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

726 Cell 157, 726–739, April 24, 2014 ª2014 Elsevier Inc.

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
to clear brain samples (Hama et al., 2011). This urea-based reagent reduced quenching, and the resulting sample was suitable for both single-photon and multi-photon-based 3D tissue
observation. Other issues, including relatively long sample
treatment periods (weeks or months) and tissue swelling,
were resolved by 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 that such simple hydrophilic reagents
do not render whole-brain samples transparent enough for single-photon-based rapid whole-brain imaging. CLARITY, a
recently reported technique for brain clearing (Chung et al.,2013), rendered brain samples transparent by aggressively
removing lipids using electrophoresis. The resulting sample
was highly transparent and suitable for 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 the issues required for theefficient clearance of brain samples, an improved protocol
was still required for whole-brain imaging with single-cellresolution.
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 beendeveloped and implemented to facilitate this process in conventional human brain imaging (Avants et al., 2011). However,this kind of computational image analysis is rare for fluorescence imaging of whole mammalian brains due to the lackof whole-tissue histological counterstaining of cell nuclei,

which can be used for anatomical orientation. 3D images
acquired with sectional tomography or tissue-clearing protocols provide information on fluorescently labeled cells, but
not on the overall shape and characteristic anatomical structures that are required for the registration and alignment steps.
Even in a trial in which the image registration step was successful, manual determination of landmark structures wasnecessary (Gong et al., 2013). The larger number of images
generated in time-course experiments makes the need forautomated processing even more acute. Thus, efficientanatomical annotation will be required for whole-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 polyhydricalcohols, 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,and immerse a whole mouse brain in one chemical mixture
and then evaluate the clearing efficiency of the chemical bymeasuring the transmittance of the fixed, cleared whole brain.
For a more efficient evaluation protocol, we exchanged theorder of the fixation and isolation steps and introduced ahomogenization step so that many chemicals can be tested using only one mouse brain (Figure 1A and Extended Experimental Procedures). This ‘‘solubilization’’ assay using a homogenized suspension instead of a whole brain enabled the
reproducible, quantitative, and comprehensive evaluation ofchemicals for their 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 noted that a series of aminoalcohols (#4, #8, #9, #10, #15,
#16, #17) showed considerable brain tissue solubilizing activity,
Cell 157, 726–739, April 24, 2014 ª2014 Elsevier Inc. 727

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whereas polyhydric alcohols without amino groups (#1, #2, #3,#5, #12, #18, #19, #20) exhibited much less activity (Figure 1B
and Table S1). This may indicate that the cationic amino groupcontributes to solvating anionic phospholipids, the most abundant lipids in the brain. In addition, basic aminoalcohols preserved and even enhanced EGFP fluorescent signals, whereassome weakly acidic amine-free alcohols (#1, #2, #3, #11)
quenched the fluorescence (Figure 1C and Table S1). Manyfluorescent 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 otherweakly 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 tothis 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

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, 726–739, April 24, 2014 ª2014 Elsevier Inc. 729

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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.

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
730 Cell 157, 726–739, April 24, 2014 ª2014 Elsevier Inc.

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

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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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,

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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 thehorizontal section (Movie S1) of a CUBIC
Plane No.
whole brain from two directions (dorsal toventral, 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) andits virtual coronal sections (Figure 3B) enabled the 3D visualization of spatial gene expression patterns and the examination ofdetailed internal structures. Furthermore, we confirmed thatbackground 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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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).

Cell 157, 726–739, April 24, 2014 ª2014 Elsevier Inc. 731

A

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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

732 Cell 157, 726–739, April 24, 2014 ª2014 Elsevier Inc.

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 have

(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.

demonstrated 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 ofexperiments (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.
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
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 microscopy (Figure S5B). Furthermore, proteins in the spine werealso preserved in the CUBIC sample by immunostaining withan antibody against synapsin I, a phosphoprotein associatedwith synaptic vesicles in neural synapses (Navone et al.,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
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), theCell 157, 726–739, April 24, 2014 ª2014 Elsevier Inc. 733

A

C

B

D

E

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.

orbital 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
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
734 Cell 157, 726–739, April 24, 2014 ª2014 Elsevier Inc.

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 theseA

identified in Figures 6B and S6B but with improved clarity due
to the sharper combined image. The CUBIC protocol combinedwith CUBIC informatics enables gene expression profiling ofadult whole brains.
DISCUSSION

B

C

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.

brains 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

CUBIC 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
Cell 157, 726–739, April 24, 2014 ª2014 Elsevier Inc. 735

A

B

E

C

D

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)736 Cell 157, 726–739, April 24, 2014 ª2014 Elsevier Inc.

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.
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(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.

Cell 157, 726–739, April 24, 2014 ª2014 Elsevier Inc. 737

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).
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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Supplemental Information
EXTENDED EXPERIMENTAL PROCEDURESMice
The R26-pCAG-nuc-3 3 mKate2 knockin mouse strain was established in our laboratory. A vector harboring all of the required elements, including the CAG promoter from pPyCAG-BstXI-IP (Niwa et al., 1991), the cDNAs encoding the nuclear localization signalof the SV40 Large T-antigen (PKKKRKV) and the triplicated mKate2 from pmKate2-C (Evrogen), the woodchuck hepatitis post-transcriptional regulatory element (WPRE) from pTRIPZ (Thermo Scientific Open Biosystems), and the bovine growth hormone polyA
sequence from pPyCAG-BstXI-IP, was constructed by inserting these sequences at the SalI and NotI sites of pENTR-1A (Invitrogen).
In addition the Puro-resistance gene cassette (pPGK-PuroR-poly A) flanked by FRT sequences was inserted at the EcoRV site of thevector. The resulting expression vector (pENTR-1A_CAG-nuc-3 3 mKate2-WPRE-PuroR) was then mixed with the ROSA26 targeting
vector (Abe et al., 2011), which contains the Reading Frame Cassette B from the Gateway Conversion System (Invitrogen) at the blunted
KpnI and AscI sites, to perform the LR recombination reaction using the Gateway system. In the resulting targeting vector, the insertion
cassette was in the same orientation as the ROSA26 gene. The targeting vector was purified and introduced into HK3i C57BL/6 mouseembryonic stem (ES) cells as described previously (Kiyonari et al., 2010) with some modification, and the homologous recombined,
puro-resistant ES cell clones were isolated for further culture and expansion. An aliquot of the cells was lysed and screened for successful homologous recombination by PCR. The integrity of the targeted region was confirmed by PCR using primers annealing outside
the homologous recombination arms and within the inserted cassette. The primers used for the screening and the confirmation of
genome integrity were as follows: 1) forward primer annealing to the region upstream of the 50 homologous arm:
50 -TGCTGGCCTACTGCTGCCTCGATCTTAC-30 , 2) reverse primer annealing to the region downstream of the 50 homologous arm:
50 -AGGACAACGCCCACACACCAGGTTAGC-30 , 3) forward primer annealing to the region upstream of the 30 homologous arm: 50 CGTGGTGGAGCCGTTCTGTGAGACA-30 , 4) reverse primer annealing to the region downstream of the 30 homologous arm: 50 GGTGAAATGCTTGACTCCTAGACTT-30 , 5) reverse primer annealing to the CAG promoter: 50 -CAGCCAGGCGGGCCATTTACCGTAAGTTAT-30 , and 6) forward primer annealing to the poly-A region of the puro-resistance gene: 50 -TCCATCAGAAGCTGGTCGATC-30 . The copy number of the inserted cassette was confirmed with a quantitative PCR assay using primers annealing to the coding
sequences of the puro-resistance gene (forward primer: 50 -CTCGACATCGGCAAGGTGTG-30 , reverse primer: 50 GGCCTTCCATCTGTTGCTGC-30 ), normalized to the amount of TATA-box binding protein gene amplification (forward primer: 50 CCCCCTCTGCACTGAAATCA-30 , reverse primer: 50 -GTAGCAGCACAGAGCAAGCAA-30 ) (Tsujino et al., 2013) using the SYBR
Premix Ex Taq GC (Takara #RR071A) and the ABI PRISM 7900 (Applied Biosystems). The selected ES cell clones were injected into
8-cell-stage ICR embryos to generate 100% ES cell-derived chimeras (Kiyonari et al., 2010).
We also used the R26-H2B-EGFP (CDB0238K) and R26-H2B-mCherry (CDB0239K) mouse strains (http://www.cdb.riken.jp/arg/
reporter_mice.html) (Abe et al., 2011) and the Thy1-YFP-H transgenic mouse strain (Feng et al., 2000) to observe static fluorescentgene-expression patterns in whole-brain imaging (Figure 3, S3, S5 and S7).
For the SCN immunostaining assay (Figure 4 and S4), R26-H2B-EGFP knockin mice housed under 12 h:12 hr light-dark conditions
were sacrificed for fixation at ZT0 and ZT12. C57BL/6 wild-type mice or Cry1–/–, Cry2–/– mice (van der Horst et al., 1999) were adapted
to light-dark conditions for over 2 weeks followed by maintenance under constant dark (DD) conditions. The sampling was performed
after 24 hr of exposure (CT12 of day-0 to CT12 of day-1) to DD conditions, at CT12 and 18 of day-1, and at CT0 and 6 of day-2.
Samples were obtained from multiple animals within 30 min.
To obtain fixed brains from these mice, each animal was anesthetized with an overdose of pentobarbital (>100 mg/kg, i. p.; Somnopentyl, Kyoritsu Seiyaku, Japan). The subject was transcardially perfused with PBS containing 10 U/mL of heparin (Wako Pure
Chemical Industries, Japan) to flush the blood vessels and followed by perfusion with 4% (w/v) paraformaldehyde (PFA) in PBS(pH 7.4) for fixation. The acrylamide-embedded brain sample of CLARITY protocol was prepared as described previously (Chunget al., 2013).
The Arc-dVenus transgenic mice (line D) (Eguchi and Yamaguchi, 2009) were housed under DD conditions for about 2.5 days
followed by 1500 lux of white light stimulation from CT2.5. After 5 hr of the stimulation, the mice were sacrificed for fixation together
with an unstimulated animal (Figure 6A). We conducted the light stimulation and sampling of multiple animals within ± 30 min. For
sampling, the mice were deeply anesthetized with ether. Then, the subject was transcardially perfused with PBS containing
5 U/mL of heparin (Wako Pure Chemical Industries, Japan) to flush the blood vessels and followed by perfusion with 4% (w/v)
PFA in 0.1 M PB (pH 7.4) for fixation.
Each excised brain was postfixed with the same fixation solution for 18 to 24 hr at 4 C. The fixed brain was immersed in 20% (w/v)
sucrose in PBS at 4 C to remove PFA and for the following cryopreservation. If necessary, the sample was stocked in O.C.T.
compound (Sakura Finetek, Japan) at –80 C until use. When the frozen sample was used for CUBIC protocol, it was thawed andwashed with PBS and then immersed in CUBIC reagents. Note that the freezing step is merely for stocking samples and notcompulsory.
All experimental procedures and housing conditions were approved by the Animal Care and Use Committee of the RIKEN Kobe
Institute or by the Gifu University Animal Experiment Committee, and all of the animals were cared for and treated humanely in accordance with the Institutional Guidelines for Experiments using animals. All mouse strains were maintained by crossing onto a C57BL/6
background.

Cell 157, 726–739, April 24, 2014 ª2014 Elsevier Inc. S1

Marmoset
We studied a newborn infant marmoset at the age of three days, an offspring of a breeding pair aged three and four years. The
animals were housed in home cages measuring 1670 3 610 3 620 mm under 12 h:12 hr light-dark conditions (light: 08:00–
20:00). Each cage had four wooden perches, a food tray, and an automatic water dispenser. Animals were fed twice a daywith solid food (CMS-1, CLEA Japan, Tokyo, Japan) soaked in water, mixed with suitable amounts of powdered milk formula,honey, gluconic acid calcium, vitamin C, and lactobacillus probiotic. In addition, their diet was supplemented with chopped
boiled eggs or bananas once a week. Water was provided ad libitum. For fixed brain sampling, the three-day-old marmoset
was anesthetized with an overdose of pentobarbital (40 mg/kg, i.p.; Somnopentyl, Kyoritsu Seiyaku, Japan) after an intramuscular injection of ketamine (30 mg/kg; Ketalar, Daiichi Sankyo Propharma, Japan). The subject was transcardially perfusedwith PBS containing 10 U/mL heparin (Wako Pure Chemical Industries, Japan) to flush the blood vessels and followed by
perfusion with 4% (w/v) PFA in PBS (pH 7.4) for fixation. The excised brain was postfixed with the same solution for 18 to
24 hr at 4 C. The fixed brain was immersed in 20% (w/v) sucrose in PBS at 4 C and finally stocked in O.C.T. compound (Sakura
Finetek, Japan) at –80 C until use. Animals were maintained and handled in accordance with the recommendations of the UnitedStates National Institutes of Health, and all procedures were approved by the Animal Care and Use Committee of the RIKEN
Kobe Institute.
Chemical Screening
The usage of one fixed whole brain per one chemical compound (or one chemical configuration) is not desirable for first chemical
screening because it is time-consuming, labor-intensive and even unethical to prepare numbers of fixed mouse brains to test
many chemical configurations. Therefore, we developed a new evaluation protocol, in which we exchanged the order of fixationand isolation, and also introduced homogenization processes (chopping and sonication) in order to test many chemical configurations using only one mouse brain. An adult mouse brain was removed from the skull, chopped with a blade, suspended and sonicatedin PBS, and gradually mixed with the same volume of 8% (w/v) PFA in PBS (pH 7.4). The resulting fixed brain suspension was washedwith PBS several times and finally used for the solubilization assay in Figure 1A and 1B to evaluate the clearing efficiency of eachcocktail. The suspension was centrifuged to remove PBS, and resuspended in candidate chemical solutions (10 wt% in water) followed by 24 hr of incubation at room temperature. The OD600 of the incubated mixture was measured with the PowerWave XS andthe attached operation software (Bio-Tek). The relative OD600s were calculated by fitting to a standard curve generated by diluting
several concentrated mixtures of the suspension in PBS. The relative transmittance value of each sample (defined as 100% minus therelative OD600) was qualitatively consistent with the expected brain-clearing capability (Figure 1A, graph of the right panel). The
chemical solutions were also mixed with recombinant EGFP (0.1 mg/mL, final concentration) and incubated for 12 hr at room
temperature, and the resulting fluorescent signals were measured with the ARVO MX (PerkinElmer, USA). The values of the chemicalsolutions (without the fluorescent protein) were also measured and subtracted as the background signal. Relative fluorescence wascalculated by normalization with an EGFP sample that was mixed and incubated with PBS, and titrated to show linearity. All data were
calculated as an average of duplicated measurements, and two independent experiments were performed (Figure 1B and C). The
chemicals were also assayed for their effects on the fluorescence of EYFP, mCherry (0.1 mg/mL, final concentration) and mKate2(0.05 mg/mL, final concentration) in addition to EGFP in Figure 2C, but are shown as raw fluorescent values. To prepare the recombinant fluorescent proteins, the genes encoding them (containing a His-tag at the N terminus) were cloned into pET15b (Merck
Millipore) and transformed into the E. coli BL21/DE3 strain. Overexpressed proteins were purified with Ni-NTA agarose (QIAGEN)
according to the manufacturer’s instructions. The refractive indices of the solutions in the chemical screening were measured by
an Abbe refractometer (ATAGO, DR-A1, Japan).
We have developed improved brain tissue clearing reagents by comprehensive chemical screening described above. During
the screening process, we followed the principle that brain clearing reagents should be developed that: a) minimize light scatteringin the sample by adjusting the inner RI to approximately 1.5, and b) preserve the signals from fluorescent proteins and antibodies
to visualize functional activities and obtain structural information. Since hydrophobic lipids are a major source of light scatteringin the fixed brain, the adjustment of RIs using hydrophobic reagents has traditionally been used to transparentize the brain. The
classic BABB protocol involves an initial dehydration step with ethanol followed by an RI-adjustment step with benzyl alcohol:benzyl benzoate (1:2 ratio), resulting in transparent brain specimens. However, both steps suppress EGFP fluorescence (Hama et al.,2011). Among hydrophobic organic chemicals, the use of THF as a dehydrant and DBE as an RI-adjusting medium increased thepreservation of EGFP fluorescence with improved tissue transparency (Becker et al., 2012). However, EYFP fluorescence was still
significantly quenched by DBE (Ertürk et al., 2012; Ke et al., 2013). Thus, it may be intrinsically difficult for hydrophobic media toprovide efficient clearing with fluorescence signal preservation. In contrast, Scale was developed based on water-soluble clearing
reagents (Hama et al., 2011). Scale contains kosmotropic glycerol (which stabilizes intermolecular interactions) and chaotropic
urea (which disturbs hydrogen networks) as hydration-promoting agents. Since Scale promotes the optical clearing of brain specimens despite its relatively low RI (1.38), the chemicals in Scale may permeate the brain tissue efficiently. Scale also preserves thesignals of fluorescent proteins. The original Scale protocol (e.g., ScaleA2) is suitable for clearing fetal mouse brains, but has drawbacks such as a long incubation time and sample swelling (Ke et al., 2013). Another hydrophilic clearing reagent, SeeDB, a highly
concentrated fructose aqueous solution that functions as an RI-adjusting medium, overcomes these issues (Ke et al., 2013). Brain
specimens treated with SeeDB are transparentized within a few days, and do not show sample expansion. These features are
S2 Cell 157, 726–739, April 24, 2014 ª2014 Elsevier Inc.

partly due to SeeDB’s high RI and osmotic pressure, which leads to the exclusion of water molecules and the permeation of fructose molecules. However, highly myelinized adult brain samples treated with these hydrophilic reagents were not sufficiently transparent for observation using rapid whole-brain imaging with LSFM. Thus, while the hydration-promoting and hydrophilic clearing
reagents were effective in some cases, improvements were needed to increase their applicability. Although it may be difficult topromote dehydration with water-soluble chemicals, the simultaneous improvements in dehydration and RI homogenization by ahighly osmotic and RI-aqueous medium like SeeDB was a promising approach. Based on these previous findings, we sought
to develop a simple and efficient tissue-clearing protocol.
We used a comprehensive chemical screening approach to gain insight into the chemical mechanisms that contribute to effective
clearing. Our improved strategy reduced the number of mice required and facilitated the quantitative evaluation of a reagent’s
clearing ability by measuring the transparency of a homogenized suspension instead of a whole brain. Although polyhydric alcohols
including glycerol have traditionally been used, their clearing ability against hydrophobic lipids and proteins had not been quantitatively evaluated. Unexpectedly, #18 glycerol and other hydrocarbon polyols [except #11 1,4-bis(2-hydroxyethoxy)-2-butyne] did
not cause a significant increase in the transparency of brain suspension in our experiment (Figure 1B). In contrast, our screening
revealed that a series of aminoalcohols transparentized the suspension quite efficiently. Positively charged amino groups may
contribute to the neutralization of negatively charged phospholipids, which are the most abundant lipids in the brain (Morell andQuarles, 1999). As expected, all of the tested detergents were relatively effective in clearing the suspension. Among them, thenonionic detergents (except for a specific Triton X-100) quenched the EGFP fluorescence (Figure 1C). Deoxycholate was the best
candidate for clearing detergents in the first screening, but led to EGFP quenching in the presence of aminoalcohol and urea. Chaotropic agents such as urea are well-known surfactants that disrupt noncovalent interactions such as protein-protein, protein-lipid,
and lipid-lipid associations. Urea has been shown to clear brain tissue effectively (Hama et al., 2011), although the chemical mechanism is poorly understood. We further investigated the clearing ability of urea-related chemicals such as aprotic solvents without
amide, urea-like amides, and other chaotropic agents. Aprotic solvents without amide (#31 acetonitrile, #39 dimethylsulfoxide,
and #40 1,4-dioxane) and urea-like amides (#29 N,N-dimethylacetamide and #30 N,N-dimethylformamide) did not significantly clear
the suspension. Other urea-like amides (#32 oxamide, #34 N-carboxymethylurea, and #37 biuret) were difficult to dissolve in water.
Among the chaotropic agents, #38 thiourea caused higher transparency than #36 urea, while #33 guanidine hydrochloride did not
clear the suspension. Our chemical screenings in this study and the previous work on Scale indicated that urea’s chemical structure
and chaotropic ability would contribute to the efficient clearing of brain tissues. Taking into account the heterogeneity of the biochemical components in the brain, we prepared reagent 1, which consisted of aminoalcohol, Triton X-100 and urea, analogous to the components in ScaleA2 reagents (Figure S1B). Another urea-like molecule, formamide, can be also used for tissue-clearing as indicatedin the recent study (ClearT) (Kuwajima et al., 2013). Comparison between urea and formamide in the CUBIC protocol will be a subjectof future investigation.
Reagent 1 treatment of the whole mouse brain resulted in a highly transparent sample (Figure 2A). The lipid-rich white matter was
not completely cleared, however, suggesting that lipid removal was incomplete and that the RIs of the reagent and the sample wereimperfectly matched. We sought to identify additional reagents that might increase brain tissue transparency through RI adjustment.
As a solute’s concentration increases, the osmotic pressure and RI increase simultaneously. Thus, large water-soluble molecules
such as sugars are excellent candidates for solutes that will yield high-RI solutions. Fructose is one of the most water-soluble solutes,
but aminoalcohols obtained from our screening would react with the aldehyde group of fructose, resulting in a brownish color via theMaillard reaction. Thus, sucrose was selected instead. Previous studies used a 60% sucrose aqueous solution for brain clearing (Tsaiet al., 2009), but the clearing efficiency was much lower than for Scale or SeeDB (Hama et al., 2011; Ke et al., 2013). We prepared ahighly osmotic, high-RI solution, reagent 2 (RI = 1.48-1.49), by mixing concentrated sucrose with cocktails based on the reagent 1
composition. When reagent-1-treated brain samples were then subjected to reagent 2, deeper regions of the brain, including thedense lipidic myelin sheath were transparentized (Figure 2A). In addition, sample swelling was minimized because the permeationof chemicals into the tissue was balanced by osmotic dehydration during reagent 2 treatment. The combination of reagent 1,
Scale-inspired chemicals that promote tissue permeation, and reagent 2, a highly osmotic, high-RI aqueous reagent that promotes
RI-homogenizing dehydration, resulted in improved clearing of the adult mouse whole brain without denaturing fluorescent proteins
and antibodies.

Measurement of Light TransmittanceBrain sample transmittance was measured with the CM-5 spectrophotometer (Konica-Minolta, Japan). For this, 100% transmittancewas calibrated with a 5 3 10 mm slit, and samples were placed on a 5 3 2 mm slit for measurement. The blank value was measured
as the transmittance of the latter slit without the dish and sample. Each value was determined as an average of duplicate measurements. The ventral-to-dorsal transmittance of the central part of the whole brain immersed in the indicated reagents was measured
for Figure 2B. The central-to-lateral transmittance of the thickest part of a hemisphere placed on a plastic bottom dish without liquid
was measured for Figure 1E and S1A, to make the procedure easier and avoid difficulties cementing samples to the dish. As a result,some of the measured values exceeded 100%, which we speculate is a consequence of the hemisphere acting as a convex lensbecause of the mismatch in RI between the air and the CUBIC samples. In this case, the values are notated as relative values(A.U.) rather than % transmittance.
Cell 157, 726–739, April 24, 2014 ª2014 Elsevier Inc. S3

The CUBIC Protocol
ScaleCUBIC-1 (reagent 1) was prepared as a mixture of 25 wt% urea (Nacalai Tesque, 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-pisooctylphenyl ether/Triton X-100 (Nacalai Tesque, 25987-85, Japan). Some technical tips regarding reagent 1 preparation are
as follows: 1) N,N,N’,N’-tetrakis(2-hydroxypropyl)ethylenediamine is a highly viscous liquid and can be used as an 80 wt% working
solution. 2) The quality of Triton X-100 product seems critical for preserving fluorescent signals and we highly recommend the product indicated above. When the same chemical from other vendors is used, the quenching effect on recombinant fluorescent proteinsshould be checked. 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, 145-05605, Japan), and 0.1% (v/v) Triton X-100. We
referred the concentration of urea in ScaleA2 (4 M) (Hama et al., 2011), for determining its concentration in CUBIC reagents (25 wt%).
Other components were added as much as possible, considering fluorescent signals and viscosity. Both reagent 1 and 2 wereprepared just prior to use. When mixed, a hot stirrer should be used except during the addition of Triton X-100. Because water
evaporation will make it difficult for the highly concentrated chemicals to dissolve, the weight should be monitored frequently forthe addition of evaporated water during the mixing step. After confirming that all the mixed chemicals are dissolved, the reagentsare cooled to room temperature, and finally Triton X-100 is added. Both CUBIC reagents should be degassed before use.
For whole-brain clearing, fixed brain samples were immersed in reagent 1 (10 g per brain) at 37 C for 3 days with gentle shaking.
The solution was then exchanged, and the brain was immersed for an additional 3 to 4 days. At that point, if the white matter is not
significantly cleared, further immersion with exchanged reagent 1 may be needed. The treated brain was washed with PBS several
times at room temperature with gentle shaking, immersed in 20% (w/v) sucrose in PBS, degassed (required to prevent air bubbles
from remaining in the ventricle), and immersed in reagent 2 (10 g per brain) for 2 to 7 days. Further immersion increased the final transparency but also caused swelling of the sample. After imaging, the sample was again washed with PBS, immersed in 20% (w/v)
sucrose in PBS, and stocked in O.C.T. compound at –80 C. For nuclear staining with SYTO 16 (Life Technologies), 0.25 to 2 mMof the stain (depending on sample size) was added during reagent 1 treatment. For nuclear staining with propidium iodide (PI, Life
Technologies), 10 mg/mL of the stain was added during reagent 1 treatment. After the step, additional staining was needed: sample
was subjected to a freeze-thaw step followed by further incubation in 10 mg/mL of PI/PBS for 3 days at 37 C with rotation. Then, thesolution was exchanged followed by staining for an additional 3 days.
3D Immunostaining of CUBIC Samples
A fixed brain block was treated with reagent 1 for 3 to 6 days, washed with PBS, immersed in 20% (w/v) sucrose in PBS, and frozen in
O.C.T. compound at –80 C overnight. The frozen sample was then thawed, washed with PBS, and subjected to immunostaining withthe primary antibodies in 750 ml of 0.1% (v/v) Triton X-100, 0.5% (w/v) bovine serum albumin, 0.01% (w/v) sodium azide in PBS for
3 days at 37 C with rotation. The stained samples were then washed with 10 ml of 0.1% (v/v) Triton X-100 in PBS several times at
37 C with rotation and then stained with the secondary antibodies in 750 ml of 0.1% (v/v) Triton X-100, 0.1% (w/v) bovine serum
albumin, 0.01% (w/v) sodium azide in PBS for 3 days at 37 C with rotation. The stained samples were then washed with 10 ml of
0.1% (v/v) Triton X-100 in PBS several times at 37 C with rotation, immersed in 20% (w/v) sucrose in PBS, degassed, and immersed
in reagent 2 for 24 to 36 hr. Note that longer treatment with reagent 2 may cause nonspecific signals in the void structures such as
vessels and ventricles. The following antibodies were used for the staining: rabbit anti-VIP (Immunostar 20077, 1:300), goat antiCopeptin (Santa Cruz Biotechnology sc-7812, 1:100), rabbit anti-Synapsin I (Millipore AB1543P, 1:250), donkey anti-rabbit Alexa
647 and anti-goat Alexa 546 (Life Technologies, 1:750 each). These antibodies were mixed with the sample after filtration with an
Ultrafree-MC GV Centrifugal Filter (Millipore UFC30GV00). We have succeeded in the efficient 3D-IHC of all antibodies in our study.
Although we have not yet encountered less effective antibodies, we could not exclude the possibility of impaired immunostaining
resulting from the urea denaturation. The most important point may be that the antibodies are polyclonal. Also, peptide antibodies
such as rabbit anti-VIP and goat anti-Copeptin would minimize the urea denaturation effect. On the other hand, in case of the antibodies recognizing a specific protein conformation, the urea denaturation may critically inhibit the antigen-antibody reaction. In our
protocol, the fixed hypothalamus block was incubated in the antibody cocktail for 3 days at 37 C. In Figure 4C and the right panel of
Figure S4C, we found that AVP immunoreactive signal was observed in the depth of at least 800 mm. Therefore, the kinetics of penetration can be conservatively estimated at 270 mm/day at 37 C. Although VIP signal was not observed in the depth of 750 mm, theresult was consistent with previous reports that VIP fibers were terminated around the subparaventricular region (Abrahamson and
Moore, 2001).
Microscopy
Whole-brain fluorescence images were acquired with light-sheet fluorescence microscopy (LSFM) (Ultramicroscope, LaVision
BioTec, Germany), combined with 488 nm and 588 nm lasers (Coherent Sapphire488LP-100 and Sapphire588LP-50), a sCMOS
camera (Andor Neo 5.5) and a macrozoom microscope (Olympus MVX-ZB10) with 0.63 3 objective lens (Olympus MVPLAPO0.63X,
NA = 0.15, working distance = 87 mm) (Figure S3A). The camera and the microscope were connected to a camera adaptor (Olympus
MVX-TV1X), tube lens (Olympus MVX-TLU), and the Ultramicroscope filter wheel unit (LaVision BioTec, emission filters: ET525/50
and ET650/60), with adaptors (LaVision BioTec, LV AD MVX_1 and LV AD MVX_2). Samples were immersed in a 1:1 mixture of silicon
oil TSF4300 (Momentive Performance Materials, RI = 1.498) and mineral oil (Sigma-Aldrich, RI = 1.467) during image acquisition.
S4 Cell 157, 726–739, April 24, 2014 ª2014 Elsevier Inc.

Images were captured using 2 3 zoom (for mouse brain) and 1 3 zoom (for marmoset brain) with the MVX-ZB10. When 2 3 zoom
was used, the theoretical X-Y resolution of acquired image is about 4.7 mm. We prepared a customized sample holder for capturing
the image of a larger marmoset hemisphere and for easier sample handling. Each plane was illuminated from both the right and left
sides, and a merged image was saved. The exposure times were adjusted according to the fluorescent signal intensities of eachsample. We acquired 25 whole-brain images in total (4 3 single-channel whole mouse brains taken from two directions, 4 3 twocolor-channels whole mouse brains taken from two directions, 1 3 single-channel marmoset hemisphere taken from one direction)
for this study.
We quantified the level of background fluorescence in CUBIC samples (Figure 3F, 3G, 3H, and 3I). For the analysis, we used images
from the R26-pCAG-nuc-3 3 mKate2 mouse brain. First, we masked the regions outside the brain to precisely evaluate the background fluorescence inside the brain. To this end, we constructed the 3D NIfTI-1 image file for this brain and aligned the
Waxholm-Space atlas (i.e., reference brain) to the brain. We then created a binary mask using the aligned atlas and applied it to
the raw TIFF images so that all pixels that are not in an annotated region (i.e., pixels that fall outside the brain) have their intensity
set to zero. We used pixels with nonzero value (i.e., inside the brain) of the resulting images for the subsequent analysis. Thisdata set includes 646 horizontal slices, numbered from 0, so we then worked on slice 322, at the center of the brain (Figure 3F,
left). Our initial step was to manually determine two thresholds that separate the low-intensity signal (smaller than 8100), mediumintensity signal (between 8100 and 35000) and high-intensity signal (greater than 35000). This was obtained by extracting theseregions from the raw image (left), normalizing the three resulting images and assigning each to a channel of the false-color RGB
image. We color-coded these signals in blue, green and red, respectively (Figure 3F, middle). This identifies very distinct regions,and background fluorescence is in the blue region, and appears well-below the signal of interest, in the green and red regions.
This is confirmed in the right panel of Figure 3F, where all pixels with an intensity below 8100 are set to black. The background isvisibly removed, while the real fluorescence signal is unaffected. To obtain more reliable background fluorescence levels while avoiding edge effects, we extended this result to slices 161-483 (50% of the total data). The background fluorescence level was associatedwith a peak in the low-intensity region of the image histogram, shown in Figure 3G for three slices (161, 322 and 483). For example, thepeak of the background fluorescence is at intensity 7,616 for slice 322. Based on these histograms and on Figure 3F, we estimatedthat most background pixels have an intensity no more than 5% higher than this peak intensity (7616+5% = 7996.8). We therefore
used this threshold (5% higher than the background peak), and considered intensity above this threshold as signals (e.g., 50th
percentile of the signal intensity corresponds the median intensity for pixels between this threshold and the maximum intensity).
We repeated this analysis for other slices 161-483, and showed 95th, 80th, 65th, and 50th percentile of the signal intensity as wellas the peak intensity of the background fluorescence in Figure 3H. We also calculated the ratio between each percentile of the signal
intensity and the peak intensity of the background fluorescence (Figure 3I), which indicates that the fluorescence signal is significantly
brighter than the background peak.
Immunostained SCN and cerebral cortex images were acquired with an inverted confocal microscope (Leica TCS SP8) with 10 3
(Leica PL APO CS, NA = 0.40, working distance = 2.2 mm) or 20 3 (Leica PL APO CS2, NA = 0.75, working distance = 0.68 mm)
objective lenses, and with another inverted confocal microscope (Carl Zeiss LSM700) with a 10 3 (Plan Apochromat, NA = 0.45,
working distance = 2.1 mm) objective lens. To detect most of the stained signals in the unsaturated range of intensity, laser output
was gradually increased according to the Z-stack acquisition.
Arc-dVenus Tg brain images in Figure S6A were captured with fluorescence stereomicroscopy using the same settings as previously described (Eguchi and Yamaguchi, 2009). Two-photon imaging was performed using an upright multiphoton microscope(Olympus FV1000, BX61WI) combined with a MaiTai DeepSee HP-OL laser (Spectra Physics) and a 25 3 objective lens for Scale(Olympus, XLSLPLN25XSVMP, NA = 0.9, working distance = 8 mm) for Figure 2D and S2, or using another upright multiphoton
microscope (LSM7MP, Carl Zeiss) combined with a MaiTai DeepSee laser (Spectra Physics) and a 25 3 objective lens (Olympus
XLPLN25XWMP, NA = 1.05, WD = 2 mm) for Figure S5B.
Image Data Processing
All raw image data were collected in a lossless TIFF format (16-bit images for LSFM and two-photon microscopy data, and 8-bit
images for confocal microscopy data). 3D-rendered images were visualized and captured with Imaris software (version 7.6.4,
Bitplane). Brightness, contrast, and gamma of the 3D-rendered images were manually adjusted with the software at minimum
when visualized. Confocal and two-photon 2D images as well as a projection image of Thy1-YFP-H brain (Figure S5A) were processed and visualized with Fiji (http://fiji.sc/Fiji). Z-projection (maximum intensity) was performed to stack 2 to 21 sequential images
with the software. Brightness and contrast were manually adjusted at minimum when visualized. Gaussian filtering was performed on
the image in Figure S5B.
To facilitate the dissemination of neuroinformatics tools, the Neuroimaging Informatics Technology Initiative (NIfTI) introduced the
NIfTI-1 data format (.nii extension), which is now supported by most major software packages. Detailed specifications are available
on the Initiative’s website: http://nifti.nimh.nih.gov/nifti-1/. The computational analysis described below relies on this format. NIfTI-1
files were visualized using ITK-SNAP (Yushkevich et al., 2006) (Figure 7 and S7).
To generate a NIfTI-1 file from each stack of TIFF images obtained from LSFM, we first converted the images to 16-bit PNG files
using ImageMagick. During this conversion, we also downscaled to 25% (i.e., keeping one of every four images and changing theresolution of these images from 2560 3 2160 to 640 3 540). This was necessary because of memory limitations of the current
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software tools. Using the Convert3D tool from ITK-SNAP, each temporary stack was converted to a NIfTI-1 file, specifying the correct
spacing (0.02064 3 0.02064 3 0.04 mm, given the downscaling) and the correct orientation (which depended on the acquisition
direction). Each resulting NIfTI-1 file was visualized and manually checked for size and orientation. These NIfTI-1 files were the input
for the subsequent computational analysis.
The reference atlas used in our analysis was the Waxholm Space Atlas (Johnson et al., 2010). The Canon T1 image wasdownloaded from the website of the International Neuroinformatics Coordinating Facility: http://software.incf.org/software/
waxholm-space, and downscaled to 216 3 512 3 256 (again for memory concerns in subsequent steps). Spacing was adjusted
accordingly. To remove surrounding nonbrain tissue, this image was then multiplied by a binary mask calculated from the WHS annotation labels (using Convert3D), so that only labeled areas, (i.e., only brain regions), were conserved in the atlas NIfTI-1 file used forregistration, as detailed below.
The CUBIC Informatics
To facilitate analysis across samples, whole-brain images are registered and aligned to reference structures. In our analysis, wealigned images by pairs, in which one image corresponded to the structural information (obtained via nuclear staining) and the otherto the signal channel. For each pair, we performed two steps: (i) registration of the structural information to a reference, and (ii) alignment of both images, i.e., applying the transformation (calculated during the registration) to both the structural image and the signal
channel image (Figure 7A).
Image registration is a very active field of research, (see e.g., (Avants et al., 2011; Woods et al., 1992)). Here, we use
ANTs (Advanced Normalization Tools), which has proved successful in a number of image registration tasks (Murphy et al.,2011). When aligning our composite images with each other or to the Waxholm Space Atlas, we use affine transformations
only (with mutual information, 64 bins and 300000 samples as the parameters of the ANTS function). When aligning images ofthe same brain obtained from opposite acquisition directions, as part of the image composition process, we allowed small
nonlinear transformations to overcome differences in sharpness. These nonlinear transformations were calculated in ANTs using
SyN, the symmetric normalization model (Avants et al., 2008; Klein et al., 2009). The affine parameters were unchanged, and weused 10 3 10 3 10 iterations for SyN. One file was generated for each transformation. Nonlinear transformations are not compulsory: it is also possible to use affine transformations only for this step. The results shown in Figure 7 did not use SyN. Finally,alignment was performed using the WarpImageMultiTransform function and the computed transformations above as parameters(i.e., the affine transformation if using affine registration only, or both the affine transformation and the diffeomorphic transformation otherwise).
The overall analysis process is summarized in Figure S7A. We first aligned and combined images of the same brain acquired from
opposite directions. We then aligned these composite images to an internal reference and to the Waxholm Space Atlas. Finally, we
compared the signal channel images of different brains, e.g., by subtracting them [using the fslmaths function from FSL (Jenkinson
et al., 2012)] to highlight differences in expression in specific regions (Figures 7D and 7E).
When images from two acquisition directions were available for the same brain, combining them into a single composite 3D image
ensured optimal sharpness of the 3D image throughout the brain. This was motivated by the fact that, in images taken in the ventralto-dorsal (V-D) direction, the ventral horizontal slices were sharper, while in images taken in the dorsal-to-ventral (D-V) direction, thedorsal horizontal slices were sharper, (Figure S7C). When comparing two images of the same object, the image with more edges canbe considered sharper. We therefore estimated via the Sobel operator (Gonzalez and Woods, 2002) the ‘‘edge content’’ of eachhorizontal plane of the D-V and V-D 3D images of the same brain. This operation defined two unique values, n and m, such thatthe relative difference in ‘‘edge content’’ was below 10% for planes between n and m, (Figure S7D). We respectively noted SVD
i
and SDVi , the horizontal slice number i from the V-D image and the D-V image previously aligned to the V-D image. We could then
= w3SDV+ ð1  wÞ3SVD
construct the composite 3D image, one horizontal slice at a time, with the weighted sum SCOMPii and weights
iw defined as follows: w = 0 if i%n, w = 1 if iRm and w = i=ðm  nÞ + n=ðn  mÞ otherwise. The resulting image was sharp throughout
the brain, (Figure S7E). We implemented the operator and the weighted sum to operate on matrices containing raw intensity values.
To obtain these values from the NIfTI-1 files, and then to export the results back to the NIfTI-1 format, we used the FSL functions
fsl2ascii and fslascii2img, respectively.
SUPPLEMENTAL REFERENCES
Avants, B.B., Epstein, C.L., Grossman, M., and Gee, J.C. (2008). Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12, 26–41.
Gonzalez, R.C., and Woods, R.E. (2002). Digital image processing, Second Edition (Reading, MA: Prentice Hall).
Jenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W., and Smith, S.M. (2012). Fsl. Neuroimage 62, 782–790.
Kiyonari, H., Kaneko, M., Abe, S., and Aizawa, S. (2010). Three inhibitors of FGF receptor, ERK, and GSK3 establishes germline-competent embryonic stem cellsof C57BL/6N mouse strain with high efficiency and stability. Genesis 48, 317–327.
Klein, A., Andersson, J., Ardekani, B.A., Ashburner, J., Avants, B., Chiang, M.C., Christensen, G.E., Collins, D.L., Gee, J., Hellier, P., et al. (2009). Evaluation of 14
nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 46, 786–802.

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Kuwajima, T., Sitko, A.A., Bhansali, P., Jurgens, C., Guido, W., and Mason, C. (2013). ClearT: a detergent- and solvent-free clearing method for neuronal and nonneuronal tissue. Development 140, 1364–1368.
Morell, P., and Quarles, R.H. (1999). Characteristic composition of myelin. In Basic Neurochemistry: Molecular, Cellular and Medical Aspects 6th edition, G.J.
Siegel, B.W. Agranoff, R.W. Albers, et al., eds., (Philadelphia: Lippincott-Raven), http://www.ncbi.nlm.nih.gov/books/NBK28221/.
Tsai, P.S., Kaufhold, J.P., Blinder, P., Friedman, B., Drew, P.J., Karten, H.J., Lyden, P.D., and Kleinfeld, D. (2009). Correlations of neuronal and microvasculardensities in murine cortex revealed by direct counting and colocalization of nuclei and vessels. J. Neurosci. 29, 14553–14570.
Tsujino, K., Narumi, R., Masumoto, K.H., Susaki, E.A., Shinohara, Y., Abe, T., Iigo, M., Wada, A., Nagano, M., Shigeyoshi, Y., and Ueda, H.R. (2013). Establishment of TSH b real-time monitoring system in mammalian photoperiodism. Genes Cells 18, 575–588.
Woods, R.P., Cherry, S.R., and Mazziotta, J.C. (1992). Rapid automated algorithm for aligning and reslicing PET images. J. Comput. Assist. Tomogr. 16, 620–633.

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Figure S1. Development of CUBIC Reagents by Comprehensive Chemical Screening, Related to Figure 1(A) Development of reagent 2, a high RI version of reagent 1. Reagent 2 candidate solutions contained 50 wt% sucrose or glycerol, 25 wt% urea, 10 wt% of anaminoalcohol and 0.1% (v/v) Triton X-100. Fixed mouse hemispheres were treated with reagent 1 (#10/#25/#36) for 5 days, washed with PBS, and immersed ineach reagent 2 for 24 hr. The refractive indices of sucrose- or glycerol-containing reagent 2 were 1.48–1.49 and 1.45–1.46, respectively. After the incubation ofreagent-1-treated hemispheres with the various reagent 2 candidate solutions, transmittance (360-740 nm) was measured as in Figure 1E. Sucrose-based
solutions resulted in higher transmittance values than glycerol-based solutions, and 2,20 ,20 ’-nitrilotriethanol/triethanolamine (#16) tended to clear the samples
more reproducibly than the other tested aminoalcohols. Note that some of the values exceeded 100 as in Figure 1E (see Extended Experimental Procedures forthe details). The values represent the average ± SEM from two independent samples. (B) Recipes of CUBIC reagents and ScaleA2. Aminoalcohols were chosen inplace of glycerol. We referred to the concentration of urea (#36) in ScaleA2 (4 M) for determining its concentration in CUBIC reagents (25 wt%). However, it wascalculated as wt% for ease of preparing the mixtures. Other components were added as much as possible, considering fluorescence signals and viscosity. (C)
CUBIC clearing using an acrylamide-embedded brain prepared according to the CLARITY protocol. The reagent 1 and 2 were used to clear an acrylamideembedded mouse brain (Chung et al., 2013). Although the clearing was less efficient compared to the results using ‘‘fix-only’’ samples (Figure 2A), the protocol
could be applied to the polymer-embedded sample.

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Figure S2. Two-Photon Deep Brain Imaging of a SCALEVIEW-A2-Treated Thy1-YFP-H Tg Hemisphere, Related to Figure 2
Two-photon deep brain imaging of Thy1-YFP-H Tg hemisphere, treated with commersially available ScaleA2 solution (SCALEVIEW-A2 from Olympus) as in thesame condition of Figure 2D (for 3 days at 37 C). The resulting sample was observed using two-photon microscopy as in Figure 2D. The performance of ScaleA2
in a Thy1-YFP-H Tg brain after treatement for 7 days is found in the Scale original paper (Hama et al., 2011), and recent update of Scale technology will beavailable at http://cfds.brain.riken.jp.

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Figure S3. Details of the CUBIC Application to Rapid Whole-Brain Imaging of Various Fluorescent Proteins, Related to Figure 3(A) Setup of the light-sheet fluorescent microscopy (LSFM) equipment used in this study. (B) The R26-pCAG-nuc-3 3 mKate2 mouse brain (from a 5-week-old
mouse) shown in Figure 3A and 3B. Images were acquired from the dorsal to ventral (D-V) direction and depicted from the ventral side in order to directly comparewith Figure 3. Z-stack: 10-mm step 3 646 planes, with 0.28 s 3 two illuminations. (C) D-V image of the R26-H2B-mCherry mouse brain (from a 6-month-old
mouse) shown in Figure 3C. Z-stack: 10-mm step 3 696 planes, with 4 s 3 two illuminations. (D) D-V image of the R26-H2B-EGFP mouse brain (from a 6-monthold mouse) shown in Figure 3D. Z-stack: 10-mm step 3 667 planes, with 4 s 3 two illuminations. (E) D-V image of the Thy1-YFP-H Tg mouse brain (from a 2month-old mouse) shown in Figure 3E. Z-stack: 10-mm step 3 733 planes, with 0.3 s 3 two illuminations. (F) and (G) Selected acquired horizontal images andreconstituted sagittal and coronal images of the R26-pCAG-nuc-3 3 mKate2 knockin brain (V-D imaging) and the R26-H2B-mCherry knockin brain (V-D imaging)shown in Figure 3A – 3B and 3C, respectively. Both of the genes were knocked into the same ROSA26 locus but were regulated by different promoters (the CAG
promoter versus the endogenous ROSA26 promoter). The expression patterns of these proteins were compared with the corresponding reconstituted sections.
Areas of differential expression are indicated. Th, thalamus; Px, ventricular choroid plexus; Me, medulla; Gu, granular layer of cerebellum. A, anterior; P, posterior;
R, right; L, left; D, dorsal; V, ventral.

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Figure S4. Reproducibility in the Application of CUBIC to the Adult Brain Imaging of 3D Immunostained Samples, Related to Figure 4(A) An independent set of 3D-IHC imaging results corresponding to Figure 4D is shown. WT and Cry1–/–, Cry2–/– samples were stained with antibodies to VIP and
Copeptin. Staining of both the WT and Cry1–/–, Cry2–/– SCN (day-2, CT0) was partially incomplete due to a scar in the sample (indicated as ‘‘s’’ in the ventral
images). (B) The R26-H2B-EGFP mice housed under 12 h:12 hr light-dark conditions were sacrificed at the indicated zeitgeber time (ZT), and the fixed brains were
subjected to a similar 3D-IHC analysis. Consistent with the results in Figure 4D, the dorsal fibers close to the third ventricle were more strongly stained at ZT0 than
ZT12. In both panels, the depth of the horizontal images is approximately 140 mm on the ventral side and 290 mm on the dorsal side (Z-projection with maximum
intensity, 1.98-mm step 3 3 planes in (A) and 3.76-mm step 3 2 planes in (B) from the most ventral plane of the SCN region. Strong signals of the neuropeptides
detected in the dorsal side of SCN at CT0/ZT0 are indicated with arrowheads. (C) Enlarged 3D-IHC analysis images of the other R26-H2B-EGFP mice SCN (leftand middle panels) and PVN (right panel), sacrificed at the ZT12 (Z-projection with maximum intensity, 3.76-mm step 3 2 planes). Images were acquired andprocessed in a similar but an optimized condition for each sample so that most of the stained signals were obtained in the unsaturated range of intensity.

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Figure S5. CUBIC Is Scalable in Its Application to Subcellular Structure Imaging, Related to Figure 5(A) Axons in the pontine-medullary region of the Thy1-YFP-H Tg CUBIC brain sample, observed with LSFM. The image is a part of the Z-projection image of theV-D YFP channel imaging data shown in Figure S7B (Z-projection with maximum intensity, 10-mm step 3 21 planes). Py, pyramidal tract. (B) Spines in the cerebral
cortex of the Thy1-YFP-H Tg CUBIC brain sample, observed with two-photon microscopy. The image is part of the Z-projection image of two-photon imaging
data (Z-projection with maximum intensity, 0.5-mm step 3 20 planes). (C) Immunostaining with a Synapsin-I antibody in the cerebral cortex of the Thy1-YFP-H Tg
CUBIC brain sample, observed with confocal microscopy. Part of an original acquired image is shown.

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Figure S6. Whole-Brain Nuclear Counterstaining in CUBIC Enables the Visualization of Neural Activities Induced by Environmental Stimulation, Related to Figure 6(A) Venus signals (green) of Arc-dVenus Tg mouse brains, which were used for Figure 6 and S6B, were also observed with fluorescent stereomicroscopy. A region
including the visual area in the cortex is indicated by a dotted ellipse, encompassing the area where the Venus signals appeared in Light+ brains. (B) The Light+ (2)Arc-dVenus Tg mouse brain was subjected to CUBIC treatment, and two-color D-V images were acquired with LSFM (Z-stack: 10-mm step 3 633 planes) as in
Figure 6B. Increased fluorescent signals were observed in the same regions as observed in the Light+ (1) brain shown in Figure 6B.

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Figure S7. Computational Image Analysis in CUBIC Enables Gene Expression Profiling of Adult Whole Brains, Related to Figure 7(A) Overview of computational analysis steps. For each brain, we first combined imaging data acquired in the ventral-to-dorsal (V-D) and dorsal-to-ventral (D-V)
directions. To do so, we first aligned the D-V and the V-D brain images and then considered the sharpness of each horizontal plane to generate a single clear 3D
image as in (C)–(E). These samples were then aligned to an internal reference brain, and to a reference atlas. This was achieved by registering the image containing
structural information, and then applying the corresponding transformation to both the structural and the signal image of the same brain. Finally, the aligned signal
images were compared and contrasted using methods such as subtraction, to highlight differences between samples. (B) A data set consisting of reconstituted
3D images acquired from a single Thy1-YFP-H Tg mouse brain subjected to whole-brain nuclear counterstaining with PI is shown. Structural information obtained
by PI staining was used to calculate the transformation matrix. Z-stack: 10-mm step 3 722 planes for V-D imaging and 10-mm step 3 797 planes for D-V imaging,with 0.6 s 3 two illuminations for YFP and with 0.15 s 3 two illuminations for PI, respectively. Scale bar: 1 mm. (C) Image sharpness depended on acquisition
directions. The raw data in (B) were converted to a NIfTI-1 file for further analysis. We downscaled original image data to 25% (i.e., keeping one of every four
images and changing the resolution of these images from 2560 3 2160 to 640 3 540). During conversion, the same plane number was assigned to equivalentplanes of both images. In this case, the ventral horizontal planes were sharper in the V-D image, while dorsal horizontal planes were sharper in the D-V image. The
images obtained in two directions were combined to obtain a single clear 3D image. (D) Estimating the edge content to combine the two acquisition directions. A
classic edge detection method is to use the Sobel operator (noted Ei,j here), which corresponds to a gradient estimation at pixel (i,j) of an image. Summing thisvalue over all pixels of the image gives us the edge content Etot of the image. Plotting Etot as a function of the plane number, for the V-D and D-V images, confirms
the observation from (C). If we note these values EVD and EDV, respectively, we can define a unique range [n,m] where jEVD - EDVj / EVD < 0.1. Finally, the values for n
and m are used to design a weighted sum of the V-D and D-V images (see Extended Experimental Procedures). (E) Combining the compensated V-D and D-Vimages from a single Thy1-YFP-H brain (NIfTI-1 file). n = 87, m = 93.

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