Resource
https://doi.org/10.1038/s41593-018-0109-1

A three-dimensional single-cell-resolution
whole-brain atlas using CUBIC-X expansion
microscopy and tissue clearing
Tatsuya C. Murakami1, Tomoyuki Mano1,2, Shu Saikawa3, Shuhei A. Horiguchi4,5, Daichi Shigeta1,
Kousuke Baba6,7, Hiroshi Sekiya8, Yoshihiro Shimizu9, Kenji F. Tanaka10, Hiroshi Kiyonari11,
Masamitsu Iino8,12, Hideki Mochizuki 6, Kazuki Tainaka1,13 and Hiroki R. Ueda 1,2,4*
A three-dimensional single-cell-resolution mammalian brain atlas will accelerate systems-level identification and analysis ofcellular circuits underlying various brain functions. However, its construction requires efficient subcellular-resolution imaging
throughout the entire brain. To address this challenge, we developed a fluorescent-protein-compatible, whole-organ clearing
and homogeneous expansion protocol based on an aqueous chemical solution (CUBIC-X). The expanded, well-cleared brain
enabled us to construct a point-based mouse brain atlas with single-cell annotation (CUBIC-Atlas). CUBIC-Atlas reflects inhomogeneous whole-brain development, revealing a significant decrease in the cerebral visual and somatosensory cortical areas
during postnatal development. Probabilistic activity mapping of pharmacologically stimulated Arc-dVenus reporter mousebrains onto CUBIC-Atlas revealed the existence of distinct functional structures in the hippocampal dentate gyrus. CUBIC-Atlas
is shareable by an open-source web-based viewer, providing a new platform for whole-brain cell profiling.

T

he mammalian brain is composed of various cellular circuitsof different physiological functions. Comprehensive analysis
of such complex cellular circuits in the entire mammalianbrain is one of the fundamental challenges in neuroscience. Toward
this goal, a number of mammalian brain atlases, including ones fornonhuman primates, have been constructed1–8 and provided usefulanatomical platforms. These mammalian brain atlases can be used(i) as a platform for mapping different cellular populations over theentire brain when combined with genome-wide expression data byin situ hybridization9 and microarrays10,11, (ii) as a platform for mapping cellular activities by measuring gene expression of immediate
early genes12–14 and (iii) as a platform for mapping cellular connections when combined with neural projection analysis based on
adeno-associated virus15 or rabies virus16,17.
Three-dimensional (3D) reconstitution of imaging data acquired
from 2D serial sections has been a common strategy for mammalian brain atlases1–3,8. Although the image-based atlases can provide
high-resolution information, the enormity of the data prevents flexible editing and update of the atlas. Considering that the cell is thebasic unit of life, a cell-based atlas would provide an attractive alternative to image-based atlases. A compact atlas of this type may beeditable via an open-source platform and therefore easily updatedby overlaying various cellular functions (for example, activity, gene
expression, cell type and neural connection).
To construct a single-cell-resolution mouse brain atlas, it is essential to accurately identify whole cells in the whole brain. To this end,

an appropriate imaging technique should provide both subcellular
resolution and whole-brain-scale coverage. The recent developmentof potent tissue-clearing methods, including BABB, 3DISCO anduDISCO, CLARITY/PACT-PARS, CUBIC and others18–24, enables
rapid single-cell-resolution imaging of an adult mouse brain whencombined with light-sheet fluorescence microscopy (LSFM).
However, rapid subcellular-resolution imaging over the entire brain
is still challenging because of the limited resolution of current LSFM
techniques and insufficient transparency of tissues.
Expansion microscopy, developed by the Boyden group25, andanother expansion protocol developed by the Gradinaru group26
provide a new approach to improving optical resolution by physically expanding tissues. The Chung group further applied expansion microscopy to whole organs27. These polymer-based expansion
microscopy methods may allow subcellular-resolution imaging ofsamples. In addition to the improved resolution, tissue expansion
is expected to provide optical advantages in tissue transparency
because the refractive indices (RI) inside tissues would become
more homogenous. However, since excess expansion makes tissue
too fragile28, moderate tissue expansion is called for.
In this study, we developed an editable, point-based mousebrain atlas with single-cell resolution by an intensive tissue-clearing method combined with tissue expansion. Describing the whole
mouse brain as an ensemble of cellular points with less than 3 GBof data was achieved by subcellular-resolution imaging of a nuclearstained brain and automatic detection of cells. With reference toDepartment of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. 2International Research Center for
Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Tokyo, Japan. 3Department of Computational Biology and Medical Sciences, Graduate
School of Frontier Sciences, The University of Tokyo, Chiba, Japan. 4Laboratory for Synthetic Biology, RIKEN Quantitative Biology Center, Osaka, Japan.
5
Department of Systems Science, School of Engineering Science, Osaka University, Osaka, Japan. 6Department of Neurology, Graduate School of
Medicine, Osaka University, Osaka, Japan. 7Department of Kampo Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan. 8Departmentof Pharmacology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. 9Laboratory for Cell-Free Protein Synthesis, RIKEN Quantitative
Biology Center, Osaka, Japan. 10Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan. 11Animal Resource Development
Unit and Genetic Engineering Team, RIKEN Center for Life Science Technologies, Kobe, Japan. 12Division of Cellular and Molecular Pharmacology, Nihon
University School of Medicine, Tokyo, Japan. 13Brain Research Institute, Niigata University, Niigata, Japan. *e-mail: uedah-tky@umin.ac.jp
1

Nature Neuroscience | VOL 21 | APRIL 2018 | 625–637 | www.nature.com/natureneuroscience© 2018 Nature America Inc., part of Springer Nature. All rights reserved.

625

Resource

NATUre NeUrosCIenCe

anatomical annotations from the Allen Brain Atlas8, this single-cellbased brain atlas, CUBIC-Atlas, allowed us to count the number ofcells in the annotated anatomical region. Region-specific cell counting in the developing mouse brain revealed a significant decrease incell numbers in the cerebral visual and primary somatosensory cortical areas during early postnatal development. By probabilisticallymapping pharmacologically activated neural cells onto CUBICAtlas, we also discovered a functionally distinct brain region in the
hippocampal dentate gyrus. This single-cell-resolution analyticalplatform, CUBIC-Atlas, is thus suitable for unbiased system-level
cellular analysis.

Results

Identification of swelling reagents by comprehensive chemical screening. To construct an imaging technique with both subcellular resolution and whole-brain-scale coverage, one feasible
approach would be volumetric imaging by tissue expansion. All
current expansion microscopy methods are based on hydrogelembedding of tissues, some of which enable imaging of fluorescentproteins26,29. Conventionally, expansion protocols have been limited
to tissue slices or cultured cells. Recently, a whole-organ expansion
protocol was reported27, termed magnified analysis of proteome(MAP), which successfully demonstrates fourfold expansion in one
dimension. However, subcellular imaging with whole-brain-scale
coverage could not be achieved due to the lack of a hyperhydrative RI matching reagent that can maintain the expansion and thelack of high-resolution objective lenses that can cover an entire
expanded brain. Furthermore, because tissue denaturation at hightemperature is required, MAP is not able to observe fluorescentproteins without immunostaining. Therefore, expansion protocols that allow both mild whole-organ expansion and retentionof fluorescent protein are still needed. Considering that too much
expansion causes fragility of tissues28 and that commercially available high-resolution objective lenses are limited in their working
distance (WD; 8-mm XLPLN-series 10×​and 25×​Olympus lenses),
we decided to develop a moderate, ten-fold volume-expansion protocol. Because simple immersion of organs into aqueous chemicalsolution retains fluorescent protein signals well, and because someaqueous chemical solutions are known to promote tissue swelling23,30,31, we endeavored to develop an expansion protocol withaqueous chemical solutions.
To identify the chemicals that achieve both tissue expansion
and RI matching, we performed comprehensive chemical screening. For parallelization of screening experiments, we used gelatin
instead of brains because of the high similarity between paraformaldehyde (PFA)-fixed gelatin and PFA-fixed delipidated brains
in terms of swelling behavior and isoelectric points (Fig. 1a,b andSupplementary Fig. 1). These results prompted us to establish highthroughput screening of swelling agents based on PFA-fixed gelatingel (type B). To quantitatively evaluate the swelling ability, we measured the water absorption at 975 nm in chemically treated gelatin gel(Fig. 1c and Supplementary Fig. 1). For the chemical screening, we
first chose 1,691 chemicals out of over 25,000 commercial chemicals
from Tokyo Chemical Industry in terms of their potential water-solubility. The screening revealed 11 chemicals (Supplementary Table 1)with greater swelling ability than that of a conventional swellingchemical, urea (Fig. 1d), and further screening using mouse brains
revealed that six of these 11 swelled the delipidated brain more thanurea (Fig. 1e). We next asked whether they can promote further
swelling if mixed together. To simplify the experiment, we classified
these final candidates into three groups (imidazole, pyrazolone andamine groups) based on their chemical structures and chose a representative chemical from each group. The combinatorial swelling
effect was tested in the forms of a mixture or a sequential procedure
with fixed gelatin blocks. The result suggested significant synergistic effect of imidazole and antipyrine (Fig. 1f). Based on this result,626

we designed the whole-brain expansion protocol by serially treating
brains with imidazole and antipyrine.
CUBIC-X for whole-brain expansion with retention of native
proteins. To ensure compatibility with 10×​volume expansion, tissue clearing and rigid sample mounting on LSFM, we developed
the following four-step protocol: (i) tissue delipidation, (ii) volume
expansion, (iii) RI matching and (iv) gel embedding. Brains weredelipidated by ScaleCUBIC-1, then immersed in aqueous imidazole
solutions as the volume expansion step. The 20% imidazole solution, termed CUBIC-X1, was the minimal concentration to achieve
the largest expansion volume (Supplementary Fig. 2). To promote
the transparency of expanded brains, we next explored a high-RI
medium compatible with the expanded brain tissue. Antipyrine,
another candidate swelling reagent, displayed remarkable watersolubility and high molar refractivity (Supplementary Fig. 2). We
found that the mixture of 5% imidazole and varying amounts ofantipyrine showed high transparency while maintaining the expansion (Fig. 2a,b). On the other hand, other examined conventionalaqueous RI media could not maintain the expanded size or lacked
high clearing ability (Fig. 2a,b and Supplementary Fig. 2c). The
clearing performance of an imidazole–antipyrine cocktail was
also quantitatively confirmed against the USAF resolution chart(Methods and Supplementary Fig. 3d–f). In view of handling, wechose the 5% imidazole +​ 55% antipyrine cocktail (RI =​  1.467) as
the RI medium, termed CUBIC-X2. In the final 5-d expansion
protocol, we obtained a 10 ×​ expanded brain with almost full transparency (Fig. 2c,d). For rigid sample mounting on a microscope,gelation was performed by adding 2% agarose to the CUBIC-X2
medium. CUBIC-X2-based agarose gel was highly transparent andsolid (Fig. 2d and Supplementary Fig. 3). A series of expanded tissue-clearing protocols, termed CUBIC-X, was compatible with thefluorescent dye staining of cell nuclei (Fig. 2d). Furthermore, our
CUBIC-X protocol was also compatible with fluorescent reporter
proteins by using a modified ScaleCUBIC-1 reagent (5% by weight(wt%) Quadrol, 10 wt% Triton X-100 and 25 wt% urea) at the delipidation step (Supplementary Fig. 4). We performed LSFM imaging
of brains expressing YFP under Thy1, PLP and Mlc1 promoters(see Methods), demonstrating clear imaging of fluorescent proteinsdeep in the tissues (Fig. 2e–g,i). High-NA (1.0) confocal imaging of
Thy1-YFP mouse brain suggested the applicability of CUBIC-X to
imaging of subcellular structure with CUBIC-X protocol (Fig. 2h).
Whole-brain nuclei imaging of a CUBIC-X expanded brain withcustomized LSFM. Because of the irregular and diversified morphologies of cells, comprehensive identification of cell bodies in awhole brain is challenging. Because relatively uniform round nuclei
facilitate global cell detection and tracing32, we decided to identify
the locations of nuclei as a surrogate for cell bodies. To this end,
propidium iodide, an organic small molecule staining DNA, wasapplied in the following experiments for whole-brain cell detection. To clarify whether CUBIC-X improves imaging quality, we
performed imaging of pre- and post-CUBIC-X brains by commercially available low-magnification LSFM (Methods). The resulting images revealed highly resolved fluorescent signals from thethalamus, located deep in the brain (Supplementary Fig. 5). We
then asked whether a CUBIC-X-expanded brain retains original
spatial information of nuclei. To verify whether brains hold nuclei
during delipidation, we performed sequential observation of thenuclei with 1-mm-thick brain slices from R26-H2B-EGFP mice
(Methods). We confirmed that none of the nuclei were lost during
delipidation (Supplementary Fig. 6 and Methods). Next, we verified that none of the nuclei were lost during expansion by observing
the nuclei in the corresponding regions of pre- and post-CUBICX brains (Supplementary Fig. 6 and Methods). Together, we concluded that none of the nuclei were lost during the whole CUBIC-X

Nature Neuroscience | VOL 21 | APRIL 2018 | 625–637 | www.nature.com/natureneuroscience© 2018 Nature America Inc., part of Springer Nature. All rights reserved.

Resource

NATUre NeUrosCIenCe
a

GH

Urea

b

2.5

GelatinBrain swelling ratio

Delipidated
brain

PBS

R 2 = 0.87

2.0

Thiourea
Urea

SD
GH

0.5
0.0
0.0

Ionic
Urea derivative

0.5
1.0
1.5
2.0
Gelatin swelling ratio

Isoelectric point (pI)

Lysozyme

10.92

Gelatin type A

9.59

Fixed gelatin type A

5.07

1.5
1.0

ProteinGelatin type B

5.00

Fixed gelatin type B

4.58

Fixed brain protein

5.01

Beforec

Chemicalapplication

9% gelatin with
0.8% PFA

Quantification
After
0.25

OD975

0.20
0.15
0.10
0.05
Gelation at 4 °CdIncubation at 20 °C for 3 d

0.00

Brain swelling ratio

In-well gelatin swelling score(blank = 0, urea = 1)

Urea

#1352

e 4.0

1.5

1.0
0.9

0.5

0.0

3.0

2.0

1.0

an
k
PB
W S
at
er
U
re
#0 a
02
#0 1
35
#0 4
76
#0 0
81
A- #1 1
IM 10
IM #0 2
-L 48
# 4
IM 104
#1 0
AN 3
5
# 2
AE 06
4
D #0 0
ET 25
#0 9
78
0

PB
W S
at
er
U
re
#0 a
02
#0 1
35
#0 4
76
#0 0
81
A- #1 1
IM 10
IM #0 2
-L 48
# 4
IM 104
# 0
AN 13
5
# 2
AE 06
4
D #0 0
ET 25
#0 9
78
0

0.0

Bl

Chemical

H 2O

PBS

f

IM-L
#1040

IM
#1352

+

AN
#0640

10% Urea

10% Urea

0.0

Gelatin swelling ratio
1.0
2.0

3.0

PBS

10% IM

10% IM

10% AN

10% AN

10% DET

10% DET

20% IM

20% IM

10% IM + 10% AN 10% IM + 10% AN
10% IM + 10% DET 10% IM + 10% DET

DET
#0780

Sequential
effect

or

AE
#0259

MixingeffectAmine group

Day 2**P = 0.0065

Pyrazolone group

Day 1
PBS

***P = 0.0002

A-IM
#0484

SingleeffectImidazole group

10% IM

10% AN

10% IM

10% DET

Fig. 1 | Identification of swelling reagents by comprehensive chemical screening. a, Correlation of the swelling ratio between delipidated hemispherebrains (n =​2) and postfixed type B gelatins (n =​3) treated with PBS, water or 12 chemicals. These 12 chemicals were chosen from our previous study23.
GH, guanidine hydrochloride; SD, sodium deoxycholate. R2 value was calculated with all individual data plots. Scale bars, 5 mm. b, Isoelectric points (pIs) of
lysozyme, type A gelatin, type B gelatin and extracted brain protein. ‘Fixed’, fixation by PFA. c, Chemical screening procedure for swelling reagents froma 1,691-chemical library. Numbers (for example, #1352) indicate chemical identification numbers. OD975, 975-nm absorption. Each bar indicates themean value. d, In-well gelatin gel-based first round of chemical screening (n =​2) identified 11 chemicals with relative swelling scores greater than 0.9 asa criterion. We highlighted several chemical groups: imidazole (pink), pyrazolone (green) and amine (blue). Each bar indicates the mean value.
e, Hemisphere brain-based second chemical screening (n =​2) identified final six chemical candidates. Colors as in d. Each bar indicates the mean value.
f, We classified the six chemical candidates into three groups based on their chemical structure: imidazole (pink), pyrazolone (green) and amine (blue).
We evaluated potential additive swelling effects, such as solvent-mixing effects and solvent-substitution effects, by gelatin gel-based assay. A-IM,
1-(3-aminopropyl)imidazole; IM-L, 2-imidazolidinone; IM, imidazole; AN, antipyrine; AE, 2-aminoethanol; DET, diethylenetriamine. ***P <​ 0.001,**P <​0.01, one-way ANOVA with Tukey’s post hoc test for multiple comparisons; n =​6. All values are mean ±​ s.d.
Nature Neuroscience | VOL 21 | APRIL 2018 | 625–637 | www.nature.com/natureneuroscience© 2018 Nature America Inc., part of Springer Nature. All rights reserved.

627

Resourceb 100

20% IM(CUBIC-X1) ScaleA2
1.382

5% IM
45% AN

5% IM
55% AN

5% IM
65% AN

1.445

1.467

1.493

12

Second reagent

First reagent

1080Transmission (%)

1.368

c

60
CUBIC-X1
ScaleA2

40

5% IM 45% AN
5% IM 55% AN

20

5% IM 65% AN

0
380

480
580
680
Wavelength (nm)780

Brain swelling ratio
vs. original brain sizea

NATUre NeUrosCIenCe

8

First
reagent
PBS

6

CUBIC-X1 CUBIC-X1
CUBIC-X1 5% IM 45% AN

4CUBIC-X1 5% IM 55% AN(CUBIC-X2)
CUBIC-X1 5% IM 65% AN

20Secondreagent
PBS

0

1

2
3
Time (d)45d
CUBIC-based delipidation and staining

Whole-brain expansion and clearing with CUBIC-X protocol

Fixation

Delipidation

Staining

Swelling

RI matching

Gel embedding

0.5 d

5–14 d

3d

2.5 d

1.5 d

0.5 d

PFA

CUBIC-1

Propidium iodide

CUBIC-X1

CUBIC-X2

Agarose in X2

e LSFMh Confocal
DorsalThy1-YFP

Thy1-YFP

Ventral

0 μm

6,000 μm

0 μm

6,000 μm

0 μm

6,000 μm

PLP-YFPf

Mlc1-YFP

Mlc1-YFPgi LSFM

Fig. 2 | CUBIC-X for whole-brain expansion and hyperhydrative RI matching. a, Transmission images of CUBIC-X1-treated whole brains after additional
treatment with CUBIC-X1, ScaleA2 and imidazole–antipyrine cocktails. Chemical cocktails of 5% imidazole and 45–65% antipyrine provided almost
fully transparent brains without shrinkage. RIs of each chemical are shown in blue. Scale bars, 2 mm. b, Transmission curves of the whole-brain samples
shown in a. Light transmittance around the visible region (380–780 nm) was measured (n =​ 3). c, Time course of swelling ratio of delipidated whole brains
after chemical treatment (n =​3). Brain swelling ratios were evaluated against original brain size. For handling purposes, we chose 5% imidazole +​ 55%
antipyrine cocktail (CUBIC-X2). d, Overview of a whole-brain expansion and hyperhydrative RI matching protocol (CUBIC-X protocol) with stainingof nuclei. The duration of CUBIC-1 delipidation can be modified to purpose. Scale bars, 2 mm. e–g, CUBIC-X was compatible with fluorescent protein
imaging of various reporter mouse brains. Thy1-YFP, PLP-YFP and Mlc1-YFP mouse brains were cleared and expanded with CUBIC-X protocol and imaged
with LSFM (10×​, NA =​0.6). Volume-rendered images are shown. Scale bars, 100 μ​m. h, Confocal image (25×​, NA =​1.0) of Thy1-YFP mouse brain. Inset:
magnified view showing spines (indicated by arrows). Scale bars, 30 μ​m (main) and 5 μ​m (inset). i, An LSFM image of an Mlc1-YFP astrocyte, from g.
Maximum intensity projection was applied over the 30-μ​m-thick volume. Scale bar, 40 μ​m. Experiments were repeated once (f), twice (e,g,i) or threetimes (h) with independent brains. Representative images are shown. For e–i, a post-CUBIC-X brain was used as a baseline. All values are mean ±​ s.d.

brain preparation procedure. The degree of expansion is not dependent on cell density (Supplementary Fig. 6). We next quantified theamount of distortion at macrostructural scale. By three-dimensionally comparing the pre-CUBIC-X brain and post-CUBIC-X brain,
628

root-mean-square errors were measured25. Root-mean-squareerrors were estimated at less than ~20 μ​m, which was ~0.8% of
the measurement length (2,500 μ​m) in the investigated regions(Fig. 3a,b, Supplementary Fig. 7 and Methods).

Nature Neuroscience | VOL 21 | APRIL 2018 | 625–637 | www.nature.com/natureneuroscience© 2018 Nature America Inc., part of Springer Nature. All rights reserved.

Resource

NATUre NeUrosCIenCe
To visualize the CUBIC-X brain in subcellular resolution using
LSFM, we implemented a customized LSFM equipped with along WD detection objective lens (Olympus XLPLN10XSVMP,
NA =​  0.6, WD =​ 8 mm; Fig. 3c and Supplementary Fig. 8). A complete list of parts is available in Supplementary Table 2. The imaging region was divided into an x–y grid, which typically consistedof 17–20 tiles in the x direction and 18–21 tiles in the y direction.
The sample was scanned in z direction with a step size of 5 μ​m toobtain a stack of images. To cover an entire brain, we also introduced rotation sequences (Fig. 3d, Supplementary Video 1 andMethods). As a result, we obtained clearly resolved images acrossthe brain (Fig. 3e). We also note that the possible displacements of
the imaging sequences were small enough (x–y tiling: ≤​12 μ​m, θ​tiling: ≤​1.3%) to perform the following whole-brain cell detection(Supplementary Fig. 9 and Methods). The resulting whole-brain
image data was approximately 14 TB with 16-bit raw TIFF format.
We also performed whole-brain imaging of brains expressing YFP
under the PLP and Thy1 promoters. These applications demonstrated subcellular-resolution imaging of fluorescent reporter proteins from whole mouse brains (Supplementary Fig. 10).
Construction of a single-cell-resolution mouse brain atlas(CUBIC-Atlas). The next challenge in constructing a single-cellresolution atlas is accurately detecting all cells from the acquired
images (Fig. 4a). To this end, we devised a cell-detection algorithm
based on two-step convolution (Supplementary Fig. 11), which
was analogous to a one-step convolution algorithm developed by
Shimada et al.33. In our two-step algorithm, we applied the first
convolution with a 2D mean filter of a kernel diameter, DM1, resulting an image with local maximum values at the center of the cellular nuclei. We limited the pixels to local maximum values, andother pixels were replaced with zero. Then we unified the multiply-detected cells by performing the second convolution upon thisimage, with a 3D mean filter of a kernel diameter, DM2. Finally, we
detected each cell by finding the local maximum in 3D by automatically comparing determined local thresholds (Supplementary Fig.
12 and Methods). By tuning diameters of two convolution kernels,
DM1 and DM2 (Supplementary Fig. 11), we achieved fully automatedcell detection. Evaluated by comparing the result to manual detection, accuracy exceeded 94% in interbrain (thalamus), midbrain
and cerebrum (except hippocampus and olfactory bulb granularlayer), 91% in hippocampal CA1, 90% in olfactory bulb granularlayer, 89% in dentate gyrus and 79% in the densest area, the cerebellum granular layer (Supplementary Fig. 13 and Methods).
Supplementary Video 2 shows a representative z-stack image analyzed by our cell-detection algorithm. We did not observe morphology- or size-dependent errors of cell detection with our algorithm.
As a result, the spatial information of whole-brain cellular nuclei
was represented as an ensemble of cellular points, occupying ~3 GB
and providing a pointillistic anatomical platform that we termed
CUBIC-Atlas. CUBIC-Atlas is an editable platform that can also
accommodate multiple types of cellular information in single-cellresolution, such as anatomical area, gene expression, cell type andcellular connections. As an initial step, anatomical information
was annotated onto CUBIC-Atlas by adopting anatomical annotations in the Allen Brain Atlas (ABA)9. Because the ABA brain wasreconstituted from a series of coronal-slice images, we created aregistration based on the virtual coronal slices (Supplementary
Fig. 14 and Methods). Using this registration, each individual cellin CUBIC-Atlas was assigned an anatomical identity from ABA(Fig. 4b–j, Supplementary Fig. 15 and Supplementary Videos 3
and 4). To examine how accurately CUBIC-Atlas was annotated, anucleus stained brain of a Thy1-YFP-H mouse was registered onto
CUBIC-Atlas. We confirmed that YFP signals of the brain could be
accurately mapped to known anatomical locations, such as cerebral
cortex layers 5 and 634 (Supplementary Fig. 16 and Methods).

Whole-brain cell counting in mice. One of the attractive applications of the CUBIC-Atlas is whole-brain cell counting in adult mice.
For this purpose, we first sampled brains of three adult C57BL/6 N
male mice (8 weeks old), which were then cleared, expanded,
imaged and profiled using the CUBIC-X pipeline described above(Fig. 4k). As a result, the total cell count in the 8-week-old male
mouse brain was found to be 7.22 (±​0.52) ×​  107 (coefficient of variation =​ 7.14%). We also registered and annotated the detected individual cells to anatomical areas in the CUBIC-Atlas (Supplementary
Fig. 17), which provided cell-number information for individual
brain areas (Fig. 4l and Supplementary Table 3).
To comprehensively analyze regional progression of cell numbers during postnatal development, we also performed wholebrain cell profiling of 1-week-old, 3-week-old and 6-month-old
C57BL/6 N male mice according to the CUBIC-X pipeline (Fig. 5a).
In this analysis, we focused on the brain areas excluding cerebellum and olfactory bulb, due to their morphological changes during early postnatal development (Supplementary Fig. 18). We firstinvestigated whether anatomical areas of the adult brain atlas could
be applied to datasets from younger ages. Automatic annotation ofcortical layers based on adult brain atlas was achieved with highaccuracy (Supplementary Fig. 19). Thus, we applied the anatomical annotations of adult (P56) mouse brain atlas to datasets fromother ages and compared cell numbers in the corresponding areas.
To characterize the developmental progression of cell numbers in
different brain regions, we analyzed the average cell number andthe s.d. over mice aged 1 week, 3 weeks, 8 weeks and 6 months ineach area (Fig. 5b,c) and found that variations in developmental
progressions over age depended strongly on brain area. Analysis of
the coefficient of variation also supported high variability in developmental progression of cell numbers (Fig. 5d,e). To further investigate the developmental progression of cell numbers across theentire brain, we first calculated the normalized cell numbers of eacharea by setting the cell number averaged over ages to 1.0. To extract
the characteristic patterns of developmental progression, we performed hierarchical clustering analysis of the normalized cell numbers (Fig. 5f and Methods). This clustering analysis revealed four
major clusters (Fig. 5g,h). The largest cluster (blue cluster) spreadover the entire brain (Fig. 5i). Two major regional clusters (pinkand yellow) were relatively localized in cortex and olfactory area,
respectively, and a fourth (purple) cluster was localized especiallyin midbrain (Fig. 5i).
We next focused on the differences of the normalized cell number between 1-week-old and 3-week-old mice, in which the normalized cell numbers in purple and yellow clusters markedly increased
and decreased, respectively. We first analyzed large brain areas, suchas cerebrum, interbrain, midbrain and hindbrain, and found thatthe midbrain displayed a larger increase in cell number than otherareas (Fig. 6a), which was consistent with the enriched distributionof the purple cluster in the midbrain (Fig. 5h,i). We then mapped thenormalized cell-number increase between ages 1 week and 3 weeks
onto the midbrain, which revealed that the cell number increase wasenriched in the anterior part of the midbrain (Fig. 6b,c). During
the early postnatal period between 1 week and 3 weeks of age, thenormalized cell number significantly increased in the motor-relatedareas and the behavioral-state-related areas, whereas the normalized cell number did not significantly change in the sensory-relatedareas (Fig. 6d). These findings in normalized cell numbers were also
confirmed by substantial changes of actual cell numbers in theseareas (Supplementary Fig. 20).
We also expected a decrease in normalized cell numbers in thecortex during the early postnatal period, based on the enriched distribution of the yellow cluster in the cortex (Fig. 5h,i). We focusedon the cortical areas excluding layer 1 because cell numbers in thisoutermost area of the brain could be influenced by the residualarachnoid matter or possible damage during dissection procedures.

Nature Neuroscience | VOL 21 | APRIL 2018 | 625–637 | www.nature.com/natureneuroscience© 2018 Nature America Inc., part of Springer Nature. All rights reserved.

629

Resourcea

NATUre NeUrosCIenCe
Region 2 (striatum)
Horizontal
Sagittal

Region 3 (cerebellum)
Horizontal
Sagittal

After CUBIC-X

60
40
20
0
60
40
20
0
60
40
20
0

Region 1

Region 2

Region 3

0

500 1,000 1,500 2,000 2,500

Measurement length (μm)

c

d

y
z

17 to 20 tiles

θ = 0°~4 d aquisition time
~1.3 million images~14 TB of datay stagez stagex
x stageyθ stageθ

Detection
objectivez

Illumination
objectiveθ = 180°x

1 stack (θ = 0°)1500 to 1600 images

18 to 21 tiles

θb

RMS error (μm)

Before CUBIC-X

Region 1 (hippocampus)
Horizontal
SagittalGPU

Server

CUBIC-X brain(2% agarose CUBIC-X2) Illumination
objectivee

Cerebral cortex

Whole-brain volume-rendered image

Olfactory bulb

Horizontal (xy)Coronal (xz)

ThalamusStriatumA
D
Hippocampus

R
Midbrain

Cerebellum

Fig. 3 | Whole-brain imaging of a CUBIC-X expanded brain with customized LSFM. a, Comparison of a propidium iodide (PI)-stained brain cleared withthe modified ScaleCUBIC-2 (pre-CUBIC-X) and a brain expanded by the CUBIC-X treatment (post-CUBIC-X). Three regions (2.5 mm ×​ 2.5 mm ×​ 2.5 mm)of a pre-CUBIC-X brain are shown in the upper row from both horizontal and sagittal views. Corresponding images of a post-CUBIC-X brain, linearlyregistered to the pre-CUBIC-X brain images, are shown in the lower row. Scale bars, 500 μ​m, based on the pre-CUBIC-X brain. b, Quantification of thenonlinear distortion caused by CUBIC-X treatment. Root-mean-square errors were quantified. Its average (blue) and s.d. (light blue) are plotted against
the measurement length. Sparsely sampled pairs of voxels were used (Methods). Note that all lengths are represented using a pre-CUBIC-X brain asbaseline. c, Image of sample mounting in the customized LSFM. A CUBIC-X brain embedded in 2% agarose CUBIC-X2 is suspended in oil (RI =​ 1.467).
d, Overview of whole-brain imaging procedures. e, Volume-rendered and single-plane images of a PI-stained CUBIC-X brain. A volume-rendered imageof a whole brain is shown in the center. An original single-plane and its magnified (10×​) images are shown for cortex, thalamus, hippocampus, midbrain,
olfactory bulb, corpus callosum and cerebellum. Both horizontal (x–y) and coronal (x–z) views are shown. Experiments were repeated more than ten times
with independent brains. Scale bars, 1,000 μ​m for whole-brain image and 50 μ​m for others, using a pre-CUBIC-X brain as baseline.
630

Nature Neuroscience | VOL 21 | APRIL 2018 | 625–637 | www.nature.com/natureneuroscience© 2018 Nature America Inc., part of Springer Nature. All rights reserved.

Resource

NATUre NeUrosCIenCeaWhole-brain cell detection

Image acquisition
LSFM

14 TB whole-brain data

2D detection

Anatomical annotation of CUBIC-Atlas

3D detection

Annotationfrom ABACUBIC-Atlas

CUBIC-Atlas with
anatomical data

C57BL/6Jb

3D single-cell resolution atlas

Horizontal view

2D single-cell resolution atlascdIsocortex

A

R
A

g

DR

R

A DR

h

Thalamus (TH) andhypothalamus (HY)A
A

Coronal viewD

Midbrain (MB)

D

D

A

i

D

A

R

j

Pons (P) and medulla (MY)D

R

Cerebellum (CB)

D

D
R

R
A

k

Sample collection
C57BL/6N

Expansion

107

A DR

DR

Imaging

Cell profiling

LSFM

CUBIC-1
Propidium iodide
CUBIC-X1
CUBIC-X2

Detection

Gel embedded
expanded brain

8-week-oldlA

f Cerebral nuclei (CNU)

A
R

Sagittal viewDRe Hippocampal formation (HPF)

A

Olfactory area (OLF)

Isocortex and OLF

HPF

Annotation
Atlas
mapping

GPU
CNU

TH

HY

MB

P

MY

CB

Cell number

106
105
104
103
102
101
100
Depth

Fig. 4 | Construction of a single-cell-resolution mouse brain atlas (CUBIC-Atlas). a, Overview of construction of the CUBIC-Atlas. First, we acquired
images of highly resolved cellular nuclei throughout the C57BL/6 J male mouse brain (image acquisition). Second, we developed an algorithm tocomprehensively detect individual nuclei (red dots) without omission or overlap (whole-brain cell detection) to construct the CUBIC-Atlas. We
annotated the individual points of CUBIC-Atlas using anatomical annotations from ABA (anatomical annotation of CUBIC-Atlas). b, The CUBIC-Atlas.
Horizontal, sagittal and coronal view of single-plane images (left) and volume-rendered images (right) of the CUBIC-Atlas. c–j, Major anatomical areasin the CUBIC-Atlas. For b–j, experiments and annotations were repeated more than ten times with independent brains. A, anterior; D, dorsal; R, rostral.
k, Overview of whole-brain cell counting in C57BL/6 N 8-week-old male mice. l, Cell numbers in each brain area, indicated as average ±​ s.d. (n =​ 3).
Colors represent anatomical areas: hue indicates parental anatomical structures; saturation indicates hierarchical depth within parental anatomical
structures. OLF, olfactory areas; HPF, hippocampal formation; CNU, cerebral nuclei; TH, thalamus; HY, hypothalamus; MB, midbrain; P, pons; MY,
medulla; CB, cerebellum.

Nature Neuroscience | VOL 21 | APRIL 2018 | 625–637 | www.nature.com/natureneuroscience© 2018 Nature America Inc., part of Springer Nature. All rights reserved.

631

Resourcea

NATUre NeUrosCIenCe
Sample collection

Expansion and clearing

C57BL/6N
1-week-old ×3

8-week-old ×3

Averagecell number
Standard deviation

c

1076
10
1054
103
102
101
100
10

1076
105
104
103
102
101
100
10
Depth

d

CNU

TH

HY

MB

P

MY

Isocortex and OLF

HPF

CNU

TH

HY

MB

P

MY

e

Standard deviation

CV (0.1406)i

CV of areas
Isocortex

105

HPF

4

Cluster of areas
Isocortex

HPF
MB

MB

CV × 2
OLF

102
101

GPU

HPF

106

103

Analysis

Isocortex and OLF

107

10

Cell profiling
Detection Annotation

Gel embedded
expanded brain

×3

3-week-old ×3 6-month-old ×3b

LSFM Imaging
LSFM

×3 CUBIC-1
PI
×3CUBIC-X1
CUBIC-X2
×3

CNU
HY

CV × 1/2

TH

OLF

TH

CNU
P

P

MY

HY

MY

100
100 101 102 103 104 105 106 107
Average cell number

1

h
2.0

2

Pink cluster

1.0

1.0

0.0
2.0

Purple cluster

1.0

Yellow clusterz = 2/3 to 1

w

3

w

ee
k
ee
8
w ks
e
6 ek
m s
on
th
s

0.0

1

8

6

k

ks

ee

ee

w

w

w
ee
ks
m
on
th
s

1.0

1

z = 1/3 to 2/3

Blue cluster

0.0
2.0

3

z = 0 to 1/3

Blue cluster

Normalized cell number

0.0
2.0

Whole brain

Pink cluster

0g

Purple cluster

Normalized cell number

Yellow clusterfFig. 5 | CUBIC-Atlas is applicable to whole-brain cell profiling of mice at various developmental timepoints. a, Overview of whole-brain cell profiling overtime in mice. In addition to the 8-week-old mouse brains described in Fig. 4l, the brain samples of 1-week-old, 3-week-old and 6-month-old C57BL/6 N malemice were collected, expanded by CUBIC-X, imaged by the customized LSFM and analyzed with the CUBIC-Atlas. b,c, Average cell numbers and s.d. overmice aged 1 week, 3 weeks, 8 weeks and 6 months in each brain area (n =​3 mice per age group). Colors represent anatomical areas. Parental hierarchicalstructures are shown as in Fig. 4l. d, Correlations between the average cell numbers and s.d. e, Coefficient of variation (CV) values for cell numbers in eacharea. f, Heatmap for hierarchical clustering of normalized cell numbers in each brain area. Mean values of three brains are shown. Four major clusters (pink,
blue, purple and yellow) were identified. g, Normalized cell numbers in each cluster. Plots were generated with normalized cell numbers from all individual
areas of three brains for each age. Box plots indicate median and 25−​75% interquartile ranges; whiskers cover full ranges of distributions; dots show
averages and center lines show medians. h, 3D distribution of each cluster in the mouse brain. i, The ratios of clusters in each area. The size of each circle
indicates the number of anatomical areas. OLF, olfactory areas; HPF, hippocampal formation; CNU, cerebral nuclei; TH, thalamus; HY, hypothalamus; MB,midbrain; P, pons; MY, medulla. Normalized cell numbers for all individual areas of twelve brains were subjected to clustering analysis (Methods).
632

Nature Neuroscience | VOL 21 | APRIL 2018 | 625–637 | www.nature.com/natureneuroscience© 2018 Nature America Inc., part of Springer Nature. All rights reserved.

Resource

NATUre NeUrosCIenCebFold change1

1.0

Frontal view (3D)

Normalized cell number(3 weeks vs. 1 week)
1

2

Side view (3D)

Fold change

Plane 1

2

Plane 2

0.5

in
M
id
b
H r ain
in
db
ra
in

rb

eb

te

In

C
er

ra

m

0.0

d

R
A

D

D R

Plane 1 Plane 2A

Midbrain, motor related
Superior colliculus,
motor relatedn.s.

Pretectal region
P = 0.0008

1.5

Normalized
cell numbercNormalized cell number(3 weeks vs. 1 week)

1.5

ru

Normalized cell number(3 weeks vs. 1 week)

a

***

1.0

Midbrain, motor related(other regions)n.s.

Periaqueductal grayP = 0.0201

*

0.5

w
ee
ks
m
on
th
s
6

8

3

1

w
ee
k
w
ee
ks

w
ee
ks
m
on
th
s
6

8

w
ee
k
w
ee
ks
3

1

1

1

w
ee
k
3
w
ee
ks
8
w
ee
ks
6
m
on
th
s

w
ee
k
3
w
ee
ks
8
w
ee
ks
6
m
on
th
s

0.0

Midbrain, behavioral state related Midbrain, sensory related

Normalized
cell number

Superior colliculus,
sensory relatedn.s.

Midbrain,behavioral state related
P = 0.0168

1.5

*

1.0

Midbrain, sensory related(other regions)n.s.

Inferior colliculus
n.s.

0.5

Normalized
cell number

Layer 5

s
th

ks

on

w
8

6

w
3

Layer 6a

m

ee

ee
w
1

ee

ks

k

s
on

m

w
8

6

w
3

th

ks

ee

ee
w
1

Layer 4

ee

ks

k

s
th
on

m

w
8

6

w
3

ee

ee

ee
w
1

Layer 2/3

ks

ks

k

s
th

ks

on

ee

m

w
8

1.5

6

e

3

1

w

w

ee

ee

k

ks

0.0

Layer 6b

1.0
0.5

s

ks

th

on

m
6

w
8

ee

ks

ee
w

w
3

1

ee

k

s
th

on

ks
m

w
8

6

w
3

1

ee

ks

ee
w

on

ee

k

s
th

ks
m

w

6

8

ee

ks

ee

w

1

3

w

on

ee

k

s
th

ks

ee
w

m
6

8

ee

w
3

w
1

ee

k

ks

s
th

ks

ee

on

m
6

w
8

ee

w

1.0

2/3
4
5
6a
6b

0.7

g

Visual areas

Primary somatosensory areas

Layer 2/3
P = 0.0036

1.5

Normalized
cell number

1.3

Fold change

Normalized cell number
8 weeks
3 weeksvs. 3 weeks vs. 1 week

G

Layer
2/3
4
5
6a
6b

3

f

U
PT
L
AU p
D
AU p
D
AU d
D
AU v
D
SS po
p
SS -unp
SS -bfp d
SS -ll
pSS m
p
SS -n
p
SS -trp
SS -ul
s
M
O
M p
O
TE s
a
VI
Sp
VI
S
VI am
S
VI al
S
VI l
Sp
VI l
S
VI pm
S
O C
R
O Bl
R
B
O m
R
B
EC vl
T
PE
R
AI I
d
AI
p
AI
v
PL

1

w

ee

k

ks

0.0

Layer 4
P = 0.0020

Layer 2/3
P = 0.0076****

Layer 4
P = 0.0265

**

1.0

*

0.5

s

6

m

on

th

ks

w
8

ee

ks
w

3

ee
1

w

on
m

6

ee

k

s
th

ks

w
8

ee

ks
ee
w

3

1

w

ee

k

s
th

ks

on
m

6

w
8

ee

ks
ee
w

3

1

w

ee

k

s

m

on

th

ks
ee

w

6

8

ee
w

3

1

w

ee

k

ks

0.0

Fig. 6 | CUBIC-Atlas revealed a cell number increase in midbrain and decreases in visual area and primary somatosensory area of cerebral cortex during
early postnatal development. a, Normalized cell numbers in large brain areas. b,c, Fold-changes of the normalized cell number in the midbrain during
early postnatal development (1 to 3 weeks). Fold-changes of the normalized cell number are shown in a volume-rendered image (b) and in single-plane
images (indicated by yellow dashed lines) with the corresponding anatomical areas (c) of the midbrain. d, Increases in normalized cell numbers in the
midbrain were significant (n =​ 3, ***P <​ 0.001, *P <​0.05, independent two-tailed t test). Background color represents each classification of the midbrain(pink, motor-related areas; blue, behavioral-state-related areas; purple, sensory-related areas). e, Normalized cell numbers in each layer of the cerebral
cortex (n =​ 3). f, Fold-changes in the normalized cell number in the cerebral cortex during early postnatal period from age 1 week to 3 weeks (top) or during
maturation period from 3 weeks to 8 weeks (bottom). To calculate fold-changes, mean values of normalized cell numbers for each age (n =​3) were used.
g, Decreases in normalized cell numbers in visual area and primary somatosensory area of the cerebral cortex were significant (n =​ 3, **P <​ 0.01, *P <​ 0.05,
independent two-tailed t test). All values are mean ±​ s.d.
Nature Neuroscience | VOL 21 | APRIL 2018 | 625–637 | www.nature.com/natureneuroscience© 2018 Nature America Inc., part of Springer Nature. All rights reserved.

633

Resource

NATUre NeUrosCIenCe

As expected, layers 2/3 and 4 displayed a decrease in normalized
cell numbers during the early postnatal period (Fig. 6e). We further
analyzed the normalized cell number changes of individual corticalareas between ages 1 week and 3 weeks during the early postnatal
period and between ages 3 weeks and 8 weeks during the maturation period (Fig. 6f). This whole-cortex cell profiling demonstrated
a decrease in normalized cell numbers during the early postnatal
period mainly in layers 2/3 and 4 of the gustatory area, posteriorparietal association area, auditory area, somatosensory area, motorarea, temporal association area and visual area. Since the visual andprimary somatosensory areas have been intensively studied in relation to their critical periods35, we focused on layers 2/3 and 4 inthese areas and analyzed the temporal patterns of normalized cellnumbers in these areas (Fig. 6g), which demonstrated a substantial
decrease in normalized cell numbers in layers 2/3 and 4 of theseareas, as well as a decrease in actual cell numbers (Supplementary
Fig. 21). Although a number of studies have been done on thedecrease of synapses during early postnatal development in the
cerebral cortex35,36, this observation was, to the best of our knowledge, the first report of a significant decrease of total cell numbers inlayers 2/3 and 4 during early postnatal development.
Probabilistic annotation of CUBIC-Atlas revealed a functionally distinct structure in the granule cell layer of hippocampal
dentate gyrus. To demonstrate the power of CUBIC-Atlas, weused it to identify functionally distinct brain areas. Because mammalian brains do not have identical cellular structures due to individual differences, we developed a probabilistic method to define
areas within the CUBIC-Atlas (Fig. 7a and Methods). As its initialdemonstration, we reanalyzed previously published brain image
data of Arc-dVenus mice to investigate pharmacologically activatedcells (high dVenus signal) in response to chronic administration of
the NMDA-receptor inhibitor MK-80114. We first confirmed thatthe activated cells annotated with CUBIC-Atlas more frequently
emerged in the cerebral cortex (Fig. 7b and Supplementary Fig.
22), which is consistent with our previous report. By assuming theGaussian probabilistic distribution around the registered activatedcells, we allocated probabilistic activity to each cell within CUBICAtlas, then performed k-means clustering analysis against all cells
in the atlas. The results revealed four clusters distinctly emerging at each circadian time (Fig. 7c). Because the cells of Cluster 1
and Cluster 2 are in the granule cell layer of dentate gyrus (DG-sg;
Fig. 7d,e), we further investigated distribution of the clusters overDG-sg. Mapping the cell cluster in the DG-sg revealed functional
differences between upper DG-sg, where the majority of dorsal cell
population belonged to Cluster 1, and lower DG-sg, where most ofcells belonged to Cluster 2 (Fig. 7f,g), suggesting the applicability of
CUBIC-Atlas to discovery of previously uncharacterized functionally distinct subareas. Notably, this clear separation of the clusters
is consistent with the inhomogeneous characteristics of the hippocampus37. Discovery of functional areas is possible even without
prior anatomical information. Based on these results, we propose a
new approach for exploring and mapping of functionally distinct
areas, using the single-cell-resolution atlas.

Discussion

To clarify the underlying cellular mechanisms in complex neuralcircuits, an alternative atlas describing whole mouse brain as an
ensemble of cellular points is a promising addition to current pixelor voxel-based brain atlases. Herein we proposed a single-cell-resolution mouse brain atlas, CUBIC-Atlas. To this end, we developed
a fluorescent-protein-compatible, intensive tissue-clearing method
combined with a tissue expansion protocol, CUBIC-X, whichenables seamless imaging of the whole mouse brain at subcellular
resolution. Thanks to the high-resolution images, almost all cells in
the whole brain could be abstracted by comprehensive and accurate634

cell detection, and therefore huge datasets were reduced to portableand editable data size (~3 GB). Because CUBIC-Atlas was derived
from seamless 3D images, our atlas would be suitable for analysis
of volumetric images generated from cleared brains. The protocol
may also play a pivotal role in global analysis of gene expression andneural circuits through continuous update of our CUBIC-Atlas byoverlaying various cellular functions.
As a test application of the CUBIC-X pipeline and CUBICAtlas, we performed whole-brain cell counting. Quantification ofcell numbers in the mammalian central nervous system has been
one of the most fundamental and technical challenges in neuroanatomy38,39. For mice, total cell numbers in brains of various strains
have been reported38,40–43, ranging from less than 6.0 ×​  107 (ref. 41) tomore than 15.0 ×​  107 (ref. 43). This large variation in total cell numbers is assumed to be derived from genetic background, age, sex
and individual differences. In our study, we reported the total cellnumbers of C57BL/6 N male mouse brain (at 8 weeks old) to be 7.22(±​0.52) ×​  107. Our extensive evaluation of accuracy (Supplementary
Fig. 13) supports this cell number as an appropriate estimate. We
also note that our cell-detection algorithm tends to underestimate
the cell number rather than overestimate especially in densely populated areas (Supplementary Fig. 13f); thus, we assume this total cellnumber is a lower-bound estimate. Further improvement of axial
resolution of LSFM will improve accuracy in densely populatedareas, such as the granular cell layer of the cerebellum. Since the
CUBIC-X pipeline allows not only counting cell numbers but also
identifying cellular locations, the application of the pipeline to pathological analysis is a good candidate for additional study. In someneurological disorders, such as Parkinson’s disease and depression,
neuronal death or dysfunction of neurogenesis in specific part ofbrains are observed44,45. Although local estimations of neuron numbers have been extensively performed, whole-brain comprehensive
analysis of cell numbers of these disease model mice is yet to beachieved. Such analysis is promising application of CUBIC-Atlas
and will provide insight to neurological disorders.
Our CUBIC-Atlas also revealed a significant decrease in cellnumbers in layers 2/3 and 4 of visual and somatosensory areas
during early postnatal development (Fig. 6). It has been reported
that cell death of presynaptic neurons can be induced by deletionof their postsynaptic target46,47. In the visual cortex, the neuronal
circuit can be reorganized by synaptic pruning mediated by protease from the extracellular matrix during postnatal critical periods35. Therefore, the cell number decreases observed in this study
might reflect apoptosis of neuronal cells in those areas due to postsynaptic pruning. Other possible explanations for the decrease incell numbers are early postnatal glial apoptosis and cortical cellularmigration48. Since the CUBIC-X protocol is compatible with cellularlabeling with fluorescent protein and fluorescent molecule, profiling cell types and tracking cellular migration during critical periods
are potential applications of CUBIC-Atlas in future studies.
Another promising application of CUBIC-X protocol is imaging with super-resolution microscopes. As shown in Fig. 2f and
Supplementary Fig. 10h, CUBIC-X expanded brains maintain thesubcellular structures of neurons. Although the expansion ratio of
CUBIC-X is smaller than those of previously reported expansion
protocols25–27, the availability of fluorescent protein without special
anchoring gel matrices or complicated immunostaining procedures
could simplify experimental protocols. Notably, the expansion canbe performed on a whole-organ scale, thereby allowing researchers
to avoid possible disruptions of microscale structure or cellular circuits caused by tissue sectioning.
An unbiased analysis revealing previously unknown areas is oneof the most attractive applications of CUBIC-Atlas. In this study,
we confirmed the existence of novel subareas in the granule cell
layer of DG by probabilistically mapping spatial information ofpharmacologically activated cells (Fig. 7). This application not only

Nature Neuroscience | VOL 21 | APRIL 2018 | 625–637 | www.nature.com/natureneuroscience© 2018 Nature America Inc., part of Springer Nature. All rights reserved.

Resource

NATUre NeUrosCIenCe
Data
collection

Registration to
CUBIC-Atlas

Probabilistic
annotation

Clustering
analysis

Condition A
Register

2.5

Cluster 2

Cluster 3

Cluster 4
MK-801+2.0

MK-801–

1.5
1.0
0.5
0.0

Registerb

Cluster 1

CT4
CT10
CT16
CT22
CT4
CT10
CT16
CT22
CT4
CT10
CT16
CT22
CT4
CT10
CT16
CT22
CT4
CT10
CT16
CT22
CT4
CT10
CT16
CT22
CT4
CT10
CT16
CT22
CT4
CT10
CT16
CT22

Condition B

cRelative activity (a.u.)

a

CT10

CT16

CT22

d

Cluster 1

Cluster 2

Cluster 3

Cluster 4

Section 2
Section 3

MK-801–

Coronal sections

MK-801+

Section 1

CT4

f

e

h

Annotator

Annotator

Annotator

Isocortex and OLF

Upper DG-sg
Upper DG-sg
Lower DG-sgR

D

Lower DG-sg
P
CUBIC-Atlas viewer(CATMAID)CUBIC-Atlasg

Lower DG-sg

Angle 1

TH CNU

HPF

Upper DG-sg

D
P

R

D

User

P

3

4
er

C

lu

st

2

er
st

er
st

lu
C

lu

1
C

st
C

lu

er

MY

Angle 2

P MB

HY

R

Cell number counting

A

V
R

A

User

Cellular activity mapping

V
R

Fig. 7 | Probabilistic annotation of CUBIC-Atlas revealed a functionally distinct structure in granule cell layer of hippocampal dentate gyrus. a, Strategic
schema of area discovery with probabilistically annotated CUBIC-Atlas. b, Temporal variation in distribution of hyperactivated cells in the whole brain.
Original image data were obtained in our previous study14 and reanalyzed in this study. Arc-dVenus-expressing cells in mice with or without chronic
administration of MK-801 are shown. Colors indicate the anatomical area of the detected cells. Eight representative brains are shown. All other brains
are shown in Supplementary Fig. 22. c, Temporal variation of relative cellular activity in each cluster with or without administration of MK-801. Box plots
indicate median and 25−​75% interquartile ranges; whiskers indicate second and 98th percentiles; dots show averages and center lines show medians.
d,e, Spatial distribution of each cluster. (d) Each cluster was mapped onto the virtual coronal section of the mouse brain. (e) Heat-map view of the cluster
distribution with parental hierarchical structures. f, Overview of upper and lower DG-sg. Coronal slice view (left) and 3D-reconstructed view (right) are
shown. g, 3D view of spatial distribution of four clusters in DG-sg. Images are shown from two different angles after volume rendering. When clusters on
upper DG-sg are shown, lower DG-sg is shown as gray (left). When clusters on lower DG-sg are shown, upper DG-sg is shown as gray (right). R, rostral;P, posterior; A anterior; D, dorsal; V, ventral. For c–e,g, clustering analysis was performed with 20 brains (MK-801+, n =​ 3 ×​4 timepoints, MK-801–, n =​ 2 ×​ 4
timepoints). h, Concept for the CUBIC-Atlas data-sharing platform via an open-source web-based viewer and editor, CATMAID.

demonstrates the feasibility of activity mapping with CUBIC-Atlas
but also proposes an approach to redefining anatomical areas. Thisbottom-up approach to build a mammalian brain atlas is synergisticwith the previously reported histologically annotated atlas. In ourstudy, both information of probabilistic annotation (spatial information of Arc-dVenus expressing cells) and the prior annotation

Nature Neuroscience | VOL 21 | APRIL 2018 | 625–637 | www.nature.com/natureneuroscience© 2018 Nature America Inc., part of Springer Nature. All rights reserved.

635

Resource

NATUre NeUrosCIenCe(DG-sg) lead to the discovery of a functionally distinct subarea in
DG-sg. The utilization of analytic tools to handle point-cloud data,such as a point-cloud library49, will further assist cellular descriptionof the mouse brain.
Since whole-organ imaging technologies are developing rapidly,
our CUBIC-Atlas would contribute to this field by making this single-cell-resolution atlas publicly available. Therefore, we establisheda universal data-sharing platform for CUBIC-Atlas (http://cubicatlas.riken.jp) via an open-source web-based viewer and annotation
editor, the Collaborative Annotation Toolkit for Massive Amountsof Image Data (CATMAID)50 (Fig. 7h). In CATMAID, a CUBICAtlas viewer and editor, annotators can assemble novel annotations
such as gene expression profiling, phenotyping of individual cells
and functional connections of individual neural circuits. We put
our CUBIC-Atlas data and source codes on our webpage (http://
cubic-atlas.riken.jp) to perform cell detection as well as to map andannotate the detected cells onto CUBIC-Atlas. We thus believe our
CUBIC-Atlas will become a useful platform for 3D brain mappingand for the digitalization and decoding of the mammalian brain.

Methods

Methods, including statements of data availability and any associated accession codes and references, are available at https://doi.
org/10.1038/s41593-018-0109-1.
Received: 26 July 2017; Accepted: 6 February 2018;
Published online: 5 March 2018

References

1. Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adulthuman brain transcriptome. Nature 489, 391–399 (2012).
2. Mikula, S., Trotts, I., Stone, J. M. & Jones, E. G. Internet-enabled highresolution brain mapping and virtual microscopy. Neuroimage 35,
9–15 (2007).
3. Amunts, K. et al. BigBrain: an ultrahigh-resolution 3D human brain model.
Science 340, 1472–1475 (2013).
4. Calabrese, E. et al. A diffusion tensor MRI atlas of the postmortem rhesus
macaque brain. Neuroimage 117, 408–416 (2015).
5. Rohlfing, T. et al. The INIA19 template and NeuroMaps Atlas for primate
brain image parcellation and spatial normalization. Front. Neuroinform. 6,
27 (2012).
6. Johnson, G. A. et al. Waxholm space: an image-based reference forcoordinating mouse brain research. Neuroimage 53, 365–372 (2010).
7. Papp, E. A., Leergaard, T. B., Calabrese, E., Johnson, G. A. & Bjaalie, J. G.
Waxholm Space atlas of the Sprague Dawley rat brain. Neuroimage 97,374–386 (2014).
8. Dong, H. W. Allen Reference Atlas. A Digital Color Brain Atlas of the
C57BL/6J Male Mouse (Wiley, Hoboken, NJ, USA, 2008).
9. Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mousebrain. Nature 445, 168–176 (2007).
10. Kasukawa, T. et al. Quantitative expression profile of distinct functional
regions in the adult mouse brain. PLoS One 6, e23228 (2011).
11. Okamura-Oho, Y. et al. Transcriptome tomography for brain analysis in the
web-accessible anatomical space. PLoS One 7, e45373 (2012).
12. Kim, Y. et al. Mapping social behavior-induced brain activation at cellular
resolution in the mouse. Cell Rep. 10, 292–305 (2015).
13. Vousden, D. A. et al. Whole-brain mapping of behaviourally induced neuralactivation in mice. Brain Struct. Funct. 220, 2043–2057 (2015).
14. Tatsuki, F. et al. Involvement of Ca(2+​)-dependent hyperpolarization in sleep
duration in mammals. Neuron 90, 70–85 (2016).
15. Oh, S. W. et al. A mesoscale connectome of the mouse brain. Nature 508,
207–214 (2014).
16. Menegas, W. et al. Dopamine neurons projecting to the posterior striatum
form an anatomically distinct subclass. eLife 4, e10032 (2015).
17. Vélez-Fort, M. et al. The stimulus selectivity and connectivity of layer six
principal cells reveals cortical microcircuits underlying visual processing.
Neuron 83, 1431–1443 (2014).
18. Dodt, H. U. et al. Ultramicroscopy: three-dimensional visualization ofneuronal networks in the whole mouse brain. Nat. Methods 4, 331–336 (2007).
19. Ertürk, A. et al. Three-dimensional imaging of solvent-cleared organs using
3DISCO. Nat. Protoc. 7, 1983–1995 (2012).
20. Pan, C. et al. Shrinkage-mediated imaging of entire organs and organisms
using uDISCO. Nat. Methods 13, 859–867 (2016).
636

21. Chung, K. et al. Structural and molecular interrogation of intact biologicalsystems. Nature 497, 332–337 (2013).
22. Yang, B. et al. Single-cell phenotyping within transparent intact tissue
through whole-body clearing. Cell 158, 945–958 (2014).
23. Susaki, E. A. et al. Whole-brain imaging with single-cell resolution usingchemical cocktails and computational analysis. Cell 157, 726–739 (2014).
24. Susaki, E. A. & Ueda, H. R. Whole-body and whole-organ clearing andimaging techniques with single-cell resolution: toward organism-level systems
biology in mammals. Cell Chem. Biol. 23, 137–157 (2016).
25. Chen, F., Tillberg, P. W. & Boyden, E. S. Expansion microscopy. Science 347,
543–548 (2015).
26. Treweek, J. B. et al. Whole-body tissue stabilization and selective extractions
via tissue-hydrogel hybrids for high-resolution intact circuit mapping andphenotyping. Nat. Protoc. 10, 1860–1896 (2015).
27. Ku, T. et al. Multiplexed and scalable super-resolution imaging of threedimensional protein localization in size-adjustable tissues. Nat. Biotechnol. 34,
973–981 (2016).
28. Chang, J. B. et al. Iterative expansion microscopy. Nat. Methods 14,
593–599 (2017).
29. Tillberg, P. W. et al. Protein-retention expansion microscopy of cells andtissues labeled using standard fluorescent proteins and antibodies. Nat.
Biotechnol. 34, 987–992 (2016).
30. Hama, H. et al. Scale: a chemical approach for fluorescence imaging andreconstruction of transparent mouse brain. Nat. Neurosci. 14,
1481–1488 (2011).
31. Tainaka, K., Kuno, A., Kubota, S. I., Murakami, T. & Ueda, H. R. Chemical
principles in tissue clearing and staining protocols for whole-body cell
profiling. Annu. Rev. Cell Dev. Biol. 32, 713–741 (2016).
32. Keller, P. J., Schmidt, A. D., Wittbrodt, J. & Stelzer, E. H. K. Reconstruction ofzebrafish early embryonic development by scanned light sheet microscopy.
Science 322, 1065–1069 (2008).
33. Shimada, T., Kato, K., Kamikouchi, A. & Ito, K. Analysis of the distribution of
the brain cells of the fruit fly by an automatic cell counting algorithm.
Physica A 350, 144–149 (2005).
34. Feng, G. et al. Imaging neuronal subsets in transgenic mice expressing
multiple spectral variants of GFP. Neuron 28, 41–51 (2000).
35. Hensch, T. K. Critical period plasticity in local cortical circuits.
Nat. Rev. Neurosci. 6, 877–888 (2005).
36. Chechik, G., Meilijson, I. & Ruppin, E. Synaptic pruning in development:
a computational account. Neural Comput. 10, 1759–1777 (1998).
37. Fanselow, M. S. & Dong, H. W. Are the dorsal and ventral hippocampus
functionally distinct structures? Neuron 65, 7–19 (2010).
38. Herculano-Houzel, S. et al. Updated neuronal scaling rules for the brains ofGlires (rodents/lagomorphs). Brain Behav. Evol. 78, 302–314 (2011).
39. von Bartheld, C. S., Bahney, J. & Herculano-Houzel, S. The search for true
numbers of neurons and glial cells in the human brain: A review of 150 yearsof cell counting. J. Comp. Neurol. 524, 3865–3895 (2016).
40. Herculano-Houzel, S., Mota, B. & Lent, R. Cellular scaling rules for rodent
brains. Proc. Natl. Acad. Sci. USA 103, 12138–12143 (2006).
41. Herculano-Houzel, S., Messeder, D. J., Fonseca-Azevedo, K. & Pantoja, N. A.
When larger brains do not have more neurons: increased numbers of cells arecompensated by decreased average cell size across mouse individuals.
Front. Neuroanat. 9, 64 (2015).
42. Williams, R. W. Mapping genes that modulate brain development: aquantitative genetic approach. in: Mouse Brain Development. Results andProblems in Cell Differentiation (eds. A. M. Goffinet & P. Rakic) 21–49.
Springer Verlag, Berlin, 2000).
43. Seiriki, K. et al. High-Speed and scalable whole-brain imaging in rodents andprimates. Neuron 94, 1085–1100.e6 (2017).
44. Dauer, W. & Przedborski, S. Parkinson’s disease: mechanisms and models.
Neuron 39, 889–909 (2003).
45. Sahay, A. & Hen, R. Adult hippocampal neurogenesis in depression.
Nat. Neurosci. 10, 1110–1115 (2007).
46. Cowan, W. M., Fawcett, J. W., O’Leary, D. D. & Stanfield, B. B. Regressive
events in neurogenesis. Science 225, 1258–1265 (1984).
47. Vanderhaeghen, P. & Cheng, H. J. Guidance molecules in axon pruning andcell death. Cold Spring Harb. Perspect. Biol. 2, a001859 (2010).
48. Rice, D. & Barone, S. Jr. Critical periods of vulnerability for the developing
nervous system: evidence from humans and animal models. Environ. Health
Perspect. 108, 511–533 (2000). Suppl 3.
49. Rusu, R.B. & Cousins, S. 3D is here: Point Cloud Library (PCL).
IEEE Int. Conf. Robot https://doi.org/10.1109/ICRA.2011.5980567 (2011).
50. Saalfeld, S., Cardona, A., Hartenstein, V. & Tomancak, P. CATMAID:
collaborative annotation toolkit for massive amounts of image data.
Bioinformatics 25, 1984–1986 (2009).

Acknowledgements

We thank all lab members at The University of Tokyo and RIKEN QBiC, in particular:
A. Millius and W. Kylius for editing, E.A. Susaki and A. Kuno for discovering the

Nature Neuroscience | VOL 21 | APRIL 2018 | 625–637 | www.nature.com/natureneuroscience© 2018 Nature America Inc., part of Springer Nature. All rights reserved.

Resource

NATUre NeUrosCIenCe
tissue-swelling phenomenon, S. Shoi for helping with statistical analysis, K. Yoshida for
helping with the decomposing transformation matrix, and C. Shimizu for supportingswelling experiments. We also thank H. Hayakawa and S. Jiang for supporting the
preparation of a C57BL/6 J mouse brain, D. Perrin and H. Yukinaga for informatics
instruction, J. Kaneshiro, T. Watanabe and Olympus Engineering for helping design the
microscope, T. Mitani and K. Matsumoto for reproducibility confirmation, S. Takano,
S. Yamazoe and T. Tsukuda for the measurements of zeta potentials and Bitplane for
instruction of Imaris 8.1.2. This work was supported by a grant from AMED-CREST(AMED/MEXT, grant number JP17gm0610006, to H.R.U.), CREST (JST/MEXT, to
H.R.U.), Brain/MINDS (AMED/MEXT, grant number JP17dm0207049, to H.R.U. and
H. M.), Basic Science and Platform Technology Program for Innovative Biological
Medicine (AMED/MEXT, grant number JP17am0301025, to H.R.U.), Translational
Research Network Program from Japan Agency for Medical Research and development(AMED, to H.M.), World Premier International Research Center Initiative (MEXT,
to H.R.U), a Grant-in-Aid for Scientific Research (JSPS KAKENHI, grant number
16 J05041, to T.C.M.), a Grant-in-Aid for Scientific Research (S) (JSPS KAKENHI,
grant number 25221004, to H.R.U.), Grant-in-Aid for Challenging Exploratory
Research (JSPS KAKENHI, grant number 16K15124, to K.T.), a Grant-in-Aid for
Scientific Research on Innovative Areas (JSPS KAKENHI, grant number 23115006, to
H.R.U., 15H01558, to H.M., 17H05688, to K.T.), and a Grant-in-Aid from the Naito
Foundation (to K.T.).

Author contributions

H.R.U., T.C.M., T.M. and K.T. designed the study. T.C.M., T.M. and S.S. performed mostof the experiments. S.A.H. contributed to CATMAID data sharing. D.S. designed CAD.
K.B. and H.M. prepared C57BL/6 mouse brains. H.S., M.I. and K.F.T. produced PLP-YFP
and Mlc1-YFP mice. Y.S. produced recombinant fluorescent proteins. H.K. produced
R26-H2B-EGFP mice. H.R.U., T.C.M., T.M. and K.T. wrote the manuscript. All authors
discussed the results and commented on the manuscript text.

Competing interests

T.C.M., K.T. and H.R.U. have filed patent application for the CUBIC-X technique. Part ofthis study was done in collaboration with Olympus Corporation.

Additional information

Supplementary information is available for this paper at https://doi.org/10.1038/
s41593-018-0109-1.
Reprints and permissions information is available at www.nature.com/reprints.
Correspondence and requests for materials should be addressed to H.R.U.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.

Nature Neuroscience | VOL 21 | APRIL 2018 | 625–637 | www.nature.com/natureneuroscience© 2018 Nature America Inc., part of Springer Nature. All rights reserved.

637

Resource

NATUre NeUrosCIenCe

Methods

Mice. We used 6-month-old Thy1-YHP-H transgenic female mice34, 10-week-old
PLP-YFP (PLP-tTA::tetO-ChR2EYFP) transgenic male mice51 and 11-weekold Mcl1-YFP (Mlc1-tTA52::tetO-YC53) transgenic female mice for whole-brain
imaging of fluorescent reporter proteins. We used 4-month-old R26-H2B-EGFP
male mice54 (accession number CDB0238K; http://www.clst.riken.jp/arg/reporter_
mice.html) to observe the influence of delipidation over nuclei, and used 8-weekold C57BL/6J male mice to build the whole-brain atlas with single-cell resolution.
C57BL/6N mice were used to analyze whole-brain cell profiling over various ages.
One-week-old male mice were purchased from CLEA Japan, and 3-week-, 8-weekand 6-month-old male mice were purchased from Japan SLC, Inc.
For sampling, the mice were deeply anesthetized with pentobarbital. Then thesubject was transcardially perfused with PBS to flush the blood vessels, followedby perfusion with 4% paraformaldehyde (PFA) in PBS (pH 7.4) for fixation. Each
excised brain was postfixed with the same fixation solution at 4 °C overnight. The
fixed brain was washed with PBS for several hours to remove PFA. If necessary, thepostfixed samples were immersed in 20% sucrose in PBS at 4 °C and then stocked
in O.C.T. compound at –80 °C until use.
All experimental procedures and housing conditions were approved by
the Animal Care and Use Committee of the Graduate School of Medicine, theUniversity of Tokyo, or by the Animal Care and Use Committee of the Graduate
School of Medicine, Osaka University, and all of the animals were cared for andtreated humanely in accordance with the Institutional Guidelines for Experiments
using animals.
Delipidation of brain sample. For Thy1-YHP-H, PLP-YFP and Mlc1-YFP
mouse brains, a modified ScaleCUBIC-1 reagent (5 wt% N,N,N’,N’-tetrakis(2hydroxypropyl)ethylenediamine (Quadrol, Tokyo Chemical Industry, T0781),
25 wt% urea (Nacalai Tesque, 35904-45) and 15 wt% Triton X-100 (Nacalai Tesque,
12967-45)) was applied to suppress quenching of fluorescent proteins. For otherbrains, ScaleCUBIC-1 reagent (25 wt% Quadrol, 25 wt% urea, 15 wt% Triton X-100)
was applied23. Brain samples were initially immersed in delipidation reagent,
diluted by half with water, at 37 °C with gentle shaking for 3 h. Then brain samples
from Thy1-YHP-H, PLP-YFP and Mlc1-YFP mice were immersed in modified
ScaleCUBIC-1 at 37 °C with gentle shaking for 8 d. Other brain samples weretreated with ScaleCUBIC-1 at 37 °C for 5–14 d. Because we noticed that long-term
delipidation made the young (less than 3-week-old) mouse brain too soft to handle
properly, we used different duration of delipidation depending on age: 5 d for
1-week-old mice, 7 d for 3-week-old mice and 14 d for 8-week-old and 6-monthold mice. The delipidation cocktails were refreshed every 4 d. After delipidation,
the brains were thoroughly washed with PBS at room temperature (20°C to 30°C)
with gentle shaking overnight.
Size measurements of gelatin blocks and brains. We used gelatin type B (Nacalai
Tesque, 16631-05) in gelatin-based assays (Fig. 1 and Supplementary Fig. 1).
To prepare gelatin blocks, 10% gelatin aqueous solution was prepared by gentle
heating until the gelatin powder dissolved, and 1 mL of the solution was dispensed
into square dies. Gelatin was allowed to set at 4 °C overnight. After gelation,
resulting gelatin blocks were fixed by 4% PFA in PBS at 4 °C for more than 12 hwith gentle shaking and washed with PBS extensively. Gelatin blocks were treatedwith 10 mL of each chemical at room temperature with gentle shaking for 1 d. We
measured the 2D projection area (mm2) of the gelatin blocks using ChemiDoc
XRS Plus (Bio-Rad) and estimated the volume assuming the gelatin blocks were
isotopically swelled or shrunk by each chemical treatment.
To measure the brain swelling ratio (Fig. 1a,e and Supplementary Figs. 1 and2), brain samples of 8-week-old mice were initially delipidated by ScaleCUBIC-1
as mentioned above. Delipidated brains were cut to make hemisphere brains.
Hemisphere brains were treated with 10 mL of each chemical at room temperature
with gentle shaking for 1 d. We measured the 2D projection area (mm2) of thehemisphere brains using ChemiDoc XRS Plus (Bio-Rad) and estimated thevolume, assuming that hemisphere brains were isotopically swelled and shrunkby each chemical treatment. The volume of each hemisphere brain was calculated
by simply taking the 3/2 power of the 2D projection area. Custom-written Python
code was used to quantify the 2D projection area. Initially, a Sobel filter wasapplied to give sufficient contrast to the edges, followed by manually adjustinga threshold to determine the contour of the brains. Finally, we measured innerarea as a 2D projection area. The swelling ratio was calculated as the ratio of theobtained volume between brain samples before and after chemical treatment.
We measured the 2D projection areas of whole brains instead of hemispherebrains and evaluated the time-courses of swelling ratios of whole-brain samples inFig. 2c. Volumes were estimated as described above. In this Fig. 2c, we used the sizeof PFA-fixed whole brain as the original (baseline) brain size.
Measurement of zeta potential. Because isoelectric points of gelatin are different
depending on the manufacturing process, we measured both gelatin type A (MPBiomedicals, 901771) and gelatin type B (Nacalai Tesque, 16631-05), which areprocessed under acidic and basic conditions, respectively. We prepared a 0.1%
concentration of gelatin in water, which we then fixed by 0.8% PFA in water at
4 °C overnight. As a control, we used 0.1% lysozyme aqueous solution (Wako Pure

Chemical Industries Ltd., 122-02673). Brain protein was prepared as follows. Adultmouse brains were removed from the skull, chopped with a blade, suspended andsonicated in extraction buffer (20 mM HEPES, 100 mM NaCl, 0.01 M DTT, andprotease inhibitors (1 ×​ cOmplete, EDTA-free Protease Inhibitor Cocktail, Roche,
05056489001)). We centrifuged 20 mL of the resulting brain suspension (from 10
brain samples) at 12,000 g for 15 min, and 1 mL of the supernatant was mixed with
14 mL of cold 80% (v/v) acetone and 20% (v/v) methanol solution. The suspension
was incubated at 4 °C for 90 min. After centrifugation, the pellet was washedwith 20 mL of acetone and adequate volume of methanol, and then dried at 37 °C
over 30 min. The pellet was fixed with 2 mL of 4% PFA in PBS at 4 °C with gentle
shaking overnight. After washing in PBS, the pellet was stored in 2 mL of stock
solution (20 mM HEPES, 100 mM NaCl, 1 mM EDTA, 1 mM DTT, 0.1% NaN3
and protease inhibitors; pH 7.4). The fixed brain protein was diluted to 1/1,000 inwater just before zeta potential measurement. Zeta potential was measured with
ZetaSizer Nano ZS (Malvern). pH was adjusted by titrating 0.05 M NaOH or 0.05 M
HCl to samples. The measured zeta potentials were plotted in Supplementary Fig.
1 and fitted to fourth-order polynomials. Isoelectric points were determined by
calculating a crossing point of the polynomial with the x-axis (Fig. 1b).
High-throughput screening of swelling reagents. For high-throughput screeningof swelling reagents (Fig. 1c,d), we measured 975-nm absorption of gelatin type B(Nacalai Tesque, 16631-05). We used 975-nm absorption because water absorptionof near infrared shows a peak around 975 nm55, and this peak is widely used todetermine the optical path-length of water56,57. We prepared a mixture of 9% gelatin
type B an 0.8% PFA; this low concentration of PFA was used because hard fixation
by 4% PFA caused less swelling of gelatin, hindering the accurate quantification ofchemical swelling abilities. We dispensed 100 µ​L of this mixture immediately into
each well of the 96-well plate. The plates were incubated at 4 °C overnight. After
gelation, gelatin plates were washed with water. For calibration, 100 μ​L of water was
put onto the gelatin, and the 975-nm absorbance was measured with PowerWave
XS (Bio-Tek). After removal of the water, 170 μ​L of 10% (w/w) chemicals wereput onto the gelatin, and bubbles were carefully removed. The plates were sealed
and incubated at room temperature for 3 d. After incubation, the supernatants
were thoroughly removed by washing the plates with water three times. Then we
added 100 μ​L of water to the gelatin to avoid surface scattering and refraction.
The 975-nm absorbance was measured again with PowerWave XS. Because somechemicals produced precipitation in or on gelatin during incubation, we alsomeasured the 900-nm absorbance as an index of scattering. The same experiments
were performed twice each. We excluded chemicals that showed more than 0.1
absorbance at 900-nm. We linearly normalized the values of 975-nm absorbance ofthe gel by the value of blank (0.0) and urea-treated gel (1.0). Then we converted the975-nm absorbance to in-well gelatin swelling scores (Fig. 1d).
Evaluation of hyperhydrative RI matching reagent. Brain samples of 8-week-old
mice were delipidated by ScaleCUBIC-1 as described above. After delipidation,
brain samples were washed with PBS and then cut to make hemisphere brains.
Each hemisphere brain was immersed in 10 mL of CUBIC-X1 (20% imidazole(Tokyo Chemical Industry, I0001)) at room temperature with gentle shaking for
1 d. CUBIC-X1-treated brains were subsequently subjected to 10 mL of RI media
at room temperature with gentle shaking for 1 d. We used 60 wt% Histodenz(RI =​ 1.467; Sigma-Aldrich, D2158)22,58, 75 wt% fructose (RI =​  1.472; Nacalai
Tesque, 16315-55), 100 wt% glycerol (RI =​ 1.473; Nacalai Tesque, 17018-25), 75 wt%
TDE (RI =​ 1.469; 2,2’-thiodiethanol, Tokyo Chemical Industry, T0202)59,60, ScaleA2(RI =​ 1.382; 4 M urea, 10% (w/v) glycerol, and 0.1% (w/v) Triton X-100)30 and
ScaleCUBIC-2 (RI =​ 1.485; 50 wt% sucrose, 25 wt% urea, 10 wt% triethanolamine)23
in addition to imidazole/antipyrine cocktails. We measured the swelling ratio of
the resulting brain samples using ChemiDoc XRS Plus (Supplementary Fig. 2). In
Supplementary Fig. 2b, RI values of imidazole/antipyrine cocktails were measured
with an Abbe refractometer (ATAGO, DR-A1, Japan).
We captured transmission images of swollen brain samples (Fig. 2a). Thesesamples were prepared as follows. Delipidated brain samples were immersed in
30 mL of CUBIC-X1 at room temperature with gentle shaking for 3 d. CUBIC-X1
was refreshed every 12 h. Then these brains were immersed in 40 mL of eachchemical cocktail at room temperature with gentle shaking for 2 d. Each reagent
was refreshed daily.
CUBIC-X protocol. The brain expansion protocol is composed of the following
major steps: delipidation, nuclear staining, swelling, RI matching and gel
embedding (Fig. 2d). Delipidation is described above. For nuclear staining, we
immersed a delipidated brain sample in 3 mL of 30 μ​g/mL propidium iodide(Life Technologies, P21493) and 1.5 M NaCl in PBS and incubated it at room
temperature with gentle shaking for 3 d. The brain sample was washed with PBS atroom temperature with gentle shaking for more than 12 h. Then we immersed thesample in 30 mL of 20% imidazole and incubated it at 4 °C (for Thy1-YFP-H, PLPYFP and Mlc1-YFP brains) or at room temperature (for other brains) with gentle
shaking for 2.5 d. Note that we incubated brain samples expressing fluorescentproteins at the lower temperature to avoid possible quenching. Since the expanded
brains were fragile, careful handling was required for all following procedures.
Each expanded brain was immersed in 40 mL of CUBIC-X2 (5% (w/v) imidazole
Nature Neuroscience | www.nature.com/natureneuroscience© 2018 Nature America Inc., part of Springer Nature. All rights reserved.

Resource

NATUre NeUrosCIenCe
and 55% (w/v) antipyrine (Tokyo Chemical Industry, D1876) cocktail) with gentle
shaking at room temperature for 1.5 d. The cocktail was refreshed every 12 h. For
gel embedding, well-cleared swollen brains were embedded in CUBIC-X2 with 2%
agarose (Wako Pure Chemical Industries Ltd., 318-01195). Prior to gel embedding,agarose powder was dissolved in CUBIC-X2 by heating, and then we cooled the
mixture to a moderate temperature. Gelation was performed at 4 °C overnight. The
gel-embedded samples were then dissected for imaging.
Measurement of light transmittance and scattering. For Fig. 2b, we measured
the light transmittance of the brain samples used in Fig. 2a, from 380 to 780 nm
at 5-nm intervals with an integrating sphere (Spectral Haze Meter SH 7000,
Nippon Denshoku Industries). Every sample was tested in 2-cm optical pathlength cells with each chemical solution. For Supplementary Fig. 3, we measured
the light transmittance of water, 2% agarose, CUBIC-X2, 2% agarose CUBIC-X2
and CUBIC-X brain in 2% agarose CUBIC-X2. We calculated the transmission
value per centimeter by the square root of the total transmission value at eachwavelength.
To measure scattering, we referred to the method described by Schwarz et al.61.
Images were taken with a macro-zoom microscope (MVX10, Olympus) withan SDFPLAPO1.6XPF (NA =​ 0.24, Olympus) objective lens. The brains were
immersed in ScaleA2 or CUBIC-X2, in glass cases placed on top of a light sourcewith a positive 1951 USAF resolution value (Edmund Optics). CUBIC-X2-treatedbrains were embedded in gel with 2% agarose before imaging. For quantification,
minimum and maximum intensity over element 2, group 2 of the USAF resolution
chart was used. The absolute differences between the minimum and the maximum
were computed, and the local contrast was calculated by normalizing it to blankcontrols (ScaleA2 reagent or 2% agarose CUBIC-X2 in CUBIC-X2).
Evaluation of application of CUBIC-X to fluorescent protein with confocal
microscopy. Purified recombinant EGFP, EYFP and mCherry were used toevaluate quenching of fluorescent proteins by various chemical treatments. Eachchemical solution was mixed with EGFP (6.7 μ​g/mL final concentration), EYFP
(8.5 μ​g/mL final concentration) or mCherry (200 μ​g/mL final concentration). We
measured the fluorescence intensity of each sample with Enspire (Perkin Elmer,
USA; Supplementary Fig. 4). To evaluate the availability of CUBIC-X protocol tobrain samples expressing fluorescent reporter proteins, we acquired images of the
hippocampal area of Thy1-YFP-H mouse brains from the ventral side at 2-μ​m
steps using an upright confocal microscope (FV1200, Olympus) equipped witha 25 ×​ objective lens (XLSLPLN25XGMP, NA =​  1.0, WD =​ 8.0 mm, Olympus).
A 473-nm laser was used for excitation. Acquired images were volume-renderedand shown in Fig. 2e.
Evaluation of distortion during CUBIC-X protocol. To assess nonlinear
deformation of chemically treated brains, we attempted to prepare a transparent
brain preserving the original shape as a standard. After delipidation and PI
staining, brains were washed with 30 mL of PBS at room temperature withgentle shaking for 12 h, and then immersed in 10 mL of modified ScaleCUBIC-2(22.5 wt% antipyrine, 22.5 wt% sucrose, 25 wt% urea, 10 wt% triethanolamine(Wako Pure Chemical Industries Ltd., 145-05605) and 80 mM NaCl), dilutedby half, at 37 °C with gentle shaking overnight. Then the brains were further
immersed in 5 mL of modified ScaleCUBIC-2 at 37 °C with gentle shaking for 2 d.
The modified ScaleCUBIC-2 was refreshed every day. After image acquisition, thebrains were washed with 30 mL of PBS at room temperature with gentle shaking
overnight, and then immersed in 30 mL of ScaleCUBIC-1 at 37 °C with gentle
shaking for 1 d. After washing in PBS, samples were expanded with CUBIC-X1,
followed by RI matching with CUBIC-X2 and gel embedding as described above.
To acquire images, we used a light-sheet fluorescent microscope (developed
by Olympus). The pre-CUBIC-X brain sample was immersed in a mixture(RI =​ 1.490) of silicon oil TSF4300 (Momentive Performance Materials, RI =​  1.498)
and mineral oil (Sigma-Aldrich, RI =​ 1.467), and the post-CUBIC-X brain
sample was in mineral oil (Sigma-Aldrich, RI =​ 1.467) during image acquisition.
Images were captured with a 0.63×​ objective lens (NA =​  0.15, WD =​ 87 mm) with2×​ optical zoom for pre-CUBIC-X brains or 1×​ zoom for post-CUBIC-X brains.
A 532-nm laser was used for image acquisition. To cover whole brains, a stage wasdesigned to move in both lateral and axial directions. When the stage was moved
axially, the detection objective lens moved in synchrony with it to avoid defocusing.
We measured the volumes of pre- and post-CUBIC-X brains by segmenting
the brains from acquired images. Segmentation was performed by combining thebinarization and morphological opening of the images. The thresholds for thebinarization and the radius of morphological openings were manually determined.
Voxel numbers of segmented brain volumes were counted, then converted into
mm3 (Supplementary Fig. 5).
For nonlinear distortion analysis, postexpansion images were registeredto pre-expansion images using affine transformation and then nonlinear
transformation. We used ANTs (Advanced Normalization Tools)62 as a registration
tool because it has proved successful in a number of image registration tasks63.
Affine transformation was performed with mutual information, 64 bins and
300,000 samples as the parameters of the ANTS function. We used 100 ×​  10 ×​  10
iterations for the nonlinear transformation, using symmetric normalization (SyN)

implemented in ANTs. We used the value in the *InverseWarp.nii.gz deformationfield file to evaluate nonlinear deformation, because this file contains nonlinear
transformation registration elements. To reduce the computational cost, we applied
the images to a 3D grid with 20-voxel spacing, and then collected the voxels in
the grid. We calculated two values of L′​ and L from each pair of the collectedvoxels, where L′​is the distance between two voxels in pre-SyN transformed
images and L is the distance between two voxels from post-SyN transformed
images (Supplementary Fig. 7). Both L′​ and L were measured using the pre-SyN
transformed image as the baseline. We created graphs of |L – L′​| as a function of
distance L, calculating the root-mean-square error and s.d. As shown in Fig. 3b,
RMS errors were less than ~20 µ​m, which is ~0.8% of the measurement length(2,500 µ​m) in the investigated regions.
Evaluation of cellular loss during a CUBIC-X protocol. We confirmed that nonuclei were lost during delipidation process (Supplementary Fig. 6). To visualize
the nuclei before delipidation, we used brains of R26-H2B-EGFP mice. The
brains were sectioned into 1-mm-thick coronal slices, then immersed in modified
ScaleCUBIC-2 before the delipidation process. Fluorescence observation of
nuclei was performed with confocal microscopy. Delipidation with modified
ScaleCUBIC-1 was performed after observation, and samples were gently shaken at
37 °C. The samples were cleared with modified ScaleCUBIC-2 1 h before confocal
imaging and delipidated again until the end of the sequential observation period.
Because repeated observation with confocal microscopy bleaches EGFP signals,
propidium iodide was introduced to enhance the signal of nuclei starting at day 3.
We also confirmed that none of the nuclei were lost during expansion bycomparing images taken before and after CUBIC-X treatment. The pre-CUBIC-X
brain was prepared by delipidation and nuclear staining and imaged with a confocal
microscope. Then the same sample was expanded and imaged with the customized
LSFM. The post-CUBIC-X brain image was linearly registered to images of the preCUBIC-X brain by ANTs using the affine transformation (Supplementary Fig. 6).
To evaluate how the intercellular expansion ratio depends on cell density, images ofthe post-CUBIC-X brain were first registered to images of the pre-CUBIC-X brain
by a similarity transformation. Then one pair of 2D images (250 µ​m ×​  250 µ​m) wereextracted. Nuclei in the images were segmented by thresholding and converted tocircular particles with the ‘analyze particles’ function in Fiji. One-dimensional celldensity was measured as the number of nuclei on a connecting line divided by thelength of the connecting line (Supplementary Fig. 6c). The intercellular expansion
ratio was measured as L2/L1 for a matched pair of nuclei (Supplementary Fig. 6d).
Finally, the relationship between the intercellular expansion ratio and the 1D celldensity was plotted (Supplementary Fig. 6e).
Construction of the customized light-sheet fluorescent microscope. Overviewsof the customized light sheet microscope are illustrated in Supplementary Fig. 8.
For notation of the xyz coordinate system, please refer to Fig. 3c,d. A full list ofparts is presented in Supplementary Table 2, and CAD drawings are available uponrequest. The excitation light source was provided by a fiber-coupled CW laser(Omicron, SOLE-3 equipped with λ​  =​ 488, 594 and 642 nm). After collimation, thebeam was split into two paths (right and left excitation) using 50:50 beam splitter(Thorlabs). Left and right paths were essentially identical. The beams struck anelectrically tunable lens (ETL, Optotune, EL-10-30) combined with offset lenswith f =​ –50 mm, positioned at the conjugated plane with the back focal plane of
the illumination objective through a series of 4 f relay lenses. The focal length of
the ETL was tuned to adjust the focus point of the light sheet. A graduated iris wasplaced to adjust the diameter of the beam, which defined the effective NA of theillumination objective (NA =​  0.1, WD =​ 30 mm, Nikon, CFI Plan 4×​). The diameterof the iris was set at 3 mm, which corresponds to an effective NA of 0.03. NA =​  0.03
corresponds to the minimum beam waist of about 10 μ​m and Rayleigh length ofabout 0.64 mm, which ensured a homogeneous light sheet thickness throughout
the field of view (in this setup, 1.4 mm in the x direction). The first galvo mirror(Thorlabs, GVS001) was placed to adjust the z-position of the light sheet so that theexcitation beam illuminated the focal plane of the detection objective. The second
galvo mirror (Cambridge Technology, VM500Plus) was used to generate the lightsheet, following the a DLSM scheme32. The mirror was swept at 500 Hz and theamplitude was adjusted to sufficiently cover the entire field of view (in this setup,1.65 mm in the y direction). The detection objective (XLPLN10XSVMP, NA =​  0.6,
WD =​ 8 mm, Olympus) was inserted directly into the hole located on the side of thecustom-made sample chamber, and the chamber was filled with mixture of high-RI
silicon oil (RI =​ 1.498, Momentive Performance Materials, TSF4300) and low-RI
silicon oil (RI =​ 1.396, Momentive Performance Materials, TSF405) whose RI was
adjusted to match to the brain samples (RI =​ 1.467). The samples were mounted ona motorized x–y–z–θ​stage through a custom-made sample holder (x and y stages,
Thorlabs, MTS50/M-Z8E; z stage, Physik Instrumente, M-112.1DG; θ​stage, Physik
Instrumente, M-116.DG). The gel-embedded brain sample was mounted onto
the sample holder. The sample fluorescence was imaged with an sCMOS camera(Andor, Neo 5.5) through an f =​ 200 mm tube lens, after passing through a custommade filter wheel equipped with φ​  =​ 32 mm filters. Since the detection objective
had a large back pupil, use of 32-mm filters was necessary to prevent clipping of thecollected photon. All electronic devices were integrated and controlled by customwritten LabVIEW software (National Instruments).

Nature Neuroscience | www.nature.com/natureneuroscience© 2018 Nature America Inc., part of Springer Nature. All rights reserved.

Resource

NATUre NeUrosCIenCe

Tiling sequence in whole-brain imaging. To image the entire brain with a limited
field of view and working distance of the detection objective, we designed thefollowing image acquisition sequence. Schematic of the procedures is presented
in Fig. 3d. Initially, the x–y coordinates of the imaging region were defined by
specifying the top-left and right-bottom corner. The program then automaticallydivided the region into two-dimensional tiles, consisting of rectangular blocks
whose size was determined by the field of view of the objective (in this setup,x =​ 1.4 mm and y =​ 1.65 mm) and the specified overlap value (10% for data shownin Supplementary Fig. 9b,c; 2% for others). The overlap value was defined to ensurethat there would not be any loss of brain regions, and overlap was considered andhandled properly in the data analysis. Next, the range of the z-scan was specified.
Images were collected by scanning the sample in the z direction, with a step size of
5 μ​m. We also implemented an autofocus function, which was executed every 200
images, to compensate for slight shifts of the focal plane of the detection objective
due to the movement of the sample. This shift was considered to be caused by thefact that there was a slight RI mismatch between brain sample and immersion
oil, and depending on the distance between the sample boundary and objective
the light traveled slightly different paths. The autofocus routine swept several
different light sheet positions by adjusting the sheet-positioning galvo mirror, andit calculated the image sharpness by applying a Sobel filter and taking the squared
sum of the intensity to find the sheet position with maximum sharpness.
After collecting images from the ventral side, the sample was rotated by 180°
using the θ​stage and the second half of the acquisition (dorsal side) was initiated.
Since the sample was rotated along the y axis, it was critical to determine thecenter of rotation in the x–z plane. This was done manually by locating identicalcells or structures near the cerebral aqueduct from the θ​  =​  0° and θ​  =​  180° sides.
In this way, two blocks of images were merged continuously (Supplementary
Fig. 9a). The errors caused by rotation as well as by x–y tiling were quantified,
as described below. The excitation laser power was typically 1 mW (measured
before the illumination objective), which was low enough to prevent fluorescencebleaching despite long imaging times. Single-image acquisition took approximately
200 ms (50 ms of exposure, 150 ms of stage motion), achieving an acquisition
rate of 5 images/s. Each image was approximately 11 MB (2,160 ×​  2,560 pixels,
16 bit), corresponding to 55 MB/s data, which can easily be handled with harddisk drive arrays. The total image acquisition was completed within 4 d, yielding
approximately 1.3 million images or 14 TB of data. The acquired data was either
recorded on local hard drives or directly transferred to storage in the server
through a 10-Gbit Ethernet cable.
Imaging of whole mouse brains expressing fluorescent proteins at subcellular
resolution. To demonstrate subcellular-resolution imaging on a whole-brain scale,
we imaged mouse brains expressing fluorescent proteins PLP-YFP and Thy1YFP-H. Prior to imaging, these brains were delipidated, PI-stained, expanded
and gel-embedded, as described above, and imaged with the customized LSFM.
To further analyze the PLP-YFP mouse brain images, we applied a Frangi filter64
to enhance the fibrous structure (Supplementary Fig. 10d). In the Frangi filter,a 3D Hessian matrix is constructed at each voxel, and the fibrousness and fiber
orientation are determined by solving the eigenvalue and eigenvector of the matrix.
Prior to filtering, image tiles were stitched together using TeraStitcher65 to correct
the stripes at tile boundaries, and the image resolution was reduced to 5 μ​m on
all axes to ensure a homogeneous resolution. The Frangi filter was implemented
using Matlab code available online (https://jp.mathworks.com/matlabcentral/
fileexchange/24409-hessian-based-frangi-vesselness-filter).
Quantitative measurement of tiling displacements. We used 10 ×​  10 (x ×​  y) tilesof 600-image stacks with the 10% overlap of each stack in both x and y directions
to quantitatively evaluate the possible loss of cell detection originating from tiledisplacement (Supplementary Fig. 9b,c). We selected 10 ×​ 10 tiles so that pictures
would not include space outside the brain. Brain samples from 1-week-old mice
were used for image acquisition. Displacement was calculated with stitchingsoftware, TeraStitcher65. Displacement search regions must be set in the alignment
step. We set sV, sH, and sD, whose searching areas are 50 pixels around 200 sliceimages per layer. Because larger searching regions return more correct value, we
set the largest values of sV, sH and sD in our computational setup. In thresholding,we set 0.5 as the threshold parameter to judge whether a tile is stitchable. In theplacement step, displacement values were calculated by the minimum spanning
tree algorithm. The resulting values in the xml:merging.xml file were analyzed. The
displacement values were extracted both from SOUTH_displacement and EAST_
displacement. We repeated the displacement evaluation twice and summarize it inSupplementary Fig. 9b.
To estimate theta tile displacement, we acquired 10 ×​  10 (x ×​  y) tiles of 600image stacks from θ​  =​  0° and θ​  =​ 180° using an identical brain, and then compared
the two stacks. Prior to analysis, these datasets were stitched with TeraStitcher
using the same parameters described above. Stitched images were further
downsampled to a voxel size of 45 μ​m ×​  45 μ​m ×​  45 μ​m (x ×​  y ×​  z), and images fromθ​  =​ 180° were registered to θ​  =​ 0° images with the ANTs similarity transformation.
Mutual information with 64 bins was used as the similarity metric. The resulting
matrix was decomposed to estimate the rotation angle around the x, y and z axes
(Supplementary Fig. 9c).

Computational system and data storage. The image processing server comprises
two 12-core CPUs (Intel Corporation, Xeon E5-2590 v3), Intel 256-GB C612
Chipset (16 GB ×​ 16 GB), DDR4-2133 SDRAM (Samsung, M393A2G40DB0-CPB,ECC Reg.), four GPUs (Nvidia Corporation, GeForce GTX TITAN X), a 12-GB/s
SAS RAID card (Avago, MegaRAID SAS 9380-8e) with 42 hard disks (Seagate,
ST100NM0033 and ST5000NM0084), two 1-TB SATA hard disks combined in a
RAID-1 for the system, and forty 5-TB SATA hard disks combined in a RAID-0
for every ten hard disks for the data. The image processing server is connected
to a workstation beside the microscope by a 10-GB Ethernet fiber (Mellanox
Technologies, MC3309130-002) via two 10-GbE switches. The storage server
comprises a CPU (Intel Corporation, Xeon E5-1620 v3), C612 Chipset (Intel
Corporation), 8-GB DDR4-2133 SDRAM (Samsung, M393A1G40DB0-CPB, ECCReg.) and a RAID controller (Avago, MegaRAID SAS 9361-8i) with thirty-six 6-TB
SATA hard disks (HGST, Inc., HUS726060ALE610). One hard disk is a hot spareand other 35 hard disks are combined in a RAID-6. Effective capacity of these harddisks is 198 TB (1 TB for the system and about 197 TB for the data).
Whole-brain cell-detection algorithms. Cell detection was performed via
our image-processing pipeline, implemented in Matlab for GPU-based 2D cell
detection and in Python for CPU-based unification of detected cells to remove
duplicated detections in the z direction. The local thresholds of the subregions
were initially determined as shown in Supplementary Fig. 12. Each subregion(216 pixels ×​ 256 pixels) was determined by dividing each single image (2,160
pixels ×​ 2,560 pixels) into 10 ×​  10 (x ×​  y) regions. Modes were calculated using ahistogram of pixel intensity with a bin size of 10.
Next, we set the background intensity at <​2 ×​ the mode intensity and extracted
background signals (blue in Supplementary Fig. 12). Next, we calculated mean
intensity and s.d. from the resulting histogram, and then set the image thresholdat intensity <​ 2 s.d. above the mean (red in Supplementary Fig. 12). We determined
the range of thresholds both to avoid cell detection in empty regions of the images
and to ensure cell detection without loss in dense regions such as the cerebellum.
We detected local maxima after applying a 2D mean filter kernel with diameter DM1
in the first 2D cell-detection step (Supplementary Fig. 11a). We determined DM1
as 8.45 μ​m (13 pixels) to maximize the accuracy of 2D detection among cerebral
cortex, hippocampus and olfactory bulb (Supplementary Fig. 11d). Each intensity
value and x–y–z coordinate of detected peaks in each stack were output in CSV
file format. This GPU-based 2D cell-detection was completed within 35 h. Among
all cells detected in the 2D cell-detection step, we excluded ‘isolated’ cells thatwere detected only in a single plane along the z direction, to remove noise. Next,
we unified the multiply-detected cells after applying a 3D mean filter kernel withdiameter DM2 in the second 3D-cell unifying step. The depth of the 3D mean filter
kernel in the z direction was set at 20 μ​m (4 pixels), and the diameter (DM2) was
determined depending on the surrounding peak number (NP) in each subregion(Supplementary Fig. 11f). This is because optimal values for DM2 are relatively
dependent on the density of cells. For example, setting DM2 =​  13.0 μ​m (20 pixels)
was the most accurate parameter value for brain regions with intermediate celldensities (20 ≤​ NP <​  40) whereas DM2 =​  18.2 μ​m (28 pixels) and DM2 =​  10.4 μ​m (16pixels) were more accurate for those with lower (NP <​ 20) and higher (40 ≤​ NP) celldensities, respectively. Then we introduced variable DM2 values optimized for thecell density of each region to maximize accuracy (Supplementary Fig. 11e,f). Aftermean filter application, local maxima in the z direction were detected.
Accuracy of this fully automated cell-detection protocol among various brain
regions was evaluated by comparing the results of automatic detection withthose from manual detection (Supplementary Fig. 13). To facilitate the accuracy
evaluation, we categorized brain areas into two types, densely populated areas(DG and CBgr) and sparsely populated areas (other brain areas). For accuracy
evaluation of sparsely populated areas, more than 200 cells were manually detected.
Accuracy was calculated as the frequency of true-positive detections divided by
the sum of frequency of true-positive detection and the penalty value. The penalty
value was calculated as the sum of the frequency of false-negative detections,frequency of false-positive detections, and frequency of overlapped detections. The
frequency of overlapped detection comprised a count of the number of overlapped
detections (for example, 1 for double detection, 2 for triple detection). For densely
populated areas, we took subvolume images with a thinner light sheet. The usual
low-illumination-NA condition was FWHM =​  11.0 μ​m (measured) with scanningstep size =​  5 μ​m while the high-illumination-NA condition was FWHM =​  5.2 μ​m(measured) with scanning step size =​  2 μ​m. Using high-NA images, two people
performed independent manual counts and the results were compared with theautomatic count of low-NA images. Accuracy was calculated as described above.
To compare our two-step cell-detection algorithm with the direct 3D celldetection algorithm33, we evaluated the computation time and cell-detection
accuracy using the three image stacks from striatum, thalamus and cerebral
cortex layer 5/6. Each stack included 100 images. All the images were analyzed forcomputation time, and parts of the images were used for accuracy estimation. For
the direct 3D cell-detection algorithm, we applied a cylinder-shaped mean filter
kernel with a diameter of 8.45 μ​m (13 pixels) and a height of 25 μ​m (5 pixels) anddetected local maxima. For the two-step algorithm, we applied a circle-shaped
mean filter kernel with a diameter of 8.45 μ​m (13 pixels) and detected local
maxima; then we used a cylinder-shaped mean filter kernel with a diameter of
Nature Neuroscience | www.nature.com/natureneuroscience© 2018 Nature America Inc., part of Springer Nature. All rights reserved.

Resource

NATUre NeUrosCIenCe
8.45 μ​m (13 pixels) and a height of 25 μ​m (5 pixels) in the second 3D cell-unifying
step. The ratio of computation time and accuracy of the cell-detection werecompared (Supplementary Fig. 11b).
Construction of a single-cell-resolution mouse brain atlas (CUBIC-Atlas).
The CUBIC-X images of C57BL/6 J mice brain were registered in the 25-μ​m
Allen Brain Atlas, and detected cells were then annotated. From the Allen Brain
Atlas website, we downloaded the atlasVolume file, which is a 3D assembled Nissl
volume of the reference brain used for registration, and the ‘annotation’ file, whichcontains annotation ID in a reconstituted brain used for annotation (http://help.
brain-map.org/display/mousebrain/API). We prepared both atlasVolume and‘annotation’ in TIFF format.
Initially, we prepared the downsampled images of CUBIC-X brain before
registration because conventional registration software is unable to handle large
files. The downsampled image size was 361 ×​  428 ×​  230 (x ×​  y ×​  z) with a voxel
size of 80.12 μ​m ×​  80.10 μ​m ×​  80.00 μ​m, saved as a TIFF file. We fitted the image
file to this size by appending pixels with a signal intensity of zero. Then thedownsampled image was registered to atlasVolume (we term this the ‘ABA brain’)
with an affine transformation using ANT software. In the affine transformation,
mutual information was used for the similarity metrics, with 64 bins and 300,000
samples as parameters of the ANTS function. The WarpImageMultiTransform
command was used to generate an aligned image. We could not smoothly map 3D
images of the CUBIC-X brain onto the ABA brain simply by using diffeomorphic
registrations such as B-spline transformation or SyN transformation. Thus, we
introduced an alternative registration based on the virtual coronal slices. Afterroughly aligning the CUBIC-X images by affine transformation, we generated 111
overlapping 3D coronal slices with a 2.2-mm thickness at 100-μ​m steps from both
the CUBIC-X and ABA brains (Supplementary Fig. 14a). Then each coronal-slice
image of the CUBIC-X brain was nonlinearly registered onto the coronal-slice
images of the ABA brain. We used a symmetric normalization model (SyN) as thenonlinear transformation. We used 100 ×​  10 ×​ 10 iterations for SyN, with crosscorrelation as the similarity metrics.
Because the transformation information was output as affine.txt files forthe affine transformation and as InverseWarp.nii.gz files for the nonlinear
transformation, we were able to trace the same registration process. In thisprocedure, each coronal plane of the CUBIC-X brain was mapped 22 times onto
the ABA brain. We then calculated the normalized cross-correlations (NCCs) ofeach CUBIC-X coronal image with the ABA coronal images. Next, we applied
individual cells to 22 different deformation fields and calculated 22 different
destinations. By referring to NCCs of coronal plane images in the individual
destinations, we identified the optimal deformation field displaying the highest
NCC. Based on this procedure, we registered individual cells of the CUBIC-X
brain to the ABA brain. Overall, it took about 3 d with our image processing
server to complete the registration of the whole brain. We note that the coronalslice-based registration demonstrated better performance in annotating detected
cells when compared with a simple affine transformation or SyN transformation(Supplementary Fig. 14).
After virtual-coronal-slice-based registration, we next annotated the detected
cells by referring to the ‘annotation’ file. We analyzed the annotation data from allof the detected cells and removed cells annotated out of space in the ABA brain.
Based on results of this registration and annotation procedure, we generated a 3D
mouse brain atlas with a single-cell resolution and named this point-based brain
atlas CUBIC-Atlas.
Tto generate 3D annotation regions, we applied the Matlab function alphashapeand input an alpha diameter of 300 as a parameter for each annotated point set.
To generate 2D annotation regions, we extracted 100-μ​m-wide point sets from theselected plane to form the alpha shape, followed by projection to the 2D plane(Fig. 6c and Supplementary Fig. 14d).

taken from mice of various ages. After registration, we detected the nearest-neighborcell as a reference in CUBIC-Atlas to the cell of interest in the obtained images. To
reduce computational cost, we performed this search in locally allocated grids(120 μ​m ×​ 120 μ​m ×​ 120 μ​m) partitioned from the atlas space (Supplementary Fig. 17).
We annotated the corresponding anatomical region of the nearest-neighbor cellin CUBIC-Atlas to the region of the cell of interest and excluded the cells thatwere annotated outside of the brain or allocated outside of the atlas space. To
characterize the developmental progression of cell numbers at each age, we first
analyzed the average cell numbers and s.d. for 1-week-old, 3- week-old, 8-week-old
and 6-month-old mice in each region (Fig. 5b,c) and revealed that variations in cellnumbers over different ages strongly depended on brain region. We calculated thecoefficient of variation (CV), i.e., normalized s.d. by the average cell number (Fig.
5d), and mapped CV values on the mouse brain schema (Fig. 5e). A dendrogram
schema was constructed using networkX66 and Gephi67 software and indicatedthe hierarchical annotation structure of ABA (Fig. 5e). To investigate
developmental changes in cell numbers across the entire brain, we calculated
normalized cell numbers for each region by setting the cell number averaged overages to 1.0. For hierarchical clustering analysis, averaged values of normalized cellnumbers within each age group were used. Brain regions were then clustered basedon their normalized cell numbers over different ages, using the heatmap.2 functionin the gplots R package68, with Euclidean distance and the ward.D2 clustering
method (Fig. 5f). Four clusters were determined by the ‘cutree’ function in the
R stats package. This clustering analysis revealed four major clusters (Fig. 5g,h).
To see how accurately brains were anatomically annotated during early
postnatal development, we visualized the nucleic images of 1-week-old and3-week-old mice with the anatomical annotations of detected cells. Cerebral
cortical layers of somatosensory areas, posterior parietal association areas andvisual areas are visualized with pseudo colors (Supplementary Fig. 19).
Probabilistic annotation of CUBIC-Atlas. Hyperactivated cells were detected as
in our previous paper14, and the detected cells were then registered and annotatedas above. Annotated cells were visualized in the brain (Fig. 7b and Supplementary
Fig. 22). For probabilistic annotation of the CUBIC-Atlas, a Gaussian probabilistic
distribution (σ​  =​  60 μ​m, using a post-CUBIC-X brain as baseline) was generated
around the registered cells. After integrating the probabilistic distribution
generated from all registered cells, we annotated the cell in the atlas by referringto the value of the corresponding location in the resulting activity distribution.
Probabilistically annotated cells in CUBIC-Atlas were then clustered with the
kmean+​+​algorithm implemented in Matlab.
Statistical analysis. Statistical analyses were performed in R version 3.3.1 orGraphPad Prism. Prior to Student’s t tests and one-way ANOVA, normality wasevaluated by Kolmogorov–Smirnov tests at a significance level of 0.05, and thehomogeneity of variance for each group was evaluated by Bartlett’s test. In bothtests, significance level was set to 0.05. To analyze swelling of gelatin blocks orbrains, one-way ANOVA with Tukey’s post hoc tests were used when the groups
displayed normal distribution with equal variance, or Dunnett’s modified
Tukey–Kramer pairwise multiple comparison test when groups displayed
normal distribution without equal variance. For comparison of computation
time, accuracy, normalized cell numbers and cell numbers, Student’s t tests
were used after confirmation of normal distribution with equal variance. In thisstudy, P <​ 0.05 was considered significant (*P <​  0.05, **P <​  0.01, ***P <​  0.001;
n.s., not significant). Data collection and analysis were not performed blindto the conditions of the experiments. No statistical methods were used topredetermine sample sizes, but our sample sizes are similar to those reported
in previous publications14,23. The mice used for whole-brain cell counting
were randomly chosen from colonies. Experiments were not performed ina blinded fashion.

Mapping a Thy1-YFP-H mouse brain onto the CUBIC-Atlas. To see how
accurately anatomical annotations of a CUBIC-Atlas are performed, we overlaid
cerebral cortical layers of a Thy1-YFP-H mouse brain onto those of CUBICAtlas. A Thy1-YFP-H mouse brain was delipidated, PI stained, expanded andgel-embedded as described above. Following fluorescence imaging of PI and YFPwith our customized LSFM, the images were downsampled before registration.
The downsampled PI signal image was then registered onto the CUBIC-Atlas
with an affine transformation followed by nonlinear SyN transformation. The
resulting affine matrix and deformation fields were applied to both the PI signal
and the YFP signal. The registered volumetric images of PI and YFP are shownin Supplementary Fig. 16. Cerebral cortical layers of the CUBIC-Atlas are alsovisualized in pseudocolors, overlaid with the registered images of PI and YFP(Supplementary Fig. 16).

Life Sciences Reporting Summary. Further information on experimental design isavailable in the Life Sciences Reporting Summary.

Whole-brain cell-profiling of mice at different ages. We performed whole-brain
cell profiling of 1-week-old, 3-week-old, 8-week-old and 6-month-old C57BL/6 N
mice according to the CUBIC-X pipeline (Fig. 5a). Detected cells from the wholebrain were registered and annotated to the anatomical regions in the CUBIC-Atlas
as a reference. In this step, we applied an affine transformation and the following
nonlinear SyN transformation to individual cells in each CUBIC-X image of brains

References

Code availability. The source codes used in detecting cells as well as mapping andannotating detected cells onto the CUBIC-Atlas are available from our website(http://cubic-atlas.riken.jp). Other software used in this study, including customwritten LabVIEW code controlling a custom LSFM, is available upon reasonable
request.
Data availability. The CUBIC-Atlas can be downloaded from our website (http://
cubic-atlas.riken.jp). The data supporting the findings of this study are available
upon reasonable request.

51. Inamura, N. et al. Gene induction in mature oligodendrocytes with a
PLP-tTA mouse line. Genesis 50, 424–428 (2012).
52. Tanaka, K. F. et al. Flexible Accelerated STOP Tetracycline Operator-knockin(FAST): a versatile and efficient new gene modulating system. Biol. Psychiatry67, 770–773 (2010).

Nature Neuroscience | www.nature.com/natureneuroscience© 2018 Nature America Inc., part of Springer Nature. All rights reserved.

Resource

NATUre NeUrosCIenCe

53. Kanemaru, K. et al. In vivo visualization of subtle, transient, and local
activity of astrocytes using an ultrasensitive Ca(2+) indicator. Cell Rep. 8,
311–318 (2014).
54. Abe, T. et al. Establishment of conditional reporter mouse lines at ROSA26
locus for live cell imaging. Genesis 49, 579–590 (2011).
55. Hale, G. M. & Querry, M. R. Optical constants of water in the 200-nm to200-microm wavelength region. Appl. Opt. 12, 555–563 (1973).
56. Cope, M., Delpy, D. T., Wray, S., Wyatt, J. S. & Reynolds, E. O. A CCD
spectrophotometer to quantitate the concentration of chromophores in living
tissue utilising the absorption peak of water at 975 nm. Adv. Exp. Med. Biol.
248, 33–40 (1989).
57. Wray, S., Cope, M., Delpy, D. T., Wyatt, J. S. & Reynolds, E. O.
Characterization of the near infrared absorption spectra of cytochrome aa3
and haemoglobin for the non-invasive monitoring of cerebral oxygenation.
Biochim. Biophys. Acta 933, 184–192 (1988).
58. Ke, M. T. et al. Super-resolution mapping of neuronal circuitry with anindex-optimized clearing agent. Cell Rep. 14, 2718–2732 (2016).
59. Aoyagi, Y., Kawakami, R., Osanai, H., Hibi, T. & Nemoto, T. A rapid opticalclearing protocol using 2,2′​-thiodiethanol for microscopic observation of
fixed mouse brain. PLoS One 10, e0116280 (2015).
60. Costantini, I. et al. A versatile clearing agent for multi-modal brain imaging.
Sci. Rep. 5, 9808 (2015).

61. Schwarz, M. K. et al. Fluorescent-protein stabilization andhigh-resolution imaging of cleared, intact mouse brains. PLoS One 10,
e0124650 (2015).
62. Avants, B. B. et al. A reproducible evaluation of ANTs similarity
metric performance in brain image registration. Neuroimage 54,
2033–2044 (2011).
63. Murphy, K. et al. Evaluation of registration methods on thoracic CT: the
EMPIRE10 challenge. IEEE Trans. Med. Imaging 30, 1901–1920 (2011).
64. Frangi, A. F., Niessen, W. J., Vincken, K. L. & Viergever, M. A.
Multiscale vessel enhancement filtering. Lect. Notes Comput. Sci. 1496,
130–137 (1998).
65. Bria, A. & Iannello, G. TeraStitcher - a tool for fast automatic 3D-stitching ofteravoxel-sized microscopy images. BMC Bioinformatics 13, 316 (2012).
66. Hagberg, A.A., Schult, D.A. & Swart, P.J. in Proceedings of the 7th Python in
Science Conference (SciPy2008) (eds. Varoquaux, G., Vaught, T., Millman, J.)
11–15 (2008).
67. Bastian, M., Heymann, S. & Jacomy, M. in International AAAI Conference on
Weblogs and Social Media https://www.aaai.org/ocs/index.php/ICWSM/09/
paper/view/154 (2009).
68. Warnes, G.R. et al. gplots: various R programming tools for plotting data. R
package version 2 https://cran.r-project.org/web/packages/gplots/index.html(2009).

Nature Neuroscience | www.nature.com/natureneuroscience© 2018 Nature America Inc., part of Springer Nature. All rights reserved.

Life Sciences Reporting Summary
Nature Research wishes to improve the reproducibility of the work that we publish. This form is intended for publication with all accepted life
science papers and provides structure for consistency and transparency in reporting. Every life science submission will use this form; some list
items might not apply to an individual manuscript, but all fields must be completed for clarity.
For further information on the points included in this form, see Reporting Life Sciences Research. For further information on Nature Researchpolicies, including our data availability policy, see Authors & Referees and the Editorial Policy Checklist.
Please do not complete any field with "not applicable" or n/a. Refer to the help text for what text to use if an item is not relevant to your study.
For final submission: please carefully check your responses for accuracy; you will not be able to make changes later.

`

Experimental design

1. Sample size
Describe how sample size was determined.

The sample sizes were determined based on the literatures in the fields. No statistical wereused to predetermine sample sizes.

nature research | life sciences reporting summary

Corresponding author(s): Hiroki R. Ueda

2. Data exclusions
Describe any data exclusions.

For developmental progression analysis, we excluded the cerebellum and olfactory bulb fromanalysis due to their significant morphological change during early postnatal development.
We also excluded cortical layer 1 because the cell numbers of this outermost area of thebrain could be influenced by the residual arachnoid matter or the possible damages during
the dissection procedures.

3. Replication
Describe the measures taken to verify the reproducibilityof the experimental findings.

CUBIC-X pipeline was repeated more than ten times with independent brains and the results
were reliably reproduced. For whole-brain cell counting, three brains for each age were used.
The cell numbers and their variations in the areas used for early postnatal developmental
analysis are shown in Figure 6 and Supplementary Figs. 20, 21. All three brains show the same
trends.

4. Randomization
Describe how samples/organisms/participants wereallocated into experimental groups.

The mice used for whole-brain cell counting were randomly chosen from colonies.

5. Blinding
Describe whether the investigators were blinded togroup allocation during data collection and/or analysis.

No blinding was done in this study because knowledge of experimental conditions was
required during data collection. Analysis was conducted using automated workflow.

Note: all in vivo studies must report how sample size was determined and whether blinding and randomization were used.

November 20171

6. Statistical parameters

n/a Confirmed
The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement (animals, litters, cultures, etc.)
A description of how samples were collected, noting whether measurements were taken from distinct samples or whether the same
sample was measured repeatedly
A statement indicating how many times each experiment was replicated
The statistical test(s) used and whether they are one- or two-sided
Only common tests should be described solely by name; describe more complex techniques in the Methods section.

A description of any assumptions or corrections, such as an adjustment for multiple comparisons
Test values indicating whether an effect is present
Provide confidence intervals or give results of significance tests (e.g. P values) as exact values whenever appropriate and with effect sizes noted.

A clear description of statistics including central tendency (e.g. median, mean) and variation (e.g. standard deviation, interquartile range)
Clearly defined error bars in all relevant figure captions (with explicit mention of central tendency and variation)
See the web collection on statistics for biologists for further resources and guidance.

nature research | life sciences reporting summary

For all figures and tables that use statistical methods, confirm that the following items are present in relevant figure legends (or in the
Methods section if additional space is needed).

` Software
Policy information about availability of computer code

7. Software
Describe the software used to analyze the data in thisstudy.

Terastitcher, MATLAB, ANTs, networkX, Gephi, Imaris, R, Graphpad Prism, Microsoft excel,
LABVIEW, Image J and Fusion360 were used. We used custom scripts for the construction of
CUBIC-Atlas with anatomical annotation, and for activity mapping on CUBIC-Atlas.

For manuscripts utilizing custom algorithms or software that are central to the paper but not yet described in the published literature, software must be madeavailable to editors and reviewers upon request. We strongly encourage code deposition in a community repository (e.g. GitHub). Nature Methods guidance forproviding algorithms and software for publication provides further information on this topic.

` Materials and reagents
Policy information about availability of materials

8. Materials availability
Indicate whether there are restrictions on availability of
unique materials or if these materials are only available
for distribution by a third party.

No unique materials were used.

9. Antibodies
Describe the antibodies used and how they were validated No antibodies were used.
for use in the system under study (i.e. assay and species).

10. Eukaryotic cell lines
a. State the source of each eukaryotic cell line used.

No eukaryotic cell lines were used .

b. Describe the method of cell line authentication used.

No eukaryotic cell lines were used .

c. Report whether the cell lines were tested for
mycoplasma contamination.

No eukaryotic cell lines were used .

d. If any of the cell lines used are listed in the databaseof commonly misidentified cell lines maintained by
ICLAC, provide a scientific rationale for their use.

No commonly misidentified cell lines were used.

November 20172

Animals and human research participants

Policy information about studies involving animals; when reporting animal research, follow the ARRIVE guidelines

11. Description of research animals
Provide all relevant details on animals and/or
animal-derived materials used in the study.

6-month-old Thy1-YHP-H transgenic female mice, 10-week-old PLP-YFP (PLP-tTA::tetOChR2EYFP) transgenic male mice and 11-week-old Mcl1-YFP (Mlc1-tTA::tetO-YC) transgenic
female mice were used to render whole-brain imaging of fluorescent reporter proteins. 4month-old R26-H2B-EGFP male mice were used for observation of influence of delipidation
over nuclei. 8-week-old male mice of C57BL/6J were used to build the whole brain atlas withsingle-cell resolution. C57BL/6N mice were used to analyze whole-brain cell profiling overdifferent aged mice. 1-week-old male mice were purchased from CLEA Japan, and 3-, 8-weekand 6-month-old male mice were from Japan SLC, Inc.

Policy information about studies involving human research participants

12. Description of human research participants
Describe the covariate-relevant population
characteristics of the human research participants.

The research did not involve human research participants.

nature research | life sciences reporting summary`

November 20173

Resource
SUPPLEMENTARY INFORMATION
https://doi.org/10.1038/s41593-018-0109-1

In the format provided by the authors and unedited.

A three-dimensional single-cell-resolution
whole-brain atlas using CUBIC-X expansion
microscopy and tissue clearing
Tatsuya C. Murakami1, Tomoyuki Mano1,2, Shu Saikawa3, Shuhei A. Horiguchi4,5, Daichi Shigeta1,
Kousuke Baba6,7, Hiroshi Sekiya8, Yoshihiro Shimizu9, Kenji F. Tanaka10, Hiroshi Kiyonari11,
Masamitsu Iino8,12, Hideki Mochizuki 6, Kazuki Tainaka1,13 and Hiroki R. Ueda 1,2,4*
Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. 2International Research Center for
Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Tokyo, Japan. 3Department of Computational Biology and Medical Sciences, Graduate
School of Frontier Sciences, The University of Tokyo, Chiba, Japan. 4Laboratory for Synthetic Biology, RIKEN Quantitative Biology Center, Osaka, Japan.
5
Department of Systems Science, School of Engineering Science, Osaka University, Osaka, Japan. 6Department of Neurology, Graduate School of
Medicine, Osaka University, Osaka, Japan. 7Department of Kampo Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan. 8Departmentof Pharmacology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. 9Laboratory for Cell-Free Protein Synthesis, RIKEN Quantitative
Biology Center, Osaka, Japan. 10Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan. 11Animal Resource Development
Unit and Genetic Engineering Team, RIKEN Center for Life Science Technologies, Kobe, Japan. 12Division of Cellular and Molecular Pharmacology, Nihon
University School of Medicine, Tokyo, Japan. 13Brain Research Institute, Niigata University, Niigata, Japan. *e-mail: uedah-tky@umin.ac.jp
1

Nature Neuroscience | www.nature.com/natureneuroscience© 2018 Nature America Inc., part of Springer Nature. All rights reserved.

Supplementary Figure 1
Development of gelatin-based high-throughput screening assay.
(a) The swelling ratio (the average and SD) of chemically treated brain samples and gelatin gel between before and after chemicaltreatment shown in Fig. 1a (brain; n = 2, gelatin; n = 3). All chemicals are chosen from our previous study (Online Methods). Urea
derivatives (blue color) remarkably swelled brain samples compared to ionic chemicals (red color). (b) The plots of zeta potential
against pH for various protein samples. Each zeta potential curve was fitted to fourth-order polynomials. The isoelectric points were
determined by calculating crossing points of polynomials with the x-axis. The measurements were repeated three times at each pH
point. (c) Correlation of the 975 nm absorbance and apparent height of type B gelatin (n = 3) treated with PBS, water, or 8 otherchemicals indicated in the panel. The 975 nm absorbance of chemically treated gelatin gels showed a linear correlation with theapparent height of those. R-squared value was calculated with all data plots. All values are mean ± SD.

Supplementary Figure 2
CUBIC-X1 and CUBIC-X2 for whole-brain expansion and hyperhydrative RI matching.
(a) Swelling ratios of delipidated hemisphere brains treated by imidazole aqueous solutions for 1 day. Delipidated hemisphere brains
were immersed in various concentration of imidazole aqueous solutions (n = 6). The 20% imidazole solution, termed CUBIC-X1, wasthe minimal concentration to achieve the largest expansion volume (***p < 0.001, *p < 0.05, one-way ANOVA, Tukey’s post hoc test for
multiple comparisons). (b) Refractive indices of cocktails of imidazole and antipyrine (n = 3). We note that the standard error of several
points cannot be shown because those measured values rounded at third decimal place resulted in the identical values. (c) Swelling
ratios of hemisphere brains by RI matching media. Delipidated hemisphere brains were treated with CUBIC-X1 for 1 day, and thenimmersed in various kinds of RI media for 1 day (n = 3, *p < 0.05, Dunnett’s Modified Tukey-Kramer pairwise multiple comparison test).
All values are mean ± SD.

Supplementary Figure 3
Embedding of a CUBIC-X brain into an agarose gel.
(a) Transmission images of water, CUBIC-X2, 2% agarose, and 2% agarose CUBIC-X2. 2% agarose CUBIC-X2 showed hightransparency while 2% agarose showed white turbidity. (b) Image of a CUBIC-X brain (2% agarose CUBIC-X2). The gel embedded
expanded brain was highly transparent and solid enough to mount on the LSFM. (c) Transmission curves of water, CUBIC-X2, 2%
agarose, 2% agarose CUBIC-X2, and CUBIC-X brain (2% agarose CUBIC-X2). Light transmittance around the visible region (380-780nm) was measured (n = 3). (d) Three regions of the brain quantified in e. (e) ScaleA2 and CUBIC-X2 treated CUBIC-X1 expanded
brains were placed on USAF chart for quantitative analysis of scattering. The CUBIC-X2 brains were embedded in 2% agarose. (f)Comparison of the local contrasts of the ScaleA2 and CUBIC-X2 treated CUBIC-X1 expanded brains. The local contrasts were
quantified by referring local intensity profiles over element 2, group 2 of USAF chart (Online Methods, n = 3, ***p < 0.001, independent
two-tailed t-test). All values are mean ± SD. Scale bars indicate 5 mm (a, d) and 500 μm (e).

Supplementary Figure 4
Intensity of fluorescent proteins in various CUBIC solutions.
Quenching test of fluorescent proteins by several CUBIC reagents. The indicated recombinant fluorescent proteins were incubated ineach reagent or PBS for up to two weeks. The bars indicate the averages (n = 2).

Supplementary Figure 5
CUBIC-X brain images acquired by commercial LSFM and commercial confocal microscopy.
(a) Using a commercial LSFM, a low-magnification images of a PI-stained brain before (upper) and after (lower) CUBIC-X wereobtained with 2× and 1× zoom lens, respectively (Online Methods). Insets: magnified views of the brains. (b) Volume-rendered imagesof a PI-stained brain before (left) and after (right) CUBIC-X in the same scale. Volumes were quantified by segmenting the brains(Online Methods). (c) PI-stained brain after CUBIC-X treatment was imaged with commercial LSFM with 4× magnification. To generate
a virtual thin light sheet, four images with different light sheet focus positions were tiled together. DG: dentate gyrus, chpl: Choroid
plexus, CTX: Cerebral cortex. (d-g) Magnified views of representative regions in brain. (h-k) Magnified views of d,e,f,g, respectively. (l)Fluorescent protein imaging of a Thy1-YFP-H mouse brain with a commercial confocal microscope. A Thy1-YFP-H mouse brain was
cleared and expanded with CUBIC-X protocol, and imaged with confocal microscopy (25×, NA = 1.0). The volume rendered image (anupper panel; a volume size of 0.5 mm × 0.5 mm × 6.3 mm) and images of maximum-intensity projections (lower panels; 225 μm, 225
μm, 10 μm, 10 μm in thickness from left) are visualized. For all panels, experiments were repeated three times with independent brains.
The representative images are shown.

Supplementary Figure 6
Multi-cellular scale evaluation of distortion and cellular loss during delipidation and expansion procedures.
(a) Sequential observation of nuclei during the delipidation process. The sliced brains of R26-H2B-EGFP mice were observed prior to
the delipidation process (day 0). The slices were repeatedly observed during the delipidation process (day 2, 4, 7 and 14). Because
repeated observation with fluorescence microscopy bleaches EGFP signal, propidium iodide was added at day 3 to enhance the signalof nuclei. Two regions of the brains (periaqueductal gray and cerebral cortex layer 5/6) are shown. Scale bars indicate 25 μm in thescale of a “before-CUBIC-X” brain. (b) Multi-cellular scale comparison of “before-CUBIC-X” brain and “after-CUBIC-X” brain. Tworegions (hippocampal molecular layer and cerebral cortex layer 5/6) are shown. The corresponding images of “after-CUBIC-X” brain,
which were linearly registered to “before-CUBIC-X” brain images by affine transformation, are also shown (Online Methods). It was
confirmed that none of the nuclei were lost in the investigated area. The merged images after registration are shown on the right. Scalebars indicate 50 μm in the scale of a “before-CUBIC-X” brain. In (a) and (b), a perspective 3D rendering was used by Imaris software forvisualization. Experiments were repeated three times with independent brain slices or brains. The representative images are shown.
(c,d) Schematic procedures of the measurement of 1D-cell density (c) and intercellular expansion ratio (d). 1D-cell density wasmeasured for every pair of cells by counting the number of nuclei between the pair. Intercellular expansion ratio was calculated forevery pair of cells as L2/L1. For the analysis, images of “after-CUBIC-X” were registered to images of “before-CUBIC-X” by similarity
transformation. (e) The relation between 1D-cell density and Intercellular expansion ratio. Blue and orange dashed lines indicate overall
expansion ratios in hippocampal molecular layer and cerebral cortex layer 5/6, respectively.

Supplementary Figure 7
Global scale evaluation of distortion during expansion procedures.
(a) Comparison of merged images of “before-CUBIC-X” (magenta) and “after-CUBIC-X” (green) brain between before- and after-nonlinear transformation. After linear registration by affine transformation, three horizontal images were merged in the hippocampal region(2.5 mm × 2.5 mm × 2.5 mm) of a “before-CUBIC-X” (magenta) brain to the corresponding images of an “after-CUBIC-X” (green) brain
in the upper row. After further non-linear transformation, the corresponding images are shown in the lower row. Scale bars indicate 500
μm in the scale of a “before-CUBIC-X” brain. (b) Schematic procedures of the measurement of root-mean-square (RMS) error value in
Fig. 3b. L’ and L were calculated by measuring the Euclidean distance between two points of before non-linear registration (shown as
A’ and B’) and after non-linear registration (shown as A and B), respectively. Both of L’ and L were measured in the scale of the before
non-linear transformed image.

Supplementary Figure 8
Optical layout of the customized light sheet microscope.
Electrically tunable lenses (ETL) were put for the adjustment of the light-sheet focus position along the x-direction. Galvanometerscanners were introduced for adjusting a light-sheet position along z-direction (shown as “sheet pos. galvo”). Autofocus (i.e. adjustmentof the light-sheet z-position) by these galvanometer scanners was performed every 1 mm.

Supplementary Figure 9
Quantitative measurements of the displacements during the imaging sequence.
(a) Coronal and sagittal image merged by θ-rotation with dorsal (θ = 0°) and ventral (θ = 180°) sides of the CUBIC-X brain. Red dashedline is the merged position. Scale bars indicate 2000 μm in the scale of a “before-CUBIC-X” brain. (b) Quantitative evaluation of
stitching displacement errors in x, y, and z-axes in the xy-direction tiling. We prepared 10 × 10 tiles of 600 images for each stack with
10% overlap in both x- and y-direction. Stitching distance was calculated with the stitching software, TeraStitcher. The displacement
errors of all possible combinations of the stacks are shown. The average ± SD are shown. (c) Quantitative evaluation of theta tiledisplacement around x, y, and z-axes. We prepared two sets [ventral (θ = 0°) and dorsal view (θ = 180°)] of 10 × 10 tiles of 600 images
for each stack with 10% overlap in both x- and y-direction. These images were stitched with TeraStitcher, and the resulting stitched
images were downsampled. The downsampled ventral images were registered to the downsampled dorsal images by ANTs. Then
Euler angle around x, y, and z-axes, and translation were calculated.

Supplementary Figure 10
Whole-brain imaging of a CUBIC-X expanded brain with customized LSFM resolves multiscale cellular structures.
(a) Volume rendered image of PLP-tTA::tetO-ChR2EYFP mouse brain, imaged by customized LSFM. (b) Magnified view of a in theregion enclosed in the green box. The maximum intensity projection along V-D axis spanning 500 μm was applied to generate theimage. (c) Detailed view of a from the striatum region. Because CUBIC-X highly retains the fluorescent proteins, subcellular structuresof a single oligodendrocyte cell (indicated by a white arrow) can be clearly imaged by LSFM. (d) From the images shown in a, the fiber
orientations were color-coded by their orientations, which were determined by applying fiber enhancement filter (Online Methods) (e)Volume rendered image of Thy1-YFP-H mouse brain, imaged by customized LSFM. (f) Magnified view of e in the region enclosed in theblue box. The maximum intensity projection along V-D axis spanning 1000 μm was applied to generate the image. (g) Detailed view of
e from the cerebral cortex. (h) A magnified view of g in the region enclosed in the red box. Branching of axons as well as single spines(indicated by white arrows) can be clearly resolved by expanding the sample and using the high-resolution LSFM. In b, c, f, g and h, theimage intensity was adjusted by taking the logarithms of raw intensity for better visual contrast. For b and f, three image tiles werestitched together by blending the overlapping regions with linear weight using “Pairwise Stitching” plugin in ImageJ. Scale bars indicate
5 mm (a), 500 μm (b), 20 μm (c), 1 mm (d), 5 mm (e), 500 μm (f), 100 μm (g), and 20 μm (h) in the scale of “after-CUBIC-X” brain.

Supplementary Figure 11
A two-step convolution algorithm for the detection of cell nuclei in a whole brain.
(a) Schematic of cell detection of nuclear-stained CUBIC-X brain images. To establish two-step cell-detection algorithm, we firstdetected individual cells in a 2D xy-plane, and then unified the multiply-detected cells in 3D volume. In each step of this algorithm, we
detected local maxima after applying a different mean filter. We used a 2D mean filter kernel with a diameter D M1 in the first GPU-based
2D cell-detection step, and 3D mean filter kernel with a diameter D M2 in the second CPU-based 3D cell-unifying step, respectively. (b)Comparison of calculation speed and accuracy in cell detection between 3D-based one-step convolution algorithm and our two-stepalgorithm (***p < 0.001, *p < 0.05, independent two-tailed t-test). The average ± SD were evaluated in the independent three regions.
(c) The effect of the first 2D mean filter in cell detection. While no filters [D M1 = 0.65 μm (1 pixel)] to the raw data resulted in multiple
detections of a single cell, large diameter [D M1 = 18.85 μm (29 pixels)] led to the failure in detecting all cells within the image. The
accuracy of cell detection depends on mean filter diameter (D M1 ). Scale bars indicate 50 μm in the scale of an “after-CUBIC-X” brain.
(d) The plots of the accuracy of 2D cell-detection against D M1 in cerebral cortex layer 2/3, hippocampal CA3, and olfactory bulb granularlayer, respectively. We found D M1 of 8.45 μm was the optimal value over the three regions. (e) The effect of the second 3D mean filter
in cell detection. After 2D cell detection, a single cell was multiply detected over the several stacked images (left). Then, we attempted
to unify the multiply-detected points by detecting local maxima of these points in the z-direction. We succeeded in the convergence ofthe multiply detected points (red arrow; local maxima of after 3D mean filter) after 3D mean filtering while failing to unify these points
before 3D mean filtering blue arrow; local maxima of before 3D mean filter. (f) The plots of the accuracy of 3D cell-unification againstsurrounding peak number (NP, Online Methods) in the three different D M2 value. We note that optimal values for D M2 were highly
dependent on the density of cells. 13.0 μm for D M2 was the most accurate parameter value for the brain region with an intermediatedcell density whereas 18.2 μm and 10.4 μm for D M2 were the more accurate for those with lower and higher cell density, respectively.

Supplementary Figure 12
The automated determination of an image threshold.
Background threshold was determined by the histogram of pixel intensity in each sub-region (1/100 of the original image) in the 2D celldetection step. We determined the intensity less than 2 × mode as background intensity, and then extracted background signals (blue).
Next, we calculated mean intensity and SD from the resulting histogram, and then determined the intensity less than 2 SD above themean intensity as an image threshold (red). Scale bars indicate 200 μm in the scale of an “after-CUBIC-X” brain.

Supplementary Figure 13
The evaluation of accuracy for the automated detection of cell nuclei in a whole brain.
(a) The representative images of cell detection in sparsely populated areas. Because cell detection was performed in a threedimensional manner, the cellular nuclei shown in single planes (magenta) were repeatedly detected in different axial positions. The
lateral positions of detected cellular nuclei (green) in surrounding z-planes (1, 3, 5, or 7) were shown in each image. CTX, cerebral
cortex; TH, thalamus; cc, corpus callosum; MOBgr, main olfactory bulb granular layer; MB, midbrain. Scale bars indicate 100 μm in thescale of an “after-CUBIC-X” brain. (b) The representative images of cell detection in densely populated areas. DG, dentate gyrus; CBgr,cerebellum granular layer. For a and b, experiments were repeated more than ten times with independent brains. The representative
images are shown. (c) Image comparison of low-illumination-NA condition (measured FWHM = 11.0 μm, scanning step size= 5.0 μm)
and high-illumination-NA condition (measured FWHM = 5.2 μm, scanning step size= 2.0 μm). Horizontal and coronal sectional imagesof DG and CBgr are shown. Scale bars indicate 50 μm in the scale of “after-CUBIC-X” brain. Experiments were repeated three times
with different brain areas. The representative images are shown. (d) Comparison of the manually annotated nuclei with theautomatically detected nuclei. Two people independently annotated the nuclei for manual annotation. Scale bars indicate 50 μm in thescale of “after-CUBIC-X” brain. (e) Projected image of manually annotated nuclei and automatically detected nuclei in DG and CBgr.
The projection of 25 μm are shown. Scale bars indicate 50 μm in the scale of “after-CUBIC-X” brain. (f) Details of the accuracy of theautomated two-step cell-detection algorithm in DG and CBgr. (g) The accuracy of the cell detection in various brain areas. Red and bluelines indicate 0.9 and 0.8, respectively. For f and g, one and three volumetric images were used for each to evaluate accuracy.

Supplementary Figure 14
Non-linear registration of coronal slices in a CUBIC-X brain to Allen Brain Atlas.
(a) A schematic diagram for the registration of a CUBIC-X brain to an Allen Brian Atlas (ABA) brain based on the virtual coronal slices.
After registration of the CUBIC-X brain images by affine transformation, we generated about a hundred overlapping 3D coronal slices
with a 2.2-mm thickness at a 100-μm step both from the CUBIC-X and ABA brains. We then registered each coronal-slice image of the
CUBIC-X brain onto the coronal-slice images of the ABA brain by non-linear transformation. (b) Coronal single-plane images of a
CUBIC-X brain with affine transformation (“Affine”), a CUBIC-X brain with non-linear transformation based on coronal slices (“Coronalslice-based SyN”), an ABA brain (“ABA”), and merged coronal single-plane images of non-linearly transformed CUBIC-X (green) and
ABA brains (red). (c) Normalized cross-correlations of a CUBIC-X brain against an ABA brain over entire coronal single-plane positions
by straightforward affine transformation (“Affine”, blue), straightforward non-linear transformation (“SyN”, green), and non-linear
transformation based on coronal slices (“Coronal slice-based SyN”, red). (d) Sagittal and coronal images of a 2D and 3D single-cellresolution mouse brain atlas (CUBIC-Atlas) based on an 8-week-old C57BL/6J mouse brain. For non-linear transformation, symmetricnormalization (SyN) in ANTs software was used (Online Methods).

Supplementary Figure 15
A single-cell-resolution mouse brain atlas (CUBIC-Atlas).
(a-c) Horizontal (a), sagittal (b) and coronal (c) single-plane images of CUBIC-Atlas. Each color indicates a corresponding anatomicalarea (see also Supplementary Table 3 for color). Scale bars indicate 5 mm in the scale of “after-CUBIC-X” brain. Each slice is shown at
1.495 mm (a), 0.845 mm (b) and 0.585 mm (c) step.

Supplementary Figure 16
Layers of Cerebral cortex in a CUBIC-Atlas overlaid with a PI-stained Thy1-YFP-H mouse brain.
A PI stained Thy1-YFP-H mouse brain was expanded by CUBIC-X and imaged with the customized LSFM. The acquired volumetricimages were downsampled, then non-linearly registered to a CUBIC-Atlas. The layer structures of CUBIC-Atlas were overlaid with theimages of PI and YFP, respectively. Cerebral cortical layers of somatosensory areas, posterior parietal association areas, and visualareas of a CUBIC-Atlas were visualized with pseudo-color codes. L1, layer 1; L2/3, layer 2/3; L4, layer 4; L5, layer 5; L6a, layer 6a; L6b,
layer 6b. Experiments were repeated twice with independent brains and the representative images are shown. Scale bars indicate 2000
μm in the scale of an “after-CUBIC-X” brain.

Supplementary Figure 17
Anatomical annotation with CUBIC-Atlas.
After registration, we detected the nearest neighbor cell as a reference in CUBIC-Atlas to the cell of interest in the obtained images. To
reduce computational cost, we performed this search in the locally allocated grids (120 μm × 120 μm × 120 μm) partitioned from theatlas space. We annotated the corresponding region of the nearest neighbor cell in CUBIC-Atlas to a region of the cell of interest(Online Methods).

Supplementary Figure 18
Whole-brain cell profiling over different developmental stages.
(a) Horizontal images of CUBIC-X brains of different aged mice before and after registration to CUBIC-Atlas. “Before-registration”
images are shown on the left. Indicated numbers mean the horizontal slice positions. In the “After-registration” images, green shows thereference brain and magenta shows the registered brains. (b) Normalized cell number over different aged mice (n = 3). The average ±SD are shown.

Supplementary Figure 19
Mapping of 1-week-old and 3-week-old mice brains onto a CUBIC-Atlas.
PI-stained 1-week-old and 3-week-old mice brains were mapped onto and annotated with a CUBIC-Atlas based on an 8-week-old
mouse brain. Cerebral cortical layers of somatosensory areas, posterior parietal association areas, and visual areas with annotations
are visualized with pseudo colors. On the left, red squares indicate the approximate positions of the magnified brain regions shown in
the right. Experiments were repeated three times with independent brains. The representative images are shown. L1, layer 1; L2/3,
layer 2/3; L4, layer 4; L5, layer 5; L6a, layer 6a; L6b, layer 6b. Scale bars indicate 1000 μm in the scale of an “after-CUBIC-X” brain.

Supplementary Figure 20
Whole-brain cell profiling over different developmental stages in midbrain.
The significant increase of the total cell number in the midbrain (n = 3, ***p < 0.001, *p < 0.05, independent two-tailed t-test).
Background color represents each classification of the midbrain (Pink for motor-related areas, blue for behavioral-state-related areasand purple for sensory-related areas, respectively). The average ± SD are shown.

Supplementary Figure 21
Whole-brain cell profiling over different developmental stages in cerebral cortex.
The significant decrease in the total cell number in visual areas (a) and primary somatosensory areas (b) of the cerebral cortex areas (n= 3, *** p < 0.001, **p < 0.01, *p < 0.05, independent two-tailed t-test). The average ± SD are shown.

Supplementary Figure 22
Probabilistic mapping of pharmacologically stimulated mouse brains expressing an Arc-dVenus reporter onto a CUBIC-Atlas.
Temporal variation of distribution of hyper-activated cells in the whole brain. Original image data were obtained in Tatsuki et al. (Online
Methods), and re-analyzed in this study. Arc-dVenus expressing cells in mice with or without the chronic administration of MK-801 areshown. The color represents an anatomical area of the detected cells. All brain samples used in the experiment are shown except thebrain samples shown in Fig. 7b.