Please cite this article in press as: Susaki et al., Whole-Brain Imaging with Single-Cell Resolution Using Chemical Cocktails and Computational
Analysis, Cell (2014), http://dx.doi.org/10.1016/j.cell.2014.03.042

Resource

Whole-Brain Imaging with Single-Cell
Resolution Using Chemical Cocktails
and Computational Analysis
Etsuo A. Susaki,1,2,3,4,14 Kazuki Tainaka,1,3,4,14 Dimitri Perrin,2,14 Fumiaki Kishino,5 Takehiro Tawara,6Tomonobu M. Watanabe,7 Chihiro Yokoyama,8 Hirotaka Onoe,8 Megumi Eguchi,9 Shun Yamaguchi,9,10 Takaya Abe,11
Hiroshi Kiyonari,11 Yoshihiro Shimizu,12 Atsushi Miyawaki,13 Hideo Yokota,6 and Hiroki R. Ueda1,2,3,4,*
1Laboratory for Synthetic Biology, RIKEN Quantitative Biology Center
2Laboratory for Systems Biology, RIKEN Center for Developmental Biology
2-2-3 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
3Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
4CREST, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan
5Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
6Image Processing Research Team, RIKEN Center for Advanced Photonics, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
7Laboratory for Comprehensive Bioimaging, RIKEN Quantitative Biology Center, OLABB, Osaka University, 6-2-3, Furuedai, Suita,
Osaka 565-0874, Japan
8Bio-Function Imaging Team, RIKEN Center for Life Science Technologies, 6-7-3 Minatojima-minamimachi, Chuo-ku, Kobe City,
Hyogo 650-0047, Japan
9Division of Morphological Neuroscience, Gifu University Graduate School of Medicine, 1-1 Yanagido, Gifu City, Gifu 501-1194, Japan
10PRESTO, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan
11Laboratory for Animal Resources and Genetic Engineering, RIKEN Center for Developmental Biology, 2-2-3 Minatojima-minamimachi,
Chuo-ku, Kobe, Hyogo 650-0047, Japan
12Laboratory for Cell-Free Protein Synthesis, RIKEN Quantitative Biology Center, 2-2-3 Minatojima-minamimachi, Chuo-ku, Kobe,
Hyogo 650-0047, Japan
13Laboratory for Cell Function Dynamics, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
14Co-first author*Correspondence: uedah-tky@umin.ac.jp
http://dx.doi.org/10.1016/j.cell.2014.03.042

SUMMARY

INTRODUCTION

Systems-level identification and analysis of cellular
circuits in the brain will require the development ofwhole-brain imaging with single-cell resolution. To
this end, we performed comprehensive chemical
screening to develop a whole-brain clearing andimaging method, termed CUBIC (clear, unobstructed
brain imaging cocktails and computational analysis).
CUBIC is a simple and efficient method involving theimmersion of brain samples in chemical mixturescontaining aminoalcohols, which enables rapid
whole-brain imaging with single-photon excitation
microscopy. CUBIC is applicable to multicolor imaging of fluorescent proteins or immunostained samples in adult brains and is scalable from a primate
brain to subcellular structures. We also developed
a whole-brain cell-nuclear counterstaining protocol
and a computational image analysis pipeline that,together with CUBIC reagents, enable the visualization and quantification of neural activities induced
by environmental stimulation. CUBIC enables timecourse expression profiling of whole adult brains
with single-cell resolution.

Whole-brain imaging with single-cell resolution is one of the mostimportant challenges in neuroscience, as it is required forsystem-level identification and analysis (Kitano, 2002) of cellular
circuits in the brain. Various cross-sectional tomographymethods have been developed to obtain high-resolution images
throughout the brain (Gong et al., 2013; Li et al., 2010). Such section-based methods require expensive, specialized equipment,
and the detailed structure between sections can be lost during
sample processing. Alternatively, brain-transparentizing and-clearing techniques have been combined with rapid threedimensional (3D) imaging using single-photon excitation microscopy, such as light-sheet fluorescence microscopy (LSFM) or
specific plane illumination microscopy (SPIM). The 3D imaging
can enable the study of systems from cells to organisms, suchas cellular behavior in developing embryos and neural circuitsin the adult brain (Dodt et al., 2007; Hägerling et al., 2013; Tomeret al., 2011). Such studies require a highly transparent sample forboth illumination and detection.
Transparency is achieved by minimizing the light scattered byan object. Light is scattered at the boundary between materials
with different refractive indices (RIs). Because lipids are a major
source of light scattering in the fixed brain, the removal of lipids
and/or adjustment of the RI difference between lipids and thesurrounding areas are potential approaches for increasing brain
Cell 157, 1–14, April 24, 2014 ª2014 Elsevier Inc. 1

Please cite this article in press as: Susaki et al., Whole-Brain Imaging with Single-Cell Resolution Using Chemical Cocktails and Computational
Analysis, Cell (2014), http://dx.doi.org/10.1016/j.cell.2014.03.042

sample transparency. In an early trial of whole-brain imaging withmacrozoom LSFM (Dodt et al., 2007), the brain tissue wascleared with BABB, a mixture of benzyl alcohol and benzyl
benzoate. Because of BABB’s hydrophobicity and because its
high RI (1.56) (Becker et al., 2012) matched that of the fixedtissue, the sample was markedly cleared and suitable for observation with LSFM. Unfortunately, the clearing medium caused
the rapid quenching of fluorescent signals. Although otherorganic chemical-based mixtures, such as tetrahydrofuran(THF) and dibenzyl ether (DBE) (Becker et al., 2012), performed
somewhat better, some frequently used fluorescent proteinssuch as yellow fluorescent protein (YFP) were still quenched(Ertürk et al., 2012).
A hydrophilic chemical mixture called Scale was developed toclear brain samples (Hama et al., 2011). This urea-based reagent
reduced quenching, and the resulting sample was suitable forboth single-photon and multi-photon-based 3D tissue observation. Other issues, including relatively long sample treatment
periods (weeks or months) and tissue swelling, were resolvedby the development of another clearing reagent, SeeDB, which
renders brain samples transparent within a few days by adjusting
RI differences between the sample lipids and the surrounding
tissue (Ke et al., 2013). SeeDB, a hydrophilic reagent lacking
detergents or denaturation agents, preserves detailed structures
as well as fluorescence signals. The remaining issue was thatsuch simple hydrophilic reagents do not render whole-brain
samples transparent enough for single-photon-based rapid
whole-brain imaging. CLARITY, a recently reported techniquefor brain clearing (Chung et al., 2013), rendered brain samples
transparent by aggressively removing lipids using electrophoresis. The resulting sample was highly transparent and suitablefor LSFM imaging; however, use of a specialized electrophoresis
device with a narrow range of optimal parameters makes parallelization and comparison among different samples difficult.
Thus, although previous protocols have addressed some of theissues required for the efficient clearance of brain samples, animproved protocol was still required for whole-brain imaging
with single-cell resolution.
The anatomical annotation of images is also critical. In conventional human brain imaging techniques such as computed
tomography (CT) (Cormack, 1973), magnetic resonance imaging(MRI) (Lauterbur, 1973), and functional magnetic resonanceimaging (fMRI) (Ogawa et al., 1990), anatomical annotationconsists of two steps: (1) acquisition of whole-brain structuraland specific signal (or functional) images and (2) computational
analysis of these images. For example, fMRI, which visualizes
neural activity, is almost always accompanied by MRI, which
acquires a structural image of the whole brain. This image isthen used for image registration and alignment to a standard
whole-brain image (‘‘reference brain’’) so that signal images
can be compared across individuals. Because computational
image analysis is so essential, informatics tools have been developed and implemented to facilitate this process in conventional
human brain imaging (Avants et al., 2011). However, this kind ofcomputational image analysis is rare for fluorescence imaging ofwhole mammalian brains due to the lack of whole-tissue histological counterstaining of cell nuclei, which can be used foranatomical orientation. 3D images acquired with sectional
2 Cell 157, 1–14, April 24, 2014 ª2014 Elsevier Inc.

tomography or tissue-clearing protocols provide information
on fluorescently labeled cells, but not on the overall shape andcharacteristic anatomical structures that are required for theregistration and alignment steps. Even in a trial in which theimage registration step was successful, manual determinationof landmark structures was necessary (Gong et al., 2013). The
larger number of images generated in time-course experiments
makes the need for automated processing even more acute.
Thus, efficient anatomical annotation will be required forwhole-brain imaging with single-cell resolution.
We have developed a simple, efficient, and scalable brainclearing method and computational analysis pipeline, CUBIC(clear, unobstructed brain imaging cocktails and computational
analysis), that enables rapid whole-brain imaging with singlephoton excitation microscopy. CUBIC can be applied towhole-brain imaging of various fluorescent proteins and 3D
imaging of immunostained adult brain samples. CUBIC isscalable from primate brain imaging to subcellular structures
such as axons and dendritic spines. In order to facilitate interindividual comparisons, we have also developed an anatomicalannotation method using a whole-brain cell-nuclear counterstaining protocol and a computational image analysis pipeline.
CUBIC enables the time-course expression profiling of adultwhole brains with single-cell resolution.
RESULTS
Development of CUBIC Reagents by Comprehensive
Chemical Screening
To develop a simple, efficient, and scalable tissue-clearing
protocol for whole-brain imaging, we started by rescreening
the chemical components of the hydrophilic brain-clearing
solution ScaleA2 (Hama et al., 2011). We started with this protocol because of its ease and its ability to preserve fluorescentsignals. We considered 40 chemicals, including polyhydric alcohols, detergents, and hydrophilic small molecules (Table S1
available online), corresponding to the components of the
ScaleA2 solution: glycerol, Triton X-100, and urea. To evaluate
the clearing efficiency of these chemicals, we first sought toimprove the experimental throughput of clearing measurements.
In a typical evaluation procedure, we first fix, isolate, andimmerse a whole mouse brain in one chemical mixture andthen evaluate the clearing efficiency of the chemical bymeasuring the transmittance of the fixed, cleared whole brain.
For a more efficient evaluation protocol, we exchanged the orderof the fixation and isolation steps and introduced a homogenization step so that many chemicals can be tested using only
one mouse brain (Figure 1A and Extended Experimental Procedures). This ‘‘solubilization’’ assay using a homogenizedsuspension instead of a whole brain enabled the reproducible,
quantitative, and comprehensive evaluation of chemicals fortheir ability to dissolve brain tissue (Figure 1B). We also evaluated the quenching effect of each chemical on EGFP fluorescence in this first chemical screening (Figure 1C). We notedthat a series of aminoalcohols (#4, #8, #9, #10, #15, #16, #17)
showed considerable brain tissue solubilizing activity, whereas
polyhydric alcohols without amino groups (#1, #2, #3, #5, #12,
#18, #19, #20) exhibited much less activity (Figure 1B and TablePlease cite this article in press as: Susaki et al., Whole-Brain Imaging with Single-Cell Resolution Using Chemical Cocktails and Computational
Analysis, Cell (2014), http://dx.doi.org/10.1016/j.cell.2014.03.042

S1). This may indicate that the cationic amino group contributes
to solvating anionic phospholipids, the most abundant lipids inthe brain. In addition, basic aminoalcohols preserved and evenenhanced EGFP fluorescent signals, whereas some weakly
acidic amine-free alcohols (#1, #2, #3, #11) quenched the fluorescence (Figure 1C and Table S1). Many fluorescent proteins,
such as EGFP, EYFP, and mCherry, contain a phenolate chromophore, which is highly pH sensitive, resulting in increased
fluorescence intensity with increasing pH (until the protein denatures) (Kneen et al., 1998). In a highly concentrated aqueous
medium without buffers, basic aminoalcohols were therefore
more suitable clearing agents than other weakly acidic or neutral
alcohols. After a second chemical screening (Figures 1D and 1E),
our cocktail finally included N,N,N’,N’-tetrakis(2-hydroxypropyl)
ethylenediamine (#10), Triton X-100 (#25), and urea (#36) (Figure S1B). We refer to this first CUBIC reagent as ScaleCUBIC-1(hereafter denoted as reagent 1). We used the words ‘‘Scale’’and ‘‘CUBIC’’ because this reagent is based on urea (Scale)and aminoalcohols (CUBIC), respectively.
Reagent 1 treatment of the whole adult mouse brain resulted
in a highly transparent brain (Figures 2A and 2B). The lipid-richwhite matter was not completely cleared, possibly due to incomplete lipid removal and unmatched RIs between the tissue andsolution. ScaleCUBIC-2 (hereafter denoted as reagent 2) was
developed by modifying the reagent 1 recipe in a third chemical
screening (Figure S1A). Addition of a high concentration of
sucrose instead of glycerol increased the reagent RI (from1.45-1.46 for glycerol-based reagent 2 to 1.48–1.49 forsucrose-based reagent 2, which is similar to the RI of SeeDB[RI = 1.49] [Ke et al., 2013]) so that it matched the tissue RI
and increased the transmittance (Figure S1A). Replacing chemical #10 with 2,20 ,20 ’-nitrilotriethanol (#16) also reproducibly
increased the transmittance (Figure S1A), motivating its inclusionin the final version of reagent 2 (Figures 2A and S1B). As expected, reagent 2 enhanced the optical clearance of lipid-richregions deeper in the brain (Figure 2A).
CUBIC Is a Simple and Efficient Procedure
As summarized in Figure 2A, the CUBIC protocol involves aseries of immersion steps followed by a washing step. The
CUBIC brain samples were sufficiently transparent for adultwhole-brain imaging. The sample was slightly swollen by reagent
1 treatment but returned to its original size following reagent 2
treatment. In addition, CUBIC-based clearing can be used toclear an acrylamide gel-embedded sample prepared according
to the CLARITY protocol (Chung et al., 2013), addressing concerns about destruction of detailed anatomical structures andprotein loss (Figure S1C).
Sample clearing with CUBIC reagents required 2 weeks forcompletion, comparable to CLARITY (10 days) (Chung et al.,2013) and faster than ScaleA2 (weeks) and ScaleU2 (months)(Hama et al., 2011). Although clearing with 3DISCO can becompleted in 5 days, fluorescence signals are quenched withina few days of preparation (Ertürk et al., 2012). To evaluate thequenching effect of CUBIC, we next incubated various recombinant fluorescent proteins with reagent 1, reagent 2, or PBS for
24 hr or 7 days (Figure 2C). The fluorescence of all of the tested
proteins, even EYFP, the most sensitive, remained high after the

7-day incubation. The CUBIC protocol achieved comparable
transparency to other recently developed methods in a reasonable time, with minimal quenching of fluorescence signals.
Notably, the red fluorescent proteins mCherry and mKate2
also showed minimal quenching. Given that red light transmits
through tissues more efficiently than shorter wavelength light(Figure 2B), these proteins are very suitable as labeling tools
for use with the CUBIC protocol.
To confirm that CUBIC samples can be used for deep brain
imaging, we performed two-photon imaging of a Thy1-YFP-H
Transgenic (Tg) mouse brain (Feng et al., 2000) with an objective
lens optimized for use with SCALEVIEW-A2 (working distance
8 mm, optimized for RI = 1.38) (Figure 2D). We treated a hemisphere with reagent 1 for 3 days and acquired a Z stack image.
Treatment with reagent 1 alone was sufficient for deep brain
imaging (up to 4 mm) with two-photon excitation microscopy(Figure 2D). The accelerated clearing obtained with reagent 1
enabled substantially deep brain imaging up to 4 mm, whereas
SCALEVIEW-A2-treated samples became transparent more
slowly (Figure S2).
CUBIC Enables Rapid, High-Performance, Whole-Brain
Imaging with LSFM
The high transparency of CUBIC samples and the two-photon
imaging results prompted us to perform whole-brain imaging
with single-photon excitation microscopy techniques such as
LSFM. Previous reports have used confocal- or multi-photonbased imaging methods (Chung et al., 2013; Ke et al., 2013),
in which image tiling and data acquisition time (days for a single
brain) were significant challenges. LSFM, which enables rapid
whole-brain imaging, was used in a previous study using
BABB (Dodt et al., 2007), though fluorescence signal loss
over time was a problem. We first set up an optimized macrozoom microscope combined with a light-sheet illuminator(LaVision Ultramicroscope) (Figure S3A) so that an entire horizontal section of a mouse brain can be acquired in a single planewith subcellular resolution (theoretical X-Y resolution in theacquired image is 4.7 mm). For rapid whole-brain imaging, we
generated a knockin mouse strain expressing three tandem
repeats of mKate2 with a nuclear localization signal, undercontrol of the CAG promoter (Niwa et al., 1991) from the
ROSA26 locus. We used mKate2 because of its signal intensity,
photostability, and rapid protein maturation (Chudakov et al.,2010), as well as its resistance to fluorescence quenching by
CUBIC reagents (Figure 2C).
We then collected Z stack images of the horizontal section(Movie S1) of a CUBIC whole brain from two directions (dorsal
to ventral, D-V, and ventral to dorsal, V-D). With our current
microscope setup, imaging took 30 min to 1 hr per brain/color/
direction. The reconstituted 3D whole-brain image of the mKate2
knockin mouse (Figures 3A and S3B) and its virtual coronal
sections (Figure 3B) enabled the 3D visualization of spatial
gene expression patterns and the examination of detailed internal structures. Furthermore, we confirmed that background
fluorescence in CUBIC samples is low enough not to impair
visualization of the fluorescent signal from mKate2 in the overall
brain image data (Figures 3F–3I and Extended Experimental
Procedures).
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Please cite this article in press as: Susaki et al., Whole-Brain Imaging with Single-Cell Resolution Using Chemical Cocktails and Computational
Analysis, Cell (2014), http://dx.doi.org/10.1016/j.cell.2014.03.042

A

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(legend on next page)

4 Cell 157, 1–14, April 24, 2014 ª2014 Elsevier Inc.

Please cite this article in press as: Susaki et al., Whole-Brain Imaging with Single-Cell Resolution Using Chemical Cocktails and Computational
Analysis, Cell (2014), http://dx.doi.org/10.1016/j.cell.2014.03.042

CUBIC Is Applicable to the Whole-Brain Imaging of
Various Fluorescent Proteins
Our successful imaging using mKate2 led us to test otherfluorescent proteins, including mCherry, EGFP, and YFP. We
performed similar whole-brain imaging with CUBIC adult wholebrains from three mouse strains: an mCherry-fused histone2B-expressing strain (R26-H2B-mCherry [CDB0239K]; Figures
3C and S3C; Abe et al., 2011), an EGFP-fused histone-2Bexpressing strain (R26-H2B-EGFP [CDB0238K]; Figures 3Dand S3D; Abe et al., 2011), and a Thy1-YFP-H transgenic strain(Figures 3E and S3E). By comparing the reconstituted sectionsof these samples, spatial gene expression patterns and theirdifferences across samples could be easily traced. For example,
we compared two knockin strains in which the gene was inserted
into the same ROSA26 locus but regulated by different promoters; the gene for mKate2 was regulated by the CAG promoter
and, for mCherry, by the endogenous ROSA26 promoter. The
whole-brain image showed expression differences in regions
such as the thalamus, hypothalamus, midbrain, granular layerof the cerebellum, and ventricular choroid plexus (Figures S3F
and S3G). These results show that various fluorescent proteinscan be used for rapid and high-performance whole-brain imaging with the CUBIC protocol.
CUBIC Is Applicable to Adult Brain Imaging of 3DImmunostained Samples
Given that CUBIC samples were highly permeabilized by lipid
removal, we predicted that the CUBIC protocol would be usefulfor the immunostaining of large blocks of brain tissue, a potential
improvement over sectional staining. We performed 3D immunohistochemistry (3D-IHC) using a brain block containing the hypothalamus region (Figure 4A). The block was subjected to reagent
1 treatment, followed by 3D-IHC with antibodies to neuropeptides expressed in the suprachiasmatic nucleus (SCN), thecentral clock of the mammalian circadian system. We used antibodies for Copeptin (a peptide cleaved from the common precursor of arginine vasopressin [AVP]; Land et al., 1982) and forvasoactive intestinal peptide (VIP) to label the AVP and VIP neurons in the nucleus. After immunostaining, the block was treatedwith reagent 2 for further clearing, and the resulting sample wasobserved with an inverted single-photon confocal microscope.
We imaged a horizontal plane of the SCN and paraventricular
nucleus (PVN) regions of the hypothalamus from the bottom of
the brain. The immunostained signals were detected at a depthof >750 mm (from the bottom of the brain to the dorsal area of
the PVN in the hypothalamus) and were visualized as a 3D reconstituted image (Figure 4B and Movie S2). In the acquired horizontal images, the cell bodies and fibers of the two types ofneurons were clearly visible (Figure 4C). High-resolution images
were also included (Figure S4C). Abrahamson and Moore havedemonstrated that a large number of VIP-positive soma weredensely packed in the ventral area of the SCN and that AVP-positive soma were localized in the dorsomedial and ventrolateralarea of the SCN and dorsally distributed even around theparaventricular nucleus (Abrahamson and Moore, 2001).
The ventral image at a depth of 45 mm (including the mostventral area of the SCN) displayed extensive VIP-positive soma
signals in the left panel of Figure S4C. The middle image at a
depth of 65 mm (including the more dorsal area of the SCN)
exhibited AVP-positive soma and VIP-positive fibers in the
middle panel of Figure S4C. The dorsal image at a depthof 780 mm (including the paraventricular nucleus) included
sparse AVP-positive soma in the right panel of Figure S4C.
Therefore, previously reported spatial distributions of AVP- and
VIP-positive soma and fibers were clearly recapitulated by
CUBIC-based 3D IHC.
We further applied 3D-IHC analysis to the SCN of both wildtype (WT) and a Cry1–/–, Cry2–/– clock gene double-knockout
strain (van der Horst et al., 1999), sacrificed at different circadiantimes (CT). For these samples, nuclear counterstaining withSYTO 16, a cell-permeable green fluorescent nucleic acid stain,
was performed during the reagent 1 treatment. Plane images atdifferent depths (Figures 4D and S4A) and reconstituted 3D
images (Movies S3 and S4) showed CT-dependent differencesin the staining patterns of these two neuropeptides. The moststriking difference was that the fibers in the dorsal region of the
SCN were more strongly stained at CT0 than at other circadiantimes. This tendency was reproduced in an independent set of

Figure 1. Development of CUBIC Reagents by Comprehensive Chemical Screening(A) Method for screening chemicals for brain-solubilizing activity using a fixed brain suspension. Turbidity of the suspension was quantitatively determined with anabsorptiometer and was expressed as an OD600 value. See also Extended Experimental Procedures and Table S1.
(B) Solubilization assay. The fixed brain suspension was mixed and incubated with 10 wt% aqueous solutions of candidate chemicals. After 24 hr, the mixtures
were subjected to OD600 measurement.
(C) EGFP quenching assay. Recombinant EGFP was incubated in 10 wt% aqueous solutions of candidate chemicals for 12 hr, after which the fluorescencesignals were measured.
In (B) and (C), the data represent the average (±SD) of two independent measurements. Chemicals without ID number were rejected before the step due to their
low solubility (<10 wt%) or unavailability (see Table S1). Chemicals chosen for the second chemical screening in (D) and glycerol (#18) are indicated with magenta
arrowheads and a green arrowhead, respectively.
(D) Second chemical screening of candidate chemicals for reagent 1. The EGFP quenching assay was performed with the final recipe of reagent 1 (Figure S1B).
Fluorescent signal intensities were measured as in (C), and the average relative fluorescence ± SD of two independent measurements is shown in the graph. #38
was rejected due to its relatively low solubility (<15 wt% in water).
(E) Effect of using aminoalcohols instead of glycerol in reagent 1. From the results in (D), we chose aminoalcohols (#10, #16, #17), Triton X-100 (#25), and urea(#36) for further evaluation. Glycerol (#18) was also tested as a Scale-based mixture, as in (D). #15 was rejected due to its cost and safety concerns (see Table S1).
Fixed mouse hemispheres were subjected to clearing with the indicated solutions for 3 days followed by measurement of transmittance (360–740 nm). The
transmittance data are shown as relative transmittance, normalized to the blank, defined as 100. The values in the graph represent the average ± SEM from two
independent samples. Note that some of the values exceeded 100, possibly due to the difference in the RI of the sample and the surrounding air (see Extended
Experimental Procedures).
See also Figure S1.

Cell 157, 1–14, April 24, 2014 ª2014 Elsevier Inc. 5

Please cite this article in press as: Susaki et al., Whole-Brain Imaging with Single-Cell Resolution Using Chemical Cocktails and Computational
Analysis, Cell (2014), http://dx.doi.org/10.1016/j.cell.2014.03.042

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Figure 2. CUBIC, A Simple and Efficient Whole-Brain Clearing Protocol(A) CUBIC protocol. A fixed whole brain (from a 6-month-old mouse) was treated with reagent 1 to remove lipid components for up to 7 days, followed by washingwith PBS. The sample was then treated with reagent 2 for 2–7 days to adjust the refractive indices of the tissue and reagent. Optional procedures are indicated inorange boxes: nuclear counterstaining during reagent 1 treatment, and sample storage at 4 C or –80 C after completion.
(B) Transmission curves. Light transmittance (360–740 nm) of fixed whole brain (n = 3), reagent-1-treated whole brain (day 7, n = 4), reagent-2-treated whole brain(day 3, n = 3), and the corresponding reagents (n = 1, for each reagent) were measured and normalized to the blank. Data shown represent the average ± SEM.
(C) Quenching test of fluorescent proteins by CUBIC reagents. The indicated recombinant fluorescent proteins were incubated in each reagent or PBS for 24 hr or
7 days, followed by measurement of the fluorescent signal intensity. Data shown represent the average ± SD of three independent measurements.
(D) Two-photon deep brain imaging of a reagent-1-treated Thy1-YFP-H Tg hemisphere. A single hemisphere was treated with the reagent 1 for 3 days at 37 C.
The resulting sample was observed using two-photon microscopy.
See also Figure S2.

experiments (Figure S4A) as well as in animals housed underlight-dark conditions (Figure S4B). These differences were notobserved in images of the Cry1–/–, Cry2–/– strain captured at
CT0 and CT12 (Figures 4D and S4A), suggesting that theobserved immunostaining differences in the AVP and VIP neurons were dependent on the circadian clock. The CUBIC
protocol is highly applicable to the 3D imaging of immunostained
adult-brain blocks.
6 Cell 157, 1–14, April 24, 2014 ª2014 Elsevier Inc.

CUBIC Is Scalable from Subcellular Structures to a
Primate Brain
In addition to the efficiency of the CUBIC protocol, its scalability is also important for a wide range of applications. One potential application of a brain-clearing protocol is to trace neuralconnections at the axon or synapse level. We therefore tested
whether detailed cell structures such as axons and spines were
preserved in CUBIC samples. We used Thy1-YFP-H transgenic

Please cite this article in press as: Susaki et al., Whole-Brain Imaging with Single-Cell Resolution Using Chemical Cocktails and Computational
Analysis, Cell (2014), http://dx.doi.org/10.1016/j.cell.2014.03.042

R26-pCAG-nuc-3×mKate2(V-D imaging)A

Figure 3. CUBIC Can Be Used for Rapid andHigh-Performance Whole-Brain Imaging of
Various Fluorescent Proteins

R26-pCAG-nuc-3×mKate2(Reconstituted coronal sections)

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mice in which neuronal subcellular structures, including
somata, axons, and dendritic spines, can be labeled and visualized by YFP fluorescence (Feng et al., 2000). A magnified
view of a CUBIC brain sample from the Thy1-YFP-H transgenic
mouse revealed that the axons and somata of neurons were
well preserved (the pontine-medullary region in the LSFM image is shown in Figure S5A). A single axon was labeled andvisualized without any breaks, making its trajectory easily traceable. In addition, the dendritic spines in the cerebral cortex of
the same strain were clearly observed by two-photon micro-

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scopy (Figure S5B). Furthermore, proteinsin the spine were also preserved in the
CUBIC sample by immunostaining withan antibody against synapsin I, a phosphoprotein associated with synaptic vesicles in neural synapses (Navone et al.,Plane No.
1984) (Figure S5C). Synaptic proteins
were preserved in the CUBIC samples,suggesting that CUBIC is scalable to subcellular structure
imaging.
Another potential application for a brain-clearing protocol is in
primate whole-brain imaging. The marmoset is becoming an increasingly popular primate model organism in neuroscience
because of its potential for genetic engineering (Sasaki et al.,2009). We tested whether the CUBIC protocol is applicable tomarmoset whole-brain fluorescence imaging with nuclear counterstaining. A postnatal-day-3 marmoset brain has 10 times
the volume of an adult mouse brain (Figure 5A). CUBIC treatment

193

9.00
8.00
7.00
6.00
5.00
4.00
3.00
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1.00
0.00
161

449

Ratio

I

483

385

417

353

289

321

193

225

20480

18496

14400

16448

12352

8256

10304

6208

64

0

2112

0.004

257

5248 7616 9280

0.008

4160

Proportion of
pixels

0.012

45000
40000
35000
30000
25000
20000
15000
10000
5000
0

161

Plane No. 161
Plane No. 322
Plane No. 483

0.016

95th percentile
80th percentile
65th percentile
50th percentile
BG peakH
IntensityG

3D-reconstituted images of mouse brains expressing various fluorescent proteins were acquired with
LSFM. A, anterior; P, posterior; R, right; L, left; D,
dorsal; V, ventral. Ventral view is shown.
(A) R26-pCAG-nuc-3 3 mKate2 mouse brain (5week-old mouse). Images were acquired from theventral-to-dorsal (V-D) direction. Z stack: 10 mmstep 3 646 planes, with 0.28 s 3 two illuminations.
(B) Reconstituted coronal sections at the indicatedpositions (1–3) shown in (A). Cc, cerebral cortex; Cp,
caudoputamen; Th, thalamus; Hy, hypothalamus; Hp,
hippocampus; Am, amygdala; Pu, Purkinje cell layer.
(C) V-D image of a R26-H2B-mCherry mouse brain(6-month-old mouse). Z stack: 10 mm step 3 709
planes, with 4 s 3 two illuminations.
(D) V-D image of a R26-H2B-EGFP mouse brain (6month-old mouse). Z stack: 10 mm step 3 665
planes, with 4 s 3 two illuminations.
(E) V-D image of a Thy1-YFP-H Tg mouse brain (2month-old mouse). Z stack: 10 mm step 3 696
planes, with 0.3 s 3 two illuminations. See also
Figure S3 and Movie S1.
(F) (Left) Raw TIFF image from plane 322 of the R26pCAG-nuc-3 3 mKate2 mouse brain. (Center)
False-color image highlighting regions with intensity<8,100 (blue), between 8,100 and 35,000 (green),
and >35,000 (red). In each channel, brighter colors
mean higher intensity within the range in the raw
image. (Right) Processed image for plane 322,
where all pixels whose intensity is below 8,100 are
set to black.
(G) Low-intensity peaks (corresponding to background peaks) in the TIFF image histogram, shownfor planes 161, 322, and 483.
(H) Background peaks (BG peak) and percentiles
for the intensity distribution of pixels whose intensity is at least 5% higher than BG peak.
(I) Ratios between the percentiles and the background peaks shown in (H).

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Figure 4. CUBIC Can Be Used for Imaging of 3D Immunostained
Adult Brain Samples(A) CUBIC treatment procedure and 3D immunohistochemistry (3D-IHC) of ahypothalamus block with anti-VIP and -Copeptin antibodies (1st ab) andAlexa-conjugated second antibodies (2nd ab).
(B) 3D view of the SCN region of R26-H2B-EGFP mouse. Sampling was performed at zeitgeber time 12 (ZT12) under 12 hr:12 hr light-dark conditions.
Horizontal images were acquired from the ventral side with an inverted
confocal microscope (Z stack: 4.99 mm step 3 173 planes). A, anterior side; P,
posterior side; D, dorsal side; V, ventral side. Asterisks indicate nonspecificsignals in the third ventricle and the vessels.
(C) Selected horizontal images (Z projection with maximum intensity, 4.99 mmstep 3 3 planes) from (B). Approximate depth from the first image of the Z
stack data are indicated.
(D) 3D-IHC results of the WT and Cry1–/–, Cry2–/– SCN, collected at different
time points under constant dark conditions. Nuclear counterstaining withSYTO 16 was performed during reagent 1 treatment. Depth of the horizontal
images was 140 mm on the ventral side and 290 mm on the dorsal side

8 Cell 157, 1–14, April 24, 2014 ª2014 Elsevier Inc.

with SYTO 16 staining of the hemisphere from a 3-day-old
marmoset resulted in clearing comparable to that achieved withthe mouse brain (a hemisphere was used instead of a whole
marmoset brain due to the size limitation of the microscope stage
used in this study; Figure 5B). The acquired raw images were subjected to 3D image reconstitution to generate a volume-rendering
image (Figures 5C and 5D) and reconstituted sections (Figure 5E).
The reconstituted image clearly depicts general and detailed
structures of the marmoset brain, such as vessels and layers of
the cerebral cortex (Figures 5C–5E and Movie S5). These results
suggest that the CUBIC protocol is scalable to larger tissues, indicating its potential application to various animals, including primates. Together, the CUBIC protocol is scalable from subcellular
structures to a primate brain, a feature that will support wideranging applications in neuroscience.
Whole-Brain Nuclear Counterstaining andComputational Image Analysis in CUBIC Enable theVisualization of Neural Activities Induced byEnvironmental Stimulation with Single-Cell Resolution
Another challenge for establishing whole-brain imaging withsingle-cell resolution is the development of anatomical annotation. For this purpose, we used Arc-dVenus transgenic mice, inwhich an unstable Venus protein is expressed under the control of a neural activation marker gene, Arc (Eguchi and Yamaguchi, 2009). Arc-dVenus transgenic mice were housed underconstant dark conditions for about 2.5 days and were thenexposed to constant light at CT2.5, followed by fixation andcollection of their brains (Figure 6A). As previously reported,
light-dependent neural activation was observed with fluorescent stereomicroscopy, showing increased Venus signals inthe visual area of the cortex in a light-exposed brain (Figure S6A, Light+ (1)), compared with a control brain (Figure S6A,
Light).
Because anatomical annotation requires the acquisition ofwhole-brain structural images, we performed nuclear counterstaining with propidium iodide (PI) to acquire whole-brain
structural images in the red channel. The brain samples were subjected to CUBIC treatment with PI counterstaining, and the resulting samples were analyzed by whole-brain imaging with LSFM.
The reconstituted 3D images clearly revealed light-responsive
neurons, such as those in the visual area (Vi) (Figures 6B and 6C
and Movies S6 and S7). Individual neurons were clearly visible,
suggesting that the whole-brain image achieved single-cell resolution. Several additional brain regions, including the orbital area(Or), the motor area (Mo), the anterior cingulate area (Ac), theorbital area, and subcortical areas in the forebrain (such as theclaustrum and endopiriform nucleus [Cl/Ep]), also exhibited
increased Venus signals in the light-exposed brain (Figure 6B),
compared to the control brain (Figure 6C). These areas are known(Z projection with maximum intensity, 1.98 mm step 3 3 planes) from the mostventral plane of the SCN region. Strong signals for the neuropeptides weredetected on the dorsal side of the WT SCN at CT0 of day 2 (arrowheads), whichwere barely detected in the Cry1–/–, Cry2–/– SCN. Images were acquired andprocessed in a similar but optimized condition for each sample so that most of
the stained signals were obtained in the unsaturated range of intensity.
See also Figure S4 and Movies S2–S4.

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Figure 5. CUBIC Is Scalable to a Primate Brain(A) Comparison of the size of an adult mouse brain and a postnatal day 3 (P3) marmoset brain.
(B) CUBIC treatment and SYTO 16 nuclear staining of the marmoset hemisphere. The images of the same marmoset hemisphere after reagent 1 and reagent 2
treatments are shown.
(C–E) A 3D-reconstituted fluorescent image of the nuclear-stained marmoset hemisphere (Z stack: 10 mm step 3 900 planes, with 0.25 s 3 two illuminations)acquired with LSFM. Magnified view of the boxed region in (C) is shown in (D). A reconstituted coronal section at the indicated position in (C) is shown in (E).
Anatomical structures such as vessels and layers of the cerebral cortex can be observed in the reconstituted images.
See also Figure S5 and Movie S5.

to be connected to each other and to respond to several sensory
stimuli (Miller and Vogt, 1984; Reep et al., 1996; Sloniewski et al.,1986). This result was reproduced in another light-exposed brain(Light+ (2) in Figures S6A and S6B).
We next focused on the development of a computational
image analysis pipeline (‘‘CUBIC informatics,’’ Figure 7A). We
first combined whole-brain images of the same brain that
had been captured from two opposite directions (dorsal-toventral [D-V] and ventral-to-dorsal [V-D]) (Figures S7A and
S7B). This was necessary because whole-brain images
captured from the dorsal-to-ventral direction (D-V images)
were clear on the dorsal side but blurred on the ventral sideand vice versa (Figures S7C and S7E, V-D image and D-Vimage). To combine the two images, we first aligned them

(Figure S7A) and then generated a single whole-brain image
by considering the sharpness of each horizontal plane (Figure S7D). The blurred areas were compensated for in the combined image (Figure S7E, combined image), which was used
for subsequent computational analysis.
Applying this pipeline to Arc-dVenus transgenic mouse brains,
we aligned the combined PI image of an internal reference brain(Light) to a reference brain (Figure 7B), aligned the PI images
captured from the Light+ and Light brains (Figure 7C), anddirectly compared the dVenus fluorescence signals of thesebrains by overlaying their aligned images (Figure 7D). Subtraction of the signal intensities clearly showed the brain regions
stimulated by light (Figure 7E). These regions included areas
identified in Figures 6B and S6B but with improved clarity due
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Figure 6. Whole-Brain Nuclear Counterstaining in CUBIC Enables
the Visualization of Neural Activities Induced by Environmental
Stimulation(A) Experimental scheme. Arc-dVenus Tg mice (11 weeks old) housed underconstant dark (DD) conditions for about 2.5 days were then stimulated by light(Light+) or maintained in the dark (Light) and were sacrificed 5 hr after
stimulation.
(B and C) Whole-brain images and reconstituted coronal sections (indicatedpositions in the whole brain image in (B) of CUBIC brains from Light+ (1) andLight mice, corresponding to Figure S6A. The brains were stained with PI(light purple), and two-color D-V images were acquired with LSFM (Z stack:
10 mm step 3 625 planes for B and 10 mm step 3 648 planes for C, with 3 s 3
two illuminations for Venus and with 0.3 s 3 two illuminations for PI, respectively). A region including the visual area is indicated by the dotted ellipse.
Areas where Venus signals were increased in the Light+ brain are indicated in
the whole-brain image and reconstituted coronal sections in (B).
Or, orbital area; Mo, motor area; Ac, anterior cingulate area; Ep, endopiriform
nucleus; Cl, claustrum; Vi, visual area; Ss, somatosensory area. See also
Figure S6 and Movies S6 and S7.

to the sharper combined image. The CUBIC protocol combinedwith CUBIC informatics enables gene expression profiling ofadult whole brains.
10 Cell 157, 1–14, April 24, 2014 ª2014 Elsevier Inc.

DISCUSSIONCUBIC Is a Simple, Efficient, and Scalable Protocol ofWide Applicability
The CUBIC protocol appears to have reconciled some of theseemingly incompatible demands of previous methods. First,
it is simple in that it requires only the immersion of tissue in
two reagents for relatively short periods (Figure 2). Simplicity
is advantageous for multi-sample imaging. The water-based
Scale and SeeDB methods also have simple protocols, though
the resulting sample transparency is insufficient for rapid
whole-brain imaging of the adult brain. 3DISCO and CLARITY
produce highly transparentized brain samples but require
careful handling of reagents or specialized devices. CLARITY
also requires expensive clearing reagents such as FocusClearfor final clearing before imaging. In contrast, CUBIC produced
a highly transparent brain specimen at lower cost (Table S1)
and using nontoxic water-soluble chemicals. Second, the
CUBIC protocol can be used with many different types of fluorescent proteins (Figures 3 and 6) and to compare multiple
samples by 3D-IHC (Figure 4). This efficient and highly reproducible method also enabled time-course expression profilingwith single-cell resolution. Third, the CUBIC protocol is scalable, as it achieved transparentization of a larger primate brain(Figure 5) while preserving detailed subcellular structures suchas the axons and spines of neurons (Figure S5). CUBIC
reagents can also be used to clear acrylamide-gel-embedded
samples prepared according to the CLARITY protocol (Chunget al., 2013), if necessary (Figure S1C). The CUBIC protocol
and reagents have the potential to be modified or optimized
based on specific sample or experimental needs. For example, our preliminary tests revealed that it was possible toprepare partially cleared samples for two-photon microscopy
by using a low-stringency version of reagent 1 for several
hours.
Rapid whole-brain imaging cannot deliver its maximum
benefit without imaging devices optimized for the cleared tissues. Because LSFM showed potential for high-performance
whole-brain imaging early in its development (Dodt et al.,2007), we optimized LSFM for lower spherical aberration (Figure S3A) and acquired 25 whole-brain images, most of whichwere collected within a few days. Our optimized LSFM setup
could capture seamless whole-brain images with relatively
high resolution. This is particularly important for comparing
multiple 3D whole-brain images with single-cell resolution
because the assembly of numerous plane images still has
issues such as unavoidable errors of mechanical stage
accuracy, the need for additional calculations to correct for
optical distortion and rotation for reconstitution, or the accumulation of huge data sets (Emmenlauer et al., 2009; Preibisch
et al., 2009). Improvements in illumination (Kalchmair et al.,2010; Leischner et al., 2009) and image processing (Mertz
and Kim, 2010) and technological sharing through open platforms such as OpenSPIM (Pitrone et al., 2013) will also
advance the speed and resolution of whole-brain imaging. In
addition, the development of an RI-adjusted objective lens
will further facilitate the high-resolution imaging of CUBIC
samples.

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Figure 7. Computational Image Analysis Enables Gene Expression Profiling of Adult Whole Brains(A) Scheme of CUBIC informatics pipeline (see Figure S7A). Structural information from a nuclear-stained image is used for registration to a reference brain so thata standardization matrix can be calculated. The resulting matrix is then applied to the corresponding signal image. Standardized images from different brains canbe merged directly to generate a subtracted signal image.
(B) Standardized PI image mapped to the Waxholm Space atlas, after V-D and D-V image combination and standardization to the reference brain. The analysis
was performed using downscaled NIfTI-1 files and visualized with ITK-SNAP (see Extended Experimental Procedures; also in C–E). Ob, olfactory bulb; Cc,
cerebral cortex; Cp, caudoputamen; Hp, hippocampus; Th, thalamus; Mb, midbrain; Po, pons; Me, medulla; Cb, cerebellum; Hy, hypothalamus.
(C) PI images of two different brains (Light+ (1), red and Light, green) and their overlays after V-D and D-V image combination, sample alignment, and standardization to the reference brain.

(legend continued on next page)

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Please cite this article in press as: Susaki et al., Whole-Brain Imaging with Single-Cell Resolution Using Chemical Cocktails and Computational
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CUBIC Enables Whole-Brain Imaging with Single-Cell
Resolution
In this study, we conducted whole-brain nuclear counterstaining using SYTO 16 or PI and successfully acquired structural
information with anatomical annotation, which is important forwhole-brain imaging with single-cell resolution (Figures 6B,
6C, and 7). Because CUBIC preserves global anatomical structure in brain specimens, the anatomical annotation was highly
reproducible; for example, the PI images of two different brains
in Light+ and Light mice show substantial overlap (Figure 7C).
In addition, the computationally standardized PI image
mapped well to a ‘‘reference’’ brain such as that in the Waxholm Space atlas (Figure 7B), enabling reliable annotation of
the observed signals. Imaging can be further improved by
combining two whole-brain images of the same brain captured
from opposite directions (D-V and V-D). Using an edge
detection algorithm, it was possible to automatically define aweighted sum between images from these two directions
and obtain a combined image that was sharp throughout
the brain (Figures 7 and S7). This improved both the alignment
to the reference structures and the clarity of the fluorescentsignal images. The CUBIC protocol, together with CUBIC
informatics, enabled the accurate comparison of multiple
whole-brain images. This high-throughput method enables efficient handling of a large number of brain samples. Thisis particularly useful when investigating dynamic biologicalphenomena.
To realize system-level identification and analysis of cellular
circuits in the brain, it will be important to combine highthroughput whole-brain imaging with other key technologies.
One interesting synergy may exist with CRISPR/Cas-mediatedgenetically engineered mice, in which knockin animals
harboring neural-activity reporters (e.g., Arc-dVenus) can begenerated within a single generation (Yang et al., 2013). The
comprehensive description of phenotype-correlated cells using
such knockin mice is an important step in determining thecellular circuits underlying biological phenomena of interest. If
neural activity modifiers such as tetanus toxin (Yamamoto
et al., 2003), Allatostatin (Tan et al., 2006), or DREADDs and
PSAMs (Armbruster et al., 2007; Magnus et al., 2011) could
be selectively introduced and expressed in a limited populationof cells together with a fluorescent protein marker, phenotyperesponsive cells could be functionally identified by whole-brain
imaging. The combination of neural activity modifiers (expressed in a limited neural population) and neural activity reporters (expressed throughout the brain) with CUBIC could
enable the identification and analysis of functional connectivity
at the whole-brain level. In addition, anterograde and retrograde neural tracers, when used with the CUBIC protocol,
could also provide direct connectivity information at thewhole-brain level (Wickersham et al., 2007). When combinedwith other emerging technologies, CUBIC should contributeto the system-level identification and analysis of cellular circuits in the whole brain and probably in the whole body.
New research fields such as whole-brain cell profiling orwhole-body cell profiling (WBC profiling) lie ahead.
EXPERIMENTAL PROCEDURES
See Extended Experimental Procedures for additional details.
The CUBIC Protocol
ScaleCUBIC-1 (reagent 1) was prepared as a mixture of 25 wt% urea (Nacalai
Tesque Inc., 35904-45, Japan), 25 wt% N,N,N’,N’-tetrakis(2-hydroxypropyl)
ethylenediamine (Tokyo Chemical Industry CO., LTD., T0781, Japan), and 15
wt% polyethylene glycol mono-p-isooctylphenyl ether/Triton X-100 (Nacalai
Tesque Inc., 25987-85, Japan). ScaleCUBIC-2 (reagent 2) was prepared
as a mixture of 50 wt% sucrose (Nacalai Tesque Inc., 30403-55, Japan), 25
wt% urea, 10 wt% 2,20 ,20 ’-nitrilotriethanol (Wako Pure Chemical Industries
Ltd., 145-05605, Japan), and 0.1% (v/v) Triton X-100. For whole-brain clearing,each fixed brain was immersed in 10 g of reagent 1 at 37 C with gentle shaking
for 3 days, after which the solution was exchanged and the sample immersedin the same volume of fresh reagent 1 for an additional 3–4 days. The treatedbrain was washed with PBS several times at room temperature while gently
shaking, immersed in 20% (w/v) sucrose in PBS, degassed, and immersed
in reagent 2 (10 g per brain) for 3–7 days. After imaging, the sample was again
washed with PBS, immersed in 20% (w/v) sucrose in PBS, and stored in O.C.T.
compound at –80 C.
Microscopy
Whole-brain fluorescence images were acquired with an LSFM (Ultramicroscope, LaVision BioTec, Germany) using 488 and 588 nm lasers, an sCMOS
camera, and a macrozoom microscope (Figure S3A). Each plane was illuminated from both the right and left sides of the sample, and a merged image
was saved. Immunostained SCN and cerebral cortex images were acquiredwith an inverted confocal microscope (Leica TCS SP8 or Carl Zeiss
LSM700). Images of Arc-dVenus Tg brains (Figure S6A) were captured witha fluorescence stereomicroscope using the same settings as previously
described (Eguchi and Yamaguchi, 2009). Two-photon imaging was performed using an upright multiphoton microscope (Olympus FV1000, BX61WIor Carl Zeiss LSM7MP).
Image Data Processing and the CUBIC Informatics
All raw imaging data were collected in a lossless TIFF format. 3D-rendered
images were visualized and captured with Imaris software (Version 7.6.4,
Bitplane). 3D brain images in the NIfTI-1 format are constructed with
Convert3D from ITK-SNAP (Yushkevich et al., 2006). The combination of
two images acquired from opposite directions into a single composite 3D
image ensured the optimal sharpness of the 3D brain images. The referenceatlas used in our analysis was the Waxholm Space Atlas (Johnson et al.,2010). To facilitate analysis across samples, whole-brain images wereregistered and aligned to reference structures. We aligned images bypairs, in which one image corresponded to the structural information(obtained via nuclear staining) and the other to the fluorescent signal
channel. Thus, the analysis of each pair required two steps: (1) registrationof the structural information to a reference and (2) alignment of both images
by applying the transformation (calculated during registration) to both thestructural image and the fluorescent image (Figure 7A). For image registration, we used advanced normalization tools (ANTs), which have proved successful in resolving a number of image registration issues (Murphy et al.,2011).

(D) Subtraction data of Light+ (1) and Light dVenus images, corresponding to sections in (B) and (C).
(E) Subtraction data of Light+ (1) and Light dVenus images, corresponding to coronal images in Figures 6B and 6C. Pink and light blue indicate regions where theVenus signals were stronger under Light+ and Light conditions, respectively. Areas where Venus signals were increased in the Light+ brain are indicated as in
Figure 6B. In (D) and (E), images were overlaid with the PI data of Light+ (1) specimen.
See also Figure S7.

12 Cell 157, 1–14, April 24, 2014 ª2014 Elsevier Inc.

Please cite this article in press as: Susaki et al., Whole-Brain Imaging with Single-Cell Resolution Using Chemical Cocktails and Computational
Analysis, Cell (2014), http://dx.doi.org/10.1016/j.cell.2014.03.042

SUPPLEMENTAL INFORMATION
Supplemental Information includes Extended Experimental Procedures, seven
figures, one table, and seven movies and can be found with this article online at
http://dx.doi.org/10.1016/j.cell.2014.03.042.
AUTHOR CONTRIBUTIONS
H.R.U., E.A.S., K.T., and D.P. designed the study. E.A.S. performed most of
the biological experiments. K.T. performed chemical screening. D.P. performed most of the computational image analysis. F.K. contributed to thechemical screening. T.T. and H.Y. contributed to the computational image
analysis. T.M.W. settled the LSFM imaging system. C.Y. and H.O. housed marmosets and prepared marmoset brains. M.E. and S.Y. prepared ArcdVenus brains. T.A. and H.K. produced R26-H2B-mCherry and -EGFP mice.
Y.S. produced recombinant fluorescent proteins. A.M. provided details on previous brain-clearing methods. All authors discussed the results and commented on the manuscript text.
ACKNOWLEDGMENTS
We thank the lab members at RIKEN CDB and QBiC, in particular, S. Hirahara,
J. Hara, H. Ukai, N. Koide, J. Yoshida, C. Imai, and Y. Niwa for their kind help in
preparing the materials and C. Jolley for helpful advice. We also thank T. Imaifor sharing the SeeDB protocol before its publication, H. Hama for testing
Scale protocol with our samples before its publication, H. Niwa for providing
a vector with a CAG promoter, H. Sekiya for supporting two-photon imaging
experiments, the LARGE, RIKEN CDB for housing the mice, Emi Kosano for
illustrating the mouse in Figure 6, and Olympus and Olympus Engineering
for optimizing the Ultramicroscope setup. This work was supported by the
Program for Innovative Cell Biology by Innovative Technology from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan, aGrant-in-Aid for Scientific Research (S) (Grant No. 25221004) and a Grant-inAid for Scientific Research on Innovative Areas (Grant No. 23115006) from
MEXT/Japan Society for the Promotion of Science (JSPS), the strategic
programs for R&D (President’s discretionary fund) of RIKEN, an intramural
Grant-in-Aid from the RIKEN Center for Developmental Biology and RIKEN
Quantitative Biology Center, a grant from Core Research for Evolutional
Science and Technology (CREST) and Precursory Research for Embryonic
Science and Technology (PRESTO) of Japan Science and Technology Agency(JST), the RIKEN Special Postdoctoral Research Program, a Grant-in-Aid fromJapan Foundation for Applied Enzymology, a Grant-in-Aid from the Shimabara
Science Promotion Foundation, and by the RIKEN Foreign Postdoctoral
Researcher Program.
Received: January 20, 2014
Revised: March 3, 2014
Accepted: March 27, 2014
Published: April 17, 2014

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