e
BI
C
-1

PB

S

A

After

Mouse kidney(legend on next page)2202 Cell Reports 24, 2196–2210, August 21, 2018

decalcification acids, heterocyclic amines without a primaryamine were found to be the optimal organic base (Figures S2M
and 3H). EDTA-related chemicals (e.g., EDTA, CyDTA, andPDTA) were still superior to other chelators, even to 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) and
EGTA, which have higher stability constants for calcium ion(logK) than EDTA (Figure 3I), suggesting that the specific chemical nature of EDTA may play an important role in an organicbase-assisted decalcification. Collectively, the combined cocktail of EDTA and imidazole appeared to be the optimal decalcification solution (Figure 3J, bottom).
Rapid and Scalable Tissue Clearing by Updated CUBIC
Protocols Combining Various CUBIC Cocktails
The above experiments independently identified a series of
CUBIC chemicals with high performance for delipidation, decoloring, RI matching, and decalcification. Because the requisite chemical properties for tissue clearing differ dependingon the nature of the sample (e.g., lipid content, FP expression,
existence of bone tissue), we sought to develop representative
CUBIC cocktails that cover existing clearing demands. We
began by developing two kinds of delipidation and decoloring
cocktails, representing upgrades of ScaleCUBIC-1: one for
FP-compatible rapid delipidation and decoloring (CUBIC-L)
and another for very rapid delipidation and decoloring(CUBIC-HL). According to our screening results, N-butyldiethanolamine and 1,3-bis(aminomethyl)cyclohexane were chosen
for the main components of CUBIC-L and CUBIC-HL, respectively, because they had both high lipid solubility and decoloringscores. The combinatorial evaluation of detergents was performed using biological tissues. Since it is difficult to distinguishthe clearing performance of individual cocktails under a longincubation time, we applied N-butyldiethanolamine-based
cocktails to mouse brain and kidney and to human brain witha brief incubation (Figures 4A and S3A). The cocktail of N-butyldiethanolamine and Triton X-100 (termed CUBIC-L) exhibitedthe highest clearing ability for all of the specimens. The
delipidation efficiency of all of the tested solutions was comparable in mouse brain and kidney, while anionic detergent-based
cocktails (SDS, CU#0631 [sodium dodecylbenzenesulfonate],
and CU#0865 [sodium nonanoate]) exhibited slightly higher

efficiencies than other solutions in human brain (Figure 4B).
Notably, the tissue transparency of CUBIC-L was superior tothat of other anionic detergent-based cocktails.
Sodium dodecylbenzenesulfonate (CU#0631) was an optimal
cocktail for clearing human brain and kidney among the 1,3-bis(aminomethyl)cyclohexane-based cocktails (Figures 4C, 4D,
and S3H). The cocktail of 1,3-bis(aminomethyl)cyclohexane
and sodium dodecylbenzenesulfonate (CU#0631) (CUBIC-HL)
achieved almost full delipidation. Next, we sought to optimize
the RI-matching medium, an upgrade of ScaleCUBIC-2, basedon N-methylnicotinamide (CU#1283). Combinatorial experiments revealed that the cocktails of N-methylnicotinamide(CU#1283)-Histodenz and CU#1283-antipyrine showed muchhigher clearing performance than the single chemicalsolutions and other N-methylnicotinamide (CU#1283)-based
cocktails (Figures 4E and S3F). In terms of cost efficiency, we
chose the cocktail containing 30 wt% N-methylnicotinamide(CU#1283) and 45 wt% antipyrine as the RI-matching
medium (CUBIC-RA). The even less expensive nicotinamide(CU#0855), a demethylated derivative of CU#1283, served asa convenient surrogate for N-methylnicotinamide (CU#1283)
in combination with antipyrine; this cocktail was named
CUBIC-R (Figure S3B).
We previously reported that a transient perfusion of
ScaleCUBIC-1 effectively cleared whole-body samples (Susaki
et al., 2015; Tainaka et al., 2014). For this purpose, we introduced
an optional CUBIC-L-based perfusion cocktail that included thedecoloring-effective 1-methylimidazole, named CUBIC-P. To
achieve efficient decalcification compatible with whole-bodyclearing, we chose the cocktail of 10 wt% EDTA and 15 wt%
imidazole, named CUBIC-B. The proposed CUBIC cocktails
except CUBIC-HL sufficiently preserved the fluorescent signal
of mCherry (Figure 4F). CUBIC-RA is more feasible for various
FPs such as EGFP and yellow fluorescent protein (YFP) than
CUBIC-R (Figure S3E). These series of CUBIC cocktails are summarized in Figure 4G (original CUBIC protocol in Figure S3G),
and four CUBIC protocols sequentially combining these cocktails are summarized in Figure 4H. By applying the protocols toa variety of tissue samples, including large human tissues, wedemonstrated the effects of the updated CUBIC protocols(Figures 4I–4N, S3C, S3D, and S3I).

Figure 4. Combinatorial Screening of CUBIC Chemicals for Rapid and Scalable Tissue-Clearing CUBIC Protocols(A) Combinatorial screening of delipidation chemicals compatible with FP. CU#0414 was mixed with various detergents. Mouse brain hemispheres, kidneys, andhuman brain blocks were delipidated with individual chemical cocktails and were then immersed in CUBIC-R.
(B) Phospholipid content of the tested biological tissues treated with CU#0414-based delipidation cocktails (n = 3).
(C) Combinatorial screening of delipidation chemicals for human brain and kidney blocks. 1,3-bis(aminomethyl)cyclohexane (CU#0070) was mixed with various
detergents.
(D) Phospholipid content of human brain blocks treated with CU#0070-based delipidation cocktails. The CU#0070-CU#1455 cocktail precipitated (n = 3).
(E) Combinatorial screening of RI matching reagents. Delipidated adult (8-week-old) mouse lungs were immersed in highly concentrated chemical cocktails. RI of
the mixtures is shown in blue.
(F) Time course of fluorescent signals from mCherry with each CUBIC cocktail (n = 3).
(G and H) Chemical components in CUBIC cocktails (G), and CUBIC protocols using serial treatments with CUBIC cocktails (H).
(I) Clearing performance of CUBIC protocol I for adult (6-week-old) mouse brain and kidney.
(J–L) Clearing performance of CUBIC protocol II for adult (8-week-old) mouse leg (J), P7 mouse head and body (K), and adult (8-week-old) mouse spinal cord
(L4–L6) (L). Grids were visible through hard bone tissue (red arrows, K).
(M) Clearing performance of CUBIC protocol III for human heart and kidney blocks.
(N) Clearing performance of CUBIC protocol IV for human brain block.
Scale bars indicate 4 mm. Data are means ± SDs. See also Figure S3.

Cell Reports 24, 2196–2210, August 21, 2018 2203

Inspection of the Tissue Integrity of CUBIC-Treated
OrgansTo demonstrate how the CUBIC protocols could outperform
other clearing methods, we compared CUBIC protocol I withpreviously reported clearing methods. The final clearing performance of the CUBIC protocols was significantly better thanthat of other protocols (Figure S4). The updated CUBIC protocol
also improved the imaging quality at deeper regions of the sample (Figure 5A). Since tissue clearing is often accompanied bymacroscale tissue deformation, we also examined the effect ofclearing on the brain macroshape. Because the final morphologyof tissues was influenced by the pH of CUBIC-R, we tested bothneutral and weakly basic CUBIC-Rs. The neutral CUBIC-R
showed little deformation, while the weakly basic CUBIC-R exhibited swelling (Figure 5B). In addition, a cleared mouse brain(CUBIC protocol I) showed preserved subcellular microstructures (Figure 5C). To evaluate protein loss during the clearing
process, we quantified the amount of residual protein after a
4-day delipidation using human brain tissue. Most of the endogenous protein was retained in the brain after CUBIC delipidation,even using the harshest CUBIC-HL protocol (Figure S4D). We
also tested the antigenicity of delipidated mouse brain by immunohistochemistry using anti-glial fibrillary acidic protein (GFAP)
and anti-NeuN antibodies and found that the antigenicity waspreserved after a 4-day CUBIC-HL treatment (Figure 5D).
In addition, using various antibodies on delipidated humanbrain, we observed that the antigenicity remained unaltered after
a 9-day CUBIC-L treatment (Figure 5E). We further demonstrated the applicability of the protocols for 3D staining using
lectin and an anti-smooth muscle actin (SMA) antibody on
CUBIC-L delipidated human brain (Figure 5F). These findings
indicated that the proposed protocols largely retain the amountand antigenicity of the proteins. We also applied our CUBIC
protocols to comprehensive cell analysis at the whole-organ
scale (Figure S5). The application of the CUBIC protocol I
revealed comprehensive segmentation of the glomeruli in amouse kidney (Figures S5A–S5E) and whole-organ cell detectionin the left medial lobe of the liver and left lung of a mouse (Figures
S5F–S5M)
Scalable Imaging of Mouse Whole Body, Including theInside of Bone, and Large Human Organ Blocks by
CUBIC Protocols
To perform exhaustive whole-body imaging, we applied
CUBIC protocol II to render transparent the whole body of a
P7 mouse and stained it with nuclear-staining dye (Figure 6A;
Video S1). The volume rendered whole-body image showedstrong fluorescence inside bones, as seen in the densely
concentrated cells in the bone marrow (Figure 6B). CUBIC
protocol II enabled brain and spine imaging at single-cell resolution in the skull and backbone (Figures 6C, 6D, S6A, andS6B). The imaging was even possible with adult mouse headand spine (Figures S6C–S6H). Next, we applied this protocol
to metastatic bone tumors in the leg of an MDA-231-D-injected mouse (Figures 6E–6H and S6I) and observed the infiltration of metastatic cancer cells from bone tissue to the adjacent soft tissues. The metastatic bone tumors were evenvisible through skull (Figures S6J and S6K)

2204 Cell Reports 24, 2196–2210, August 21, 2018

To examine the scalability of the CUBIC protocols, we applied
CUBIC protocol III to large human tissue blocks; a large chunk ofdissected human lung (approximately 43.4 3 16.0 3 26.4 mm3;
Figure S6L; Video S2), dissected human kidneys (Figure S6N),
and a dissected human liver. Using light-sheet fluorescence microscopy (LSFM), multidirectional imaging was performed to acquire autofluorescence images throughout the blocks. We
confirmed that the fluorescent signal was sufficient for imaging
even deep within the blocks (Figures 6I, S6M, and S6O–S6Q).
To examine the feasibility of volumetric imaging of human kidney
at single-cell resolution, we visualized the 3D structure of theglomerulus after nuclear staining. Confocal imaging of the highly
cleared tissue enabled the identification and segmentation ofglomeruli in the magnified view (Figure 6J) and even the comprehensive detection of glomerular cells in the magnified view (Figures 6K and 6L).
We next performed the whole-brain imaging of an adultmarmoset. The brain of a 1-year-old male marmoset was clearedusing a modified CUBIC protocol I with propidium iodide (PI)staining, resulting in a cleared PI-stained brain without apparent
morphological distortion (Figure S6R). The whole-brain images
were acquired with LSFM and subjected to 3D image reconstitution to generate a volume-rendered image (Figures 6M and 6N;
Video S3). Individual cells were distinguishable even in the
deep regions of the brain (Figure 6O).
Multiple Visualization of Transgenically Labeled FPs in
Mouse Brain
Simple immersion-based CUBIC protocols should be applicable and highly reproducible for tissue clearing without thesignificant quenching of FPs. We next used CUBIC protocol
I combined with the visualization of a series of fluorescently
labeled mouse samples. We prepared mouse brains with awide variety of genetically encoded FPs (Thy1-YFP [Fenget al., 2000], P(CAG)-EGFP [Okabe et al., 1997], Drd1-mVenus[Nagai et al., 2016], Th-EGFP [Matsushita et al., 2002], Mcl1YFP [Mlc1-tTA (Tanaka et al., 2010)::tetO-YC (Kanemaru
et al., 2014)], Plp-YFP [Plp-tTA {Inamura et al., 2012}::tetOChR2EYFP {Tanaka et al., 2012}], and Htr5b-YFP [Htr5b-tTA{Tanaka et al., 2012}::tetO-YC]). The brain samples were subjected to CUBIC protocol I, with CUBIC-RA as an RI matching
reagent to preserve the FP signals. All of the brains weretreated with a nuclear staining dye (RedDot2) for registration,and images were acquired with LSFM, resulting in two volumetric images (one for the nuclear stain and the other for
the FP signal) for each brain. We registered the nuclear images
to that of a reference brain, and directly compared the FP
signals by overlaying their registered images, as we reported
previously (Susaki et al., 2014, 2015), thus achieving virtual
multiplex whole-brain imaging (Figures 7A, S7A, and S7B).
The overlaid image made it possible to compare the global
distribution of pyramidal neurons (Thy1-YFP), astrocytes(Mlc1-YFP), oligodendrocytes (Plp-YFP), dopaminergic neurons (Th-EGFP), serotonin receptor 5B (Htr5b-YFP), and dopamine receptor D1 (Drd1-mVenus) at the whole-brain scale (Figures 7B and S7C; Video S4). This systematic approach toanalyzing the global distribution of expressed genes enabled
us to investigate the distribution of neural subtypes in the

A Protocol 0

Mouse brain

PVN

x

x

y

z

PVN

500 μmx

Figure 5. Tissue Integrity of CUBIC-Treated Organs

B

Protocol Ixyz

500 μm
~7.5 mm2500 μm

Mouse lungx

~6.5 mm2500 μmxz2500 μm

Mouse liverxz

2500 μmxz2500 μmz

2500 μm

C Protocol I, FP, mouse brain

D Protocol III, IHC mouse brain
DAPI
GFAP

Thy1-YFP mouse brain(confocal)

100 μm
DAPI
NeuN

50 μm

10 μm

100 μm(A) LSFM images of nucleus-stained 8-week-old brain, lung,and left medial lobe of the liver after CUBIC protocol 0 and
CUBIC protocol I. To compare the image qualities fairly,
imaging was performed only from one side of the tissue. x
indicates the illumination direction and z indicates thedetection direction of the LSFM. For brain, the hypothalamic
nuclei of paraventricular nuclei (PVN) are visualized in theinsets. The scales for CUBIC protocols 0 and I were slightly adjusted to correct for shrinkage caused by CUBIC protocol 0.
(B) Deformation analysis of cleared 8-week-old mouse brainsusing CUBIC protocol I. The cleared brains were registered
to a standard brain, and then the deformation fields werevisualized on the projection images of the brains. In the RImatching step, neutral CUBIC-R (without #0414; middle) andbasic CUBIC-R (pH 8–9 adjusted with CU#0414; bottom)
were applied; see also STAR Methods for details.
(C) Subcellular imaging of a 6-month-old Thy1-YFP mousebrain after CUBIC protocol I. A cellular view (left) and amagnified view (right) are shown. A representative spine isindicated by a red arrow (right).
(D) Immunohistochemistry of an 8-week-old mouse brain
after a 4-day CUBIC-HL treatment. Anti-GFAP (top) and antiNeuN (bottom) antibodies were used.
(E) H&E (HE) staining (top left) and immunohistochemistry ofhuman brain after CUBIC protocol IV. Antibodies for glial
fibrillary acidic protein (GFAP), ionized calcium-binding
adaptor molecule 1 (Iba-1), neurofilament (NF), myelin basic
protein (MBP), and laminin were examined.
(F) Visualization of vascular structures in thick slices of ahuman brain. Lectin (for 0.4-mm-thick brain slice; left) andanti-a-SMA antibody (for 1.5-mm-thick slice; right) wereapplied. The 3D volumetric images were obtained with
LSFM.
See also Figures S4 and S5.

E Protocol IV, HE and IHC, human brain

HE

20 μm

GFAP

20 μm

Iba-1

40 μm

NF

40 μm

MBP

40 μm

Laminin

40 μm

F Protocol IV, IHC, human brain
Lectin AF(0.4-mm-thick)

SMA AF(1.5-mm-thick)

100 μm500 μmCell Reports 24, 2196–2210, August 21, 2018 2205

A

P7 mouse
V-D view

B P7 mouse leg

Mouse leg (> 8-week-old)

Sectional view
MDA-231-D-Luc2-mCherry

Femur

G Frontal view

Orthogonal view

Fibura400 μm

Tibia
Frontal view 3D magnified

1000 μm1000 μm

A

Brain, sagittal(Confocal)

Spine, sagittal(Confocal)

400 μm

H Segmented tumor, 3D magnified view

view

D

D

Orthogonal view

mCherry
RedDotTM2

A1000 μm

I

2000 μm

kidneyJ HumanSegmented glomeruli

Human lung26.4 mm

Segmented glomeruli

16.0 mm
300 μm43.4 mm

300 μmHuman kidney

K Segmented glomeruliL

xy-plane(Confocal)(Confocal)

50 μm

y

5000 μm

100 μmM Adult marmoset brain

100 μm

5000 μm
Sagittal view

N

z
x

50 μm
Horizontal view

50 μm

O

~9 mm
~12 mm200 μmsegmented

4000 μm

5000 μm

5000 μm

200 μmFigure 6. Scalable Imaging of Mouse Whole Body, Including Bone, and of Large Human Tissue Blocks with Single-Cell Resolution(A and B) Volume-rendered images of whole mouse body (P7) stained with nuclear-staining dye. Ventral-dorsal whole-body view (A) and magnified view of left leg(B). Inset of (B), sectional view of the leg. CUBIC protocol II was applied.
(C and D) Confocal images of the brain inside the skull in a sagittal plane (C) and of the spine in a sagittal plane (D). Yellow arrows indicate bone tissues.
(E) Bioluminescence image of an MDA-231-D-injected immunodeficient BALB/c-nu/nu mouse.
(F) Volume-rendered image of the right leg of the mouse in (E). Magenta and cyan indicate metastasized cancer cells (mCherry) and nuclei (RedDot2), respectively.
(G and H) Frontal (G, left), orthogonal (G, right), and magnified (H) views of (F). For (H), the segmented metastatic colony was visualized. For (F)–(H), CUBIC
protocol II was applied.
(I) Volume-rendered image of the autofluorescence in a dissected human lung. CUBIC protocol III was applied.
(J) Volume-rendered image of nuclear-stained human kidney. Several glomeruli were segmented and are shown at right. CUBIC protocol III was applied.
(K and L) Confocal imaging of a single glomerulus in the human kidney. Volume-rendered image (K), sectional image (L, top) and magnified single-cell-resolution
view of the sectional image (L, bottom).
(M–O) Adult marmoset brain stained for nuclei. Volume-rendered image of whole brain (M), sectional views (N), and highly resolved images (O). All images acquired with LSFM. Modified CUBIC protocol I was applied (STAR Methods).
See also Figure S6.

cerebral cortical layers (Figures 7C, 7D, and S7D). In utero
electroporation is another method for introducing an exogenous gene. We unilaterally introduced P(CAG)::mRFP (monomeric red fluorescent protein) into mouse brain by in utero
electroporation (Hayashi-Takagi et al., 2015) and analyzed
the spatial distribution of the induced genes by virtual multi-

2206 Cell Reports 24, 2196–2210, August 21, 2018

plex whole-brain imaging; this demonstrated that the mRFPs
were mainly expressed into layer 4 (Figures 7E–7G). Furthermore, FP expressed in metastatic tumor cells (MDA-231-D
cancer cells expressing mCherry) was also clearly visible using
our CUBIC protocol I, allowing the morphological analysis ofcancer metastasis (Figure 7H).

A

B

Adult mouse brain

Thy1

Mlc

500 μm

Drd1

Htr5b

Plp

P(CAG)

Th

Merge

1500 μm

D L4

C

L5

L2/3

Thy1
Htr5b
Th

E

F

G EP P(CAG)-mRFP H

MS mCherry
RedDotTM2(Confocal)

L61000 μm

Thy1
Htr5b
Th
RedDotTM2

500 μm

1500 μm

Thy1
Thy1
EP P(CAG)-mRFP
EP P(CAG)-mRFP
RedDotTM2 500 μm
RedDotTM2

50 μm

2000 μm

500 μmFigure 7. Multiple Visualization of Transgenically Labeled FPs in Mammalian Brains and Scalable Imaging of Brains with Single-Cell
Resolution(A–D) Virtual multiplex images of FP-expressing mouse brains registered to a standard brain. Overlaid images of seven different FPs (horizontal view; 300-mmthick projection) (A). Individual expression patterns and merged images in magnified views of the indicated region of (A) (B). Overlaid images of three different FPs(coronal view; 300-mm-thick projection) (C) and magnified view of the indicated region of (C) (D). Cortical layer structures are indicated in (D).
(E and F) Multiplex images of mouse brain labeled with EP-induced mRFP overlaid with Thy1 and nuclear images. Volumetric image (E) and coronal view of the
300-mm-thick projection in (E) and (F).
(G) Subcellular resolution imaging of mRFP-expressing cells by confocal microscopy.
(H) Metastasized colonies of MDA-231-D cells expressing mCherry (MS mCherry) in mouse brain; volumetric image overlaid with nuclear image. Inset shows
segmented volumes of cancer metastasis.
For (A)–(H), CUBIC protocol I was applied. See also Figure S7 and STAR Methods.

DISCUSSION
Here, by applying comprehensive chemical profiling for delipidation, decoloring, RI matching, and decalcification, we designed aset of chemical tools for tissue clearing. Organic salts had poor
efficiencies for delipidation and decoloring (Figures S2A and
S2F). The undesirable effect of ionic charge in these steps wasprobably related to its effect on the molecular flux between a tissue and the external medium (Hama et al., 2011; Hou et al.,2015). Deionization by an osmotic-pressure gradient is astraightforward way to increase molecular flux; therefore, nonionic cocktails that promoted molecular flux were advantageousin these steps. Detergents also had unexpectedly poor efficiencies for the delipidation and decoloring of tissue samples,
given their high scores in the in vitro assay (Figures 2F, 2H,
S2A, and S2F). Tissue delipidation and decoloring using detergents is often achieved by combining them with other tissue-

permeable chemicals (Hama et al., 2015; Susaki et al., 2014) orwith chemophysical assistance (Chung et al., 2013; Murray
et al., 2015; Tainaka et al., 2014; Yang et al., 2014). Detergents
generally show slow tissue-permeation kinetics as a result oflarge micelle formation, which could explain their poor efficiency
in tissue. In contrast, amine derivatives with higher logP values
are expected to be more cell permeable and more misciblewith hydrophobic lipids. Among the current decoloring reagents,
N-alkylimidazole showed the highest decoloring ability. N-Alkylimidazole facilitates heme elution under basic conditions bycompeting with histidine for binding to the heme iron center.
In the RI-matching step, an external medium with a high RI
value similar to that of endogenous proteins (RI R1.45) replaces
the low-RI water (RI = 1.33) inside tissue. The final tissue transparency improves as the RI value of the medium increases (upto 1.52) under a given chemical composition (Kubota et al.,2017). Organic solvent-based RI media have high RI values

Cell Reports 24, 2196–2210, August 21, 2018 2207

(e.g., benzyl alcohol/benzyl benzoate [BABB], 1.56; dibenzyl
ether [DBE], 1.56). Our chemical profiling demonstrated that aromatic groups have high RI values because of their high electron
density (Figure 3A). Thus, an aromatic derivative is likely to be asuitable constituent of RI media. In addition, the tissue clearing
performance in a variety of RI media demonstrated that the final
transparency was determined not only by the RI value but also by
the chemical nature of the components of the RI medium (Figures 3C and 3E). Among the clearing protocols, sugar-based
RI medium (e.g., see deep brain [SeeDB, Ke et al., 2013] andScaleCUBIC-2) elicits moderate clearing compared with RI media consisting of contrast reagents (e.g., CLARITY, refractive index matching solution [RIMS], SeeDB2 [Ke et al., 2016], and
SWITCH). The effectiveness of antipyrine and nicotinamide in
our screen indicated that the high clearing efficiency of contrast
reagents is derived from their aromatic amide group. Since highly delipidated tissues are regarded as protein-based biomaterials (Murakami et al., 2018), it is plausible that hydrophilic aromatic amides contributed to RI homogenization because of
their efficient solvation of protein backbone amides. A future
strategy for improving RI media will be to explore chemical combinations that promote the solvation of the major proteins intissues.
Our updated protocols have a few potential limitations. Sinceboth of the delipidation cocktails are alkaline-buffered solutions,almost all of the endogenous RNA would be degraded. To perform
RNA profiling, a 1,2-hexanediol-based delipidation cocktail needs
to be developed. In addition, much of the presynaptic and postsynaptic fabric is reported to disappear with thorough delipidation
protocols such as 3D imaging of solvent-cleared organs(3DISCO), PACT, and the original ScaleCUBIC (Hama et al.,2015). Therefore, our protocols would be not suitable for theobservation of these structures by electron microscopy. Furthermore, while our decoloring reagents are effective for bleachingporphyrine-related pigments such as myoglobin and cytochromec, they are unlikely to affect melanin, which is formed by the oxidative polymerization of amino acids (Sealy et al., 1980). Another decoloring strategy is needed to bleach such polymerized pigments.
In summary, we applied comprehensive chemical profiling asa rational design strategy for obtaining purposeful clearing cocktails. A visual guide for this design strategy is shown in Figure S8.
The application of these protocols should contribute to a rich variety of studies, including the prospective atlas of human cells.
STAR+METHODS
Detailed methods are provided in the online version of this paper
and include the following:d KEY RESOURCES TABLE
d CONTACT FOR REAGENT AND RESOURCE SHARING
d EXPERIMENTAL MODEL AND SUBJECT DETAILSB Marmoset
B Mice Model
B Human Tissued METHOD DETAILSB High-throughput Chemical Screening
B Chemical Parameters2208 Cell Reports 24, 2196–2210, August 21, 2018

B Multivariate Linear Regression Analysis
B CUBIC Protocols
B Deformation Analysis of Cleared Brains
B Long-term Tissue Preservation
B Immunostaining
B Microscopy
B In Vivo Bioluminescence Imaging
B Histological Examination
B Image Analysis
d QUANTIFICATION AND STATISTICAL ANALYSISB Protein Quantification
B Quantification of Image Quality
B Statistical Analysis
d DATA AND SOFTWARE AVAILABILITY

SUPPLEMENTAL INFORMATION
Supplemental Information includes eight figures, two tables, and four videos
and can be found with this article online at https://doi.org/10.1016/j.celrep.
2018.07.056.
ACKNOWLEDGMENTS
We thank all of the laboratory members at The University of Tokyo and RIKEN
Quantitative Biology Center (QBiC), in particular, K. Yoshida for help with thepolynomial regression; S. Shi, G.A. Sunagawa, and R.G. Yamada for statisticalanalysis; and R. Ohno for housing mice. We also thank Y. Morishita and T.
Tanda for histological analysis, S. Ehata and J. Nishida for experimental designof the metastasis model, H. Nawa and H. Namba for helping to prepare the ThEGFP mouse brain, T. Mano for reproducing the image processing pipeline,
Tokyo Chemical Industry for the CUBIC chemical library, Olympus Engineeringfor helping with the microscope design, and Bitplane for instruction in Imaris
8.1.2. This work was supported by grants from AMED-CREST (AMED/MEXT
to H.R.U.) and CREST (JST/MEXT to H.R.U. and JPMJCR1652 to H.K.);
PRESTO from JST(JPMJPR15F4 to E.A.S.); Brain/MINDS (AMED/MEXT to
H.R.U.); the Basic Science and Platform Technology Program for Innovative
Biological Medicine (AMED/MEXT to H.R.U.); the Japanese Initiative for
Progress of Research on Infectious Disease for Global Epidemic (AMED,
JP17fm0208023 to M.M. and K. Tainaka); Strategic Research Program from
Brain Sciences (AMED, JP18dm0107120 to H.K.); Grant-in-Aid for Challenging
Exploratory Research (JSPS KAKENHI grant 16K15124 to K. Tainaka); Grantin-Aid for Young Scientists (A) (JSPS KAKENHI grant no. 15H05650 to E.A.S.);
Grant-in-Aid for Scientific Research (JSPS KAKENHI grant no. 16J05041 to
T.C.M.); Grant-in-Aid for Scientific Research (S) (JSPS KAKENHI grant no.
25221004 and 18H05270 to H.R.U. and 26221001 to H.K.); Grant-in-Aid for
Scientific Research (B) (JSPS KAKENHI grant no. 18H02105 to K. Tainaka
and 18H02540 to T.S. and K. Tainaka); Grants-in-Aid for Scientific
Research on Innovative Areas (KAKENHI grant no. 23115006 to H.R.U. and
17H05688 and 18H04543 to K. Tainaka, and 17H06328 to E.A.S.)
from MEXT; Grant-in-Aid from the Naito Science & Engineering Foundation(K. Tainaka); Grant-in-Aid from the Tokyo Biochemical Research Foundation(K. Tainaka); Grant-in-Aid from the Cell Science Research Foundation (K. Tainaka); Grant-in-Aid from the NOVARTIS Foundation (Japan) for the Promotionof Science (K. Tainaka); Grant-in-Aid from the Takeda Science Foundation(H.R.U. and E.A.S.); Grant-in-Aid from the Japan Foundation for Applied Enzymology (E.A.S.); and grant from the Brain Sciences Project of the Center for
Novel Science Initiatives of the National Institutes of Natural Sciences(BS291001 to E.A.S.).
AUTHOR CONTRIBUTIONS
H.R.U., K. Tainaka, and T.C.M. designed the study. K. Tainaka, T.C.M., andC.S. performed most of the experiments. K. Tainaka performed the chemical
profiling for tissue clearing. T.C.M. performed the computational image

analysis. E.A.S. developed the modified CUBIC protocol for marmoset brain
and performed LSFM imaging of the marmoset brain. R.S. and A.K. preparedhuman brain tissues and performed the clearing and LSFM imaging of humanbrain tissues. S.N., M. Ikemura, T.U., E.M., and M.F. prepared human tissues.
T.I. housed the marmosets and prepared marmoset brains. K. Takahashi and
K.M. produced the experimental metastatic model. A.H.-T. and H.K. produced
exogenously FP-labeled mice by in utero electroporation. Y.S. produced therecombinant fluorescent proteins. H.S., Y.A., K.F.T., M. Iino, M.M., T.S., andK.K. housed a series of mice used in this study and prepared organ samples.
H.R.U., K. Tainaka, and T.C.M. wrote the manuscript. All of the authors discussed the results and commented on the manuscript text.
DECLARATION OF INTERESTSThe RIKEN Quantitative Biology Center has filed a patent based on this work inwhich T.C.M., K. Tainaka, and H.R.U. are co-inventors. Part of this study wasdone in collaboration with Olympus Corporation.
Received: January 31, 2018
Revised: May 21, 2018
Accepted: July 15, 2018
Published: August 21, 2018
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STAR+METHODSKEY RESOURCES TABLEREAGENT or RESOURCESOURCEIDENTIFIER

Antibodies
GFAP

Sigma-Aldrich

Cat#C9205; AB_476889

NeuN

Millipore

Cat#MAB377; AB_2149209

Donkey anti-mouse IgG

Thremo Fisher Scientific

Cat#A21203; AB_2535789

GFAP

Dako

Cat#Z0334; AB_10013382

Iba1

Wako

Cat#019-19741; AB_839504

Laminin

Dako

Cat#Z0097; AB_10013382

MBP

Dako

Cat#A0623; AB_2650566

NF

Monosan

Cat#MON3004; AB_2314914

a-SMA

Novus Biological

Cat#IC1420R

LectinVector Laboratories

Cat#RL-1062

Kohjin-Bio

Cat#12070110

Pentobarbital

Nacalai TesqueCat#02095-04

Paraformaldehyde

Nacalai TesqueCat#02890-45

QuadrolTokyo Chemical Industry

Cat#T0781

Triethanolamine

Wako Pure Chemical industries

Cat#145-05605

Triton X-100

Nacalai TesqueCat#12967-45

Urea

Nacalai TesqueCat#35904-45

Sucrose

Nacalai TesqueCat#30403-55

SDS

Nacalai TesqueCat#31606-75

Histodenz

Sigma-Aldrich

Cat#D2158

N-Butyldiethanolamine (CU#0414)

Tokyo Chemical Industry

Cat#B0725

1,3-Bis(aminomethyl)cyclohexane (cis- andtrans- mixture) (CU#0070)

Tokyo Chemical Industry

Cat#B1005

1-Methylimidazole

Tokyo Chemical Industry

Cat#M0508

Antipyrine (CU#0640)

Tokyo Chemical Industry

Cat#D1876

N-Methylnicotinamide (CU#1283)

Tokyo Chemical Industry

Cat#M0374

Nicotinamide (CU#0855)

Tokyo Chemical Industry

Cat#N0078

EDTA

Tokyo Chemical Industry

Cat#E0084

Biological Samples
Horse blood
Chemicals, Peptides, and Recombinant Proteins

Imidazole (CU#1352)

Tokyo Chemical Industry

Cat#I0001

Hydroxyapatite (HAp)

Sigma-Aldrich

Cat#289396

RedDot 2

Biotium

Cat#40061

Propidium iodide

Life Technologies

Cat#P21493

Agarose

Nacalai TesqueCat#01163-76

Silicon oil (RI = 1.555)

Shin-Etsu Chemical

Cat#HIVAC-F4

Mineral oil (RI = 1.467)

Sigma-Aldrich

Cat#M8410

CUBIC chemicals for screening

Tokyo Chemical Industry

Table S1

Critical Commercial Assays
LabAssay Phospholipid

Wako Pure Chemical industries

Cat#296-63801

LabAssay Cholesterol

Wako Pure Chemical industries

Cat#296-65801

Microplate BCA Protein Assay KitReducing Agent Compatible

Thermo Fisher Scientific

Cat#23252(Continued on next page)

Cell Reports 24, 2196–2210.e1–e9, August 21, 2018 e1

Continued
REAGENT or RESOURCESOURCEIDENTIFIER

Ehata et al., 2007

N/A

Tanaka et al., 2012

N/A

Experimental Models: Cell Lines
MDA-MB-231-5a-D
Experimental Models: Organisms/Strains
Htr5b-tTA:tetO-YC

Kanemaru et al., 2014
Plp-tTA:tetO-ChR2EYFP

Inamura et al., 2012

N/A

Tanaka et al., 2012
Mlc-tTA:tetO-YC

Tanaka et al., 2010

N/A

DrD1-mVenus

Nagai et al., 2016

N/A

Th-EGFP

Matsushita et al., 2002

N/A

Thy1-YFP-H

Feng et al., 2000

N/A

P(CAG)-EGFP

Okabe et al., 1997

N/A

P(CAG)::mRFP

Hayashi-Takagi et al., 2015

N/A

Excel 2016

Microsoft

N/A

Origin 7.0

OriginLab

N/AR

R Core Teamhttps://www.r-project.org

Software and Algorithms

GraphPad Prism 5

GraphPad software

N/A

ChemDraw

Perkin Elmer

N/A

Fiji

Schindelin et al., 2012https://fiji.sc/

ImarisBitplane

N/A

MATLAB

Mathworks

N/A

Customized MATLAB code for virtual
multiplex imaging

This paper

https://github.com/DSP-sleep/
Landscape_pipeline.

CONTACT FOR REAGENT AND RESOURCE SHARING
Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Hiroki R.
Ueda (uedah-tky@umin.ac.jp).
EXPERIMENTAL MODEL AND SUBJECT DETAILS
Marmoset
We studied an adult marmoset at the age of 12 months. Animal was maintained and handled in accordance with the recommendations of the United States National Institutes of Health, and all procedures were approved by the Animal Care and Use Committee of
the Graduate School of Medicine, Kyoto University, Japan.
Mice Model
For the chemical screening (Figures 1, 2, 3, 4 and S1, S2, S3 and Tables S1 and S2), the organs of adult mice (C57BL/6N, ICR, or
BALB/c-nu/nu) were used. The mice were sacrificed by an overdose of pentobarbital (> 100 mg/kg, Nacalai Tesque, 02095-04),
then transcardially perfused with 15 mL of PBS (pH 7.4) and 20 mL of 4% paraformaldehyde (PFA, Nacalai Tesque, 02890-45) inPBS. Organs were dissected, then post-fixed with 4% PFA-PBS at 4 C overnight. The fixed organs were then washed with PBS prior
to the experiments.
The Htr5b-tTA (Tanaka et al., 2012):tetO-YC (Kanemaru et al., 2014) (7 weeks old), Plp-tTA (Inamura et al., 2012):tetO-ChR2EYFP(Tanaka et al., 2012) (10 weeks old), Mlc-tTA (Tanaka et al., 2010):tetO-YC (11 weeks old), Drd1-mVenus (Nagai et al., 2016) (7 monthsold), Th-EGFP (Matsushita et al., 2002) (6 months old), Thy1-YFP-H (Feng et al., 2000) (6 months old), and P(CAG)-EGFP (Okabe et al.,1997) (8 weeks old) mouse strains were used to observe the expression patterns of fluorescently labeled cells in the brain.
P(CAG)::mRFP was transduced into mouse brain (8 weeks old) by in utero electroporation as described previously (Hayashi-Takagi
et al., 2015).
The human breast cancer cells, MDA-MB-231-5a-D (MDA-231-D), are a highly metastatic clone derived from MDA-MB-231(Ehata et al., 2007). We established MDA-231-D cells that co-expressed luciferase and mCherry according to our recent
report (Kubota et al., 2017). To elicit experimental bone metastasis by intracardiac (i.c.) injection, MDA-231-D cells

e2 Cell Reports 24, 2196–2210.e1–e9, August 21, 2018

(MDA-231-D: 1 3 105 cells/mouse) were injected into BALB/c-nu/nu mice (4-weeks old, female) by puncture into the left ventricle ofheart. Mice were sacrificed 4 or 5 weeks after injection.
All experimental procedures and housing conditions were approved by the Animal Care and the Use Committee of the Graduate
School of Medicine, University of Tokyo, and by the Animal Use and Care Committee of Niigata University, and all of the animals werecared for and treated humanely in accordance with the Institutional Guidelines for Experiments using animals.
Human Tissue
Human tissues used for this study were obtained from 10 patients (59–90 years old; 9 males and 1 female). Written informed consent
for autopsy including the use of tissue for research purposes was obtained from the next of kin. Experiments using human tissues
were approved by the institutional review boards of Osaka University School of Medicine, Osaka, Japan, the Graduate School of
Medicine, University of Tokyo, Tokyo, Japan, and Niigata University School of Medicine, Niigata, Japan.
METHOD DETAILSHigh-throughput Chemical Screening
Delipidation
To comprehensively assess the ability of chemicals to solubilize lipids in a high-throughput manner, we used a previously reported
method (Susaki et al., 2014). In brief, 20 ml of homogenized mouse brain was mixed with 130 ml of each 10%(wt/wt) chemical solution
in 96-well plates and incubated at 37 C overnight. After gentle shaking, the OD600 of the mixture was measured with a PowerWave
XS (Bio-Tek). The lipid solubility score was calculated as follows. First, the OD600 of each chemical solution alone was subtracted
from the OD600 of the mixture. The lipid solubility score was then determined by plotting the resulting OD600 on a line created byassigning the OD600 of PBS-suspension as 0.0 and the OD600 of the chemical solution alone as 1.0. Among the non-detergent
and salt-free chemicals, those that were colored (OD600 R 0.1), of low solubility, or lacked a LogP value (octanol-water partition
coefficient) were excluded from the chemical profiling. To examine the relationship between tissue phospholipid or cholesterol content and transparency, adult mouse half brains were weighed and then immersed in various delipidating CUBIC chemicals (Figures
2E and S2D). After 1 day of delipidation at 37 C with gentle shaking, the brains were washed with PBS at room temperature for a half
day with gentle shaking. The washed samples were then subjected to phospholipid or cholesterol quantification. To quantify thephospholipid content, the samples were thoroughly crushed, then sonicated in 10% Triton X-100 in PBS to obtain a homogenizedsuspension. After adjusting for the total weight of each sample, the phospholipid was quantified with LabAssay Phospholipid (Wako
Pure Chemical Industries, 296-63801) according to the instruction manual, with OD600. The absolute amount of phospholipid wasdetermined by fitting the OD600 value to a standard curve generated by a serially diluted pure phospholipid standard. The cholesterol
content was similarly quantified using LabAssay Cholesterol (Wako Pure Chemical Industries, 296-65801).
To measure the relative transparency, delipidated samples were washed with PBS, and immersed in modified ScaleCUBIC-2 [22.5
wt% antipyrine (Tokyo Chemical Industry, D1876), 22.5 wt% sucrose (Nacalai Tesque, 30403-55), 25 wt% urea (Nacalai Tesque,35904-45), and 10 wt% triethanolamine (Wako Pure Chemical Industries, 145-05605)] overnight at room temperature with gentle
shaking. The transmittance was quantified with a Spectral Haze Meter SH 7000 (Nippon Denshoku Industries), as described in
our previous study (Tainaka et al., 2014).
After comprehensive screening for effective delipidation chemicals, we further assessed their delipidation ability when used in
combination with other chemicals, as shown in Figures 4A-4D, S3A and S3H. Adult mouse half brains, adult mouse kidneys, and human brain blocks were used to evaluate the chemicals’ delipidation ability. Each tissue was weighed prior to the experiment if
required for normalization. The mouse tissues were immersed in the cocktails for more than 1 day at 37 C with gentle shaking, while
the human tissues were immersed in the cocktail for more than 4 days at 37 C with gentle shaking. After delipidation, the samples
were washed with PBS overnight. If required to improve the transparency of the tissue, the samples were then subjected to RI-matching procedures, consisting of 1:1-diluted CUBIC-R for one day followed by CUBIC-R for one more day. The washed samples werethen subjected to phospholipid quantification as described above.
Decoloring Ability
To comprehensively evaluate the decoloring ability of chemicals in a high-throughput manner, we used a previously reported method(Tainaka et al., 2014) with minor modifications. Because a large amount of blood was required for this screen, we used horse blood(Kohjin-Bio, 12070110). Prior to screening, the blood was fixed in an equal volume of 8% PFA in PBS, and the mixture was thendiluted 4-fold with PBS. The fixation was performed at 37 C for 16 hours. After removing clots by filtration, the sample was centrifuged at 1500 G for 3 minutes, and the supernatant was discarded. The pellet was washed with PBS. This centrifugation-washingprocedure was repeated for a total of three times. The final pellet was diluted in the same amount of PBS as the original volumeof blood. For the comprehensive screening, 50 ml of fixed blood and 150 ml of 10%(wt/wt) CUBIC chemical solution were mixed in
96-well plates. The samples were gently shaken for 1 day at 37 C, then spun at 1500 G for 5 minutes, and 100 ml of the supernatant
was transferred to another 96-well plate. OD420 and OD700 were measured with an EnSpire plate reader (Perkin Elmer, USA). To
calculate the decoloring score, we first subtracted the OD700 from the OD420 to account for light scattering. The resulting value
was then divided by the value obtained for Quadrol. Among the non-detergent and salt-free chemicals, low-solubility chemicals
were excluded from the chemical profiling. After this comprehensive screening for decoloring chemicals, we used adult mouse

Cell Reports 24, 2196–2210.e1–e9, August 21, 2018 e3

spleen to examine the chemicals’ decolorization ability in biological tissues. An adult mouse spleen was immersed in 3 mL of the
CUBIC chemical solutions at 37 C for 3 hours. Then, 100 ml of each supernatant was subjected to OD400 measurement. These results are summarized in Figure 2H.
RI Matching
To screen for chemicals with effective RI matching, we assumed that such chemicals would satisfy at least two conditions: colorlesswith a high RI in 10%(wt/wt) solution, and highly soluble in water. To first identify chemicals with a high RI in 10%(wt/wt) solution,
we measured the OD400 with a PowerWave XS and the RI value of each CUBIC-chemical solution with an Abbe refractometer(Atago, DR-A1, Japan) (Figures 3A and 3B). Next, we examined whether the chosen chemicals were soluble in water at 60-70 wt%. The candidates satisfying these two criteria were then evaluated for their clearing performance in mouse lung. Prior to the RImatching step, the lungs were delipidated with ScaleCUBIC-1 for 2 weeks at 37 C. For Figures 3C and 3E, the lungs were dissected
into small (3-5-mm) blocks, which were then immersed into 400 ml of 60-70 wt% CUBIC chemical at room temperature overnight. The
resulting samples were transferred into 96-well plates, and the OD600 was measured with a PowerWave XS. For combinatorial
screening, the whole lungs were first immersed in 2 mL of the chemical cocktail diluted 1:1 with deionized water, then gently shaken
at room temperature overnight. The lungs were then immersed in 2 mL of the chemical cocktail with gentle shaking at room temperature overnight. Pictures of the samples taken after RI matching are shown in Figures 4E and S3F.
Decalcification
To measure the decalcifying ability of a chemical, we used a screen based on hydroxyapatite (HAp), a major component of bone, as asurrogate for bone. Because the specific gravity of HAp (about 3.16) is much higher than that of water ( = 1), we dissolved 200 mg/ml
or 100 mg/ml HAp in 40%(v/v) glycerol to prevent its precipitation during the experiments. We then mixed 20 ml of homogenized HApsolution with 130 ml of the 10%(wt/wt) chemical solutions in 96-well plates. After gentle shaking at 37 C overnight, the plates were
centrifuged at 1500 G for 3 minutes, and the OD600 of the mixture was then measured with a PowerWave XS. The decalcification
score was calculated by plotting the resulting OD600 on a line in which the OD600 of PBS-HAp suspension was 0.0 and the
OD600 of EDTA-2Na-treated suspension (Figures 3F and 3I) or EDTA titrated with CU#0414-treated suspension (Figures 3H andS2M) was 1.0.
Quenching of Fluorescent Proteins (FPs) by Chemicals
The pattern and expression level of FPs in tissue varies due to individual differences, making it difficult to accurately quantify the FP
quenching of chemicals, while an in vitro FP quenching assay is more quantitative and has higher reproducibility. Moreover, clearing
protocols usually involve some kind of chemical fixation (e.g., PFA, glutaraldehyde, or a Clarity-related embedding hydrogel), and the
FP signals can be dramatically affected by the fixation conditions (e.g., the fixative chemicals, concentration, duration, and temperature). For these reasons, we conducted an in vitro FP quenching assay in this study.
To comprehensively measure the effect of CUBIC chemicals on the fluorescence intensity of fluorescent proteins (FPs), we used
EGFP and Sirius (Figures S1D and S1E). Recombinant EGFP at 0.05 mg/ml and Sirius at 0.2 mg/ml were prepared in PBS. Then, 20 mlof the FP solutions were mixed with 130 ml of each 10%(wt/wt) chemical solutions in 96-well plates. After gentle shaking at 37 C overnight, we measured the 507-nm emission for EGFP by 488-nm excitation, and the 427-nm emission for Sirius by 355-nm excitation,with an EnSpire. A series of standard buffers with a pH range of 2-14 was prepared, including 200 mM glycine (pH 2.5, 3, 3.5, 12, 12.5,
and 13; Nacalai Tesque, 17109-35), 100 mM citrate (pH 4, 4.5, 5, 5.5, 6, 6.5, and 7; sodium citrate tribasic dehydrate, Sigma-Aldrich,
S4641), PBS (pH 7.5 and 8), 10 mM borate (pH 8.5, 9, 9.5, and 10; borate reference solution, Horiba), 50 mM NaHCO3 (pH 10.5 and 11;
Nacalai Tesque, 31212-25), and 100 mM NaH2PO4 (pH 11.5; Nacalai Tesque, 31718-15). To further demonstrate the quenching effectof each CUBIC protocol, we also used 16 mg/ml YFP and 15 mg/ml mCherry. The fluorescence intensities of these FPs were
measured as described above, using the appropriate excitation and emission wavelengths (Figures 4F and S3E).
pH evaluation
Because pH can significantly influence the chemical parameters in tissue clearing, we established a high-throughput pH measurement assay using universal pH indicator paper (As One Corp., 1-1254-01). The papers were cut into circles and placed in the bottomof 96-well plates, followed by the application of 2 ml of CUBIC chemicals. The absorbance at 550, 570, 610, and 660 nm wasmeasured with an EnSpire, then the pH was predicted according to a regression model.
To establish the regression model, we prepared a series of standard solutions with a pH range of 0-14: 200 mM KCl (pH 1, 1.5, and2; Nacalai Tesque, 28514-75), 200 mM glycine (pH 2.5, 3, 3.5, 12, 12.5, and 13; Nacalai Tesque, 17109-35), 100 mM citrate (pH 4, 4.5,
5, 5.5, 6, 6.5, and 7; sodium citrate tribasic dehydrate, Sigma-Aldrich, S4641), PBS (pH 7.5 and 8), 10 mM borate (pH 8.5, 9, 9.5, and10; borate reference solution, Horiba), 50 mM NaHCO3 (pH 10.5 and 11; Nacalai Tesque, 31212-25), and 100 mM NaH2PO4 (pH 11.5;
Nacalai Tesque, 31718-15). The pH of the standard solutions was adjusted with hydrochloric acid or sodium hydroxide, andmeasured with a benchtop pH meter (Horiba, LAQUA F-71). The predicted pH values were less accurate in the low (pH < 3) orhigh (pH > 12) pH range by the single linear regression model. Therefore, we introduced three regression models: an acid-range,a neutral-range, and a base-range regression model. The pH was first predicted with the neutral-range regression model. If the predicted pH was higher than 11.1, then the base-range regression model was applied. If the predicted pH was lower than 5.5, then the
acid-range regression model was applied. These thresholds were statistically determined by comparing the mean square error of thepredicted pH and the measured pH.

e4 Cell Reports 24, 2196–2210.e1–e9, August 21, 2018

Concern about Potential False Negatives
Each chemical was used as a 10%(wt/wt) solution in all of the first chemical screenings. We hoped to minimize false negatives in thefirst screening, so we would not miss potential candidates. Although chemicals were likely to have concentration-dependent effects
on each parameter, we could avoid false negatives by taking advantage of the chemical diversity of our chemical library. That is,owing to our comprehensive chemical profiling, we could predict whether the performance of a chemical in the first screening
was under-estimated or not from its chemical structure.
We also considered the possibility of chemicals with more dramatic concentration-dependent behavior. Concentration-dependent
self-aggregation of the solute is one of the most likely causes of dramatic concentration-dependent behavior. In aqueous medium,hydrophobic chemicals precipitate and amphiphilic chemicals form micelles in a concentration-dependent manner. Since precipitates would have no effect on tissue clearing, we focused on the ability of amphiphilic chemicals such as detergents to form
water-soluble micelles. To avoid false negatives in the amphiphilic chemicals, we prepared a wide variety of detergents, as shownin Figure S2B. In this figure, we categorized a series of detergents according to the polarity head group, because each group of detergents forms micelles with similar chemical properties at concentrations above the critical micelle concentration (CMC). In general,
the CMC of a detergent tends to decrease as the hydrophobic part of the detergent molecule grows. Figure S2B indicates that themicelle state exhibited much higher delipidation activity than the monomer state. This means that amphiphilic chemicals with a high
CMC were potential false negatives in our delipidation screening. That is, the delipidation efficiency of these chemicals in vitro may bemuch higher at concentrations above the CMC. However, micelle molecules are liable to have low permeability in real tissue as
described above, and higher solute concentrations often have a quenching effect on fluorescent proteins. Thus, we did not perform
additional experiments on the concentration dependence of such amphiphilic chemicals. In the decoloring screen, micelle molecules
had a similar tendency for low permeability as in real tissue (Figure 2H). For the RI matching and decalcification assays, we did notobserve significant positive outputs for amphiphilic chemicals.
Taken together, our chemical profiling approach applies chemical insights and hypotheses to tissue clearing, and these insights
are helpful for preventing potential false-negatives. Of course, our profiling is not perfect for false-negative detection due to unknown
chemical phenomena. Nevertheless, we are confident that our screening system has sufficient detection sensitivity without redundancy and complexity to be useful for this field at the present time.
Comparison of Organic Solvents and Hydrophilic Chemicals
To examine the effects of organic solvents and hydrophilic chemicals, we included several organic solvents in this study, such as
tetrahydrofuran (THF, CU#1427), dimethyl sulfoxide (DMSO, CU#1510), 2-propanol (CU#1514), methanol (CU#1515), ethanol(CU#1516), benzyl alcohol (CU#1517), benzyl benzoate (CU#1518), acetone (CU#1520), 1,4-dioxane (CU#1521), acetonitrile(CU#1522), ethyl acetate (CU#1523), and N,N-dimethylformamide (DMF, CU#1524). However, we could not perform a comprehensive evaluation of organic solvents due to experimental feasibility. First, almost all organic solvents (e.g., dichloromethane and diphenyl ether), with the exception of several alcohols and aprotic polar solvents, as listed, are immiscible with water. Second, plastic
experimental tools (e.g., microtubes and microplates) are highly labile to many organic solvents (especially at high concentration).
Therefore, we included only the above-listed organic solvents in this study. In our chemical screening, we prepared 10%(wt/wt)
aqueous mixtures of the organic solvents because of the above concerns and to make fair comparisons. However, these aqueous
mixtures displayed quite low performances (see Table S2); thus, organic solvents would exert their clearing performance at a highconcentration or in a fully dehydrated condition.
Chemical Parameters
To obtain a chemical profile for the chemicals identified from the screens, we analyzed a series of chemical parameters (e.g., LogP
and PSA) using the ChemOffice software. The LogP and LogS value of each chemical was calculated by the MOSES descriptor
Community edition (https://www.mn-am.com/services/mosesdescriptors). Solubility parameters (e.g., dD and dP) of each chemical
were calculated based on the Hansen solubility parameters (Hansen, 2007). The stability constant of various chemicals for Ca2+ ion
according to the NIST Standard Reference Database 46 was used (Smith et al., 2003).
Multivariate Linear Regression Analysis
We prepared a series of parameters consisting of three classes of parameters: functional parameters, solubility parameters(calculated by Hansen solubility parameters), and structural and topological parameters (Figure S1F). The lists of parameters
used for delipidation, decolorization, and RI matching are shown in Figure 2C, S2I, and 3D, respectively. After normalizing the scoresand parameters, we performed partial least-squares (PLS) regression using four major PLS components. The variable importance inprojection (VIP) scores of two major PLS components were calculated from the results.
CUBIC Protocols
CUBIC Protocol I for Adult Mouse Organ Samples and Marmoset Brain
For the efficient clearing of mouse organs, we first performed an optional transcardiac perfusion of CUBIC-P reagents. After themouse was sacrificed by an overdose of pentobarbital, 15 mL of PBS was perfused followed by 20 mL of 4% PFA-PBS. After another
15 mL of PBS was perfused, the transcardiac perfusion of 100 mL of CUBIC-P [mixture of 5 wt% 1-methylimidazole (Tokyo
Chemical Industry, M0508), 10 wt% CU#0414 (N-Butyldiethanolamine, Tokyo Chemical Industry, B0725) and 5 wt% Triton X-100

Cell Reports 24, 2196–2210.e1–e9, August 21, 2018 e5

(Nacalai Tesque, 12967-45)] from the left ventricle for 10 minutes was performed. After perfusion, the organs were dissected anddirectly immersed in CUBIC-L (mixture of 10 wt% CU#0414 and 10 wt% Triton X-100). The organs in CUBIC-L were incubatedwith shaking at 37 C for 3-7 days. The immersion period was based on the sample size. If the immersion period was longer than
4 days, the CUBIC-L was refreshed at least once. After delipidation, the organs were washed in PBS with gentle shaking at room
temperature overnight. If required, the organs were stained with RedDot 2 (1:100, Biotium Inc, #40061) or propidium iodide(10 mg/ml, PI; Life Technologies, P21493) in 0.1 M phosphate buffer (PB) containing 0.5 M NaCl with shaking at room temperaturefor 3-5 days. The organs were then immersed in 1:1 water-diluted CUBIC-RA [recommended for FP-expressing organs, a mixtureof 45 wt% CU#0640 (antipyrine, Tokyo Chemical Industry, D1876) and 30 wt% CU#1283 (N-methylnicotinamide, Tokyo
Chemical Industry, M0374)] or in 1:1 water-diluted CUBIC-R [for other organs, mixture of 45 wt% CU#0640 and 30 wt%
CU#0855 (nicotinamide, Tokyo Chemical Industry, N0078)] with gentle shaking at room temperature for 1 day. The organs werethen immersed in CUBIC-RA or CUBIC-R with gentle shaking at room temperature for 1-2 days. The pH of CUBIC-R and CUBICRA can be optionally adjusted to approximately 8-9 with CU#0414. If required, the cleared organs were embedded in CUBIC-agarosegel [2% agarose (Nacalai Tesque, 01163-76) dissolved in CUBIC-RA or CUBIC-R].
The clearing and staining of adult marmoset brain was performed as follows. The marmoset was sacrificed by an overdose ofpentobarbital (> 100 mg/kg, Nacalai Tesque, 02095-04), then transcardially perfused with 200 mL of cold PBS (pH 7.4) and
300 mL of 4% PFA-PBS. The brain was dissected, then post-fixed with 4% PFA-PBS at 4 C overnight. After washing in PBS, thebrain was immersed in CUBIC-L with shaking at 37 C for a total of 5 weeks, and then at 45 C for 1 week. The CUBIC-L was refreshed
every 1-2 days throughout the delipidation. After washing in PBS, the brain was immersed in 10 mg/ml propidium iodide (PI) in PB
containing 1.5 M NaCl with shaking at 37 C for a total of 19 days. The staining solution was refreshed after day 12. After washingin PBS, the brain was immersed in 10 wt% imidazole (Tokyo Chemical Industry, I0001) aqueous solution with gentle shaking atroom temperature for 3 days. The imidazole solution was refreshed every day. The brain was then immersed in 1:1 water-diluted
CUBIC-R (pH adjusted to approximately 8-9 with CU#0414) with gentle shaking at room temperature for 3-4 days. Finally, the brain
was immersed in CUBIC-R (pH adjusted to approximately 8-9 with CU#0414) with gentle shaking at room temperature for 7 days. The
CUBIC-R was refreshed after day 4.
CUBIC Protocol II for Mouse Body or Tissues That Include BonePFA-fixed samples were immersed in CUBIC-L with shaking at 37 C for 3-7 days, then washed with PBS at room temperature overnight. The immersion period was based on the sample size. If the immersion period was longer than 4 days, the CUBIC-L was refreshed at least once. The samples were immersed in CUBIC-B [10 wt% EDTA (Nacalai Tesque, 15105-35) and 15 wt% imidazole]with shaking at 37 C for 5-7 days, then washed with PBS at room temperature overnight. During decalcification, the CUBIC-B wasrefreshed at least once. The samples were immersed in CUBIC-L with shaking at 37 C for 2-4 days, then washed with PBS at room
temperature overnight. If required, the organs were stained with 10 mg/ml PI in 0.1 M PB containing 0.5 M NaCl with shaking at 37 C
for 5-7 days. The samples were immersed in 1:1 diluted CUBIC-RA or CUBIC-R with gentle shaking at room temperature for 1 day.
The samples were then immersed in CUBIC-RA or CUBIC-R with gentle shaking at room temperature for 1-2 days. If required, thecleared samples were embedded in CUBIC-agarose gel.
CUBIC Protocol III for Aggressive Tissue Clearing
Human tissues were stored in formalin at 4 C until use. The dissected blocks were washed with PBS overnight prior to clearing. The
tissue blocks were immersed in CUBIC-HL [mixture of 10 wt% CU#0070 (1,3-bis(aminomethyl)cyclohexane, Tokyo Chemical Industry, B1005) and 10 wt% CU#0631 (sodium dodecylbenzenesulfonate, Tokyo Chemical Industry, D0990), whose pH was adjusted to12.0 by p-toluenesulfonic acid (Tokyo Chemical Industry, T0267)] with shaking at 37 C (for human brain or kidney) or 45 C (for humanheart, liver, lung, or spleen) for 1-2 weeks, then washed with PBS at room temperature. The immersion period was based on the sample size. Since the apparent opacity inside the sample gradually disappears as delipidation progresses, the delipidation period could
be easily judged by the appearance of the sample soaked in CUBIC-HL. Note that prolonged delipidation may damage the sample
morphology. If delipidation needs to be prolonged beyond 2 weeks, we recommend that the subsequent delipidation be done at alower temperature or in CUBIC-L. During delipidation, the CUBIC-HL should be refreshed at least once. If required, the organs were
stained with 10 mg/ml PI in 0.1 M PB containing 0.5 M NaCl with shaking at 37 C for 5-7 days. After washing in PBS, the samples were
immersed in 1:1 diluted CUBIC-R with gentle shaking at room temperature for 1 day. The samples were then immersed in CUBIC-R
with gentle shaking at room temperature for 1-2 days. If required, the cleared organs were embedded in CUBIC-agarose gel.
CUBIC Protocol IV for Large Blocks of Human Brain Tissue
Human brain blocks were stored in phosphate-buffered formalin at 4 C until use. The dissected blocks (around 1 cm3) were washedwith PBS overnight prior to clearing. The brain blocks were immersed in CUBIC-L with shaking at 45 C for 1-2 weeks, then washedwith PBS at room temperature. The immersion period was based on the volume of white matter. During delipidation, the CUBIC-L
was refreshed at least once. After washing in PBS, the samples were immersed in 1:1 diluted CUBIC-R with gentle shaking atroom temperature for 1 day. The samples were then immersed in CUBIC-R with gentle shaking at room temperature for 1-2 days.
We noted that the autofluorescence of cerebral cells decreased as the delipidation period increased. We recommend that delipidation be finished by approximately one week to preserve sufficient autofluorescent signals. If required, the cleared samples wereembedded in CUBIC-agarose gel.

e6 Cell Reports 24, 2196–2210.e1–e9, August 21, 2018

Deformation Analysis of Cleared Brains
Brains after PBS, CUBIC protocol I without pH adjustment, and CUBIC protocol I with pH adjustment to 8-9 were subjected to deformation analysis. Two brains were prepared for each procedure, and then projection images were captured by a ChemiDoc XRS Plus
(Bio-Rad). The brains were manually segmented, then registered to a standard post-fixed brain. The average of the two obtained
deformation fields was visualized and mapped onto a representative projection image of the brain.
Long-term Tissue Preservation
To preserve the cleared tissue for a long time, we recommend storing it in O.C.T. compound at 80 C after washing it with PBS as
described in our previous paper (Susaki et al., 2015). In this storage medium, FP signals can be preserved for several months to years.
Immunostaining
For Figure 5D, an 8-week-old ICR mouse brain was washed with PBS after 4 days of delipidation with CUBIC-HL. The brain was
immersed in 40% sucrose for 1 day, embedded in O.C.T. compound (Sakura Finetek), and sectioned with a cryostat (CM3050S,
Leica). The brain sections were immersed in 200 ml of PBST with 1% anti-GFAP-Cy3 (C9205, Sigma-Aldrich) or 0.1% anti-NeuN(MAB377, Millipore) antibodies. For NeuN detection, a secondary antibody (A21203, Thermo Fisher Scientific) was applied. For
Figure 5F, the human brain was washed with PBS overnight and sliced after 9 days of delipidation with CUBIC-L (CUBIC protocol
IV). The slice was placed into 400 ml of immunostaining buffer (mixture of PBS, 0.5% Triton X-100, 0.25% casein, and 0.01%
NaN3) containing 1:20 diluted rhodamine-conjugated lectin (RL-1062, Vector Laboratories) or 1:10 diluted Alexa647-conjugated
a-SMA (alpha smooth muscle actin) antibody (IC1420R, Novus Biologicals) for 5 days at room temperature with gentle shaking.
For the control experiment, the immunostaining buffer without antibody was applied. After washing the samples with PBS for several
hours, the samples were immersed into CUBIC-R according to CUBIC protocol IV.
Microscopy
Light-sheet Fluorescence Microscopy (LSFM)
Macroscopic whole-body or organ images were acquired with two custom-built LSF microscopes (MVX10-LS, developed by
Olympus). Images were captured using a 0.63 3 objective lens [numerical aperture (NA) = 0.15, working distance = 87 mm] withdigital zoom from 1 3 to 6.3 3 . The first LSF microscope was equipped with lasers emitting at 488 nm, 532 nm, 590 nm, and639 nm. The second was equipped with lasers emitting at 488 nm, 532 nm, 594 nm, and 637 nm. When the stage was moved tothe axial direction, the detection objective lens was synchronically moved to the axial direction to avoid defocusing. The RI-matchedsample was immersed in an oil mixture (RI = 1.525) composed of silicon oil HIVAC-F4 (RI = 1.555, Shin-Etsu Chemical) and mineral oil(RI = 1.467, M8410, Sigma-Aldrich) during image acquisition.
A customized light-sheet microscope was used for the high-resolution-imaging of the organs of adult mice in Figure S5. Fluorescence excited by a 488 nm CW laser (Omicron, SOLE-3) was captured with a sCMOS censor (Andor, Neo) using a 10 3 objective lens
(XLPLN10XSVMP, NA = 0.6, working distance = 8 mm, Olympus). The details of the light-sheet microscope are described in our previous study (Murakami et al., 2018). To obtain complete views, the regions of interest were divided into 7 3 4 tiles (for the left medial
lobe of the mouse liver), or 6 3 8 tiles (for the left lung of the mouse). Images were collected by scanning the sample in the z-direction,with a step size of 5 mm.
Confocal Microscopy
An upright confocal microscope (FV1200, Olympus) was used for microscopic 3D imaging. The microscope was equipped with a
25 3 objective lens (XLSLPLN25XGMP, NA = 1.0, WD = 8.0 mm, Olympus). A 473 nm laser was used for image acquisition. During
image acquisition, RI-matched samples were immersed in a mixture of silicon oil (RI = 1.525) as described above.
In Vivo Bioluminescence Imaging
Mice were anesthetized with avertin, and D-luciferin potassium salt (Promega) was injected intraperitoneally. Ten to fifteen minutes
after injection, the luciferase activity was measured using a NightOWL II LB983 (Berthold Technologies GmbH & Co. KG, Bad
Wildbad, Germany).
Histological Examination
After CUBIC-based clearing, biological samples were washed with PBS, embedded in paraffin, and subjected to HE staining as previously described (Hoshino et al., 2015) and to a series of immunostainings (Figures 5E and S6I).
Image Analysis
The Analysis of Cellular Nuclei
The cellular nuclei in organs were comprehensively detected by our image-processing pipeline (Murakami et al., 2018). The process
can be divided into two steps: 2D detection of cells and 3D unification of multiply detected cells. The 2D detection of cells was performed by detecting local maxima after applying a mean filter to raw images. Then, 3D local maxima were detected after a 3D mean
filter was applied to the 2D-detected cells. The detected cellular nuclei (‘‘points’’ or ‘‘point clouds’’) were further analyzed for the segmentation of tubular structures. To this end, we used the alpha shape function with the specific alpha radius by using MATLAB. The

Cell Reports 24, 2196–2210.e1–e9, August 21, 2018 e7alpha radius was set at 30 mm for liver and 50 mm for lung. After forming the alpha shape, the surface points composing the alphashape were used. Since the surface points also included cells located in the outer layer of the organ, we removed those points byapplying a manually generated mask. The points are shown in red or magenta in Figures S5J and S5L.
Multi-directional Image Fusion
For Figures 6, 7, S5 and S6, multi-directional image fusion was performed after the acquisition of images from multiple directions
unless noted. All the samples were embedded in agarose, and mounted in the LSF microscope. We first performed the imaging
with both optical arms of the LSF microscope (i.e., left and right) from one direction (top-to-bottom). The samples were then flipped180 manually, followed by image acquisition (bottom-to-top) with both optical arms. The imaging sequences were then repeated for
multi-color imaging. These processes produced four stacks per channel: a right-side-illuminated top-bottom stack (TB-R), left-sideilluminated top-bottom stack (TB-L), right-side-illuminated bottom-top stack (BT-R), and left-side-illuminated bottom-top stack(BT-L). For the registration process, we set the TB-L stack as the fixed images, then three other stacks were registered to TB-L stack.
Prior to registration, the stacks were reduced to less than 4 GB by cropping marginal regions, or by downsampling them into isotropic
voxel sizes to reduce the computational cost. Then, TB-R and BT-L were directly registered onto TB-L with an affine transformation
using ANTs (Avants et al., 2011). Because the direct registration of BT-R to TB-L often ends in misalignment due to the intensity gapsof these two stacks, we performed indirect registration by registering BT-R onto BT-L, followed by the application of the affine matrix
obtained from the registration of BT-L onto TB-L. These mutually registered images were fused using the Fiji software, Multiview
Reconstruction (Schindelin et al., 2012).
Segmentation
For segmentation of the mouse kidney glomeruli in Figure S5, we used HLoG (Hessian based Difference of Gaussian) developed byZhang et al. (Zhang et al., 2015) with minor modifications. In the original paper, renal glomeruli were extracted by unsupervised clustering. Because this method was originally optimized for MRI-scanned images and therefore could not show high accuracy, we introduced a manual threshold for the smallest eigenvalue to achieve higher accuracy.
For the segmentations of metastatic cancer in Figures 6H and 7H, the thresholds were applied based on absolute intensity. Manual
segmentation was performed to segment the human kidney glomeruli (Figures 6J and 6K).
Virtual Multiplex Fluorescence Imaging and Analysis of Whole Brain
Fluorescently labeled mouse brains were stained with a far-red nuclear stain, RedDot 2, prior to the RI-matching process. The
cleared brains were embedded in agarose gel as mentioned above. Brain images were captured using a 0.63 3 objective lenswith a 1.25 3 zoom. The brain structures were visualized by the RedDot 2 fluorescent signal at 637-nm excitation. The sectional images were taken at 7.5-mm steps from both the right and left sides with a fixed focal position of the light sheet. The FP signal was thenvisualized using a laser of appropriate wavelength with sequential shifting of the focal position of the light sheet. The thinnest focal
point of the LSF microscope was horizontally scanned 6 times per plane to reduce defocus derived from the Gaussian shape of thebeam. The scanning with focus shifts was performed from both sides of the illumination arm as described previously (Gao, 2015;
Santi et al., 2009).
The images of the identical horizontal positions (2 images for RedDot 2 signal, or 12 images for FP signals) were fused using thecontent-based fusion algorithm proposed by Preibisch et al. (Preibisch et al., 2009). To register individual brains, the fused volumetricnucleus images were downsampled 5:1 in one direction. These downsampled images were registered using the ANTs software in thesymmetric normalization (SyN) manner. The obtained transformation information was applied to the original volumetric FP images,
thus achieving registration without losing the resolution. To apply the transformation information to large data, we used our customized MATLAB code, because direct application of the transformation with the default algorithm in ANTs requires a huge amount ofmemory, which makes it practically impossible. The overall process (fusion, registration, application of transformation information)
was fully automated.
Volume Rendering and Image Visualization
To generate the tubular structures shown in Figures S5K and S5M, visual mapping of the alpha shape was implemented by MATLAB.
For other volume rendered images, Imaris software (version 8.1.2, Bitplane) was used. The section images were generated by Imarisor Fiji software.
QUANTIFICATION AND STATISTICAL ANALYSIS
Protein Quantification
Human brain tissue was weighed and then immersed in PBS, ScaleCUBIC-1, CUBIC-L, or CUBIC-HL. After 4 days of delipidation at
45 C with gentle shaking, the brain samples were washed with PBS at room temperature for half a day with gentle shaking. The
washed samples were then subjected to protein quantification. To quantify the residual protein content, the samples were thoroughly
crushed, then sonicated in 1% Triton X-100 in PBS to obtain a homogenized suspension. After adjusting for the total weight of eachsample, the protein was quantified with the Microplate BCA Protein Assay Kit-Reducing Agent Compatible (ThermoScientific,#23252) according to the instruction manual, with OD570. The absolute amount of protein was determined by fitting the OD570 value
to a standard curve generated by a serially diluted pure Albumin standard.

e8 Cell Reports 24, 2196–2210.e1–e9, August 21, 2018

Quantification of Image Quality
We used discrete cosine transformation Shannon entropy (DCTS) (Royer et al., 2016) to quantify the image quality. The DCTS calculation was performed for every plane image in Figure S5B, and for every 10 plane images in Figures S5H and S5I.
Statistical Analysis
Statistical analyses were performed by R version 3.1.0. and Origin 2016 (OriginLab, Northampton, MA, USA).
For the chemical profiling involving functional analysis and pH dependency (Figures 2A, 2B, 2G, 3A, 3F, S1E, and S2H), normality
was evaluated by the Kolmogorov-Smirnov test with a significance level of 0.05. These analyses showed that not all members of thegroup were normal. The chemical functional group and pH dependency were compared to the control (alcohol group and pH 7-8,
respectively) by the Steel test.
For the other chemical analyses, normality was evaluated by the Kolmogorov-Smirnov test with a significance level of 0.05. When
all of the members of the group were normal, the homogeneous variance for each group was evaluated by Bartlett’s test with a significance level of 0.05. When all of the groups were normal distributions with equal variance, the Turkey-Kramer test was used, when
the groups were normal distributions without equal variance, the Games-Howell test was used, and otherwise the Steel-Dwass test
was applied. In this study, p < 0.05 was considered as significant (*p < 0.05, **p < 0.01, ***p < 0.001, and n.s. for not significant
evaluations).
DATA AND SOFTWARE AVAILABILITY
Our customized MATLAB codes used for the virtual multiplex fluorescence imaging and analysis are publicly available at https://github.com/DSP-sleep/Landscape_pipeline. The other computer codes used in this study are available upon request.

Cell Reports 24, 2196–2210.e1–e9, August 21, 2018 e9

Cell Reports, Volume 24

Supplemental Information

Chemical Landscape for Tissue Clearing
Based on Hydrophilic Reagents
Kazuki Tainaka, Tatsuya C. Murakami, Etsuo A. Susaki, Chika Shimizu, Rie Saito, Kei
Takahashi, Akiko Hayashi-Takagi, Hiroshi Sekiya, Yasunobu Arima, Satoshi
Nojima, Masako Ikemura, Tetsuo Ushiku, Yoshihiro Shimizu, Masaaki Murakami, Kenji F.
Tanaka, Masamitsu Iino, Haruo Kasai, Toshikuni Sasaoka, Kazuto Kobayashi, Kohei
Miyazono, Eiichi Morii, Tadashi Isa, Masashi Fukayama, Akiyoshi Kakita, and Hiroki R.
Ueda

yellow

pH
0
1.1
1.6
2.6
4.0
4.5
5.0
5.5
5.9
6.4
7.0
7.4
8.0
8.5
9.1
9.5
9.9
10.5
11.1
13.1
13.3
14.0

magenta cyan

Relative absorbance

1.2
1.0
0.8
0.6
0.4
0.2
0
350

Relative FluorescenceD400

450 500 550 600
Wavelength (nm)E

Buffer solution
1.4

1.0
0.8
0.6
0.4

EGFP
Sirius

0
2

4

6

8

Acid

14

NeutralC Original pH indicators

Base

Acid-range regression
Neutral-range regression
Base-range regression

12
10
8

Predicted pH value

6
4
2
0

700

5.5
0

2

4

11.1
1

6

8

10

12

0

14

pH

14

Measured pH

CUBIC chemical solution

pH 0-1pH 1-2pH 2-3pH 3-4pH 4-5pH 5-6pH 6-7pH 7-8pH 8-9pH 9-10
pH 10-11
pH 11-12
pH 12-13
pH 13-14

1.2

0.2

650

B

Predicted pH

A 1.4

CUBIC chemical solution

11
60
67
41
52

***
***
***
***
***

11
55
65

***

EGFP

***
***

Sirius

38

***

50
187
181
76
51
45

**

187
180

***
***

76
51
45
38
40
104
51

***
***

10 12 14

0.0

0.5

***
***

1.0

0.0

Relative fluorescence

pH

F

38
40
104
51

***
***

0.5

1.0

2

C-Csum = 8

Relative fluorescence

G
Class

Parameter

Abbreviation
1

Functional parameter

Solubility parameter

Structural andtopological parameter

Alkyl chain number

C-CsumAlkyl chain length between polar groups

C-Clength

Amine group number

Nsum

Hydroxyl group number

OHsum

Molecular networks LogP

LogP

Molecular networks LogS

LogS

HSP dispersion energydD

HSP polar energydP

HSP hydrogen bonding energydH

HSP hydrogen bonding donor energydHD

HSP hydrogen bonding acceptor energydHA

pH of 10 wt% solution

pH

Molecular weight

MWt

Molecular volume

MVol

Molecular density

MDens

Polar surface area

PSA

Connolly solvent accessible surface area (Å²)

CSASA

Connolly molecular surface area (Å²)

CMSA

Connolly solvent excluded volume (Å³)

CSEV

Ovality

Ovality

Topological diameter

Topo

Balaban index

BindexMolecular topological index

MTindex

Figure S1

H2N

1

3

2
1

8

5

4

2

3

7

6

4

5

N

1
1

3
2

2

C-Clength = 5
Nsum = 2
OHsum = 0

CU#0070

1

NH2

1

OH

C-Csum = 3
C-Clength = 2
Nsum = 1
OHsum = 1

CU#0589

1

1

N
H
CU#0772

2

C-Csum = 2
C-Clength = 0
Nsum = 1
OHsum = 0Figure S1. High-throughput pH and Fluorescent Protein (FP) Signal Quenching
Estimation Assay, Related to Figures 1-3.
(A) Absorbance spectra of pH indicator papers after their immersion in pH standard
solutions. To determine the pH of the chemical solutions, we measured theabsorbance at 550, 570, 610, and 660 nm of pH indicators soaked in each chemicalsolution. Data are mean ± SD (n = 3). (B) Relationship between predicted pH andmeasured pH with three regression models. The pH of chemicals was first evaluatedwith the neutral-range regression model, then regressed again with an acid-range orbase-range regression model for more accuracy. (C) Image of the pH indicators
soaked in CUBIC chemical solutions (top) and predicted pH (bottom). (D) pH
dependency of the relative intensity of each FP in buffer solutions. Various pH
conditions were examined. Data are mean ± SD (n = 2). (E) Dependency of the relative
fluorescence intensity of EGFP (left) and Sirius (right) on the pH of CUBIC chemicalsolutions compared with pH 7-8. Box and whisker plots show the 25th–75th percentile(boxes), 10th–90th percentile (whiskers), median (vertical lines), and mean (cross).
The numbers next to the plots are the numbers of the chemicals. (F) Classes andabbreviations of parameters used in the chemical analysis in this study. (G) Examples
of counting functional groups. **p < 0.01, and ***p < 0.001, see also STAR Methods
for details.

A

B
2.0

C
1.0

1.2

0.8

1.0

n.s., p = 0.11

***

***

Lipid solubility score

Lipid solubility score

1.0

0.0

1.0

2.0

0.6
Xanthate
Pyridinium
Ammonium
Carboxylate
Sulfonate
Polyether
Cholesterol

0.4

0.2

0.0

Lipid solubility score

***
**

0.8
0.6
0.4
0.2
0.0

Detergentn = 49D

Non-detergent
and salt-free chemicaln = 6651Salt
n = 339

2

3

4

5

6

7

Cationic det.
n=7

8LogP of hydrophobic group in detergent

E

F

100

Anionic det.
n = 12

10.0

***

2.0

1.0

0.5

Plot
Linear fit
95% Confidence band
95% Prediction band

0.0

R2 = 0.7685
p 0.001

Decoloring score

Transmission (%)

1.5

60

40
Plot
Linear fit
95% Confidence band
95% Prediction band

20

R2 = 0.4670
p 0.001

***

***

8.0

80

Cholesterol content (%)

Non-ionic det.
n=5

6.0

4.0

2.0

0.0

0
45

50

55

60

65

70

75

80

-0.4

85

0.0

0.4

0.8

1.2

1.6

2.0

H

G

3.0
2.0
1.0
0.0
Cationic det.
n=7Anionic det.
n = 12

***
1.0

2.0

3.0

4.0

Decoloring Score

L

K
6.0

Aliphatic Aromatic

1.4

***
***

4.0

Aromatic amide

1.2

OD630after/OD630before

5.0

3.0
2.0
1.0

O
OH

N
H

1.0

HEBZA

0.8

M

0.6
0.4
0.2

0.0

CU#0070
C-Clength = 5

OH

CU#0589
C-Clength = 2

CU#0772
C-Clength = 0+ 10 wt% CU#0640

Figure S2

HEBZA

CU#1283

CU#0855

Histodenz

CU#1140

N
H

CU#0307

N

CU#1525

NH2

C-Clength = 0
n = 17

CU#0299

H2N

C-Clength = 1-2
n = 64PBS

0.0
C-Clength 3
n = 31

1.0

0.0

***
0.0

1.5

0.5

*
***

Non-ionic det.
n=5

JDecoloring score

2.0

*
**

10 wt% CU#0640

Decoloring score

4.0

Component 1
Component 2***

VIP score

***

5.0

Salt
n = 290

2.5

pH 0-1 (n = 8)
pH 1-2 (n = 41)
pH 2-3 (n = 35)
pH 3-4 (n = 27)
pH 4-5 (n = 28)
pH 5-6 (n = 65)
pH 6-7 (n = 111)
pH 7-8 (n = 47)
pH 8-9 (n = 38)
pH 9-10 (n = 26)
pH 10-11 (n = 22)
pH 11-12 (n = 26)
pH 12-13 (n = 60)
pH 13-14 (n = 40)***

Non-detergent
and salt-free chemicaln = 620

I

7.0
6.0

Detergentn = 41

LogP

Relative transmittance (%)

OD600 of
HAp suspension (a.u.)

40

2.0
1.0
0.0
0

10

20

30

Concentration of HAp (mg/ml)
Relative decalcification score

5 35

C-CsumC-Clength
Nsum
OHsum
LogP
LogS
dD
dP
dH
dHD
dHA
pH
MWt
MVol
MDens
PSA
CSASA
CMSA
CSEV
OvalityTopo
Bindex
MTindex

0

1.5

1.0

0.5

0.0

4
0
5
0
7
7
9
6
14
=
13 = 3 = 8 = 6 = 4 = 8 n =
=
=
,
n
,n
n
n
n
n
n
ed e, n fur, en, tic, en, en, rate us,
t
l
t
a
la
g
g Bo
g
or
re xylaSu itro rom alo
xy
ph
O
H
N
A
TA rbo
os
h
D
P
E
Ca

Figure S2. Comprehensive CUBIC Chemical Profiling for Delipidation,
Decoloring, RI match, and Decalcification, Related to Figures 2-3.
(A) CUBIC chemicals were divided into three groups (detergents, non-detergent andsalt-free chemicals, and salts) and their lipid solubility scores were compared. (B)
Relationship between the LogP of the hydrophobic group in a detergent and the lipid
solubility score. A series of detergents bearing seven different kinds of polar headgroup (xyanthate, pyridinium, ammonium, carboxylate, sulfonate, polyether, andcholesterol) was investigated. (C) Lipid solubility scores of cationic, anionic, and nonionic detergents. Anionic and non-ionic detergents had higher lipid solubility scoresthan cationic detergents. (D) Scatter plot of the relative transmittance and cholesterol
content of brain hemispheres of adult mice treated with high lipid-solubilizing
chemicals. Data are mean ± SD (n = 2). (E) Scatter plot of the relative transmittanceand LogP of the amine groups. Data are mean ± SD (n = 2). (F) CUBIC chemicals
were divided into three groups (detergents, non-detergent and salt-free chemicals, andsalts) and their decoloring scores were compared. (G) Decoloring scores of cationic,
anionic, and non-ionic detergents. Cationic detergents showed better decoloring ability
than anionic or non-ionic detergents. (H) Dependency of decoloring scores on pH
compared with pH 7-8. (I) VIP scores of two major components for each varianceamong decoloring amines that had a high delipidaiton efficiency. (J) The amines weredivided into three groups: multipolar amines with C-Clength ≥ 3, multipolar amines with
C-Clength1-2, and single amines (C-Clength = 0). Multipolar amines with C-Clength ≥ 3
exhibited a marked decoloring ability compared to the other amines. (K) The variation
in OD630 of human brain samples treated with 10/30 wt% CU#0640/RI candidates
were compared. The 8 chemicals were divided into three groups: aliphatic compounds,
aromatic

compounds,

andaromatic

amides.

“HEBZA”

indicates

N-(2-hydroxyethyl)benzamide. We used 10 wt% CU#0640-based cocktails due to the low
water-solubility of HEBZA. (L) Quantitativeness of the HAp-based screening system
in Figure 1E. OD600 of the HAp suspension increased linearly as the HAp

concentration was raised from 0 to 10 mg/ml. Data are mean ± SD (n = 4). (M) Relative
decalcification scores of chemical cocktails containing various kinds of acids listed in
the CUBIC chemical library. All of the acids were neutralized with CU#0414 (pH 7-8).
Over 200 acids bearing various kinds of functional groups were tested. In this analysis,
we compared the tested acids except for EDTA-related chemicals according to theirfunctional group. Box and whisker plots show 25th–75th percentile (boxes), 10th–90th
percentile (whiskers), median (horizontal lines), and mean (cross). *p < 0.05, **p <0.01, and ***p < 0.001, see also STAR Methods for details.

20
C w
U t%
#0
41
4

C
C U#0
U
#1 41
51 4
1

C
C U#0
U
#1 41
46 4
0

C
C U#0
U
#1 41
45 4
5

C
C U#0
U
#0 41
86 4
5

C
C U#0
U
#0 41
63 4
1

B

45 wt%
CU#0640+30 wt%
CU#1283

Mouse
brain

A

45 wt%
CU#0640+30 wt%
CU#0855

O

Mouse
lung

Mouse
liver

O
N
H

NH2
N

Mouse
spleen

Mouse
heart

Mouse
Pancrea

20
C w
U t%
#0
41
4

C
C U#0
U
#1 41
51 4
1

C
C U#0
U
#1 41
46 4
0

C
C U#0
U
#1 41
45 4
5

D

Mouse
arm

Magnified
view

PBS

46 wt%
CU#0640+30 wt%
CU#1283

Mouse
head

Magnified
view

Human
Brain

C
C U#0
U
#0 41
63 4
1

C
C U#0
U
#0 41
86 4
5

Mouse
lung

N

Mouse
kidney

C

E

3.0

YFP

PBS

CUBIC-L

CUBIC-R

CUBIC-P

CUBIC-B

CUBIC-RA

F

1.0
0
1.5

G

Protocol Name

0.5

Protocol5 - 14 days, 37°C / RT-37°C

CUBIC
Protocol 0

day 4

day 2

day 0

ScaleCUBIC-1 / 1A

2 - 3 days
PBS

ScaleCUBIC-2
50% 100%

Perfusion of
50% ScaleCUBIC-1 (optional)I

CUBIC protocol III
Human
liver

After

Before

20
C wt
U
#0 %
07
0

day 4

day 2

day 0

day 4

day 14

C
C U#0
U
#1 07
15 0
1

day 2

day 0

day 4

day 14C
Tr U#0
ito 0
n 70
X10
0

day 2

day 0

day 0
day 2

C
C U#0
U
#1 07
46 0
0

day 4

day 14

day 2

C
C U#0
U
#0 07
86 0
5

day 0
Human brain
Human kidney

46 wt%
CU#0640+30 wt%
Histodenz

EGFP
1.0

0

H

46 wt%
CU#0855+
30 wt%
CU#1283

Mouse
lung

Relative fluorescence intensity

2.0

46 wt%
Histodenz+
30 wt%
CU#1283

Figure S3

Human
spleen

Human
brain

Human
lung

Figure S3. Combinatorial Screening of CUBIC Chemicals for Rapid and Scalable
Tissue-Clearing CUBIC Protocols, Related to Figure 4.
(A) Combinatorial screening of delipidation chemicals compatible with FP (related to
Figure 4A). Mouse brain hemispheres, kidneys, and human brain blocks weredelipidated with individual chemical cocktails, and then immersed in CUBIC-R. (B) RI
matching performance of the CUBIC-RA cocktail of CU#0640 and CU#1283, and the
CUBIC-R cocktail of CU#0640 and CU#0855. Adult (8-week-old) mouse lungs weredelipidated and then treated with CUBIC-RA or CUBIC-R. Similar clearing
performances were observed. (C) Clearing performance of CUBIC protocol I forvarious adult (6-week-old) mouse organs. (D) Clearing performance of CUBIC protocol
II for adult (8-week-old) mouse arm and head. (E) Time course of fluorescent signalsof EGFP and YFP with each CUBIC cocktail. Data are mean ± SD (n = 3). (F)Combinatorial screening of RI-matching reagents. Delipidated adult (8-week-old)mouse lungs were immersed in the highly concentrated chemical cocktails. (G)Original CUBIC protocol, named as CUBIC protocol 0 hereafter. (H) Combinatorial
screening of CU#0070-based delipidation reagents (related to Figure 4C). Humantissue blocks were delipidated with individual chemical cocktails, and then immersed
in CUBIC-R. (I) Clearing performance of CUBIC protocol III for large blocks of various
kinds of human tissues. Scale bars indicate 4 mm.

0.0

0.4

0.2

0.0

***
***

0.4

0.2

Figure S4

F
2.4

2.0

1.6

0.0

***
***

0.1

n.s.

100

G

2.4
8000

CUBIC-HL
n=6

CUBIC-Ln=60
ScaleCUBIC-1
n=6

20

PBS
n=6

40

Autofluorescence Intensity

PBS
n = 1860

Protein (mg/ml)

120

Protocol IV

0.6
1.6

Protocol I

**

Day 0

Protocol III

2.0

160

3DISCO*****

Protocol IV

2.0

***

D

Protocol III

2.4

***

3DISCO

2.8

500 nm transmission

PBS

3.2

OD630

After
800

Protocol IV

3DISCO
700
Protocol 0

100

Protocol III

Before600
Wavelength (nm)PACT

3DISCO
PACT
Protocol 0
Protocol I

3DISCO

60

3DISCO

80

Transmission (%)

B 100
C

PBS

E
500

OD570

20
Protocol I

Protocol IV

40

Protocol III

400
Protocol 0

3DISCO

0
PACT

PBS

8-week-old ICR
mouse brain
3DICSO

OD490

Transmission (%)

A

n.s.
Day 4

80
140
n.s.

*

80

60

40

20
0

***
***

6000

4000

2000

0

Figure S4. Comparison among Tissue Clearing Protocols, Related to Figure 5.
(A-C) Comparison of clearing protocols. 3DISCO, PACT, CUBIC protocol 0, and
CUBIC protocol I were compared. Adult (8-week-old) mice were used. Whitebackground transmission images (A) and quantified transmission (B and C) are shown.
Data are mean ± SD (n = 3). Transmission spectrum (B) and transmission of the 500nm wavelength in B (C) are shown. (D) Quantification of the protein content of humanbrain tissue after delipidation. Mean are shown. (E) Clearing performance of 3DISCO
for human brain block. Scale bars indicate 4 mm. (F) Absorbance at 490 nm, 570 nm,
and 630 nm after the clearing protocols for human brain samples (n = 6). (G)Autofluorescence intensity of human brain samples after the clearing protocols (n = 6).
Mean are shown. Scale bars indicate 4 mm. **p < 0.01 and ***p < 0.001, see alsoSTAR Methods for details.

A

Sample preparation
Perfusion &Delipidation
dissection
Mouse kidney

N

Image acquisition and analysisGelembedding

Clearing

Staining

N

O

L

N
HO

OH

+
N I

OH
O
n

n

OH

+
N I

OH

-

O

N

NH2

-

NH2

O
NH2

N

NH2
HO

N
H

B

0.005

region 1

DCTS

OH

O

O O
O

HO

O

OH

flip

n

region 1

0

HLoG

D

E

Segmented glomerular

Segmented glomerular

400 m

region 2

region 2

400 m

F1000 m1000 m

400 m

G

Image acquisition and analysis
Image acquisition

Cell detection

1000 m1000 m

Mouse
heart

Mouse
kidney1000 mMouseleft lung

Mouse
liver, left medial lobe

Point cloud analysis

LSFM

Detected cell coordinate

Mean filter
application
DM1

H Mouse liver, left medial lobe
Region 1
Region 3 Region 2

z = 2025 m
0.0057

Region 1

xy view

Region 2
Region 32550 m3602.5 m

Local maxima
detection

Extracted structure0 0 0 1 0 0 0
0 1 1 1 1 1 0
0 1 1 1 1 1 0
1 1 1 1 1 1 1
0 1 1 1 1 1 0
0 1 1 1 1 1 0
0 0 0 1 0 0 01000 mtheta = 0°2550 m
0.00523075 m
0.0045

theta = 180°3602.5 m 4127.5 m
0.00960.01050.00480.00530.00691000 m

I Mouse left lungxy view

20 m

yz view

0.01040.0079

Region 2

Region 3

Region 1

0.0062

J Mouse liver, left medial lobe

1000 mR

yz view

400 m

3075 m4127.5 m

Gluomerularsegmentation

Segmentation

C

DCTS

xy view

2025 mL

0.00480.00420.00690.0078925 m

yz view

2275 m3620 mK

1600 m2950 m

L Mouse left lung1500 mFigure S51000 m1000 mtheta = 0°1600 m2275 mtheta = 180°
2950 m3620 m0.01050.00730.00650.00850.01430.00930.00740.00670.00980.00890.00670.00490.01210.0113z = 925 m
0.0103

Region 1

O

R

Region 2

N

R L

Region 3

HO

N

Bottom Top

N

N

Image
fusionImage registration

Image aquisition

O

M

Figure S5. Comprehensive Cell Detection in Highly Cleared Mouse Organs by
High-Resolution Imaging, Related to Figure 5.
(A) Scheme of CUBIC-based protocol for kidney glomerular segmentation. Samples
were prepared using CUBIC protocol I. (B) Sectional images of multi-directionally
fused images of kidney. The discrete cosine transformation Shannon entropy (DCTS)(Royer et al., 2016) is shown above the sectional images as an indicator or image
quality. (C) Sectional image of segmented glomeruli of the kidney. Segmented renal
glomeruli are shown in magenta. The renal glomeruli in the kidney werecomprehensively analyzed by a Hessian-based difference of Gaussian (Zhang et al.,2015), see also STAR Methods for details. (D,E) Three-dimensional reconstitution of
fused images of kidney (D), and segmented renal glomeruli (E). (F) Scheme of the
CUBIC-based protocol for whole-organ cell analysis. (G) Volume-rendered mouseorgan images obtained with high-resolution light sheet microscopy. Organs were
stained with nuclear-staining dye. (H,I) Representative images of nuclear-stained liver(H) and lung (I). Images with the lowest DCTS are framed in red. DCTS value is shownin the upper-left corner of each image. (J,K) Partial liver is represented as an ensembleof detected cellular nuclei (“point cloud”). Tubular structures were extracted from thepoint clouds. (L,M) Point cloud representation of the left lung. Tracheas were extracted
from the point clouds.

C Intact head of adult mouse

A Mouse brain over skull

DE

Sagittal plane

G Spine of adult mouse

Coronal plane

xy plane 1
xy plane 2

1 2 3 4
Sagittal plane2000 m1000 mF

B

2000 m2

1

2000 m

3

y z

H

4

Horizontal plane

I

Mouse tibia

xy plane 1

D2000 m1000 m2000 mxxy plane 2

D1000 mR

R

1000 m

MDA-231-D K Adult mouse head (MDA-231-D-Luc2-mCherry)
J-Luc2-mCherry
Horizontal plane

Magnified view

500 m

A

3000 mR

Human lung
L Beforeclearing

After clearing

4 mmkidney #1
N HumanBefore clearing

Human kidney #2
Before clearing

After clearing

After clearing

2000 m

4 mm
4 mm

M

Human lungxy-view xz-view

5000 m 5000 m

R

P Human liver

Before clearing (day 1)

4 mm

O Human kidney #1

Human kidney #2

R

3000 mD

A

After clearing and PI staining

4 mm

Q

4 mm

Figure S63000 mRA

xy-view

xz-view

2000 m2000 mD

Figure S6. Scalable Imaging of Mouse and Primate Tissue, Related to Figure 6.
(A,B) Brain images of P7 mouse inside the skull. Sagittal plane (A) and horizontal
plane (B) are shown. (C) Nuclear stained adult (8-week-old) mouse head. The head
was cleared with CUBIC protocol II, and observed with LSFM. (D,E) Sagittal andcoronal section images of adult mouse head. Clear nasal structures were observed.
(F) Serial sectional views of the nasal cavity. (G) Volume-rendered image of the spine(L4-L6) of an adult (8-week-old) mouse. Autofluorescence was observed. (H)Sectional images of the spine shown in G. (I) HE staining of the metastasized mouse
tibia shown in Figures 6F-H. Infiltrating cancer cells can be clearly observed. (J)Bioluminescenceimagingofasecondindividual

MDA-231-D-injected

immunodeficient BALB/c-nu/nu mouse. (K) Metastasized brain of the model mouse in
J. Metastatic foci were clearly detected through the skull after the CUBIC II protocol.
(L) Human lung before and after the clearing used in Figure 6I. (M) Human lung xy
and xz views; maximal projection over a 3300-μm-thick volume. The identicalvolumetric image shown in Figure 6I was re-analyzed. (N) Human kidney sample
before and after clearing. (O) Volume-rendered image of the autofluorescence indissected human kidneys. (P) Volume-rendered image of human liver tissue. (Q)
Human liver xy and xz views; maximal projection over an approximately 650-μm-thickvolume. For L-Q, CUBIC protocol III was applied. (R) Adult marmoset brain was
cleared by a modified CUBIC protocol I. The detailed experimental procedure isprovided in the STAR Methods. Bright field images of the adult marmoset brain justafter starting clearing (Day 1) and after complete clearing and staining are shown.

A RedDot 2 (cell nuclei)

B

TM

Thy1
(standard)

Mlc

Plp

P(CAG)

Horizontal
Plane 2

Plane 1
Mlc1-YFP brain 1

Sagittal
Plane 3

Plane 42000 m2000 m

Drd1

C

Htr5b

Th

Thy1

Mlc1-YFP brain 2

Mlc

Plp

Htr5b

Th

P(CAG)

500 m

Drd1

D

L4

L2/3

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Thy1

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L4

Htr5b

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500 m

Figure S7

L2/3

Th

Plane 5

Figure S7. Virtual Multiplex Images of FP-expressing Brains Registered to aStandard Brain, Related to Figure 7.
(A) Volumetric images after non-linear registration. All of the nuclear stained imagesof genetic reporter mouse brains are displayed. Horizontal planes are shown. (B) To
further verify the registration accuracy, two independent brains of the Mlc1-YFP
reporter mouse were registered on the standard brain. After registration of the nuclear
image, transformed YFP signals were observed. The YFP signals (magenta for brain
1, green for brain 2) were merged with the standard nuclear image (grey). Threehorizontal section images and two coronal section images are shown. The YFP signal
pattern was similar in the two brains, indicating that the registration was accurate. (C)Typical volume-rendered images of the seven types of FP-expressing brain. For
navigation, each position of the acquired image is indicated in the inset image using
the identical volumetric image of a nuclear-stained standard brain. (D) Individual
expression pattern of the three different FPs shown in Figure 7D. The cortical layer
structures are indicated.

A

Procedures to develop customized delipidating chemical cocktails

I

Screen candidate chemicals by refering our first screening
result (lipid solubility, cost, pH, FP). Classify screened
chemicals into detergent (group I), aliphatic amine andN-alcohol (group II), and other (group III).

II

Choose one chemcial from group II which
satisfies your purpose. If none satisfies the demand, choose
one from group III.

III

Observe the chemical ability of delipidation with the tissueof your interest using 10 wt% of the chemical. If result is notsatisfactory, combine the 10 wt% chemical with 10 wt% ofgroup I chemical. Choose the optimal combination.

IV

B

Procedures to develop customized RI matching chemical cocktails

I

Screen candidate chemicals by refering our first screening
result (RI, cost, pH, FP). Exclude ionic chemicals andprioritize chemicals with amide or amidine.

II

Measure solubilities of the candidate chemicals. Narrow down
the candidates by refering solubility and RI.

III

Confirm the chemical compatibility with fluorescent protein
or fluorecent molecule of your interests in in vitro.

IV

Observe RI matching abilities of the candidate chemicals
with delipidated tissues (adult mouse lung recommended).
if transparency is satisfying

Optimize concentration, duration and temparature.
Confirm the compatibility to FPs of the protocol if necessary.

if transparency is not satisfying

Combine two candidate chemicals. Observe compatibility within vitro fluorescence and abilities of RI matching. Optimize
concentration ratio until the optimal condition is determined.
V(optional)

Further screen the derivatives of the identified chemical(s).
V

Choose one chemical or chemical combination
with the highest performance.

VI(optional)

Further screen the derivatives of the identified chemical(s).

Figure S8

Figure S8. Flowcharts for Developing Customized Tissue-clearing Cocktails,
Related to Figure 1.
(A) Procedure for developing customized delipidating chemical cocktails. (B)Procedure for developing customized RI-matching chemical cocktails.