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https://doi.org/10.1038/s41596-019-0240-9

Advanced CUBIC tissue clearing for whole-organcell proﬁling

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Katsuhiko Matsumoto 1,10, Tomoki T. Mitani1,2,3,10, Shuhei A. Horiguchi1,4,10, Junichi Kaneshiro5,
Tatsuya C. Murakami6, Tomoyuki Mano7,8, Hiroshi Fujishima1, Ayumu Konno 9,
Tomonobu M. Watanabe5, Hirokazu Hirai9 and Hiroki R. Ueda1,6,7,8*
Tissue-clearing techniques are powerful tools for biological research and pathological diagnosis. Here, we describe
advanced clear, unobstructed brain imaging cocktails and computational analysis (CUBIC) procedures that can be applied
to biomedical research. This protocol enables preparation of high-transparency organs that retain ﬂuorescent protein
signals within 7–21 d by immersion in CUBIC reagents. A transparent mouse organ can then be imaged by a high-speed
imaging system (>0.5 TB/h/color). In addition, to improve the understanding and simplify handling of the data, thepositions of all detected cells in an organ (3–12 GB) can be extracted from a large image dataset (2.5–14 TB) within 3–12 h.
As an example of how the protocol can be used, we counted the number of cells in an adult whole mouse brain and otherdistinct anatomical regions and determined the number of cells transduced with mCherry following whole-brain infection
with adeno-associated virus (AAV)-PHP.eB. The improved throughput offered by this protocol allows analysis ofnumerous samples (e.g., >100 mouse brains per study), providing a platform for next-generation biomedical research.

Introduction
Tissue-clearing techniques and high-resolution volumetric imaging by light-sheet ﬂuorescencemicroscopy1–4 with appropriate ﬂuorescent labeling can provide comprehensive cell information suchas cell type, shape, state and distribution of cells of interest throughout an organ. In particular, tissueclearing techniques combined with light-sheet ﬂuorescence microscopy are a powerful tool toquantify rare cells such as stem cells, metastatic cells or activated neurons in a whole organ forbiological research and pathological diagnosis5–9. Furthermore, advancing biomedical researchrequires numerous experiments and veriﬁcations; thus a simple high-throughput and stable protocol
is important to facilitating comprehensive whole-organ cell proﬁling. Here, we describe a wholeorgan cell proﬁling pipeline that includes advanced tissue-clearing methods, use of a novel high-speed
volumetric imaging system with a customized light-sheet ﬂuorescence microscope (LSFM) and ahigh-speed cell-nucleus-detection algorithm (Fig. 1). We have previously used this protocol to createtransparent whole mouse organs or human organ blocks10, as well as whole-brain cell proﬁling ofmice from 1 week old to 8 weeks old, demonstrating substantial changes in cell numbers in particularareas during postnatal development11.

Tissue-clearing methods
To prepare a transparent organ for volumetric imaging, three major approaches have been reported:hydrophobic, hydrophilic and hydrogel-based tissue-clearing methods. Hydrophobic tissue-clearing
methods (e.g., BABB, 3DISCO, uDISCO, iDISCO)4,12–15 lead to high-transparency organs within a
few days. However, some organic solvents require careful handling14,] and the cleared organ
will shrink, which is a disadvantage for high-resolution imaging. Hydrogel-based methods (e.g.,
CLARITY)16 provide high-transparency organs within a week, but it is difﬁcult to apply to many
1
Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, Osaka, Japan. 2Faculty of Medicine, Osaka University, Osaka,
Japan. 3Osaka University Hospital, Osaka, Japan. 4Department of Electrical Engineering and Information Systems, Graduate School of Engineering, The
University of Tokyo, Tokyo, Japan. 5Laboratory for Comprehensive Bioimaging, RIKEN Center for Biosystems Dynamics Research, Osaka, Japan.
6
Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. 7Department of Information Physics andComputing, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan. 8International Research Center for
Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Tokyo, Japan. 9Department of Neurophysiology & Neural Repair, Gunma University
Graduate School of Medicine, Maebashi, Japan. 10These authors contributed equally: Katsuhiko Matsumoto, Tomoki T. Mitani, Shuhei A. Horiguchi.
*e-mail: uedah-tky@umin.ac.jp

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Clearing step

Dissection
Step 1

Imaging/data analysis step

Mouse
Perfused PBS and 4% PFA
10–15 minFixation
Step 2

Postfixation in 4% PFA
0.5–1 dWash
Step 3

PBS
0.5 d
LSFM
5–12 hImaging
Steps 6–12

Lipid removal
Step 4A(i–iii)(Optional)
Staining
Step 4A(iv & v)

CUBIC-L
3–6 d

Lipid removal
Step 4B(i–iii)

CUBIC-1
6–15 dRaw images (4–12 TB)

Staining
4d(Optional)
Staining
Step 4B(iv & v)

Alignment
Steps 19–21
1–8 h

Cell
detection
Steps 13–18
2–8 h
Staining
4d

Raw image

RI matching
Step 4A(vi & vii)

CUBIC-R+2d

Gel embedding
Step 5Swelling
Step 4B(vi)

CUBIC-X1
2.5 d

RI matching
Step 4B(vii)

CUBIC-X2
1.5 d

2% agarose
1d

Point data

Direct comparison
or
CUBIC-Atlas

Fig. 1 | Overview of the advanced CUBIC pipeline in whole-organ cell proﬁling. This pipeline comprises three major
stages (tissue clearing, imaging and image analysis). We provide two kinds of clearing protocols: a rapid and highquality protocol using CUBIC-L and CUBIC-R+ (Step 4A), which takes at least 7 d for adult mouse brain, and a tissue
expansion protocol for high-resolution imaging using CUBIC-X (Step 4B), which takes up to 21 d for adult mousebrain. In addition, both protocols can include staining with an appropriate nuclei-staining dye, which takes 4 d. Rapid
volumetric imaging can be performed with a customized LSFM with the MOVIE system (Steps 6–12). From thecollected volumetric images, cells are detected by GPUs and CPUs and converted to point data. When analyzing themouse brain, the result data are analyzed with CUBIC-Atlas. Scale bars, 5 mm. All experiments followed the relevant
governmental and institutional guidelines for animal experiments.

samples without dedicated equipment for electrophoresis. In addition, acrylamide has been reported
to be carcinogenic and toxic in the liquid state17. To overcome these disadvantages, the passive clarity
technique (PACT)18,19 for parallel handling and acrylamide-free methods such as SHIELD20, whichuses a safer ﬂexible polyepoxide instead of acrylamide for hydrogel formation, has been developed.
Hydrophilic tissue-clearing methods using X-ray contrast agents, sugars or PEG were ﬁrst describedby the Tuchin group21–24 and have led to the development of other hydrophilic tissue-clearing
methods (e.g., CUBIC, Scale and SeeDB)25–29. Desirable attributes for tissue-clearing techniques inroutine biomedical research are safety, simple operation, scalability, low environmental burden andhigh transparency. Hydrophilic tissue-clearing approaches have the potential to fulﬁl all of theserequirements. CUBIC reagents that are based on hydrophilic reagents have been developed toprioritize safety and decrease environmental burden, and these protocols require only sample
immersion in clearing media, in order to minimize the number of experimental procedures and to
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c

b

After
dissolution

Beforedissolution

a

Brain

g

f

e

Beforedissolution

d

y:

After
dissolution

i

h
50

mm

x: 3

z : 14 mm

2m

j

k

m

l

Top
MarginSampleMarginBottomFig. 2 | Procedures for tissue clearing and gel embedding. a, Preparation of CUBIC-R+. This reagent can be easily dissolved by mild heating andstirring. b,c, An incubator with a shaker (b) and a hybridization incubator (c) that we use for steps requiring temperature control for the clearing
procedure; inset shows tube containing brain. d, Preparation of 2% agarose–CUBIC solution. Agarose can be dissolved by repeated heating using a
microwave and shaking. e, A water bath for the degassing step. f, 2% agarose–CUBIC solution after the degassing step. g, The sample is immersed in
2% agarose–CUBIC solution before gelation. h, A mold for gelation. It is designed for mouse brain. i, x–y view after gelation. Scale bar, 10 mm. j, y–z
view after gelation. Scale bar, 5 mm. A margin of 1 mm or more is recommended on the top and bottom of the sample. k, Removing the sample gelfrom the mold. l, Fixation of the gel on the customized sample holder. All experiments followed the relevant governmental and institutional guidelines
for animal experiments.

enable easy handling of multiple samples. An additional advantage of the CUBIC reagents is thatamino alcohols are able to decolor endogenous heme in mouse whole-organ and whole-body samples27,30. Since CUBIC reagents were ﬁrst reported, the reagents and protocols have been continuously updated to improve the transparency and to reduce the handling time10,11. In the present
protocol, we describe two types of whole-organ clearing procedures: the latest (to our knowledge)
protocol for rapid and high-quality organ clearing using CUBIC-L and CUBIC-R+ (Step 4A) and thetissue expansion–based CUBIC-X protocol for high-resolution imaging (Step 4B). These procedures
are applicable to a whole mouse brain of any strain, age and sex. To apply them to other mouseorgans or organisms, users may need to make some modiﬁcations to the procedure, such as changing
reagent volumes and immersion durations. The details are described below (Fig. 2 and Steps 1–5).

Methods of volumetric imaging
Historically, imaging technologies have been critical to advancing biomedical research and, recently, it
has become important to be able to image whole organs with single-cell resolution. Almost all biomedical research requires analysis of numerous samples; therefore, increasing imaging throughput isessential to research efﬁciency. Confocal microscopy and multiphoton-excitation ﬂuorescence microscopy are often used to obtain volumetric images. However, in general, the imaging speed is not suitable
for large specimens such as a whole adult mouse brain31–33. Therefore, destructive methods such asserial-sectioning-based tomography or confocal microscopy are used for volumetric imaging. For
example, a high-speed serial-sectioning imaging system (FAST) that enabled imaging of a whole mousebrain in 2 h and 24 min was reported34. However, such destructive methods destroy the sample and3508

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prevent users from being able to reanalyze the samples. Light-sheet ﬂuorescence microscopy can beused to non-destructively obtain volumetric images of the intact transparent organ because imaging
throughput is high and the light and physical damage is lower than that of any other imaging
methods3,35. Using the homogeneous 2.2-fold expansion protocol (CUBIC-X) and a customized LSFM
equipped with a 10× objective lens, we previously succeeded in high-resolution imaging of a whole
mouse brain (amounting to 14 TB of data) from which all the cell nuclei were detected and analyzed11.
However, this imaging speed cannot be applied to many samples because the image acquisition of the
expanded whole mouse brain required 4 d. To overcome this challenge, we developed the moving
observation with efﬁcient real-time autofocus (MOVIE) system for efﬁcient volumetric imaging, which
integrates (i) continuous acquisition (MOVIE-scan), (ii) real-time autofocusing (MOVIE-focus) and(iii) skipping of blank images (MOVIE-skip). Consequently, imaging throughput was increased by ﬁve
to eight times. The details are described below (‘Whole-organ imaging’ and ‘MOVIE advanced highspeed imaging system’ in ‘Experimental design’, Figs. 3–6 and Steps 6–12).

Cell-nucleus-detection algorithm
In parallel with the rapid technological advancements in volumetric imaging, informatics tools havealso been improved. In recent years, whole-organ-scale analysis of mammals, ﬁsh or ﬂies has beendeveloped2,11,15,27,30,36–42. For cellular-resolution volumetric images, segmentation of cell bodies or
nuclei has been attempted in many studies5,41,43–52. Various algorithms for blob (cell-nucleus)
detection were reviewed by Xing and Yang in 2016 (ref. 53). Most algorithms locate points of interestand then delineate the boundaries; however, our algorithm, which is based on the algorithm reported
by Zhang et al.54, performs the segmentation without locating points. We extended their algorithm inseveral places to make it applicable to 3D whole-organ images.
Here, we summarize three distinct challenges for blob detection in 3D whole-organ-scale images.
First, touching or overlapping blobs are difﬁcult to separate correctly. Three-dimensional images haveanother dimension in which blobs can be connected in more complex ways. Second, heterogeneity inmorphology and brightness of blobs over a single organ prevent us from applying most of the simple
algorithms. Heterogeneity in brightness could stem from congestion of blobs and decay of laser
illumination intensity. Third, a massive number of images have to be processed in a short time forpractical usage. In terms of effective utilization, the throughput of the informatics pipeline should be
about the same as that of the volumetric imaging.
To overcome these challenges, we propose a cell-nucleus-detection algorithm based on 3D
Hessian-based difference of Gaussian (3D-HDoG) with intensity normalization and have implemented a highly parallelized algorithm to utilize the parallel processing capabilities of multiple CPUs
and graphics processing units (GPUs)11,54. In regard to accuracy, this algorithm attained an F score of>90% overall for the adult whole mouse brain cleared by the CUBIC-X protocol. It also achieved a>90% F score for the CUBIC-L/R+-treated adult whole mouse brain, except for the areas withextremely high cell densities, such as the olfactory bulb and cerebellum. Using a higher-magniﬁcation
objective should improve the F score in areas with high cell density. Because CUBIC-X expands
organs, it has consequences similar to those for increasing the magniﬁcation or using a thinner lightsheet. Details of this cell-nucleus-detection algorithm are described below (‘Cell-nucleus detection
and annotation’ in the ‘Experimental design’ section, Figs. 7,8 and Steps 13–21).
Experimental design
Overview of the advanced CUBIC pipeline
Here, we focus on describing (i) the rapid and highly transparent protocol (CUBIC L/R+; Step 4A)
and tissue expansion protocol (CUBIC-X; Step 4B), (ii) high-throughput whole-organ imaging with acustomized LSFM (Steps 6–12) and (iii) cell-nucleus detection using multiple GPUs and CPUs, andcomparison between samples and annotation in single-cell resolution using CUBIC-Atlas (Steps
19–21) (Fig. 1).
Tissue clearing
Here, we provide two organ-clearing procedures: a rapid and high-performance CUBIC-L/R+ protocol(Step 4A) and a tissue expansion–based CUBIC-X protocol for high-resolution imaging (Step 4B).
These procedures have been used previously10,11, but more details are given here (Fig. 2a–c). Users
should select appropriate clearing protocols for their research. The CUBIC-L/R+ protocol (Step 4A) is
suitable for high-quality imaging, cell counting of low-cell-density organs or regions, or low-resolution
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NATURE PROTOCOLScbf = 100 f = 50
z-Galvo

Mirrorz Stagef = 50y Stageθ Stagef = 50Dichroic mirror

Detection
objective lensx Stage
Detection
objective lensf = 100

Illumination
objective lensOil chamber

Tube lensIllumination
objective lenssCMOS
FilterFilterIris

f = 50

11–15 tilesGelf = 100
sCMOS

Mirroryf = 50y-Galvo x
Mirrorx

f = 100 f = 50

Light sheet

z-Galvo

Detection
objective lens

Laser
z

θ
z

e

θ = 180°

14–20 tiles

f = 30Irisθ = 0°

x-Galvo
f = 50f = 150xdTube lensFlipping
mirroryzyx

Cerebral cortex

Olfactory bulb

ThalamusStriatum

Horizontal (x, y )Coronal (x, z )ymCherryRD2x
Hippocampus

Midbrain

Cerebellum

Fig. 3 | Whole-organ imaging with a customized LSFM. a, Schematic of a customized LSFM. b, Illustration of observation part of a customized LSFM.
c, Picture of sample gel after installation on an LSFM. d, Procedure for imaging. Each stack of images is collected at the z range at a 7–7.5 mm
thickness; 12 × 16 tiles (total 192 tiles) were acquired for half of a CUBIC-L/R+-treated mouse brain. After acquisition from the θ = 0° side, it
automatically rotates and acquires the θ = 180° side. e, Volume-rendered and single-plane images of a brain transduced with AAV-PHP.eB:NSE-H2BmCherry (mCherry, green) counterstained with RD2 (red), which is cleared by CUBIC-L/R+. Overlapped signals are shown in yellow. A volumerendered image is shown in the center. Single-plane and magniﬁed images are shown for cerebral cortex, thalamus, hippocampus, midbrain,
cerebellum, olfactory bulb and striatum. Both horizontal (x–y) and coronal (x–z) views are also shown. Scale bars, 2 mm (3D image), 200 μm (singleplane images) and 25 μm (magniﬁed images). All experiments followed the relevant governmental and institutional guidelines for animal experiments.
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PROTOCOLNATURE PROTOCOLSa Stop and exposure
Acquisition cycle time
Exposure
50 msStage move
150 msExposure
50 msStage move
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MOVIE

Stage move
Exposure
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Exposure
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10
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MOVIE
100 μm each
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sit -shion ee
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)

0.8
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The stage moving distance in an
acquisition cycle time (μm/55 ms)

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MOVIE
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1
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FWHM (μm)

2,000 3,000 4,000
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Ideal
Measured

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1/Exposure time (s–1)

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1,000 2,000 3,000 4,000 5,000120
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2030Relative DCTS

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Imaging speed (fps)

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xy

xz

Fig. 4 | MOVIE system for high-speed and high-quality volumetric imaging. a, Imaging cycle of conventional stopand-exposure method and MOVIE-scan method. Figure shows the case of a 50-ms exposure time. b, Imaging
throughput with various stage positioning times. ‘Ideal’ shows an ideal case, in which an acquisition cycle time isequal to an exposure time. c, The axial FWHM (resolution along the z axis) of different MOVIE-scan speeds
measured by acquiring images of ﬂuorescent beads by light sheet with a 7.8-μm FWHM. ‘Ideal’ shows calculated
FWHM of the light sheet. d, Simulation of MOVIE-focus and ‘stop and exposure’ autofocusing at 100, 250, 500 and
1,000 μm each. Ideal focus positions were calculated by DCTS metrics. e, Complementary cumulative distribution(1 − cumulative distribution) of error in MOVIE-focus and various ‘stop and exposure’ autofocus simulations. The
error is deﬁned as a distance between a simulated light-sheet position and an ideal focus position. f, 3D landscape ofvarious autofocus simulations. Simulated points were illustrated by depth (μm), light-sheet position (μm) andrelative DCTS (relative DCTS of an ideal focus position at each depth was deﬁned as 1). The trajectory of MOVIE
focus was described as orange line. g, Result of MOVIE-skip described by 3D images of PI-stained mouse brain. A
region within the yellow frame (left) was acquired by the previous imaging algorithm. After MOVIE-skip, only thenon-blank region was imaged (center and right-hand images). The number of images acquired with MOVIE-skip was
decreased by 57% compared to the full, non-skipped images. Scale bars, 1 mm.

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NATURE PROTOCOLS– Light-sheet position (z )+Light-sheet position (z )Sheet

Ideal focus position
Light-sheet position

Amplitude of light-sheet oscillation (1.4 μm)+

SampleImage 1 Image 2

Sheet

Light-sheet oscillation center

Image 1–

Detection lens

1.4 μmImage 2Depth (z )Depth (z)bComparison of
DCTS score

Calculation of DCTS score
1

Block n1w

Block mChoose blocks(the top x % of area
by NORM_L2)

Calculate NORM_L2
of each blockh

Calculation of
focus value

If DCTS score of image 2
is higher than that of image 1,
Δ Focus value = +1

DCTS score 2 = 0.00356

ii

Image 2 (I (x,y ))Δ Focus value = +1

If DCTS score of image 1
is higher than that of image 2,
Δ Focus value = –1
k = ‘block size’iImage 1 (I (x,y ))cm = 1,2,3,...,w, n = 1,2,3,...,hB(m,n) = I(x,y) (k (m –1) ≦ x < km ⋂ k (n –1) ≦ y < kn)

DCTS score 1 = 0.00353

NORM_L2 =DCTS score = Σ DCTS of B(m,n)

i

ii
2
Σ
(I (x,y ) – I (x,y ))(x,y ) ∊ B(m,n)

B(m,n) ∊ BTOP x %Ideal focus position
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oscillation
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Light-sheet position (z )+Light-sheet oscillation
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3512

Focus value negative threshold

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Fig. 5 | Details of MOVIE-focus. a, Scheme of MOVIE-focus. During scan, images with oscillating light-sheet
positions were acquired, and depending on the image quality metrics (as detailed in b), the light-sheet positions were
continuously updated. Typically, the step size (i.e., amplitude) of the light-sheet oscillation was 1.4 μm. b, The
scheme of image quality evaluation. To achieve real-time processing, we divided the images into blocks and selected
the blocks with high NORM_L2 values. Typically, we divided a 2,160 × 2,560-pixel image into 128 × 128-pixel blocks(k = 128), resulting in 320 blocks, and selected the top 32 blocks (top 9.5% of image size, x = 9.5) by NORM_L2
values. The set of pixel indices (x,y) in the block (m,n) is denoted as B(m,n) and the set of selected blocks is denotedas ‘BTOP’ x%. Then, Shannon entropy of the normalized discrete cosine transform (DCTS) of each selected block
was calculated, and the DCTS score was obtained by taking the summation of all selected blocks. Last, the DCTS
scores of two images were compared, and the image with the higher DCTS score was chosen as the better focus
direction, and ‘focus value’ was updated by +1 or −1, corresponding to the better focus direction. c, Real-time
autofocus sequence in MOVIE-focus is shown. As the z-stack image scan proceeds, the light-sheet position was
oscillated with a small step size (shown as red line). The image quality metrics of two images before and after thelight-sheet position change were evaluated (as detailed in b), and the focus value was updated by either +1 or −1,
corresponding to the better focus direction (shown as cyan line). Once the focus value crosses the threshold (in thisﬁgure, we use 3 or −3 as the threshold), the center of the light-sheet oscillation was updated (‘focus update’, shownas green arrows). After the update of a light-sheet position, the focus value was initialized to 0 and the autofocus
sequence was repeated.

imaging for purposes such as screening. The CUBIC-X protocol (Step 4B) is suitable when images ofhigh-cell-density organs or regions such as the cerebellum of the mouse brain are required.
Before application of CUBIC, animals are transcardially perfused with cold Dulbecco’s PBS (DPBS) and 4% paraformaldehyde (PFA) phosphate buffer solution and the organs are dissected for
postﬁxation (Steps 1 and 2). After washing out the remaining PFA with PBS (Step 3), the ﬁxed organs
are treated with lipid-removal reagent (CUBIC-L) or a delipidation step. For the CUBIC-X protocol(Step 4B), the organs are treated with CUBIC-X1 solution for expansion. Finally, the refractive index(RI) of the organs is adjusted by CUBIC-R+ (Step 4A(vii)) or CUBIC-X2 (Step 4B(vii)). Both
protocols can also stain the cell nucleus with an appropriate nuclear stain after the delipidation step
(Table 1). This procedure can be paused at various points, which are indicated in the procedure.
Whole-organ imaging
We constructed a customized LSFM with a detection objective lens with a magniﬁcation of 10×,numerical aperture (NA) of 0.6, working distance (WD) of 8 mm and a pair of scientiﬁc CMOS
cameras (Zyla 5.5, Andor) for whole-organ imaging (Fig. 3a–c). For fully automated imaging, thecleared organ is embedded in transparent agarose gel and ﬁxed on the motorized x–y–z–θ stage. Gelembedding steps are shown in Fig. 2d–l, and these details are described below (Step 5A). Calibrationgels including ﬂuorescent beads (Step 5B) are used to calibrate the optical path of dual-color imaging
and measure the point spread function (PSF). The ﬁeld of view of our customized LSFM is 1.40 mm ×1.66 mm in the x–y plane, and the imaging region is divided into a grid, with a 0.13-mm overlap ofeach stack in both the x and y directions (Fig. 3d). In this customized LSFM, the beam waist was
adjusted to ~10 μm so that consequent Rayleigh length was 0.53 mm at 594 nm, which corresponded
to a uniform light-sheet thickness throughout the ﬁeld of view in the immersion oil for CUBIC-X and
CUBIC-R+. The full-width at half-maximum (FWHM) of the axial direction was measured as 7.8 μm
by using PSFj55. We chose a scanning step size of 5 μm in the z direction, close to half the width of thebeam waist. Because the WD of the detection objective lens determines the imaging range of thez direction, acquisition of the sample images is performed from two opposite directions, using a
θ-stage rotation to collect images of the entire large specimen. For example, when an adult whole
mouse brain with >10-mm thickness in the z axis is imaged, >5.5-mm stacks are imaged from thedorsal side, and the sample is then rotated by 180° and >5.5-mm stacks are imaged from the ventral
side. Typically, we set a >0.5-mm overlap, but it depends on the verticality of the θ stage. We also
demonstrated the simultaneous dual-color imaging of a brain transduced with AAV-PHP.eB:NSEH2B-mCherry56–58 and counterstained by RedDot2 (RD2) for nuclear staining, which is cleared by
the CUBIC-L/R+ protocol (Supplementary Fig. 1 and Supplementary Video 1). The two signals weremerged at exactly the same position and could thus be analyzed easily (Fig. 3e).
MOVIE advanced high-speed imaging system
When scanning samples in the z direction (depth), conventional microscopes use the of ‘stop andexposure’ scheme, which is performed with the stage stopping each time after moving to the speciﬁed
position, and, therefore, total imaging duration is mainly determined by the positioning time because
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PROTOCOLNATURE PROTOCOLSaBlank region

Pre-scanned image stack (200-μm span)

After MOVIE-skip analysis

Blank region

Acquire regionb(i) Divide blocks(ii) Thresholding(block mean
intensity > threshold)
1
1

1 1
1
1
1 1
1 1
1 1 1

1

1
1
1
1
1
1

Thresholding(intensity ≧9)1
1 Convolution
1+1 +1 +1
1
+1 +1 +1 =1 * +1 +1 +1
1
1

Convolution+1 +1 +1
+1 +1 +1
* +1 +1 +1 =

1
2
2
1

1
2
2
1

1
2 1 2 32 1 3 5
1 1 4 7
1 4 7
2 5 8
1 4 7 92 5 8 9

1
4
7
9
9
9
9
9

(iii) Determine theregion to be acquired
3
6
8 If containing at least one
positive block
9
9
9
9
9

AcquireIf containing no
positive block
1 1 1
1 1 1
1 1 1

Skip

Fig. 6 | Details of MOVIE-skip. a, The olfactory bulb of a PI-stained mouse brain is illustrated; the image contains
blank regions. After MOVIE-skip, only the speciﬁed region was imaged (i.e., the blank regions were not imaged).
b, For stable skipping, the block convolution method was developed. Pre-scan was performed at the time of goingback to the z-start position of the next stack at a speed of ~200 μm/exposure time. We use the following scheme(i−iii) in order to ignore small particles outside of the sample; two examples are shown in this ﬁgure using thezoomed area. (i) Divide an image into some size of blocks (e.g., when the block size of 40 × 40 pixels is chosen, an
image whose size is 2,160 × 2,560 pixels will be divided into 3,456 blocks). (ii) Perform threshold binarization,comparing the mean intensity of each block with the threshold. The threshold was typically set to two times thevalue of the mean intensity of the image in the blank region. In this ﬁgure, positive blocks were painted white. (iii)
Convolve the image with the described square 3 × 3 kernel, and perform threshold binarization with nine. With atleast one positive block, the image is acquired; otherwise the image is skipped. Scale bars, 100 μm.

Table 1 | Nuclear stain stability in CUBIC reagents

Propidium iodide
RedDot2
SYTO 16CUBIC-R+

CUBIC-X2

StableStableUnstable

StableUntested
Untested

the positioning takes much longer than the actual exposure time (e.g., 150 ms of stage motion
between exposures in our previous work)11 (Fig. 4a). Figure 4b shows the imaging speed at various
stage-positioning times. The stage-positioning time depends on the motor type and accuracy of theposition. When using a typical stepping-motor-type stage, the positioning time is estimated to be~100–150 ms. Therefore, in the case of a short exposure time, the imaging time is mainly determined
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NATURE PROTOCOLSPROTOCOL

by the stage-positioning time. Stage acceleration and deceleration are time consuming and may cause
the image to blur. When using a piezo-type stage, the stage-positioning time can be shortened, but, in
our experience, it is ~30–50 ms. To overcome this limit, we developed the MOVIE-scan method.
With MOVIE-scan, the combination of uniform motion of the stage with synchronized continuous
acquisition can reduce the overall imaging time (Fig. 4a). The relationship between exposure time andthe imaging throughput is shown in Fig. 4b. The apparent sheet thickness depends on the stagemoving distance in an acquisition cycle (Fig. 4c). Typically when using MOVIE, we set the acquisition
cycle time of an image to 55 ms, the moving distance to 5 μm and the z-stage speed to 5 μm/55 ms(i.e., 90.9 μm/s). Although the apparent sheet thickness corresponds to 8.95 μm, evaluated by the
FWHM of the axial direction (Fig. 4c), compared with 7.80 μm without MOVIE (in a ‘stop andexposure’ mode using the same stage), the imaging throughput became 18.2 frames per s (fps)(Fig. 4b, 1/exposure time is 20), compared with 4.4 fps without MOVIE.
The required imaging time can be shortened by MOVIE-scan. However, when using MOVIE-scan(and also a ‘stop and exposure’ mode), an additional issue is that the focal plane can shift for various
reasons. Some samples may be less transparent at deeper areas because of limitations of thetissue-clearing method, storage duration and ﬁxation condition of samples. It is also difﬁcult to matchthe RIs of the sample, embedded gel, and immersion oil completely. Therefore, efﬁcient autofocusing
techniques have been developed59–63. Typically, autofocus must be performed frequently to collectimages that are in focus throughout. Conventional autofocus systems need extra tens of images todetermine the optimal focus with stage stopping (we refer to this as ‘stop and exposure’ autofocus),and this scheme is commonly used for autofocusing. In the MOVIE system, an efﬁcient real-time
autofocus mode (MOVIE-focus) is used to adjust light-sheet positions without acquisition of any
extra images. (Fig. 5) The image quality can be evaluated by various image quality metrics. We chose
Shannon entropy of the normalized discrete cosine transformation (DCTS) for image quality
metric calculations, as this was suggested as the best method in a previous report60. Next, we
simulated the autofocus method by using datasets from a propidium iodide (PI)-stained mouse brain
with 400,000 images, which were acquired at 0- to 5,000-μm depth every 5 μm with 400 different
light-sheet positions at 0.35-μm intervals. The frequency of autofocus was mainly inﬂuenced by the
number of images required for determining the next light-sheet position, and the time for calculating
the image quality metric for each image. In MOVIE-focus, we continuously generate and evaluate twoslightly different light-sheet positions by calculating the image quality metrics of two images
before and after the change of the light-sheet position, and the ‘focus value’ is calculated at each lightsheet position (Fig. 5b). Once the ‘focus value’ crosses the threshold (in this paper, we used 3 or −3),
the center of the light-sheet oscillation is updated (Fig. 5c). After updating a light-sheet position,the ‘focus value’ is initialized to 0 and the autofocus sequence is repeated. In this protocol, the ‘focus
value’ calculation is performed for each exposure, but MOVIE-focus can also work if used
for every second or third exposure. If the frequency is set to be low, the tracking ability will be poorand may cause defocusing with non-uniform samples. To achieve real-time processing in high-speed
imaging, the processing time is decreased to <10 ms per image by dividing the images into blocks andselecting the blocks with high L2-norm values. Typically, we divide a 2,160 × 2,560-pixel image
into 128 × 128-pixel blocks, resulting in 320 blocks, and selected the top 32 blocks (top 9.5%of the image size) by L2-norm. Then DCTS of each selected block is calculated and the DCTS score isgiven by taking the summation of all selected blocks. Last, the DCTS scores of two images arecompared, and the image with higher DCTS score is chosen as the better focus direction,and the ‘focus value’ is updated by +1 or −1, corresponding to better focus direction. The details of
the various parameters are explained in Supplementary Fig. 2. Typical results of autofocus simulation,ideal focus positions, MOVIE-focus and stop-and-exposure autofocus at each 100–1,000 μm
are shown in Fig. 4d. The distances from ideal light-sheet positions are shown in Fig. 4e.
In MOVIE-focus, >97% of light-sheet positions were within 3 μm of the ideal focus positions. The
mean distance from ideal focus positions was 1.15 μm, corresponding approximately to stop-andexposure autofocus every 250 μm. Figure 4f illustrates the tracks of MOVIE-focus on the landscape ofthe relation among light-sheet position, depth and relative DCTS value. As shown in Fig. 4f, MOVIEfocus keeps the ﬁne light-sheet position throughout; the images are shown in Supplementary Figs. 3,4
and Supplementary Video 2. Typically, we use the nuclear staining channel for MOVIE-focus,
because the signal is usually high and distributed throughout the sample. In addition, when applying
a ﬂuorescent protein or immunostaining channel to MOVIE-focus, it performs well with the same
focusing parameters, as long as the signal is present in all imaging areas to allow the evaluation of theimage quality metric.
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PROTOCOLNATURE PROTOCOLSThe blank regions outside the sample are usually imaged because of the entire coverage of thewhole organ, and plenty of extra images are acquired, extending total imaging time and downstream
data analysis time. In the MOVIE system, to shorten imaging time by skipping blank regions, thez-start and z-end positions are speciﬁed at each stack by the evaluation of whether the intensity ofthe pre-scanned images is beyond the speciﬁed threshold. Pre-scan is performed during the time inwhich the scan goes back to the z-start position of the next stack at the speed of up to 200 μm peracquisition cycle time. The total data amount for a whole mouse brain was reduced by >50% by using
MOVIE-skip (Figs. 4g,6 and Supplementary Video 3).

Cell-nucleus detection and annotation
The HDoG_gpu.py script, followed by the MergeBrain.py and HDoG_classiﬁer.py scripts, which
will be obtained in Step 13 (‘Software setup’), is used to attempt to detect all cells in raw images(Figs. 7,8a). The ﬁrst step of cell-nucleus detection is normalization of the signal intensity because theillumination light-sheet intensity at deep areas in a sample is decreased by light absorption of dyes orother light-absorbing substances. The normalized images are processed by a 3D difference-ofGaussian (DoG) ﬁlter, which is a band-pass ﬁlter deﬁned as the difference between two sizes ofGaussian-ﬁltered images. This ﬁlter discards spatial information smaller or larger than the two-ﬁlter
size while preserving information in the scale between the two sizes. Although the theoreticalfoundation of this scale selection by Laplacian of Gaussian (LoG) ﬁltering was pioneered by Lindeberg64, DoG is more suitable to processing on GPUs. After scale selection by the 3D-DoG ﬁlter, theconvexity of intensity variation at each voxel, which is the negative deﬁniteness of the Hessian matrix,
is veriﬁed. If a voxel is judged to be convex, it is considered to be part of a cell nucleus. If theneighboring voxels are also convex, these connected voxels are labeled with the same ID number andconsidered to be a cell-nucleus candidate. Fast implementations of connected component labeling forGPUs have been considered previously65,66. For each region, maximum intensity in the region of thenormalized image and structureness index are calculated as features of that region and manuallyclassiﬁed into two clusters. A cluster with higher intensity and higher structureness corresponds tothe cluster of cell nuclei (Figs. 7,8a). Structureness index is deﬁned by using eigenvalues of the
Hessian matrix, a principle ﬁrst proposed by Frangi et al.67. After the identiﬁcation of true cell nuclei
by features, the centroid coordinates of identiﬁed regions are transformed to the whole-organ
coordinate system (Fig. 8a). The accuracy of the cell-nucleus-detection program was evaluated bycomparison with manual detection. An adult mouse brain cleared with the CUBIC-X protocol
showed >90% accuracy over all the brain and the CUBIC-R+-treated brain showed >90% accuracy,
except for the areas with extremely high cell densities such as the olfactory bulb (main olfactory bulb
(MOB), 14%) and cerebellum (CB, 82%) (Fig. 8b,c).
For comparative analysis, cell coordinates are further transformed to a standard coordinate
system, which is deﬁned by a reference organ. Even when a standard reference is not available, anarbitrary selection from among the samples is enough to be a reference for inter-sample comparison.
Transformation parameters are calculated by ANTs software68 with downscaled images. When users
analyze the mouse brain, the AtlasMapping.py script can be used for alignment and annotation of the
anatomical region information.

Fig. 7 | Details of cell-nucleus-detection algorithm. First, raw images (Iraw) are normalized (Inormalized) considering
local amplitude of intensity variation. If the intensity of a voxel is the local minimum, which roughly corresponds tothe tail of the cells or an empty region, its normalized intensity is deﬁned as 0. On the other hand, if the voxel is the
local maximum, which corresponds to the center of a cell, normalized intensity is deﬁned as 1. As the simple local
maximum (Idilation) or local minimum (Ierosion) images are sensitive to small noises in intensity, they are smoothed bya uniform ﬁlter U(rx,ry,rz). Next, normalized images undergo scale selection by DoG ﬁlter. The DoG ﬁlter is deﬁned as
the difference between the larger Gaussian Gðσ L ;σ L ;σ L Þ ﬁltered image and the smaller Gaussian Gðσ S ;σ S ;σ S Þ ﬁltered
x y z
x y z
image. Each voxel in DoG-ﬁltered images is checked for whether the Hessian matrix is negative deﬁnite or not. If theintensity proﬁle is convex upward at the voxel, the Hessian matrix at the voxel is negative deﬁnite and C[l,m,n] is 1.
Otherwise, C[l,m,n] is 0. Next, connected component analysis is performed and each connected region is labeledwith a unique ID, and these are deﬁned as cell candidate regions. Finally, the candidates are veriﬁed by their features.
Max intensity is deﬁned as maximum normalized intensity in the region, whereas structuredness of the region isdeﬁned as the sum of squared eigenvalues of the Hessian matrix of that region. Note that the Hessian matrix of aregion is deﬁned as the sum of the Hessian matrix of all voxel components. This 2D feature vector represents how
much each candidate region resembles a cell. As it is plotted in the ﬁgure, true cells (top-right cluster) and noisy
candidates (bottom-left cluster) show clear separation. Scale bars, 50 μm.
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PROTOCOL

0

12,000

8,000

4,000

NATURE PROTOCOLS

Iraw[l,m,n]

Iraw[l,m,n] – U(rx, ry, rz)⊗ Ierosion [l,m,n]

Inormalized[l,m,n] ≔U(rx, ry, rz)⊗ Idilation [l,m,n] – U(rx, ry, rz)⊗Ierosion[l,m,n]

Intensity
Normalized image⊗ = Convolution operator
Idilation[l,m,n] ≔minIraw [i,j,k]l – rx ≤ i < l + rx
m – r y ≤ j < m + ry
n – rz ≤ k < n + rz

Ierosion[l,m,n] ≔max
Iraw [i,j,k]l – rx ≤ i < l + rx
m – r y ≤ j < m + ry
n – rz ≤ k < n + rz

1.0
0.8
0.6
0.4
0.2
0.0

U(rx, ry, rz) : Uniform kernel with size (rx, ry, rz)

Inormalized[l,m,n]

Intensity

IDoG[l,m,n] ≔ G(σL σL σL) ⊗ Inormalized[l,m,n] – G(σS σS σS) ⊗Inormalized[l,m,n]
x,

y,

x,

z

y,

z

2

2

2

G(σx, σy, σz): Gaussian kernel with covariance diag (σx , σy , σz )

4
3
2
1
0
–1

Scale image∂2

IDoG[l,m,n]

Intensity2

H [l,m,n] ≔ ∇ IDoG[l,m,n] =

∂x 2
∂2
∂y∂x

∂2
∂x∂y
∂2

∂2
∂x∂z
∂2

∂y 2
∂2
∂z∂y

∂y∂z

∂2
∂z∂x

H [l,m,n]

IDoG[l,m,n]

∂2
∂z 2

1, (if H[l,m,n] is negative definite)C [l,m,n] ≔0, (otherwise)C [l,m,n]Connected component label
L[l,m,n] = L[i,j,k], if C [l,m,n] = C[i,j,k]. ((i,j,k) ∊ Ne(l,m,n))Ne(l,m,n) : 26 neighborhood of (l,m,n)

0.0

0.5

1.0

Cell candidate regions

C [l,m,n]Intensity
X [label ID] ≔log10(max intensity[label ID]
log10(structureness[label ID]
Max intensity[label ID] ≔max

Inormalized[l,m,n]L[l,m,n] = label ID
22

Structureness[label ID] ≔ ∣λ1∣ + ∣λ2∣ +∣λ3∣

Structureness106

2

λ1, λ2, λ3 : eigenvalues of Hc (λ1< λ2 < λ3 < 0)104

Hc[label ID] ≔102

Σ

H[l,m,n]L[l,m,n] = label ID

0

10

10–2

0.0

–0.5

–1.0

–1.5

–2.0

10–4

Normalized intensity

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PROTOCOL
Normalized image

Normalized image

Normalization
filter

3D-DoG filterzxx,y = 65 μm
z = 40 μm

xLyL

Region extraction
zs

–

zL
y

Extracted regions

xs

Negativedefinitenessofhessian matrixys

xL yL zL
= == 1.5
xs ys zs

Point image

True

Alignment between samples
or annotation using CUBIC-Atlas

Convert topoint data

Reference

104
102
100

False

10–2
10–4

–1
.5
–1
.0
–0
.5

Identification of characteristic
regions or cells

Structureness

ANTs

Verification by features

Raw image0a

NATURE PROTOCOLSNormalized intensity

CUBIC-X
0.987 0.959 0.965
0.936 0.950 0.957 0.946
1.0c

F scoreF score

0.4

0.6
0.4

0.2

0.2

0

0
CTX

e
107

TH

CC

CA1

DG

MOB

CB

400
0.817

0.8

0.6

450

1.0 0.969 0.938 0.940 0.951 0.946

0.8

10

d 500

CUBIC-R+

No. of regions

b

350
300
250
200
150
100

0.140

50
0

CC

CA1

DG

MOB

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Coefficient of variation

CTX

TH

CB

Isocortex and OLF

HPF

CNU

TH

HY

MB

P

MY

CB

FT

VS

Isocortex and OLF

HPF

CNU

TH

HY

MB

P

MY

CB

FT

VS

6

Averagecell no.

105
104
103
102
101
100

f
1.0

Positive cell no.
/ total cell no.

0.8
0.6
0.4
0.2
0

g
1.0
0.6
0.2

y
z
x

xy

yz

xz

Using this cell-nucleus-detection algorithm, we re-counted the cells in the 8-week-old C57BL/6J
mouse brain from which data had been obtained when preparing the CUBIC-Atlas in our previous
report11. As a result, we observed 1.03 × 108 cells, and the CUBIC-Atlas was updated using this result
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NATURE PROTOCOLSPROTOCOL

Fig. 8 | Cell-nucleus detection and alignment. a, Scheme of cell-nucleus detection and alignment between samples
and reference organ. Scale bars, 50 μm. b, Validation of cell-nucleus-detection accuracy at each anatomical region ofa CUBIC-X-treated brain. c, Validation of cell-nucleus-detection accuracy at each anatomical region of a CUBIC-L/R+-treated brain. d, A histogram of coefﬁcient of variation for each anatomical region. e, Average cell number of all
anatomical regions in three 8-week-old C57BL/6N mouse brains. Only the edge regions (i.e., having no child region)
are shown. f, Positive cell number ratio of each anatomical region in AAV-PHP.eB (NSE-H2B-mCherry)-infectedmouse brain. g, 3D and cross-section images of the positive cell number ratio map of AAV-PHP.eB (NSE-H2BmCherry)-infected whole mouse brain. Voxel size, 80 μm. Scale bars, 2 mm. CA1, ﬁeld CA1; CB, cerebellum;
CC, corpus callosum; CNU, cerebral nuclei; CTX, cerebral cortex; DG, dentate gyrus; FT, ﬁber tract;
HPF, hippocampal formation; HY, hypothalamus; MB, midbrain; MOB, main olfactory bulb; MY, medulla;
OLF, olfactory areas; P, pons; TH, thalamus; VS, ventricular system. All experiments followed the relevant
governmental and institutional guidelines for animal experiments.

(termed v.1.2). Next, we also reanalyzed the cell number of the whole brain and each anatomical
region of three 8-week-old mice (C57BL/6N) that were cleared with CUBIC-X protocol (Fig. 8e).
As a result, the average total cell number was found to be 9.77 (±0.46) × 107, in contrast to 7.22(±0.52) × 107 in our previous report11. The coefﬁcient of variation (CV) was 4.75% for whole brain,
and 67.8% of the regions (455/671 regions) showed <5% CV. (Fig. 8d). We next quantiﬁed thewhole-brain infection efﬁciency of AAV-PHP.eB (NSE-H2B-mCherry). We used the signal intensity
ratio of the ﬁrst channel (nuclei stain) to the second channel (mCherry) to determine which cells werepositive, because intensity of the excitation laser was decreased in the deep areas and signal intensity
detected through an objective lens was also decreased, requiring correction. The ratio of mCherry+cells in each anatomical region was quantiﬁed by using CUBIC-Atlas (Fig. 8f). In addition, we
visualized the localization of mCherry+ cells by using the mCherry+ cell number ratio in each 80-μm
voxel (Fig. 8g). These results show that the cortex and hippocampus, corresponding to the pyramidal
layer of Ammon’s horn (CAsp) and the granule cell layer of the dentate gyrus (DG-sg), respectively,
showed higher positive ratios than other areas. These analysis programs conﬁrmed that the expanded
CUBIC-X-treated mouse brain could be analyzed within 8 h (14 TB) and CUBIC-L/R+-treatedbrains could be analyzed within 2 h (2.5 TB) using our analysis server with 28 dual-CPU cores andfour GPUs.

Expertise needed to implement the protocol
The procedures described here require some prior experience in sampling organs, building light-sheet
microscopes and programming or software development. The most effective and time-efﬁcient
implementation of these protocols is achieved when anatomical knowledge and skills in organ
sampling are available, as well as knowledge and familiarity with Python for cell-nucleus detection,
annotation and comparison of different samples with CUBIC-Atlas or ANTs software. If a commercially available microscope is not available, an optical engineer who has experience in building
optical instruments and controlling electronics for hardware and automation is required.
Limitations of the current CUBIC pipeline
CUBIC reagents and procedures have been improved to facilitate preparation of highly transparent
whole-organ and whole-body samples and have been applied to various research ﬁelds, including
neuroscience, cancer research and pathology. Further applications are expected, because our recent
report also provided clearing methods for bone and human organ blocks10. Other groups have also
improved CUBIC and applied it to various organisms such as ﬂy (FlyClear)69, crustaceans70 andplants (ClearSee)71. Transparency of whole mouse organs with the current CUBIC protocol is sufﬁcient for high-resolution imaging and it is also compatible with ﬂuorescent protein and immunostaining. However, the current CUBIC reagents are not capable of clearing some pigments, such as
melanin and lipofuscin, completely. In addition, the lipid removal from large specimens such as rat,
marmoset and human are still inefﬁcient. Therefore, improvements in the transparency and processing time of whole-organ or whole-body clearing of larger organisms are required. Regarding
volumetric imaging, we showed that it is possible to markedly increase the volumetric imaging speedwith the MOVIE system, but it still has resolution limits due to optical limits, especially thosepertaining to z-resolution-related issues with light-sheet generation. Regarding data analysis, there arestill challenges for segmentation and detection of the precise morphology of each cell at the wholeorgan scale within a practical time. Because the clearing reagents, imaging methods and data analysis
algorithms used in the CUBIC pipeline are continually being improved and developed, there is apossibility that these problems will be solved in the near future.
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PROTOCOLNATURE PROTOCOLS

Materials
Biological materials●

Animal samples. We usually use C57BL/6N (CLEA Japan, cat. no. C57BL/6NJcl) or C57BL/6J (Charles
River Laboratories, cat. no. C57BL/6J) mice to prepare cleared organs. For virus-infected samples,
8-week-old mice receive intravenous administration of AAV-PHP.eB (NSE-H2B-mCherry, 6.4 × 1011
viral genome) through the retro-orbital sinus as previously described72. The perfusion ﬁxation is done
at 1 month after viral injection ! CAUTION Animal experiments must be performed in accordance withgovernmental and institutional rules. All animal experiments and housing conditions in this reportwere authorized by the animal care and use committees of the RIKEN Institute, The University ofTokyo and Gunma University, and all animals were treated humanely in accordance with the relevant
institutional guidelines for experiments using animals.

Reagents
Perfusion, ﬁxative and storage reagents● PBS tablets (pH 7.4; Takara, cat. no. T9181)● Sodium azide (Nacalai Tesque, cat. no. 31208-82) ! CAUTION Sodium azide is toxic. Avoid its
inhalation and contact with the skin or eyes.
● D-PBS (no calcium, no magnesium; Gibco, cat. no. 14190-144)●
Paraformaldehyde phosphate buffer solution (PFA; 4%; Wako, cat. no. 163-20145) ! CAUTION PFA is
toxic. Perform all procedures in a fume hood. Avoid inhalation and exposure to the skin or eyes.
● Sucrose (Nacalai Tesque, cat. no. 30403-55 or 30404-45)● NaCl (Nacalai Tesque, cat. no. 31320-05 or 31319-45)● Tissue-Tek OCT compound (Sakura Finetek, cat. no. 4583)● Ethanol (Japanese Pharmacopoeia, cat. no. 324-00037)● dH O2● Anesthetizing agents (such as pentobarbital (Kyoritsu Seiyaku, cat. no. 21009), ketamine (Daiichi
Sankyo, cat. no. 1119400A2038)/xylazine (ZENOAQ, cat. no. 904150) or isoﬂurane (Pﬁzer Japan,
cat. no. 114133403))ccTissue-clearing and nuclei-staining reagents●
Tissue-clearing reagent CUBIC-L (for animals; Tokyo Chemical Industry, cat. no. T3740)
CRITICAL CUBIC-L can be prepared as described in the ‘Reagent setup’ section, but pre-mixed
and quality-controlled solution is also commercially available.
● Tissue-clearing
reagent CUBIC-R+ (for animals; Tokyo Chemical Industry, cat. no. T3741)
CRITICAL Tissue-clearing reagent CUBIC-R+ (for animals) can alternatively be prepared as
described in the ‘Reagent setup’ section.
● N-Butyldiethanolamine (Tokyo Chemical Industry, cat. no. B0725)● PEG mono-p-isooctylphenyl ether (Triton X-100; Nacalai Tesque, cat. no. 12967-45)● 2,3-dimethyl-1-phenyl-5-pyrazolone (antipyrine; Tokyo Chemical Industry, cat. no. D1876)● Nicotinamide for CUBIC-R+(nicotinamide (N)) (Tokyo Chemical Industry, cat. no. N0078)● N-Methylnicotinamide
for CUBIC-R+(N-methylnicotinamide (M)) (Tokyo Chemical Industry,cat. no. M0374)● N,N,Nʹ,Nʹ-Tetrakis(2-hydroxypropyl)ethylenediamine (Quadrol; Tokyo Chemical Industry, cat. no. T0781)● Urea (Nacalai Tesque, cat. no. 35904-45 or 35907-15)● Imidazole (Tokyo Chemical Industry, cat. no. I0001)● Propidium iodide (PI; Life Technologies, cat. no. P21493) ! CAUTION
PI is light sensitive.
● RedDot2 (RD2; Biotium, cat. no. 40061) ! CAUTION RD2 is light sensitive.

ccImaging reagents● Agarose for CUBIC-R+ (Nacalai Tesque, cat. no. 01163-76)
CRITICAL Gelation properties ofagarose vary depending on the producer. To fully reproduce our results, use appropriate agarose fromlisted vendors. If the combination of CUBIC and agarose is not suitable, it will not gel or become
transparent. If recommended agaroses are not available, high-melting-point or high-strength agaroses
may be alternatives.
● Agarose S for CUBIC-X2 (Nippon Gene, cat. no. 318-01195)
CRITICAL The same cautions as above
apply to the appropriate choice of gelatin.
● Mineral oil (Sigma-Aldrich, cat. no. M8410)
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HIVAC F-4 (Shin-Etsu Chemical, cat. no. HIVAC-F4)
Sphero ﬂuorescent particles (purple; 0.1–0.3 µm, 1.0% (wt/vol); Spherotech, cat. no. FP-0262-2)

Equipment
Tissue clearing● Tubes (5 ml; Eppendorf, cat. no. 0030 119.401)● Conical tubes (15 ml; Corning, cat. no. 352096 or Greiner, cat. no. 188271)● Conical tubes (50 ml; Corning, cat. no. 352070 or Greiner, cat. no. 227261)● Intravenous injection needle (23 gauge; butterﬂy type; Terumo, cat. no. SV-23CLK)● Injection needle (26 gauge × 1/2-inch; Terumo, cat. no. NN-2613S)● T-shape stopcock (Terumo, cat. no. TS-TL2K)● Disposable syringes (10 or 20 ml; Terumo, cat. no. SS-1010SZ or SS-20ESZ)●
Incubation devices. We use a hybridization incubator (Taitec, model no. HB-80) or incubator(Eyela, model no. FMS-1000 or MHS-2000)● Shaker (Taitec, model no. Wave-PR or MixerXR-36)● Magnetic stirrer (Ikeda Scientiﬁc, cat. no. IS-20P)● Hot stirrer (Advantec, cat. no. SRS710HA)● Water bath (Advantec, cat. no. TBX183SB)● Abbe refractometer (Atago, cat. no. DR-A1)● pH meter (Horiba Scientiﬁc, LAQUAtwin model)● Microwave oven (National, cat. no. NE-S12)● Fume hood (Shimadzu, cat. no. CBR-Rc)● Gel embedding mold●Acrylic plates with polished edges (Hazaiya, 50 mm × 14 mm × 6 mm (2×), 45 mm × 14 mm × 6 mm(2×), 75 mm × 56 mm × 6 mm (2×))
Customized LSFM and operation computer● Optical table with tapped holes (Herz, model no. h-TDIS-3012LA M6-25XY tap)
● SOLE- Laser Light Engine (Omicron Laserage, model no. SOLE-3)● Optical posts (Thorlabs, part nos. TR50/M (24×),TR150/M (8×), RS5/M (4×), RS4P/M (4×), RS25/M (4×))● Post holders (Thorlabs, part nos. PH50/M (24×) and PH100/M (8×))●
Base adaptor (Thorlabs, part nos. BE1/M (6×) and CF125/M (6×))● ER assembly rods (Thorlabs, part nos. ER1.5 (16×), ER2 (40×) and ER4 (48×))● Lens mounts (cage plates; Thorlabs, part nos. CP03/M (16×), CP02T/M (2×), CP02B (4×), CXYZ05/M(2×), CXY1 (14×))● Achromatic doublet lenses (Thorlabs, part nos. AC127-030-A-ML, AC127-050-A (2×), AC254-050-A(6×), AC254-100-A (4×) and AC254-150-A-ML)● Iris (Thorlabs, part no. CP20S (2×))● Motorized ﬁlter ﬂip mounts (Thorlabs, part no. MFF101/M) for laser switching● Broadband dielectric mirrors (Thorlabs, part no. BB1-E02 (4×))● Kinematic mirror mounts (Thorlabs, part no. KM100CP/M (3×))● Galvano mirrors (Thorlabs, item nos. GVS001/M (6×), GCE001 (6×), GCM001 (6×), B1C/M (6×) and
GHS003/M (6×))● Galvo system linear power supply (Thorlabs, model no. GPS011 (3×))● Objective lenses for excitation (CFI Plan Achromat 4×; Nikon, part no. MRL00042 (2×))● Detection objective lens (Olympus, model no. XLPLN10XSVMP)● Lens adaptors (Thorlabs, item nos. SM1A12 (2×), SM1L10 (2×))● Customized sample chamber (inner size: width (W) 80 mm × depth (D) 60 mm × height (H) 120 mm
or W 80 mm × D 60 mm × H 160 mm, acrylic plastic; ISAX)● Customized sample holder (inner size: W 35 mm × D 15 mm × H 14 mm; ISAX)● Breadboard (OptoSigma, model no. OBC-2030 25XY-M4 tap)
● Precise lab jack (OptoSigma, model no. LJA-16223)● xy stages (Thorlabs, part nos. MTS50-Z8E (2×) and MTS50C-Z8)●
z stage (Autex, part no. RTP-2035/00100)● θ stage (Physik Instrumente, part no. M-116.DG)● Emission ﬁlter (2-inch; Chroma Technology, part no. ET625/30m)● Emission ﬁlter (1-inch; Thorlabs, part no. FB700-40)
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Fixed mirror mounts (2-inch; Thorlabs, part no. FMP2/M)
SM1 lens tube (Thorlabs, part no. SM1L03)● Adaptor with external C-mount (Thorlabs, part no. SM1A9)● Dichroic mirror (Thorlabs, part no. DMSP650L)● Detection tube lenses (Thorlabs, part no. AC508-180-A-ML (2×))● Two-axis miniature dovetail translation stages (Thorlabs, part no. DT12XY/M)● Post-mountable xy translation mounts (Thorlabs, part no. LM2XY (2×))● sCMOS cameras (Andor, model no. Zyla 5.5 (2×))● Medium metric low-proﬁle optical rail (800 mm; OptoSigma, part no. OBT-800SH)● Large metric low-proﬁle optical rail (500 mm; OptoSigma, part no. OBT-500LH)● Medium metric optical carrier (60 mm; OptoSigma, part no. CAA-60LS (4×))●
Large metric optical carrier (120 mm; OptoSigma, part no. CAA-120L (3×))● Virtual bench (National Instruments, model no. VB-8012)● Compact data acquisition (DAQ) devices (National Instruments, model nos. NI cDAQ-9171 and NI
cDAQ-9174)● Analog output module (National Instruments, model nos. NI9263 (4×))●●

Computer● PC (Unitcom, model no. Sol-TT9W-Xe5D-GXS; Windows 10 Pro, 64 bit; CPU: dual Intel Xeon E52630V4; memory: DDR4-2400 ECC Reg, 8 GB (16×); GPU: GeForce, model no. GTX 1080, SerialATA SSD (solid-state drive) (240 GB))
● Computer server (analysis server; Japan Computing Systems, model no. VC82690v4-4UXQTA2-GPK;data storage server, JBOD)● InﬁniBand card (Mellanox, product no. MCX353A-FCBT FDR PCIe3.0) for microscope operation PC● InﬁniBand card (Mellanox, product no. MCX354A-FCBT, FDR PCIe3.0) for data server● InﬁniCable (Mellanox, product no. MC220731V-010, 56 GB/s, QSFP, 10m)
● InﬁniBand Switch system (Mellanox, product no. MSX6012F-1BFS)● CPUs (Intel, Xeon E5-2690v4, 2.60 GHz 14C/28T (2×))● GPUs (NVIDIA, GeForce GTX1080Ti, 11 GB (4×))● Memory (DDR4 2400 ECC, registered 16 GB (16×))● SSD (Intel, S4500 1.92 TB (5×), RAID 0)●
Raid card (Broadcom, cat. no. LSI 3108)●
Case (4U rackmount; Japan Computing Systems, cat. no. VCXQTA2-GPKT)● Operating system (CentOS 7.5, https://www.centos.org/download/)● Uninterruptible power supply (APC, Smart-UPS XL 3000 RM 200V)● Chassis (4U rackmount; Japan Computing Systems, cat. no. HDD 44 Bay JBOD (2×))● Hard drive (HDD; SATA, 8TB, 7,200 r.p.m. (60×), RAID 60)
Software and tools● ImageJ (free-software from the US National Institutes of Health (NIH), https://imagej.nih.gov/ij/)●
Imaris (Bitplane, http://www.bitplane.com/imaris/imaris) for visualization of 3D images●
Python v,3.6 (https://www.python.org/downloads/)● GNU Compiler Collection 4.8 for compiling C++11 (https://gcc.gnu.org/releases.html)● CMake 2.8 (https://cmake.org/download/)● CUDA Toolkit 9.0 (https://developer.nvidia.com/cuda-90-download-archive)● Visual Studio C# for MOVIE system (https://visualstudio.microsoft.com/downloads)● 64-bit LabVIEW for LSFM control (https://www.ni.com/en-us/support/downloads/software-products/
download.labview.html#305933)● ANTs 2.1.0 (freeware from stnava: https://github.com/ANTsX/ANTs/releases)● CUBIC-Atlas v.1.2 (http://cubic-atlas.riken.jp)

Reagent setup
CUBIC-L
CUBIC-L is a mixture of 10% (wt/wt) N-butyldiethanolamine and 10% (wt/wt) Triton X-100 in
dH2O. For example, to prepare 100 g of CUBIC-L solution, mix 10 g of N-butyldiethanolamine and
10 g of Triton X-100 in 80 g of dH2O with a stirrer at room temperature (22–26 °C). The reagent
can be stored at room temperature for up to 6 months. CUBIC-L can alternatively bepurchased (Reagents).
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Half-diluted CUBIC-L
Half-diluted CUBIC-L is an equivalent-volume mixture of CUBIC-L and dH2O. Half-diluted
CUBIC-L can be stored at room temperature for up to 6 months.
CUBIC-R+(N)
CUBIC-R+(N) is a mixture of 45% (wt/wt) antipyrine, 30% (wt/wt) nicotinamide, 0.5% (vol/vol)N-butyldiethanolamine and dH2O. To prepare 100 g of CUBIC-R+ solution, dissolve 45 g of antipyrine and 30 g of nicotinamide in 25 g of dH2O. After complete dissolution, add 250 μl of
N-butyldiethanolamine to 50 ml of the mixture so that the pH becomes 9.6–9.8 and the RI is 1.522(see ‘Troubleshooting’ section). CUBIC-R+(N) can be stored at room temperature forup to 6 months.

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CUBIC-R+(M)
CUBIC-R+(M) is the same formula as that for CUBIC-R+(N), except that it contains N-methylnicotinamide in place of the nicotinamide. CUBIC-R+(M) can alternatively be purchased (Reagents).
CRITICAL The N-methylnicotinamide in CUBIC-R+(M) retains ﬂuorescent protein signals better
than nicotinamide. When observing tissues labeled with ﬂuorescent proteins, use CUBIC-R+(M);otherwise, CUBIC-R+(N) can be used, which is inexpensive.
Half-diluted CUBIC-R+
Half-diluted CUBIC-R+ is an equivalent volume mixture of CUBIC-R+ (50% (vol/vol) ﬁnal concentration) and dH2O (50% (vol/vol) ﬁnal concentration). Half-diluted CUBIC-R+ can be stored atroom temperature for up to 6 months.
ScaleCUBIC-1
ScaleCUBIC-1 is a mixture of 25% (wt/wt) Quadrol, 25% (wt/wt) urea and 15% (wt/wt) Triton X-100.
For example, to prepare 100 g of ScaleCUBIC-1 solution, dissolve 25 g of urea and 25 g of Quadrol in
35 g of dH2O with a hot stirrer. After complete dissolution, cool to room temperature and add 15 g of
Triton X-100. ScaleCUBIC-1 solution can be stored at room temperature for up to 1 month.
Modiﬁed-ScaleCUBIC-1
Modiﬁed-ScaleCUBIC-1 is a mixture of 5% (wt/wt) Quadrol, 25% (wt/wt) urea and 10% (wt/wt)
Triton X-100. For example, to prepare 100 g of modiﬁed-ScaleCUBIC-1 solution, dissolve 25 g of urea
and 5 g of Quadrol in 60 g of dH2O, using a hot stirrer to stop the temperature of the solution fromdecreasing too much, because this slows dissolution. After complete dissolution, cool to room
temperature and add 10 g of Triton X-100. Modiﬁed-ScaleCUBIC-1 can be stored at room temperature for up to 1 month.
CUBIC-X1
CUBIC-X1 is a mixture of 20% (wt/vol) imidazole and dH2O. For example, to prepare 100 ml of
CUBIC -X1, dissolve 20 g of imidazole in dH2O at room temperature. CUBIC-X1 can be stored atroom temperature for up to 6 months.
CUBIC-X2
CUBIC-X2 is a mixture of 5% (wt/vol) imidazole and 55% (wt/vol) antipyrine in dH2O. For example, toprepare 100 ml of CUBIC-X2, dissolve 5 g of imidazole and 55 g of antipyrine in dH2O at room temperature(see ‘Troubleshooting’ section). CUBIC-X2 can be stored at room temperature for up to 6 months.
Staining buffer
Staining buffer is 1.5 M NaCl in PBS. Staining buffer can be stored at room temperature for up
to 1 month.
Immersion oil
For use with CUBIC-R+, mix an ~3:2 ratio of HIVAC F-4 (RI = 1.555) and mineral oil (RI = 1.467)
completely with a stirrer. The resulting RI should be 1.522, identical to that of CUBIC-R+, and the
mixture ratio should be ﬁnely adjusted by monitoring the actual RI. Mineral oil (RI = 1.467) is used
for CUBIC-X2. These oils can be used repeatedly for imaging by ﬁltering and clearing contaminants(e.g., clearing reagents, dusts). These oils can be used at room temperature.

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Equipment setup

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Gel embedding mold
The design of the gel embedding mold is shown in Fig. 2h. The top plate and bottom plate can be
replaced with glass slides. For a mouse brain, we use a gel mold with an inner size of 32 mm × 50 mm
× 14 mm (the volume is 22.4 ml). CRITICAL The thickness of a gel mold depends on the WD of theobjective lens as well as the sample size. CRITICAL The ﬂatness and any scratches on the gel
embedding mold plates’ surfaces deﬁne the gel surface quality and have an effect on image quality. The
gel embedding mold must be ﬁrmly wiped out with 70% (vol/vol) ethanol or water with a lint-free tissue
between uses. CRITICAL The squareness of the gel embedding mold deﬁnes the gel’s squareness. If thegel squareness is incorrect, the laser light can curve, which has an effect on the optical quality of thesamples. When assembling the gel embedding mold, ensure that the gel embedding mold is square.

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Customized LSFM for dual-color imaging
We constructed a customized LSFM that was capable of simultaneous dual-color imaging (Fig. 3a).
The x-galvanometer mirrors are arranged to generate the illumination light sheet, following
the digital-scanned light-sheet microscopy (DSLM) scheme2. The y-galvanometer mirrors arearranged to perform the multidirectional selective plane illumination microscopy (mSPIM)73 toreduce absorption and scattering artifacts. The z-galvanometer mirrors are arranged to control thez-position of the illumination light sheet. Two sCMOS cameras and a dichroic mirror are used fordual-color imaging.
At ﬁrst, expand and collimate the laser beam with an iris and two achromatic doublet lenses(f = 30 and 50 mm). Then place an iris and adjust its diameter to the appropriate size, whichcorresponds to the illumination sheet thickness (we adjusted to 3 mm, which corresponded to a 7.8μm illumination sheet thickness). After the iris, switch the laser beam into two paths (right and left
excitation) with motorized ﬁlter ﬂip mounts. Before the z-galvanometer mirror, shrink the laser beamwidth with two achromatic doublet lenses (f = 100 and 50 mm) because of the limitations of thegalvanometer size (5 mm). Place the center of the rotation axis of the z-galvanometer mirror at thefront focal points of the before and after achromatic doublet lenses (f = 50 mm). Place the center ofthe rotation axis of the y-galvanometer mirror at the back focal points of the before and after
achromatic doublet lenses (f = 50 mm). Place the center of the rotation axis of the x-galvanometer
mirror at the front focal point of the before and after two achromatic doublet lenses (f = 50 mm).
After that, collimate the laser beam with an achromatic doublet lens (f = 100 mm). Place theillumination objective lenses before an oil chamber. For detection, use an oil immersion detection
objective lens with a long (8-mm) WD and high (0.6) NA. Fix the detection objective lens vertically tothe side of the custom-made oil chamber. Split the emission signals into two optical paths with adichroic mirror, then ﬁlter and focus the paths onto two sCMOS cameras with achromatic lenses witha 180-mm focal length. Adjust the positions of the achromatic lenses so that the two images merge
completely (see ‘Troubleshooting’ section). Fix the sample to a motorized x–y–z–θ stage through acustom-made sample holder (Fig. 3b). Fill the chamber with an RI-adjusted, appropriate oil(Fig. 3c). We integrate all electronic devices and control them with LabVIEW and a C# program.
CRITICAL By using an optical cage system, it is easier to align the optical system. Adjust the positionand direction of the lenses and mirrors to pass through the laser beam at the center of an alignment
target. We recommend setting the mount screw for the cage rod in the same direction, because a
different direction of mount screws causes deformation of the cage system and makes it difﬁcult toadjust the laser beam to the center position.

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MOVIE system setup
MOVIE-scan is based on the synchronization of the operation of the z stage as a uniform motion andimage acquisition by a sCMOS camera. In addition, MOVIE-focus and MOVIE-skip are necessary forsynchronizing galvanometer mirrors (x, y and z) with image analysis. We use digital-to-analog (D/A)
converters to synchronize a transistor–transistor logic (TTL) output to the sCMOS camera, laser andgalvanometer mirrors. The TTL timing is synchronized with the z stage by LabVIEW, and real-time
image analysis of MOVIE-focus and MOVIE-skip is performed by C#. CRITICAL The speed of thez stage must be adjusted, depending on the z-scan range and acquisition cycle time. For example, in thisprotocol, the z-scan step is adjusted to 5 μm and the acquisition cycle time is 55 ms (exposure timeis 50 ms and light-sheet positioning time is 5 ms); thus, the z-stage speed must be set to 91 μm/s
(5 μm × 18.2 fps).
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High-speed data communication system setup
When using the dual-color MOVIE system, the acquisition rate achieves 36–80 images/s (exposure
time, 20–50 ms). Each image is ~11 MB (2,160 × 2,560 pixels, 16 bit), corresponding to 400–890 MBof data/s. We transfer these data directly to an over-storage server with RAID 60 devices, bundling>40 HDDs through a 56-Gbps (gigabits per s) InﬁniBand system.

Procedure
Dissection ● Timing 10–15 min
Deeply anesthetize the mouse with pentobarbital (~150 mg/kg of body weight) or ketamine (100mg/kg of body weight)/xylazine (10 mg/kg of body weight) by intraperitoneal (i.p.) administration
with a 1-ml syringe and a 26-gauge, 1/2-inch injection needle, or with isoﬂurane. Perfuse the mousewith 20 ml of cold D-PBS to remove the blood from the organ. Next, perfuse with 20 ml of cold 4%
PFA at <10 ml/min. Carefully dissect the whole brain or other organs with scissors and tweezers so
as not to damage the sample.
! CAUTION All experiments must follow the relevant governmental and institutional guidelines foranimal experiments.
CRITICAL STEP Cooling of D-PBS and 4% PFA is important for successful perfusion.

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Fixation ● Timing 0.5–1 d
Fix dissected organs in 10 ml of 4% PFA with gentle shaking at 4 °C for 12–24 h.
CRITICAL STEP Keep ﬁxation duration and temperature as speciﬁed to avoid overﬁxation.
Overﬁxation (from, e.g., >2 d of ﬁxation or high temperature) causes lower clearing efﬁciency.

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PBS wash ● Timing 0.5 d

To remove the remaining PFA, wash the ﬁxed organs with 10 ml of PBS with 0.01% (wt/vol)sodium azide for at least 2 h twice at room temperature.
PAUSE POINT The ﬁxed organs can be stored at −80 °C. First, place the organs in 30 ml of
30% (wt/vol) sucrose and gently shake them at 4 °C. After the organs sink to the bottom (will takealmost 1 d), put them into OCT compound and store them at −80 °C for up to 6 months. To start
the tissue-clearing steps, in order to remove the sucrose and OCT compound, thaw the samples atroom temperature and wash with PBS at least twice, with each wash lasting 1 h.

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3Tissue clearing ● Timing 6–24 d
In this step, we describe two tissue-clearing procedures. Option A is a rapid tissue-clearing protocol
for highly transparent mouse organs. Option B is a tissue-expansion protocol for high-resolution
imaging of mouse organs. These protocols may need some modiﬁcations, such as changes inreagent volumes and immersion duration, for application to organs other than brains. The reagent
volume and tube size described here are optimized for mouse brain, but the user can modify themappropriately according to organ size.
(A) Advanced whole-organ clearing protocol using CUBIC-L and CUBIC-R+(i) Half-diluted CUBIC-L treatment. Immerse the ﬁxed organ in 10 ml of half-diluted CUBIC-L withgentle shaking at 37 °C for at least 6 h (Fig. 2b,c). Overnight incubation can also be used. This step
may be optional, but we recommend performing this step to prepare highly transparent organs.
(ii) CUBIC-L treatment. Replace the 10 ml of CUBIC-L solution and continue gentle shaking at
37 °C for 2–5 d. Refresh the CUBIC-L every 2 d. After CUBIC-L treatment, all organs
should become translucent white.
! CAUTION Because CUBIC-treated organs are soft, in order to avoid physical damage, we
recommend handling them with round-edged spoons, instead of tweezers.
CRITICAL STEP Young mouse brains (e.g., <3 weeks) or small organs are sufﬁciently
delipidated by 2 d of immersion. On the other hand, elderly mouse brain (e.g., >24 weeks)or samples ﬁxed long-term with formalin or PFA (e.g., >1 week) need >5 d of immersion.
(iii) PBS wash. To stop the delipidation reaction and wash out the remaining CUBIC-L reagent,
wash the sample with 10 ml of PBS with gentle shaking at room temperature for at least 2
h. Repeat this washing step more than three times.
PAUSE POINT CUBIC-L-treated organs can be stored at 4 °C for up to 1 week inPBS–0.01% (wt/vol) sodium azide. After storing, you can restart the staining or
RI-matching steps without additional procedures.
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(iv) (Optional) Nuclear staining. Immerse the sample in 10 ml of staining buffer with gentle
shaking at room temperature twice for at least 2 h each time. Then replace the staining
buffer with staining buffer containing nuclei-staining dye (PI: 30 μg/ml (ﬁnalconcentration), RD2: 20 μl of 200× solution in 3 ml of staining buffer) and continue
gentle shaking at 37 °C for 3 d. The reagent volume and concentration can be optimized forthe organ size.
! CAUTION Some nuclear staining dyes, such as SYTO16, are incompatible with CUBIC-R+ (see Table 1 for guidance).
(v) (Optional) PBS wash. Wash the sample with >20 ml of PBS with gentle shaking for at least
2 h. Repeat this washing step more than three times. Overnight washing can alternatively
be used.
(vi) Half-diluted CUBIC-R+ treatment. Immerse the sample in 5 ml of half-diluted CUBIC-R+
with gentle shaking at room temperature for at least 6 h. Overnight immersion can also be
used. The reagent volume and tube size can be adjusted for the organ size.
? TROUBLESHOOTING(vii) CUBIC-R+ treatment. Immerse the sample in 4 ml of CUBIC-R+ in a 5-ml tube andgently shake it at room temperature overnight. The next day, replace the reagent with fresh
reagent and incubate for a further 24 h. The reagent volume and tube size can be adjusted
for the organ size.
! CAUTION When imaging ﬂuorescent-protein-labeled organs, the signal intensity may
decrease in the CUBIC-R+ solution; therefore, long-term storage at this point is notrecommended.
? TROUBLESHOOTINGPAUSE POINT Organs can be left in CUBIC-R+ at room temperature. The maximum
storage period depends on the staining dye. Organs stained with PI and RD2 can be stored
for >1 month at room temperature in a dark environment. Avoid storage at 4 °C, so thatthe samples do not crystallize.
(B) Whole-organ expansion and clearing protocol using CUBIC-X for high-resolution imaging(i) Half-diluted ScaleCUBIC-1 treatment. Immerse the ﬁxed organ in 10 ml of half-diluted
ScaleCUBIC-1 (50% (vol/vol) ScaleCUBIC-1 and 50% (vol/vol) dH2O) with gentle shaking
at 37 °C for at least 3 h. Overnight incubation can also be used.
(ii) ScaleCUBIC-1 treatment. Replace the half-diluted ScaleCUBIC-1 solution with 10 ml of
ScaleCUBIC-1 solution and gently shake the mixture at 37 °C for 5–14 d. The delipidation
solution should be refreshed every 4 d. After ScaleCUBIC-1 treatment, all organs should
become translucent white.
! CAUTION When delipidating organs labeled by ﬂuorescent proteins, replace ScaleCUBIC-1 with modiﬁed-ScaleCUBIC-1.
! CAUTION Because CUBIC-treated organs are soft, in order to avoid physical damage, we
recommend handling them with round-edged spoons, instead of tweezers.
CRITICAL STEP To avoid the quenching of ﬂuorescent proteins, the delipidation durationis reduced. Typically, we treat for 8 d for mouse brain expressing ﬂuorescent proteins.
(iii) PBS wash. Wash as described in Step 4A(iii).
(iv) (Optional) Nuclear staining. Stain as described in Step 4A(iv).
(v) (Optional) PBS wash. Wash as described in Step 4A(v).
(vi) CUBIC-X1 treatment. Immerse the sample in 30 ml of CUBIC-X1 and shake gently at room
temperature or 4 °C for 2.5 d. Refresh the CUBIC-X1 solution twice a day (ﬁve times in total).
! CAUTION Because the expanded organ is fragile, be careful while handling it in all thefollowing procedures.
CRITICAL STEP We recommend that samples expressing ﬂuorescence protein be
incubated at the lower temperature to avoid quenching.
? TROUBLESHOOTING(vii) CUBIC-X2 treatment. Replace the CUBIC-X1 with 30 ml of CUBIC-X2 and continue
gentle shaking at room temperature for 1.5 d. Refresh the CUBIC-X2 solution twice a day(three times in total).
Gel embedding. To mount a CUBIC-R+-treated sample on a customized LSFM, the sample shouldbe embedded in an agarose gel mixed with CUBIC-R+. For a CUBIC-X2-treated sample, CUBICX2 must be used instead. Because the illumination laser may be curved at the interface between thegel and the oil, a sample must be embedded at a distance from the surface. The speciﬁc gravity of

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the gel mixture can change during the gel embedding process because of additional agarose andwater evaporation; as a result, the sample can sink or ﬂoat in the gel mixture and the sample will beembedded near the gel surface. Therefore, a three-layer protocol was developed to avoid thedifference in speciﬁc gravity between the gel mixture and a sample being an issue (Fig. 2j). For an
example of a three-layer protocol using the gel mold (the inner size is 32 mm × 50 mm × 14 mm,
Fig. 2h), an adult mouse brain treated with CUBIC-R+ has an ~10-mm thickness, so the length of
the top and bottom margins is determined to be 2 mm (margin = (14–10)/2 = 2 mm).
Follow option A to embed a clear organ and option B to embed a ﬂuorescent bead gel for
calibration purposes.
(A) Gel embedding for a cleared organ(i) Preparation of agarose–CUBIC reagent. Before dissolving the agarose, thoroughly diffuse
the 2% (wt/wt) agarose powder in CUBIC-R+ at room temperature by vigorous stirring(Fig. 2d). For example, to prepare four samples to be embedded using a gel mold (Fig. 2h),
add 3 g of agarose to 147 g of CUBIC-R+ solution in a capped glass bottle. Heat thethoroughly diffused agarose–CUBIC-R+ mixture in a microwave until it starts to boil. Be
sure to loosen the bottle cap before heating. Remove the bottle from the microwave, cap thebottle and shake it vigorously. Repeat the microwave heating and shake the bottle several
times until the agarose has dissolved completely (Fig. 2d).
! CAUTION Tightly capped bottles should not be heated in the microwave because they
may explode.
CRITICAL STEP Checking whether the agarose–CUBIC-R+ mixture becomes transparent is important when determining whether it has dissolved.
CRITICAL STEP Gelation properties of agarose vary depending on the producer. To fully
reproduce our results, use appropriate agarose from listed vendors (Nacalai Tesque for
CUBIC-R+, Nippon Gene for CUBIC-X). If the combination of CUBIC solution andagarose is not suitable, it will not gel or become transparent. If the recommended agaroses
are not available, high-melting-point or high-strength agaroses may be an alternative.
? TROUBLESHOOTING(ii) Degassing incubation. Incubate the agarose–CUBIC-R+ solution bottle in a hot water bathat 60 °C for at least 30 min in order to remove bubbles from the agarose–CUBIC solution(Fig. 2e,f).
(iii) Preparation of the bottom gel layer. Transfer 3.2 ml of agarose–CUBIC-R+ solution(corresponding to 2 mm of the gel mold) to each gel mold with a 10-ml pipette. Spread the
agarose–CUBIC solution in the gel mold uniformly. Promptly remove any bubbles
carefully with a 1-ml micropipette before viscosity increases. Then leave theagarose–CUBIC solution covered with a lid at 4 °C for 20 min. If the agarose–CUBIC
solution does not turn into a gel, then incubate at 4 °C for an additional 20 min.
CRITICAL STEP To keep the agarose–CUBIC solution at 60 °C, return the
agarose–CUBIC solution bottle to the hot water bath immediately after pouring.
CRITICAL STEP Agarose-CUBIC solution is easily crystallized by water evaporation, so a
lid is needed when incubating agarose–CUBIC solution.
CRITICAL STEP Longer incubation times, such as several hours at 4 °C, can makesamples crystallize. Timekeeping and frequent checking are essential.
? TROUBLESHOOTING(iv) Making of the middle gel layer. Carefully transfer the cleared samples to a 50-ml tube. Pour
16 ml (corresponding to a 10-mm height of the gel mold) of agarose–CUBIC solution into
the 50-ml tube (Fig. 2g). Immediately decant the mixture into the gel mold in a carefulmanner. Use a spoon to set a sample at the center position and remove any bubbles
existing under the sample (Fig. 2i,j). Promptly remove the bubbles carefully with a 1-ml
micropipette before gel viscosity increases. Leave the gel mold covered with a lid at 4 °C for
30 min in a place away from light. If the solution cannot turn into a gel, incubate at 4°C for
an additional 20 min.
? TROUBLESHOOTING(v) Making of the top gel layer. Decant the agarose–CUBIC solution into a gel mold until thesurface protrudes slightly. Remove the bubbles carefully and cover with a top acrylic plate
carefully so as not to introduce bubbles inside; incubate at room temperature for at least 6 h.
(vi) Stick the sample to the sample holder. Remove the gel from the mold; it should come out
easily (Fig. 2k). To mount the sample on a customized LSFM, stick the gel-embedded
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PROTOCOLNATURE PROTOCOLS

j

c

c

sample to the sample holder by using 2% (wt/wt) agarose–dH2O gel as an adhesive (Fig. 2l)and incubate at room temperature for 30 min. 2% (wt/wt) agarose–dH2O gel can be madeby dissolving 0.4 g of agarose in 19.6 g of dH2O, using microwave heating.
CRITICAL STEP If necessary, make the inner surface of the sample holder (i.e., where theholder touches the gel) uneven to prevent slipping. We use epoxy adhesives to achieve this.
(vii) RI matching after gel embedding. Put the gel-embedded sample into a plastic bag with azipper and add enough CUBIC-R+ or CUBIC-X2 to immerse it.
CRITICAL STEP This incubation step recovers the transparency of the samples bymatching the RIs of the sample to that of the gel.
PAUSE POINT Gel-embedded organs stained with PI and RD2 can be stored for>1 month at room temperature in the dark. Avoid storing at 4 °C so that the samples will
not crystallize.
(B) Calibration gel preparation(i) For ﬂuorescent bead gel preparation, dissolve 4% (wt/wt) agarose powder in 96% (wt/wt)
CUBIC-R+ agarose and incubate at 60 °C as described in Step 5A(i,ii).
(ii) Diffuse Sphero ﬂuorescent particles into CUBIC-R+ and mix with an equal amount of
CUBIC-R+–4% (wt/wt) agarose solution at room temperature. For example, typically, 5 μlof ﬂuorescent beads (Sphero ﬂuorescent particles, purple, 0.1–0.3 μm, 1.0% (wt/vol))are diffused into 7.5 g of CUBIC-R+ and mixed with 7.5 g of CUBIC-R+–4% (wt/wt)
agarose solution.
(iii) Transfer the ﬂuorescent beads diffused in CUBIC-R+–2% (wt/wt) agarose solution into an
appropriate mold and allow the solution to gel as described in Step 5A.
? TROUBLESHOOTING

Imaging of the cleared organ with light-sheet ﬂuorescence microscopy ● Timing 2–12 h
per sample

c

c

c

c

CRITICAL To image the cleared organ at single-cell resolution, we used a customized LSFM, which we
describe in detail below. Alternative imaging systems can be used in place of this. A tiling scheme wasintroduced because the ﬁeld of view is limited in the x–y plane. For example, an adult mouse wholebrain sample cleared by CUBIC-R+ is usually separated into 11–15 tiles in the x direction and 14–20
tiles in the y direction with sufﬁcient overlap. Each tile is scanned in the z direction for up to a 7.5-mm
thickness, and the distance per acquisition cycle is set to 5 μm. After one tile is ﬁnished, the ﬁeld of view
is moved to another tile position and the scan is started in the z direction. This is repeated until all tiles
have been completed. In addition, to cover a thick organ in the z direction, the sample is rotated by 180°,
and then the opposite side of the sample is imaged using the same tiling scheme as that used for the 0°side (Fig. 3d). The acquisition sequence can be controlled by LabVIEW and Visual Studio C#.
6 Setting a gel sample for microscopy. Fix the sample to the θ stage through the sample holder after
removing the excess CUBIC reagent on the surface (Fig. 3b,c). Typically, we wipe the sample softlywith lint-free tissue and dip it into the RI-adjusted oil mix several times.
7 x–y–z–θ positioning. To acquire whole-organ images using the imaging system with a limited ﬁeldof view and WD of the detection objective, we suggest x–y positioning at ﬁrst. Deﬁne the x–yposition arrays of the imaging region by identifying the start and end positions of both the x
direction and y direction with changing z positions and searching the edge of a sample in the x–yplane. Calculate the x–y position arrays on the basis of the effective imaging area determined by theﬁeld of view of the objective (in this setup, x = 1.40 mm and y = 1.66 mm) and the appropriate
overlap value (130-μm overlap for both x and y axes).The overlap value is deﬁned to ensure that
there will not be any loss of sample regions.
CRITICAL STEP To avoid signal quenching, the laser power must be weakened during theadjustment of position and focus. For example, we use 2 mW for the 594-nm laser for adjustment
and 500 ms of exposure time, as compared with 20 mW for image acquisition and 20–50 ms of
exposure time.
CRITICAL STEP The accuracy and straightness of the x–y–z–θ stage must be taken into
consideration for overlap value.
CRITICAL STEP The left and right halves of the sample should be illuminated by the left and rightillumination arms, respectively, for homogeneous illumination intensity.
8 Next, carry out z–θ positioning. When imaging thick organs, which are larger than the WD of thedetection objective lens, we use rotation sequences. Rotate the sample along the y axis and3528

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PROTOCOLNATURE PROTOCOLScdetermine the center of rotation in the x–z plane manually by searching identical structures fromthe θ = 0° and θ = 180° sides. Set the z start position with an appropriate overlap value for the zaxis, and merge the two sides of the image blocks continuously.
! CAUTION Be careful not to hit the chamber with the sample. θ-stage rotation must be performed
at a sufﬁcient distance from the chamber wall.
9 Set the laser setting. Typically, the excitation laser power is 20 mW (measured at each side of thelaser illumination before the illumination objective lens) and the exposure time is 20–50 ms.
However, the exposure times may have to be adjusted, depending on the ﬂuorescence signal
intensities of sample.
! CAUTION Strong lasers may cause photobleaching of samples during imaging.
10 Check two images by dual-color imaging in order to merge them completely. The ﬂuorescentbeads (Sphero ﬂuorescent particles) can be used for the calibration of dual-color imaging
by 488-, 594-, and 642-nm lasers, a dichroic mirror, a 625-nm band-pass ﬁlter and a 720-nm
long-pass ﬁlter.
11 Set an appropriate threshold for MOVIE-skip. Typically, we use two times the value of the mean
intensity of the image in the blank region, but it depends on the signal intensity. Set the initial lightsheet position for MOVIE-focus automatically for each stack, as follows: typically, 20 images
separated by 3.5 μm are acquired (thus the total range is 66.5 μm), and the best light-sheet position
is determined by evaluating DCTS scores.
12 Acquire images. When using the MOVIE system, the system can run as fast as 18–40 fps per color(Fig. 4b). Acquire images and save appropriately. Each image size is ~11 MB (2,160 × 2,560 pixels,
16 bits per pixel), and we transfer them to the server and save them to hard disks or SSDs in realtime through a 56-Gbps InﬁniBand system. Each stack (one color, one x–y position) is saved to adifferent folder and folders of the same y position are located in the same directory. The hierarchy
and folder names are used in downstream data analysis. The total image acquisition of CUBIC-R+-treated whole mouse brain should be completed within 2–5 h, yielding ~360,000 images (4–5 TB
of data) with the MOVIE system.
CRITICAL STEP Keep the microscopy room dark to decrease the background. The MOVIE
system may be susceptible to background change.
? TROUBLESHOOTING

Cell-nucleus detection ● Timing 2–8 h per samplec

CRITICAL For quantitative characterization of the sample, we implemented an informatics pipeline
to detect cell nuclei in the acquired images. To reduce labor and time, the algorithm is designed
to work with less manual preparation and is more time efﬁcient while maximizing accuracy asmuch as possible. We implemented the image-processing program by CUDA, C++ and Python.
All the settings and parameters should be provided as a JSON text ﬁle. This parameter ﬁle includes
imaging-related settings (image size, overlap size, pixel size in micrometers and ﬁle naming conventions)and algorithmic parameters (sets of ﬁlter sizes for image processing and threshold values for suppressing
obviously noisy candidates). The processing time can vary according to different computer
speciﬁcations. In the following, we denote the Python scripts in the ‘script’ directory in theprovided code set with a ‘.py’ extension and the parameter ﬁles in the ‘param’ directorywith a ‘.json’ extension.
13 Software setup. In this step, download and install the requisite programs. First, download the sourcecode by running the following command:
git clone https://github.com/lsb-riken/CUBIC-informatics
Then build the source code. To build the programs under the ‘build’ directory, run the following
commands in the ‘CUBIC-informatics’ directory.
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release -DCMAKLE_INSTALL_PREFIX=. ..
makemake install

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If the necessary software programs (Software and tools), including the CUDA Toolkit, are installed
correctly, it will ﬁnish within a few minutes without any error messages in the output. In addition,
install the required Python libraries by running the following command in the ‘CUBIC-informatics’
directory:
pip install -r requirements.txt
14 Parameter ﬁle preparation. Before running the programs, prepare the parameter ﬁles. The tilingcheck, cell candidate detection and candidate veriﬁcation (Steps 15–17) for one organ require two
image sets (θ = 0°, 180°) and four parameter ﬁles, which we call HDoG_FW, HDoG_RV, Merge andClassify. Both the HDoG_FW (for θ = 0°) and HDoG_RV (for θ = 180°) parameter ﬁles should
specify the settings for the source and destination directories, GPU identiﬁcations (IDs) to be used,
input image speciﬁcation (image size and margin in pixels), coordinate information (pixel size in
micrometers and relative position of origin in global coordinates) and the algorithmic parameters(normalization ﬁlter size, 3D-DoG ﬁlter size, and other minor parameters) for HDoG_gpu.py. The
Merge parameter ﬁle should specify the destination directory, FW/RV boundary positions, whether
or not to rotate and ﬂip before tiling, downscaled voxel size in micrometers and whether or not to
ﬂip after tiling. The Classify parameter ﬁle should specify the destination directory and coefﬁcients ofthe decision boundary line. Example parameter ﬁles and detailed descriptions of each parameter areprovided in param_example_HDoG_FW.json, param_example_HDoG_RV.json, param_example_
mergebrain.json and param_example_classify.json which are included in the ‘param’ directory of our
CUBIC-informatics program. For second-channel veriﬁcation, in addition to the same set of fourparameter ﬁles for the second-channel images, a MultiChannel parameter ﬁle is required, whose
example is provided as param_example_multichannel.json.
15 Tiling check (MergeBrain.py). Before cell-nucleus detection, to check if any issues arose during theimaging steps, such as missing images or stacks, downscale the acquired x–y tile images (from0.65 × 0.65 × 5 μm/voxel to 13 × 13 × 10 μm/voxel) and reconstitute to a whole-organ image in
16-bit TIFF format by using the MergeBrain.py script. Run the following command, replacing theparameter ﬁle name with the one you speciﬁed in the previous step.
python script/MergeBrain.py images param/param_example_mergebrain.json
This program can convert 1 TB of image data to 2.5 GB of image data in <30 min. It allows theimages to be rotated and ﬂipped before being appended so that the x–y tile will be correctly aligned.
Check whether x–y tile images have been properly acquired by using visualization software such as
ImageJ. If an improper stack is found, it is necessary to acquire the stack again. By using x–y tile
images, the 3D reconstitutions can be visualized with the Imaris software (Fig. 3e).
16 Cell candidate detection (HDoG_gpu.py, MergeBrain.py). To extract the cell candidates from the raw
image data, process the images by normalizing signal intensities, applying a 3D-HDoG ﬁlter andlabeling connected components by running HDoG_gpu.py, followed by MergeBrain.py, to merge
the results of the θ = 0° and 180° image sets and transform cell positions in the whole-organ
coordinate system. Run the following commands, replacing the parameter ﬁle name with the one
you speciﬁed in Step 14.

python script/HDoG_gpu.py param/param_example_HDoG_FW.json
python script/HDoG_gpu.py param/param_example_HDoG_RV.json
python script/MergeBrain.py cells param/param_example_mergebrain.jsonc

The cell-nucleus-detection pipeline extracts cell-like objects from images using GPUs. This step
takes the longest time in the pipeline, but it can be shortened if multiple GPUs are available.
With four GPUs, we found that the system processed ~2 TB/h. When the program ﬁnishes, it
outputs a collection of centroid positions and regional features for each cell candidate region(3–12 GB), which are used in the next step.
CRITICAL STEP Voxel values are normalized using the minimum and maximum intensity of thesurrounding 65 × 65 × 40-μm range, but the normalization kernel size can also be changed byparameter ﬁle modiﬁcation. If the size is too small, the normalization process may produce a false
patterns, whereas it is computationally intensive to use a large kernel size. To avoid artifacts andﬁnish this process quickly, the optimization of the size is important.

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PROTOCOLNATURE PROTOCOLScCRITICAL STEP Users should choose appropriate 3D-DoG (scale) parameters for accurate cellnucleus detection because this step is dependent on the physical and optical magniﬁcation. The
parameter can be adjusted easily by using the HDoG_intermediate.py script. We set the 3D-DoG
ﬁlter sizes as σxy(large) = 4.875 (7.5), σz(large) =11.25 (2.25) and σxy(small) = 3.25 (5.0), σz(small) = 7.5(1.5) μm (pixel) for the CUBIC-X mouse brain and σxy(large) = 3.9 (6.0), σz(large) =7.5 (1.5) andσxy(small) = 2.5 (4.0), σz(small) = 5.0 (1.0) μm (pixel) for the CUBIC-R+ mouse brain.
17 Candidate veriﬁcation (HDoG_classiﬁer.py). In this step, each cell candidate is classiﬁed as either areal cell or just noise according to its features (maximum intensity in the region and regional
structureness). Run the following command, replacing the parameter ﬁle name with the one you
speciﬁed in Step 14.
python script/HDoG_classify.py param/param_example_classify.json
This program outputs a classiﬁer ﬁle to be used in subsequent steps and plots cell candidate points
in 2D feature space. In most cases, real cells and noisy regions show clear separation in feature
space. Each cell candidate is classiﬁed according to at which side of the decision boundary its
feature point is located. Deﬁne an appropriate decision boundary by looking at feature space
distribution and set it in the parameter ﬁle. We use the following linear decision boundary for most
samples: 6:0 X1 þ 0:75 X2 þ 1 ¼ 0. Note that X1 is the logarithm of maximum intensity and X2 isthe logarithm of structureness (Fig. 7).
18 Second-channel veriﬁcation (MultiChannelVeriﬁcation.py). In addition to nuclear staining images forthe ﬁrst channel, use images of other channels to detect any cell signals. Run the following command:

pythonscript/MultiChannelVeriﬁcation.py
multichannel.json

param/param_example_cBased on the position of a cell from the ﬁrst channel, we can judge whether it has a positive signal
or not, by comparing the corresponding intensity in the second channel. For AAV-PHP.eB:NSEH2B-mCherry-infected samples, we computed the ratio of log intensity between mCherry andRD2 at the centroid of the cells. We set the threshold manually and also performed unsupervised
clustering to distinguish the positive from the negative cells. These two methods produced
similar results, and we used 1.0 as the threshold for the ratio of log intensity.
CRITICAL STEP We used the intensity ratio because the raw intensity value is affected by thestrength of the excitation laser, which is not uniform over the whole brain.
? TROUBLESHOOTING

Alignment and annotation ● Timing 1–8 h per samplec

CRITICAL To quantify the number of cells in each anatomical region, use the mouse brain atlas(CUBIC-Atlas) and a program for mapping (AtlasMapping.py). Mapping is performed in two stages.
The ﬁrst stage is image-based registration to the reference and the second stage is point-based
annotation using the atlas ID. We implemented the mapping tool with Python. Of course, this program
can also be used to align other organs, using an appropriate reference organ image, but in such cases, itcannot perform the annotation step.
19 Reference data and parameter ﬁle preparation. First, prepare the reference data. For mouse brain,
download the CUBIC-Atlas reference data from http://cubic-atlas.riken.jp/, which include reference
images and point-based annotation data. For other organs, prepare the appropriate reference
images by yourself. Next, create a parameter ﬁle for the registration and annotation step, whose
example is provided as param_example_mapping.json in the ‘param’ directory of our CUBICinformatics program. It should contain the destination directory, the installed ANTs directory, theinstalled CUBIC-Atlas directory, the path to classiﬁer ﬁle produced in the candidate veriﬁcation
step, the voxel size in the ANTs registration and the maximum distance for a transformed detected
cell to be annotated with the ID of the nearest reference point.
20 Registration (AtlasMapping.py). Before annotation, register the nuclear-stained whole-brain image
to the reference image of the CUBIC-Atlas by running following command.
python script/AtlasMapping.py
mapping.json

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registration

param/param_example_

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It calls the ANTs registration command with our recommended parameters. We provide reference
images in 40- and 80-μm voxel sizes. We mainly use 80-μm voxel images for quick registration(about 1 h). Because this step does not require the results of cell-nucleus detection, the cell-nucleusdetection steps and this step can be performed simultaneously if the computer has enough capacity(CPU cores and RAM).
21 Annotation (AtlasMapping.py). Map cell positions to the atlas coordinate system by applying
nonlinear transformations obtained in the previous step and assign the atlas ID of the nearest
reference point for each detected cell. Run the following command.

python script/AtlasMapping.py annotation param/param_example_mapping.json
This annotation step should take about 10 min. Because the cell detection step tends to detectbubbles or dust outside the organ, we assign ID = 0 to the detected cells far from any of thereference points in CUBIC-Atlas. We use 160 μm as the maximum distance, but this can be
adjusted in the parameter ﬁle.
? TROUBLESHOOTING

Troubleshooting
Troubleshooting advice can be found in Table 2.
Table 2 | Troubleshooting table
Step

ProblemPossible reason

Solution

Reagent setup

Undissolved solute remaining
in CUBIC-R+ or CUBIC-X2
reagent
Crystallization of CUBIC-R+
or CUBIC-X2 reagent
Entirely unmatched ﬁelds ofview from two sCMOS
camerasReduction of solubility by heat ofdissolution

Use mild heating and mix with a stirrer or use overnight
stirring at room temperature

Water evaporation or decreasing
temperature
Misalignment of thedichroic mirror

Check and adjust the temperature and RI value with dH2O

Equipment setup

Adjust the position and angle of the dichroic mirror with akinematic mount

Chromatic aberration

4A(vi,vii)

4B(vi))
5A(i,iii,iv),
5B(iii)

6–12

Correct by extending or shortening the distance between
tube lens and the sCMOS camera
Diminishing of signals at the Clipping of the collected photon by Align them closely as much as possible, or replace them
periphery
the dichroic mirror, tube lens, or with larger onesﬁlters after detecting objective lensPoor organ clearing
Insufﬁcient incubation in CUBIC-L Increase delipidation timeor ScaleCUBIC-1
Insufﬁcient RI matching
Exchange the CUBIC-R+ or CUBIC-X2 more than once or
twice additionally
Poor organ swelling
Insufﬁcient incubation in CUBIC-X1 Increase the length of the swelling step with CUBIC-X1
Solution does not become a Not using appropriate agarose for Try 1–3% (wt/wt) high-melting-point or high-strength
solid gel
CUBIC reagent
agarose
Bubbles in the agarose–CUBIC Increased viscosity of
Agarose–CUBIC solution becomes more viscous at room
solution when gel-embedding agarose–CUBIC solution
temperature; hence, the bubbles have to be removed until
the gel solidiﬁes. Be careful of bubbles at the border andcorner of the gel mold, because these are harder to ﬁnd
Gel becomes cloudy white
Recrystallization of solute
CUBIC-R+ or CUBIC-X2 is easily crystalized whencooling. Perform a 4 °C incubation for <30 min
Incubation in CUBIC-R+ or CUBIC-X2 warmed at 60 °C.
The crystal will be dissolved
Inhomogeneous nuclear signal Insufﬁcient penetration of nuclear- Increase the staining time or optimize the staining
staining dye
conditions
Weak or non-detectable
Quenching of ﬂuorescent protein Use CUBIC-R+(M) for RI matching
ﬂuorescent protein signals
Decrease the incubation temperature for all steps
Insufﬁcient ﬂuorescence signal
Choose bright ﬂuorescent protein expression system byusing a strong expression promoter
Table continued

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PROTOCOLNATURE PROTOCOLS
Table 2 (continued)
Step

ProblemPossible reason

SolutionHigh autoﬂuorescence
Poor z-resolution

16–18

19–21 (alignment
and annotation)

Insufﬁcient delipidation
Increase CUBIC-L or ScaleCUBIC-1 treatment time
Wrong setting of the light-sheet
Adjust the light-sheet distance from the detection
position
objective lensLaser is too thin before illumination Expand the illumination laser diameter
objective lensRemove the obstruction
Shade inside the sample
Obstruction, such as bubbles,
crystallized CUBIC reagentsand dust
Increase the amplitude of the y-galvo
Light absorption by high-cellTry staining with a lower concentration of dye
density region
DefocusWrong combination of immersion Use appropriate oil. For CUBIC-R+, completely mix
oil and CUBIC reagents
HIVAC F-4 (RI = 1.555) and mineral oil (RI = 1.467) in a
3:2 ratio with a stirrer. The mixture’s RI is 1.522,
comparable to that of CUBIC-R+. For CUBIC-X2, use amineral oil (RI = 1.467)
Mismatch between the update
Synchronize the timing of the z-galvo and sCMOS
timing of light-sheet position and cameras
image analysis of MOVIE-focus
Lack of images near the edges Incorrect speciﬁcation of theDetermine the xy range of the sample region carefully,of the sample
acquisition area
while changing the z-position
Low effect of MOVIE-skip
Wrong threshold used in MOVIE- Change the threshold of MOVIE-skip, and check theskip
whole-organ coverage
MOVIE-skip detects the intensity Optimize the block size (typically, we use 40 × 40 pixelof particles of dust outside
blocks), corresponding to the particle size
the sample
Image save error
Insufﬁcient network
Use a 56-Gbps InﬁniBand system, directly transferring
communication speed
data to storage in the server. Make a program toreacquire the stack of images when unexpected errors
happen. The remote Windows10 desktop may disturb
network communication; therefore, turn it off during
imaging
Incorrect tile images
Wrong parameters used for
Change the options to rotate or ﬂip the images before
MergeBrain.py
tiling. Check the boundary position of the front side andthe back side
Inappropriate detection
Weak or nondetectable
Increase the exposure time and laser power of the(overcounting or
ﬂuorescenceimaging condition
undercounting)
Filter size is too small for intensity Run HDoG_intermediate.py, check the extracted region
normalizationand optimize the parameters
Complex morphology
This algorithm is optimized for sphere-signal detection.
Samples must be prepared to be suitable for thisalgorithm
Inappropriate detection
Oversuppression of noisy regions Check the maximum intensity and volume of the cells in(undercounting)the images. Too high a threshold value will discard thetrue cells
Overcounting and multiple
Scale parameter is too small
Run HDoG_intermediate.py, check the extracted region
detection of single cells
and optimize the parameters
Undercounting and single
Scale parameter is too large
Run HDoG_intermediate.py, check the extracted region
detection for multiple cells
and optimize the parameters
Decision boundary is wrong
Plot cell candidate point in images according to theInappropriate veriﬁcation
feature value. It may help in understanding the(overcounting or
relationship between image and feature. Then set theundercounting)decision boundary manually to optimize the performance
Wrong registration result
Downscaled image is not in the
Check the scale after downscaling in the parameter forsame scale as the reference image MergeBrain.py and the scale of reference in theparameter for AtlasMapping.py
Plot the 3D downscaled image with ImageJ or Imaris andDownscaled image is not in the
check that the orientation of the sample is almost thesame orientation as thesame as the reference image. If not, change the parameter
reference image
for AtlasMapping.py by ﬂipping or rotating

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PROTOCOLNATURE PROTOCOLSTimingStep 1, dissection: 10–15 min
Step 2, ﬁxation: 0.5–1 d
Step 3, PBS wash: 0.5 d
Step 4A(i–iii), delipidation: 3–6 d
Step 4A(iv,v), (optional) nuclear staining: 4 d
Step 4A(vi,vii), RI matching: 2 d
Step 4B(i–iii), delipidation: 6–15 d
Step 4B(iv,v), (optional) nuclear staining: 4 d
Step 4B(vi), swelling: 2.5 d
Step 4B(vii), RI matching: 1.5 d
Step 5, gel embedding: 1 d
Steps 6–12, imaging: 2–12 h per sample
Steps 13–18, cell-nucleus detection: 2–8 h per sample
Steps 19–21, alignment and annotation: 1–8 h per sample

Anticipated results
The CUBIC protocol can be used to prepare highly transparent (Step 4A; CUBIC-L/R+) or expanded(Step 4B; CUBIC-X) organs with good retention of ﬂuorescent protein signal with simple immersion
procedures. These transparent organs can be imaged by a conventional or customized LSFM. Afterthe tissue-clearing procedure using the CUBIC-L/R+ protocol, highly transparent whole mousebrains with strong signals of the ﬂuorescent protein mCherry can be obtained (Fig. 3e). If using apoorly cleared organ, the images of the deep regions become dark and blur. When the CUBIC-X
protocol is used for a whole brain, it will be expanded about 2.2-fold and can generate high-resolution
images. The images of CUBIC-L/R+-treated whole mouse brains can be acquired in about 24 h by aclassic stop-and-exposure scheme with ~6 TB of data/color. On the other hand, the MOVIE system
can reduce the acquisition time to 5 h (2.5 TB of data/color × 2 colors) for CUBIC-L/R+-treatedwhole mouse brains.
Whole-organ scale data can be efﬁciently analyzed at a data throughput of 2 TB/h by the 3DHDoG-based cell-nucleus-detection algorithm using multiple-GPUs and multiple-CPU processing.
The F-scores of cell-nucleus detection should be more than 90% for a CUBIC-X-treated whole mousebrain and, except in high-cell-density regions such as the cerebellum and olfactory bulb, for a CUBICR+-treated mouse brain (Fig. 8b,c). Furthermore, the 3D-HDoG-based cell-nucleus-detection algorithm, when combined with dual-color imaging, can rapidly and accurately obtain information about
the cellular state. Because this point-based organ analysis can reduce the data size, downstream
analysis will become much simpler and it is easier to handle the entire raw dataset. Our future
biomedical studies will further develop and investigate this point-based concept.

Reporting Summary
Further information on research design is available in the Nature Research Reporting Summary
linked to this article.
Data availability
CUBIC-Atlas v.1.2 reference data are available from http://cubic-atlas.riken.jp. Other datasets generated or analyzed during the current study are available from the corresponding author upon
reasonable request.
Code availability
All source code and a brief guide for data analysis are available on https://github.com/lsb-riken/
CUBIC-informatics. Source code for the microscope and MOVIE system is available from the corresponding author upon reasonable request.
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Acknowledgements
We thank the lab members at RIKEN BDR and The University of Tokyo, in particular M. Kuroda, for his kind advice on sample gel
embedding; D. Shigeta, for his kind help in 3D image preparation; C. Shimizu for support with swelling experiments; and E. A. Susaki,
K. Tainaka and A. Kuno for help with developing CUBIC reagents. We thank K. Wilkins for proofreading the manuscript. T.T.M. was
supported by the Osaka University Medical Doctor Scientist Training Program. This research was partially supported by the program forBrain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) from the Japan Agency for Medical Research andDevelopment, an AMED grant (JP19dm0207057 to H.H.), HFSP (RGP0019/2018 to H.R.U) and JSPS KAKENHI grants 18H02521(to H.H.) and 19K06899 (to A.K.).

Author contributions
H.R.U., K.M., T.T.M. and S.A.H. designed the study. T.C.M. developed the CUBIC-X protocols. J.K., T.T.M., T.M. and T.M.W. designed
and constructed a customized LSFM. K.M. and T.T.M. developed the MOVIE system. T.M., T.C.M. and S.A.H. prepared the CUBICAtlas and analysis programs. H.F. and T.C.M. performed tissue clearing. A.K. and H.H. prepared AAV-PHP.eB (NSE-H2B-mCherry) andthe infected mouse. K.M., T.T.M., S.A.H., T.C.M., T.M. and H.R.U. wrote the manuscript. All authors discussed the results andcommented on the manuscript text.

Competing interests
H.R.U. and T.C.M. have ﬁled a patent application (PCT/JP2014/070618, 2013-168705) for the CUBIC protocol.

Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/s41596-019-0240-9.
Reprints and permissions information is available at www.nature.com/reprints.
Correspondence and requests for materials should be addressed to H.R.U.
Peer review information Nature Protocols thanks Ludovic Silvestri and other anonymous reviewer(s) for their contribution to the peer
review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional afﬁliations.

Received: 3 May 2019; Accepted: 28 August 2019;
Published online: 20 November 2019

Related links
Key references using this protocol
Murakami, T. C. et al. Nat. Neurosci. 21, 625–637 (2018): https://doi.org/10.1038/s41593-018-0109-1
Tainaka, K. et al. Cell Rep. 24, 2196–2210.e9 (2018): https://doi.org/10.1016/j.celrep.2018.07.056
Kubota, S. I. et al. Cell Rep. 20, 236–250 (2017): https://doi.org/10.1016/j.celrep.2017.06.010

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Last updated by author(s): May 8, 2019

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