Article

CUBIC-Cloud provides an integrative computationalframework toward community-driven whole-mousebrain mapping
Graphical abstract

Authors
Tomoyuki Mano, Ken Murata,
Kazuhiro Kon, ..., Etsuo A. Susaki,
Kazushige Touhara, Hiroki R. Ueda

Correspondence
uedah-tky@umin.ac.jp

In brief
CUBIC-Cloud is a cloud-native image
analysis platform for whole-mouse-brain
analysis, offering analysis, visualization,and sharing of the 3D images through aninteractive web browser interface. Mano
et al. demonstrate the applications of
CUBIC-Cloud in (1) cell type mapping, (2)IEG profiling, and (3) connectivity
mapping by using rabies virus.

Highlightsd

CUBIC-Cloud is a cloud-native software for whole-mousebrain analysisd

CUBIC-Cloud allows scalable analysis of dozens of whole
mouse brainsd

Whole-brain, cell type, and IEG mapping by using antibody
labeling was demonstrateddConnectivity of Kiss1 neurons in ARH was investigated byusing rabies virus

Mano et al., 2021, Cell Reports Methods 1, 100038
June 21, 2021 ª 2021
https://doi.org/10.1016/j.crmeth.2021.100038

llll
OPEN ACCESS

ArticleCUBIC-Cloud provides an integrative
computational framework toward
community-driven whole-mouse-brain mapping
Tomoyuki Mano,1,2,6 Ken Murata,3 Kazuhiro Kon,4 Chika Shimizu,2 Hiroaki Ono,4,7 Shoi Shi,2,4 Rikuhiro G. Yamada,2Kazunari Miyamichi,3,8 Etsuo A. Susaki,2,4,9 Kazushige Touhara,3,5 and Hiroki R. Ueda1,2,4,10,*
1Department of Information Physics and Computing, Graduate School of Information Science and Technology, The University of Tokyo,
Bunkyo-ku, Tokyo 113-0033, Japan
2Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, Suita, Osaka 565-5241, Japan
3Department of Applied Biological Chemistry, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo
113-8657, Japan
4Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
5International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
6Present address: Computational Neuroethology Unit, Okinawa Institute of Science and Technology, Onna, Okinawa 904-0495, Japan
7Present address: Laboratory of Retinal Regeneration, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047, Japan
8Present address: Laboratory for Comparative Connectomics, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047,
Japan
9Present address: Department of Biochemistry and Systems Biomedicine, Graduate School of Medicine, Juntendo University, Bunkyo-ku,
Tokyo 113-8421, Japan
10Lead contact*Correspondence: uedah-tky@umin.ac.jp
https://doi.org/10.1016/j.crmeth.2021.100038

MOTIVATION With the advancement of tissue clearing technology and 3D imaging methods, researchers
are able to rapidly obtain whole mouse brain images at single-cell resolution. Because of the massive size ofthe resulting images, however, a high-throughput yet highly accessible image analysis method is needed toscale up 3D profiling of the brain. In addition, effective data sharing is critical to maximize the potential valueof the 3D image dataset. Here, we address these challenges by designing an end-to-end image analysis
platform embracing the latest cloud technology, named CUBIC-Cloud.

SUMMARY

Recent advancements in tissue clearing technologies have offered unparalleled opportunities for researchers to explore the whole mouse brain at cellular resolution. With the expansion of this experimental
technique, however, a scalable and easy-to-use computational tool is in demand to effectively analyze
and integrate whole-brain mapping datasets. To that end, here we present CUBIC-Cloud, a cloud-based
framework to quantify, visualize, and integrate mouse brain data. CUBIC-Cloud is a fully automated system
where users can upload their whole-brain data, run analyses, and publish the results. We demonstrate thegenerality of CUBIC-Cloud by a variety of applications. First, we investigated the brain-wide distribution offive cell types. Second, we quantified Ab plaque deposition in Alzheimer’s disease model mouse brains.
Third, we reconstructed a neuronal activity profile under LPS-induced inflammation by c-Fos immunostaining. Last, we show brain-wide connectivity mapping by pseudotyped rabies virus. Together, CUBIC-Cloud
provides an integrative platform to advance scalable and collaborative whole-brain mapping.
INTRODUCTION
Massive and collective observation of complex systems (often
referred to as omics approaches) is the driving force of modern
biology. It would also be true for studying highly evolved
mammalian brains, in which a complex system of intricately con-nected cells gives rise to intelligent behaviors. In particular,
comprehensive approaches to identifying the properties of every
single cell in situ within this complex system would be pivotal.
Recent advancements in tissue clearing technology havebrought new breakthroughs in this landscape (Dodt et al.,€rk et al., 2012; Ke et al., 2013;2007; Hama et al., 2011; Ertu

Cell Reports Methods 1, 100038, June 21, 2021 ª 2021 1
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

ll
OPEN ACCESS

Chung et al., 2013; Yang et al., 2014; Susaki et al., 2014; Tainaka
et al., 2014; Belle et al., 2017). Combined with light-sheet fluorescence microscopy (LSFM) (Power and Huisken, 2017) and genetic, viral, and immunohistochemical labeling techniques, tissue clearing now enables high-speed volumetric imaging of
mammalian (most prominently mouse) brains at cellular resolution (Susaki et al., 2014; Renier et al., 2014; Murray et al.,2015; Hama et al., 2015; Chan et al., 2017; Kubota et al., 2017;
Cai et al., 2019; Kirst et al., 2020). Built on top of these technological advancements, we recently reported the construction of
CUBIC-Atlas (Murakami et al., 2018), a 3D mouse brain atlaswith single-cell resolution, where all of the cells in the brain(amounting to approximately 0.1 billion) were digitally analyzed
and recorded.
These scientific advancements encourage us to conceive afuture where whole-brain mapping projects, which conventionally required institution-scale resources and efforts, can be carried out by individual laboratories, or even by a single researcher(Tainaka et al., 2016; Susaki and Ueda, 2016; Mano et al., 2018;
Gradinaru et al., 2018). In this regard, the current technologicalstage can be thought of as parallel to the dawn of genome
sequencing technology in the early 2000s. In genome science,
the importance of the data repository cannot be overstated.
The emergence of the database to browse and search genomes(such as the UCSC Genome Browser) (Karolchik et al., 2009)
played a critical role in integrating data collected in numerous
sites across the globe. Such distributed collaboration prompted
a rapidly growing coverage of various organisms and individuals,
pioneering the data-driven discoveries of gene functions andnew therapeutics. In neuroscience, several large-scale mousebrain datasets have been constructed, such as the Allen Mouse
Brain Atlas (Wang et al., 2020) and Brain Architecture Project(Kim et al., 2017), primarily by using serial sectioning tomographymethods. However, a common platform embracing the tissueclearing and rapid brain-scanning techniques that offers the opportunity for the community to submit and share new data has
yet to appear. On the basis of these considerations, we suggestthat it is now possible to construct a community-supported
mouse brain data repository, borrowing collaborative ideas
from the genome sciences.
By referring to the previous image analysis pipelines for tissue clearing samples (Frasconi et al., 2014; Susaki et al.,€rth et al., 2018),
2015; Renier et al., 2016; Cai et al., 2019; Fu
we postulated that the following elements should be considered in composing a framework for whole mouse brain mapping. First, a reference brain (equivalent to the template
sequence), to which all brain data are aligned, is necessary.
We think that CUBIC-Atlas would play a central role in addressing this challenge. Second, the framework should be constructed around the research community, which allows researchers to submit the data, as well as browsing andsearching the brains in the previous studies. Third, becauseof the complexity and the large size of the whole-brain datasets, the framework should be equipped with a toolkit to visualize and quantify the data to assist intuitive understanding.
Fourth, these software tools should offer superior accessibility
and usability to the end users, without requiring specialized
expertise in programming or powerful computer resources.

2 Cell Reports Methods 1, 100038, June 21, 2021

ArticleIn this paper, we present a computational framework for single-cell-resolution whole mouse brain analysis, named CUBICCloud. We built CUBIC-Cloud on top of the latest cloudcomputing technologies. After identifying single cells from a 3D
image stack, users can upload the structure image and detected
cell list to the CUBIC-Cloud server. The CUBIC-Cloud server
automatically aligns individual brains with the CUBIC-Atlas andconstructs the user’s own mouse brain database. CUBIC-Cloudalso offers graphical user interface (GUI) tools to perform various
kinds of quantification tasks. Uploaded brains can be interactively visualized by using the 3D brain viewer. The scientific results obtained thereby can be easily published in CUBIC-Cloud’s
public repository, to allow other researchers to view the data.
CUBIC-Cloud is hosted at https://cubic-cloud.com.
After describing the software architecture, we extensively
demonstrate the capability and generality of the CUBIC-Cloud
framework by analyzing over 50 whole mouse brains, covering
four important application domains: (1) investigating the distribution of targeted cell types (‘‘cell type mapping’’), (2) quantifying
the pathological state by disease markers, (3) reconstructing
the neuronal activity profile by imaging the expression of c-Fos(‘‘activity mapping’’), and (4) deciphering brain-wide connectivity
by using rabies virus (RV) tracers (‘‘circuit mapping’’). With theseapplications, we established a general framework to effectively
integrate whole-brain mapping experiments, offering new opportunities for data-driven discoveries in neuroscience.
RESULTSCUBIC-Cloud whole-brain analysis framework
To design a unified workflow for whole mouse brain analysis, weconstructed CUBIC-Cloud on top of our previously published tissue clearing protocols and brain mapping strategies (Susaki
et al., 2014; Murakami et al., 2018; Tainaka et al., 2018). The
workflow of whole-brain analysis using CUBIC-Cloud is illustrated in Figure 1. Briefly, the workflow divides into (1) tissue
clearing and image collection, (2) single-cell detection, and (3)uploading the data to CUBIC-Cloud, where brain registration,
brain-wide quantification, and visualization are performed.
In this study, mouse brains were cleared by CUBIC-L and CUBIC-R+ reagents (Tainaka et al., 2018) (step 1 in Figure 1). In
addition, nuclear counterstaining (for brain registration) and,optionally, immunostaining were applied by following the CUBIC-HV protocol (Susaki et al., 2020) (STAR Methods). In mostexperiments, cleared brains were imaged using macro-zoom
LSFM with 6.5 mm isotropic XYZ voxel size (step 2 in Figure 1
and STAR Methods). The transparency of the cleared brain
was quantitatively evaluated by imaging fluorescent beads
embedded in the tissue, validating homogeneous image quality
throughout all brain regions (Figure S2 and STAR Methods).
After we obtained the 3D image stack, we detected labeledcells by using ilastik software (Sommer et al., 2011; Berg et al.,2019) (step 3 in Figure 1). ilastik uses supervised machinelearning (random forest algorithm) to classify pixels into multiple
classes (such as cells and backgrounds), which performed
robustly well even when cells from different brain regions presented diverse brightness and morphology. We created a Python script, which wrapped ilastik to automate the workflow

ll
OPEN ACCESS

ArticleClearing & staining (step 1)

CUBIC-Cloud
Automatic registration (step 4)
Non-linear

Individual brains warping (SyN)

Quantification (”notebook”) (Step 6a)

CUBIC-Atlas

4mm

CUBIC-L & CUBIC-R+,
staining by CUBIC-HV

Whole-brain LSFM imaging (step 2)
1x macro-zoom

Whole-brain database (step 5)
Cell A

6.5
isotropic
resolution

Cell C

Cell B

3D brain viewer (”studio”) (Step 6b)

Cell detection by ilastik (step 3)

Search...

Raster image

Point cloud
Cell-type

Structure image(nuclear staining)

Share & Publish(step 7)

Point cloud

UploadActivity(IEGs)
Circuit(AAV/RV)

Figure 1. CUBIC-Cloud: A cloud-based computational framework for whole mouse brain analysis
Overview of the whole-brain analysis pipeline by CUBIC-Cloud. In this study, mouse brains were cleared by CUBIC-L and CUBIC-R+ reagents and 3D stained by
the CUBIC-HV protocol. Cleared brains were imaged by using macro-zoom LSFM. From the obtained image stacks, single cells were isolated by using ilastik
software, converting the raw raster image into an ensemble of discrete cells (i.e., point cloud). Users then upload the point-cloud cells and structure image to
CUBIC-Cloud. In the cloud, brain registration is automatically performed to align individual brains with the reference brain. Thereby, the user’s own brain databaseis constructed. Then, the user can perform various kinds of brain-wide quantification using ‘‘notebook.’’ CUBIC-Cloud also offers an interactive 3D whole-brain
viewer (‘‘studio’’). Last, CUBIC-Cloud lets users share and publish their point-cloud whole-brain data, as well as notebooks and studios, to allow broad access toresearchers. Abbreviation are as follows: AAV/RV, adeno-associated virus/rabies virus; IEGs, immediate-early genes.

and add some custom analysis routines (STAR Methods). The
accuracy of cell counting is described in the corresponding sections. By this cell counting procedure, raw raster images wereconverted into a discrete ensemble of cellular points, or a point
cloud, where positions, volumes, and fluorescence intensitiesof all labeled cells in the brain were recorded. Of note, users
can opt to use their own cell detection routine, as long as theoutput follows the format required by CUBIC-Cloud.
After cell detection, the rest of the analyses were carried out
by CUBIC-Cloud (Video S1). Users can go to the CUBIC-Cloudwebsite (https://cubic-cloud.com) and upload their cellular
point cloud and corresponding structure image (nuclear staining channel) to the server. CUBIC-Cloud is deployed on
Amazon Web Service (AWS by) using the so-called serverlessarchitecture. Serverless architecture allows the cloud todynamically scale its capacity based on the computationalload, enabling the cloud to handle a practically unlimited number of tasks in parallel while minimizing the idling time. The
detailed implementation is described in the STAR Methods(also see Figure S1). Once the brain data are uploaded to the
CUBIC-Cloud server, they are automatically sent to the prepro-

cessing task queue, which is powered by ECS and EC2 on
AWS. The preprocessing task first performs brain registration
to align the submitted brain with the CUBIC-Atlas (step 4 in Figure 1). CUBIC-Cloud uses the symmetric diffeomorphic image
registration (SyN) algorithm implemented in the ANTs library(Avants et al., 2008). Using the nuclear staining channel, SyN
iteratively optimizes the warp field to maximize the normalized
cross-correlation, which measures the similarity between two
images (Figure S3I and S3J and STAR Methods). After registration, cell coordinates are transformed to the CUBIC-Atlas
space, and all cells are given anatomical region IDs (following
the Allen Brain Atlas CCF v.3) (Wang et al., 2020).
By repeating the above procedures, users can construct theirown brain database in CUBIC-Cloud. As the database grows,
users can search brains using tags attached to them, such ascell labels and project names, along with other metadata. To
perform quantitative analysis of the brains in the database, CUBIC-Cloud offers a ‘‘notebook,’’ a feature that allows users tocreate various kinds of graphs with a GUI (step 6a in Figure 1).
To give some concrete examples, many of the figure items presented in this paper were created by CUBIC-Cloud’s notebook.

Cell Reports Methods 1, 100038, June 21, 2021 3

ll
OPEN ACCESS

A

Merge

Article
PV

B

C

SST

D

ChAT

TH

E

Iba1

F

Fluorescence intensity (AU)

Fluorescence intensity (AU)

Fluorescence intensity (AU)

Fluorescence intensity (AU)

Fluorescence intensity (AU)

0

0

0

0

0

8000

12000

15000

20000

15000
Normalized cell count (%)

G

0.05 0.1

1.0

10

H

ChAT
(n=4)

J

TH
(n=4)

K

Merge

V

L
P

A

L
V

Overlap
0.5 mm

LDT

L
Volume overlap (%)

I

LC

Figure 2. Whole-brain analysis of PV-, SST-, ChAT-, and TH-expressing cells
Whole-brain distribution of PV-, SST-, ChAT-, TH-, and Iba1-expressing cells was investigated by applying 3D immunostaining and by using the CUBIC-Cloud
analysis framework.
(A–F) Whole-brain views of labeled cells. Each point (i.e., single cell) was assigned a pseudo-color on the basis of its fluorescence intensity. (A) Merge, (B) PV, (C)SST, (D) ChAT, (E) TH, (F) Iba1.
(G) Relative population heatmap of all brain regions outside the isocortex. The number of each cell type was normalized by the total number of cells in each region,
derived from CUBIC-Atlas (n = 4 for PV, SST, ChAT, and TH; n = 7 for Iba1).
(H) Density of PV- and SST-expressing cells in the isocortex. Data are shown as the mean ± SD (n = 4).

(legend continued on next page)

4 Cell Reports Methods 1, 100038, June 21, 2021

ll
ArticleCUBIC-Cloud also offers an interactive 3D brain viewer, afeature called ‘‘studio’’ (step 6b in Figure 1). Here, brain data
are visualized in point-cloud format, where the whole brain isrendered as an ensemble of discrete cellular points, each carrying biological attributes such as protein expression levels.
Point-cloud format is more efficient for delivering data to theremote clients than sending raw raster data, while carrying theessential biological information. The viewer runs on standard
web browsers, and the point clouds are adaptively queried
from the server upon the viewer’s camera movement, enabling
one to interact with millions of cells in real time.
The last essential component of CUBIC-Cloud is sharing andpublishing. Users can share their whole-brain data with specificusers, such as research collaborators, and grant access to thedata. Users can opt to publish their data in the CUBIC-Cloud’s
public repository. Once published, any users can view the brain.
The share and publish capability is also supported for the notebooks and studios. Therefore, users can transparently show theiranalysis results to the research community. To demonstrate thisconcept, all of the brain data investigated in this study are deposited on the CUBIC-Cloud public repository, as well as the notebooks and studios that performed the analysis. Together, CUBIC-Cloud offers a cloud-native and integrative software
solution for whole mouse brain mapping.
To maintain the cloud service, CUBIC-Cloud offers both freeand paid subscription plans. With the free subscription, users
can view the published data and try the analysis of several
brains. By publishing the brain data in the public repository,
users are offered a bonus token, which can be used to analyze
other brains. To analyze more brains than the free plan limit,
users need to sign up for the paid subscription. The subscription
policies are subject to changes in the future and can be checkedat the website (https://cubic-cloud.com/subscription_plans).
Whole-brain analysis of PV-, SST-, ChAT-, and THexpressing cells
In the following, we use CUBIC-Cloud in several neuroscience
applications to show the generality of the proposed framework.
As the first application, we attempted to quantify the whole-brain
distribution of distinct cellular subtypes that express thefollowing markers: parvalbumin (PV), somatostatin (SST), choline
acetyltransferase (ChAT), tyrosine hydroxylase (TH), and ionized
calcium-binding adapter molecule 1 (Iba1). Whole-brain-scale
analyses of PV-, SST-, and ChAT-expressing neurons were previously reported using a knock-in transgenic mouse line with Cre
recombinase and fluorescent proteins (Kim et al., 2017; Zhang
et al., 2017; Li et al., 2018). However, no such whole-brain-scale
analyses have been done so far using immunostaining and tissue
clearing. Immunostaining offers several orthogonal advantages
over genetic approaches, such as the capability to assess theabsolute expression amount, and thus is of significant value.
Therefore, we used the CUBIC-HV protocol (Susaki et al.,OPEN ACCESS

2020) to label the whole adult brain tissue with PV, SST, ChAT,
TH, and Iba1 antibodies (STAR Methods).
The obtained images contained strong and soma-localized
signals as well as moderately bright fibrous structures. Here,
we attempted to quantify the number of immunoreactive cellbodies, and thus, the ilastik classifier was trained to isolate cellsoma and reject fiber-like signals. The accuracy of the cell detection was extensively evaluated (Figures S3C–S3G). Our cell
detection demonstrated 75%–95% precision and 60%–85%
sensitivity for the regions evaluated, which we concluded was
accurate enough to perform quantitative analysis.
Whole-brain views of the investigated cell types are shown in
Figures 2A–2F (rendered by CUBIC-Cloud’s 3D viewer). Here,cells are pseudo-colored, reflecting the fluorescence intensityof the immunostaining. In our analysis, the total numbers of detected cells were ð6:1 ± 0:7Þ3105 (PV, n = 4), ð6:7 ± 0:3Þ3105(SST, n = 4), ð6:5 ± 0:2Þ3104 (ChAT, n = 4), ð6:9 ± 1:2Þ3104(TH, n = 4), and ð2:72 ± 0:14Þ3106 (Iba1, n = 4) (Tables S1 andS2). In this paper, mean ± SD is used unless otherwise specified.
The relative ratio of each cell type in all brain regions except isocortex is shown in Figure 2G. Here, the ratio was calculated by
dividing the detected cell count by the total number of cells reported in CUBIC-Atlas (Murakami et al., 2018). We will discuss
the brain-wide distribution of these cell types in more detail in
the STAR Methods.
Within the isocortex, PV neurons were most densely populated in somatosensory and auditory areas, whereas there
were relatively sparse populations in medial frontal and lateral
association areas (Figure 2H). Compared with PV, SST neurons
were more homogeneously observed in the isocortex, consistent
with the Cre-based study by Kim et al. (2017). Laminar density
analysis revealed that there were almost no PV cells in layer 1,
and that cell density reached its peak in layer 4 or 5 for both
PV and SST (Figures S5A–S5D), successfully recapitulating theprevious observations (Gonchar et al., 2007; Xu et al., 2010).
There were sparse populations of ChAT neurons, whose ChAT
expression levels were very low (Figures S5E and S5F). TheseChAT neurons were most dense in layer 2/3 or 4, corroborating
the previous observation (Gonchar et al., 2007). For TH neurons,no significant population above false-positive detection level
was found in the isocortex. Iba1-expressing cells were ubiquitously observed in the isocortex, with similar density acrosslayers.
In terms of the expression levels per cell, again, PV+ neurons
showed large variance across cortical areas. As shown in Figures S5I–S5N, some of the regions, like the primary somatosensory area, mouth domain (SSp-m), and primary visual area
(VISp), had a long tail in the distribution of the expression level,
meaning that there was a population of neurons expressing ahigh amount of PV. On the other hand, regions like the ILA andectorhinal area (ECT) had a very short-tailed profile, meaningthat most of the cells had a weak PV expression. In stark(I) From the cluster of ChAT-expressing cells, the boundary surface including LDT was extracted (n = 4).
(J) From the cluster of TH-expressing cells, the boundary surface including LC was extracted (n = 4).
(K) Merge of the two boundary surfaces ( color coding is as follows: yellow, ChAT; magenta, TH; cyan, overlapping region).
(L) The volume overlaps of the boundary surfaces. Data are shown as mean ± SD (n = 4). Brain region acronyms follow the ontology defined by the Allen Brain
Atlas.

Cell Reports Methods 1, 100038, June 21, 2021 5

ll
OPEN ACCESS

ArticleTable 1. Brain region acronyms used in this paper

Table 1.

Acronym

Full name

Acronym

Full nameAAAanterior amygdalar area

PVT

paraventricular nucleus of the thalamus

ACB

nucleus accumbens

RAmbmidbrain raphe nuclei

ADP

anterodorsal preoptic nucleus

RL

rostral linear nucleus raphe

AHN

anterior hypothalamic nucleus

RR

midbrain reticular nucleus, retrorubral area

ARH

arcuate hypothalamic nucleusRTreticular nucleus of the thalamus

AVP

anteroventral preoptic nucleus

SFO

subfornical organ

BST

bed nuclei of the stria terminalis

SNcsubstantia nigra, compact part

CEA

central amygdalar nucleus

SNrsubstantia nigra, reticular part

CLI

central linear nucleus rapheSOsupraoptic nucleus

DMH

dorsomedial nucleus of the hypothalamus

SOC

superior olivary complex

DMX

dorsal motor nucleus of the vagus nerve

SUB

subiculumDTN

dorsal tegmental nucleus

TRS

triangular nucleus of septumFL

flocculus

VMH

ventromedial hypothalamic nucleus

HATA

hippocampo-amygdalar transition area

VTA

ventral tegmental areaIAintercalated amygdalar nucleus

ZI

zona incerta

IC

inferior colliculus

ILA

infralimbic area

IO

inferior olivary complex

LC

locus coeruleus

LDTlaterodorsal tegmental nucleus

LH

lateral habenula

LRN

lateral reticular nucleusLSlateral septal nucleus

LSrlateral septal nucleus, rostral (rostroventral)
part

LPO

lateral preoptic area

MEA

medial amygdalar nucleus

MEPO

median preoptic nucleusMH

medial habenula

MRN

midbrain reticular nucleus

MPN

medial preoptic nucleus

MPO

medial preoptic area

NLL

nucleus of the lateral lemniscus

NOD

nodulus (X)NTB

nucleus of the trapezoid body

NTS

nucleus of the solitary tract

PAG

periaqueductal gray

PB

parabrachial nucleus

PH

posterior hypothalamic nucleus

PP

peripeduncular nucleus

PVH

paraventricular hypothalamic nucleus

PVa

periventricular hypothalamic nucleus,
anterior part

PVi

periventricular hypothalamic nucleus,intermediate partPVp

periventricular hypothalamic nucleus,
posterior part

PVpo

periventricular hypothalamic nucleus,
preoptic part

6 Cell Reports Methods 1, 100038, June 21, 2021

Continued

contrast, the expression distribution of SST+ neurons was quite
homogeneous across cortical areas (Figures S5O–S5T). In thecortex, the PV expression level is known to be correlated withplasticity during the learning process (Donato et al., 2013).
Thus, the inhomogeneity of the PV expression might reflect differences in the computation logic across different cortical areas.
From our cell-type mapping data, certain cell types were clustered in specific areas in the brain, offering clues to delineate theregion boundaries. For instance, we observed that ChAT neurons were densely populated in and around the LDT (for the listof region acronyms, see Table 1). From this cluster of ChAT neurons, we defined the polygonal boundary enclosing these cells
by using the alpha-shape algorithm (Figure 2I and STAR
Methods). We performed the same analysis targeting TH neurons in and around the LC (Figure 2J). The polygonal boundaries
defined by ChAT+ and TH+ neuron clusters were neighboringeach other with a small overlap (Figure 2K). We then evaluatedthe overlap between polygonal boundaries using the Dice metric(STAR Methods). The result showed about 70% overlap between
ChAT-ChAT and TH-TH pairs (Figure 2L). Furthermore, the overlap of the ChAT-TH pair was 2.84% ± 0.9%. These results support the accuracy of our registration method with an orthogonalevaluation metric, which is largely independent of information
present in the nuclear staining channel. This result thus offers
the possibility of accurately delineating the brain regions by collecting more cell-type mapping data in CUBIC-Cloud.
Last, to assess whether our analysis pipeline can accurately
quantify the expression amount of the proteins, we evaluatedthe expression of Iba1 under an artificially induced inflammation
state, because Iba1 expression is known to be correlated withmicroglial activation (Ito et al., 1998). In our experiment, 1 mg/
kg of lipopolysaccharide (LPS), a purified extract of the outermembrane of Gram-negative bacteria, was administered tomice via intraperitoneal (i.p.) injection. The brains were sampled
24 h after injection (STAR Methods). In the isocortex, no subregions displayed significant change in the cell density (p>0:1,

llArticleWelch’s t test, Figure S5I), whereas the mean expression amountper cell was increased significantly in all subregions (p< 0:05,
Welch’s t test, Figure S5J). The observation that cortical microglia does not proliferate, but increases its Iba1 expression
upon LPS administration, was reported in Chen et al. (2012),
and our results confirmed that it is globally true in all corticalareas. Outside the isocortex, most regions showed increases
in Iba1 expression (Figure S5K) and had varying degrees ofchange. For instance, Iba1 expression amount barely changedin the cerebellum. On the other hand, we confirmed that the increase in Iba1 expression was markedly high in the SFO and
IO, which are part of the circumventricular organs having a highly
permeable blood-brain barrier (Furube et al., 2018).
Whole-brain analysis of Ab plaque accumulation in an
AD model mouse brain
We next applied the CUBIC-Cloud analysis framework to quantitatively understand the pathological state of the Alzheimer’s
disease (AD) model mouse. To demonstrate this, the whole brain
from an AppNL-G-F/NL-G-F AD model mouse (Saito et al., 2014) (9to 10 months of age) was cleared and stained with anti-Ab antibody (n = 4, STAR Methods). In LSFM images, Ab plaques wereobserved as dim blobs often accompanying a bright spot at thecore. On the other hand, no plaque staining pattern wasobserved in the control wild-type mouse brain (9 to 10 monthsof age, n = 3, data not shown).
We first quantified the density (number of individual plaques
per volume) and the volume ratio (computed as (total plaque volume in the region)/(region volume)). In both metrics, Ab plaqueamounts were highest in the cerebral cortex and cerebral nuclei,
and relatively lower amounts of plaques were observed in thebrain stem and cerebellum (Figures 3A and 3B). The effective
radius of the plaque (computed as r = f3V=ð4pÞg1=3 , where V
is the plaque volume) tended to be larger in the isocortex andhippocampus and smaller in the cerebellum (Figure 3C). Within
the isocortex, a relatively stronger accumulation of Ab wasobserved in visual and auditory areas, whereas plaques wererelatively sparse in the medial frontal areas (Figures 3D and
3E). Layer-wise abundance of Ab plaques showed a concave
profile, with its peak in layer 4 (Figure 3F). The whole-brain
cartoon heatmap showing the Ab volume ratio is shown in Figure 3G. In the brain stem, the plaque volume ratio was typically
0.5%–1.0%. Some brain-stem regions, however, showednotably larger or smaller amounts of Ab accumulation. For
example, the SNr and VMH had relatively higher amounts of Ab
compared with neighboring regions (Figures 3H and 3I). The
ARH, right next to the VMH, had almost no Ab plaques. We
also observed that the regions around the ventricles showed
relatively lower amounts of plaques, including TRS, DTN, MH,
LH, and PVT (Figures 3G and 3J–3L).
Whole-brain analysis of c-Fos expression underlying
pharmacological sleep induction by LPS
The next important application domain of CUBIC-Cloud is toreconstruct the neuronal activity profile by imaging the protein
expression of immediate-early genes (IEGs) such as c-Fos.
Such automated analysis would allow comprehensive identification of cellular clusters that underlie an animal’s behavioral

OPEN ACCESS

phenotype (Susaki et al., 2014; Renier et al., 2016; Tatsuki
et al., 2016; Salinas et al., 2018). Reciprocally, one could define
an animal’s phenotype in a bottom-up manner based on the activity pattern of neuron ensembles. Here, we focused on the effect of LPS. Phenotypically, LPS induces acute sleep in mice,
and we thought that IEG-based activity reconstruction would
be suitable to track the relatively slow dynamics of wake-sleep
cycles.
Administration of 150 mg/kg LPS to mice via i.p. injection atzeitgeber time = 14 caused an acute immune response accompanied by prolonged sleep duration, as confirmed by Snappy
Sleep Stager (SSS) measurement (Figures S7A–S7C and STAR
Methods). In a replicate experiment, brains were sampled 2–
3 h after LPS injection, and the whole-brain c-Fos expression
profile was analyzed (STAR Methods). The accuracy of the cFos cell detection is shown in Figure S3H. Figure 4A shows thewhole-brain 3D rendering of the detected c-Fos-expressing cells(also see Table S4). We comprehensively searched for the activated or repressed brain regions by both region-wise andvoxel-wise statistical analysis (Figure S7D). Our analysis revealed that c-Fos expression in some of the isocortical areas
was reduced (Figures 4B and 4C), which included motor and somatosensory areas, presumably reflecting the mouse’s restingstate. We also found that some distinct brain nuclei were activated by LPS. Among those, the most notable regions included
the BST, PVH, PVT, CEA, PB, NTS, and DMX (Figure 4D).
In our result, the specific part of the BST (the oval region;
ovBST) was strongly activated by LPS (Figure 4D). Indeed, according to a recent study, the ovBST is responsible for inflammation-induced anorexia, and it receives inputs from the CEA and
PB (Wang et al., 2019). Our result was able to successfully identify elevated c-Fos expression in these spatially separated yetfunctionally related neurons.
We also observed heterogeneous c-Fos activation in PVT. In
terms of the number of c-Fos+ cells, the increase in number
was more pronounced in the posterior PVT (pPVT) than the anterior PVT (aPVT) (Figures 4E–4G). In terms of the expression level,
pPVT and aPVT showed similar levels of increase (Figures 4Hand 4I). Recently, Gao et al. (Gao et al., 2020) identified two classes of distinct neurons in PVT. Type I neurons, densely located in
the pPVT, respond to aversive stimuli. On the other hand, type II
neurons, dominantly located in the aPVT, become silent upon
aversive stimuli. It is also reported that the type II neurons areactive during sleep. In the pPVT, our observation aligns with
the insight of Gao, where the activated population was likely
type I neurons. In the aPVT, our result might reflect the mixed
response of the type II neurons, where aversive inflammatory
stimuli and induced sleep were both present. It should also be
noted that type I neurons in the pPVT project to the CEA, ILA,
and ACB. We indeed observed that the ILA and ACB wereweakly activated (Figure S7D).
Whole-brain analysis of input cells projecting to
ARHKiss1+ neurons
As the third application domain of CUBIC-Cloud, we show brainwide connectivity analysis by using pseudotyped RV. To demonstrate this application, we focused on a population of neurons
that secrete kisspeptin (a neuropeptide encoded by the Kiss1

Cell Reports Methods 1, 100038, June 21, 2021 7

ll
OPEN ACCESS

Article

A

B

C

F

D

0

1

2

3

4

5

6

E

G

H

I

J

LSr

CA3
SNr

K

LSc

L

PAG

ICe
MH

AQ

LH

LSv
TU

ICd

TRS

SF
VMH

CA1

ICc

LDT

MEPO

DTN
PVT

ARH

MD

PCG

Figure 3. Whole-brain analysis of Ab plaque accumulation in an AD model mouse brain
Using the AppNL-G-F/NL-G-F AD model mouse brain (9 to 10 months of age), brain-wide accumulation of Ab plaques was quantified by applying whole-mount
3D immunostaining and by using the CUBIC-Cloud analysis framework.
(A) Density of Ab plaques (number of plaques/mm3) in major brain divisions (n = 4). Data are shown as the mean ± SD (n = 4).
(B) Volume ratio of Ab plaques in major brain divisions (n = 4), computed as ðtotal plaque volume in the regionÞ=ðregion volumeÞ. Data are shown as the mean ±SD (n = 4).
(C) Distribution of effective radius of Ab plaques in the isocortex, hippocampus (HPF), striatum (STR), midbrain (MB), and cerebellum (CB) (n = 4).
(D and E) The volume ratio of Ab plaques in the isocortex (n = 4). In (E), data are shown as the mean ± SD.
(F) Layer-wise average of (D). Data are shown as the mean ± SD (n = 4).
(G) Cartoon heatmaps showing the Ab plaque volume ratio in each brain region (n = 4).
(H–L) Raw 6E10 immunostaining images around SNr (H), VMH (I), TRS and MEPO (J), LDT and DTN (K), and MH, LH, and PVT (L).

8 Cell Reports Methods 1, 100038, June 21, 2021

ll
OPEN ACCESS

ArticleA

Saline

B

c-Fos

LPS

RSP
VIS
PTLp

Saline (CT = 16)

SS
MO

AUD

TEa

FRP

D
GU

R

P

PERI & ECT

VISC

ACA

LPS (CT = 16)

0

10000
Fluorescent intensity (AU)C0|log10(p)|decr.

P-value by c-Fos+ cell density

2

4

0

incr.

2

4

12000
Fluorescent intensity (AU)D
B -5.4

4
2

5

6

decr.

2

4

2

0

-1

-5.4
-7.4

1 mm
3

Saline #1

1

4

CEAc

PVH

BST

6

5

NTS

PVT

PB

LPS #1c-Fos intensity (AU)
(MIP of 200
thickness)

DMX

p = 0.085

**

*

**

**

Saline / LPS

p = 0.064

12000

2

2500

p value map

1

0.1
-0.8
-1.7

Bregma3

1

B -7.4incr.

B -1.7|log10(p)|B -0.8

4

B 0.1

**

A

ter

An

ior

s
Po

ior
ter

I
c-Fos expression level per cell (AU)H

p = 0.09

c-Fos expression level per cell (AU)D

G
Number of c-Fos+ cells

F
Number of c-Fos+ cells

E PVT (Saline / LPS)

*

te
An

r
rio

**

r
rio

ste

Po

Figure 4. Whole-brain analysis of c-Fos expression level changes by LPS administration
LPS acutely induces sleep in mice. Brain-wide neural activity change induced by LPS was quantified by applying whole-mount 3D immunostaining of c-Fos andby using the CUBIC-Cloud analysis framework.
(A) Whole-brain views of all c-Fos+ cells, showing saline- (top) and LPS- (bottom) administered brains. Each point (i.e., single cell) was assigned a pseudo-color
based on its fluorescence intensity.

(legend continued on next page)

Cell Reports Methods 1, 100038, June 21, 2021 9

ll
OPEN ACCESSgene) located in the ARH, hereafter termed as ARHKiss1+. Those
neurons were shown to play an important role in reproduction
behavior in mammals by regulating pulsatile release of gonadotrophin-releasing hormone at around 0.3 to 1.0 pulses per hour
(Herbison, 2018). Intriguingly, the pulse frequency changes
through the estrus cycle in females, but not in males. As such,
we comprehensively investigated the neural inputs to ARHKiss1+
neurons to search the mechanism of pulse generation/modulation on the basis of neural circuitry.
To achieve cell-type-specific targeting of virus infection, weused the Cre/loxP system and RV transsynaptic tracing combined with Cre-dependent AAV vectors (Miyamichi et al., 2013)(Figure 5A and STAR Methods). After virus injection, brains(both male and female) were cleared by CUBIC reagents andanalyzed by CUBIC-Cloud pipeline (STAR Methods). We firstchecked the distribution of starter cells (GFP+ and mCherry+)to ensure that the injection was successful and the starter cells
were well confined within the ARH (Figure 5B). In the present
study, the criterion for selecting successful samples was definedas more than 45% of starter cells localized in the ARH or PVp.
(Note that the area annotated as PVp in the Allen Brain Atlas belongs to a part of the ARH in the Paxinos atlas; Paxinos andFranklin, 2012.) With this criterion, of 20 injections, n = 3 andn = 4 brains were assessed as successful for male and female,respectively (Figure 5B).
Figure 5C shows the whole-brain overview of all input (GFP+
and mCherry) cells. The accuracy of the GFP+ cell detection
is shown in Figure S3B. Our quantitative analysis identified
ð3:1 ± 0:5Þ3104 input cells in the male brain (n = 3), the majorityof which (>85%) were located within the hypothalamus (Figure 5D and Table S5). As is shown in Figure 5E, ARHKiss1+ neurons receive inputs from dozens of discrete structures
throughout the forebrain and brain stem, including the striatum(LS), pallidum (BST), thalamus (PVT), hypothalamus (MPO,
MPN, AHN, PVH, DMH, VMH, and PH), hippocampal formation(HATA and SUB), midbrain (MRN and PAG), and pons (PB).
Remarkably, extremely sparse populations, only a few dozens
of cells per region, were reproducibly identified (Figures 5F and
5G). In terms of cell density, those populations were often equivalent to less than 10 cells/mm3, which could be easily overlookedwith slice-based approaches. These sparse populations were
not reported in the past literature (Yeo et al., 2019).
We next performed statistical analysis comparing the number of
input cells between male and female brains (STAR Methods).

ArticleOverall, binary connectivity differences (i.e., zero in one sex andsome finite number in the other sex) were not observed (Figure S8A). Weak differences were suggested in LS, MPO, MPN,
and AVP (Figures S8B–S8H and STAR Methods), which are neighbors to one another. The difference was most pronounced in theLSr, which is known to inhibit the lordosis behavior during matinginteractions (Tsukahara et al., 2014). The sexually dimorphic circuit from LSr to PAG is reported, where female brains containmore neurons in the LSr that project to the PAG (Tsukahara andYamanouchi, 2002). Our results suggest that LSr sends sexuallydimorphic projections to ARHKiss1+. The identities of these populations can be fully characterized in future studies.
DISCUSSION
In this study, we presented an integrated computational framework for single-cell-resolution whole-brain analysis, named CUBIC-Cloud. Inspired by the scientific data platforms developed ingenomics, we postulated that the framework should provide (1) areference brain atlas and automatic mapping to the reference, (2)open opportunities for the research community to contributenew data, (3) a toolkit to visualize and quantify data, and (4)easy and universal accessibility. As we have shown in this study,
CUBIC-Cloud addressed these requirements by designing a
new software stack embracing the latest cloud technologies,
widely available for researchers in neuroscience (Figure 1). Users
can access CUBIC-Cloud at https://cubic-cloud.com.
After describing the software infrastructures offered by CUBIC-Cloud, we extensively evaluated and validated the accuracy and reproducibility of the proposed framework by various
applications. First, we quantified the exact number of various
cell types, including PV-, SST-, ChAT-, TH-, and Iba1-expressing cells (Figure 2). We were surprised by the small variation between individual animals in terms of labeled cell numbers(quantified SD usually less than 10%), which not only highlighted the accuracy of our analysis but also implied the intricate regulation of cell proliferation in the brain. We further
showed that CUBIC-Cloud can also be used to quantitatively
understand the pathological state of the AD model mouse (Figure 3). Together, these demonstrations encourage the futureapplication of CUBIC-Cloud to construct datasets of diseasemodels and drug effects, where decreases or increases inparticular markers in specific brain regions might correlatewith disease progression.

(B) Magnified 3D views of (A), where the left isocortex is selectively displayed. Orientation arrows stand for R (right), D (dorsal), and P (posterior).
(C) A p value heatmap showing the isocortex regions whose c-Fos+ cell density was significantly affected by LPS (n = 4 each). The p value was computed bycomparing the c-Fos+ cell count. The color lookup table is log scaled (base 10), where red represents the regions that were activated (i.e., more c-Fos+ cells) by
LPS, and blue represents the repressed regions. Regions with no statistical significance (p > 0.05) were assigned a gray color.
(D) Distinct brain regions activated by LPS (n = 4 each). The top row shows the voxel-wise p value map. Color lookup table follows that of (C). The second and thirdrows are the raw c-Fos images of saline- and LPS-administered groups, respectively. The fourth row shows the number of c-Fos+ cells of the identified regions.
Data are shown and mean ± SD (n = 4).
(E) Plot of c-Fos+ cells in PVT. Cells are pseudo-colored with their intensity values. Pink (blue) dots are from LPS (saline)-administered brains, respectively.
(F) The number of c-Fos+ cells in the PVT in 10 divisions along the anterior-posterior (AP) axis. Data are shown and mean ± SD (n = 4).
(G) The number of c-Fos+ cells in the anterior and posterior half of the PVT. The boundary between anterior and posterior regions was set at the center of the PVT
along the AP axis. Data are shown and mean ± SD (n = 4).
(H) The c-Fos expression levels per cell in the PVT, 10 divisions along the AP axis. Data are shown and mean ± SD (n = 4).
(I) The c-Fos expression levels per cell in the anterior and posterior halves of the PVT. Data are shown and mean ± SD (n = 4). *p<0:05, **p<0:01, Welch’s t test.
Brain region acronyms follow the ontology defined by the Allen Brain Atlas.

10 Cell Reports Methods 1, 100038, June 21, 2021

ll
OPEN ACCESS

ArticleCAG

mCherryCAG

oG

mCherry0

10 20 30 40 50

TOTAL

170
196
249
493
334
679
407

Male

AAV

Kiss1-Cre

B

TVA

A

RV

Starter

RV

ARH (left hemisphere)

C

E

3

)

101

102

103

Male

Male

Male

D
4

Male

CTXsp
STR
TH
HY
MB

Male

MY
CB

G

B 1.9

B 0.2

B -2.8B -3.3B -5.2

RedDot2

F

1 mm
4000

MGm

Bregma
1

-1

BLAp
TTd

AON

TRS

BMAp

MRN

BST

1.9 0.2 -2.8 -3.3 -5.2
200

COApl

COApm

Figure 5. Whole-brain analysis of input cell populations projecting to ARHKiss1+ neurons(A) Virus injection scheme. AAV carrying mCherry, TVA receptor, and optimized glycoprotein (oG) was injected into the ARH of Kiss1-Cre transgenic mouse,
followed by injection of modified rabies virus carrying GFP. Cells expressing both mCherry and GFP are the starter cells.
(B) Quantification of starter cell localization. The ratio was computed by dividing the cell count in each region by the total number of starter cells. The total numberof starter cells in each sample is shown on the right end of the heatmap.
(C) Whole-brain view of all input cells.
(D) Total cell count and the distribution of input cells. Only male brains were considered here.
(E) Cell-density heatmap of all brain regions (excluding the isocortex and cerebellum, where virtually no input cells were detected). The means of male and female
brains are shown.
(F) The plot shows extremely sparse input cell populations in previously unidentified brain regions (n = 3). Only male brains were considered here. Data are shownand mean ± SD (n = 3).
(G) Raw GFP (black) and nuclear staining (RedDot2, purple) images showing the regions identified in (F). Macro views (top) and zoomed-in views (of boxed areas;bottom) are shown. Brain region acronyms follow the ontology defined by the Allen Brain Atlas.

We have also explored the application of CUBIC-Cloud toreconstruct a whole-brain neuronal activity profile by using cFos immunostaining. Our whole-brain analysis showed thatadministration of LPS, which induces sleepiness, repressed
the c-Fos expression in some of the cortical areas, whereas it
activated several distinct brain regions (Figure 4). For example,

Cell Reports Methods 1, 100038, June 21, 2021 11

ll
OPEN ACCESS

our analysis revealed that a distinct subpopulation in the ovBST
strongly responded to LPS, and that the response in PVT neurons is heterogeneous. CUBIC-Cloud facilitates sharing suchknowledge through a widely accessible data repository, andusers will know exactly, with %100 mm spatial resolution in 3D
space, where the relevant region is. By iterating such experiments, CUBIC-Cloud opens up the possibility of functionally dissecting the mouse brain with much finer detail and specificity.
Furthermore, CUBIC-Cloud was used to comprehensively
identify brain-wide neuronal connections. As a proof-of-concept
demonstration, input of neuronal populations projecting to
ARHKiss1+ neurons was investigated by using RV tracers (Figure 5). Our analysis was successful in identifying extremely
sparse populations (less than 10 cells/mm3) with high reproducibility. Our results also implied sexually dimorphic projection
from LSr to ARHKiss1+. Again, we emphasize that CUBIC-Cloudcan serve as a central hub for researchers to share suchanatomical knowledge.
The above analysis results are all openly accessible at CUBICCloud. The raw image data, along with the markers highlighting
the detected cells, are available through the CATMAID image
browser (Saalfeld et al., 2009), hosted at http://cubic-atlas.
riken.jp.
Open data sharing and reanalysis have been highly successful
in neuroscience over the past decade. For instance,
NeuroMorpho.org (Ascoli et al., 2007) is a community-driven
storage for single-neuron reconstructions, which now hosts
over 100,000 curated cells. NeuroData.io (Vogelstein et al.,2018) is another initiative for sharing neuroscientific data, primarily focused on sharing the large raw image files through thecloud. CUBIC-Cloud serves a distinct role among these resources, by focusing its mission on the whole mouse brain dataset, represented as a cellular point cloud. We believe that allthese cloud services are complementary to each other. Namely,
researchers can submit the curated point-cloud data in CUBICCloud, and the raw image data can be deposited in NeuroData.io. A smooth and coherent integration of these services via
public API would be beneficial in future developments.
CUBIC-Cloud has pioneered the cloud-based framework toshare and publish whole mouse-brain datasets. We believe,
however, that several technical and administrative advancements need to be made to render it a fully functional and userfriendly scientific data platform for neuroscience. First, the current implementation of CUBIC-Cloud asks the user to perform
cell detection locally. The cell (object) detection routine, alongwith the machine learning training, should be integrated into
the cloud in future developments. Indeed, there are alreadyseveral reports on embracing cloud computing to analyze
massive light and electron microscopy images (Haberl et al.,2018; Falk et al., 2019; Bannon et al., 2019; Wu et al., 2019).
An interesting bonus of running object detection in the cloud isthat the training datasets can also be shared, which will allow
the training of more general and accurate deep learning models(Bannon et al., 2019). Furthermore, CUBIC-Cloud is currently
aimed at analyzing single-cell-resolution image data, where signals of interest are given as dots localized in cell nuclei/soma. Inclusion of an object detection routine in the cloud would expand
the scope to include fiber and synaptic structures. Technically,

12 Cell Reports Methods 1, 100038, June 21, 2021

Articlesuch high-resolution image data can be readily obtained bystate-of-the-art LSFM, and resolution can be boosted by expansion microscopy (Chen et al., 2015; Ku et al., 2016; Tomer et al.,2014; Murakami et al., 2018). Future software infrastructure developments, including ones discussed here, will pave the way for
new science toward bottom-up and data-driven elucidation ofneuronal functions and circuitry.
Limitations of the study
CUBIC-Cloud has been rigorously tested and validated with the
CUBIC clearing method by registering over 50 brains. CUBICCloud should be compatible with clearing methods with similar tissue deformation characteristics (e.g., hydrophilic clearing
methods, which often soften and expand the tissue). However,
we found that the registration accuracy was compromised, especially in the olfactory bulb, when iDISCO, a hydrophobic clearing
method, was used (Figure S4 and STAR Methods). The current
registration algorithm cannot correctly overcome the large deformation present in the olfactory bulb, and calls for future work.
Using CUBIC-Cloud, we were able to quantify the number ofcells labeled by immunostaining and the protein abundancelevels. As quantified in Figure S2, however, the fluorescence intensity from deeper brain regions such as the thalamus wasattenuated by a factor of around 2, due to the absorption and residual scattering of the tissue. A certain calibration process
would be ideally required to correct the intensity attenuation in
the deep brain regions. Using tissue-embedded beads could
be a possible solution, but the bead aggregation might complicate the automatic calibration. As an alternative solution, we
think that a nuclear staining channel could be used to calibrate
the intensity. Assuming that all cells have a single copy of DNA
in the nucleus, the brightness of the nuclear staining of single
cells would be almost the same. Furthermore, the absolute density of the cells of all brain areas is available from CUBIC-Atlas.
Given this, one might compute the correction factor by dividing
the raw image intensity of nuclear staining by the cell density.
Although we have not implemented this analysis in our pipeline,
ideas along this line would be useful in future studies.
The proposed whole-brain analysis and sharing pipeline wasspecifically designed for mouse brain studies. However, giventhat tissue-clearing-based high-throughput imaging is applicable to exploring the brains of rarely explored organisms(Pende et al., 2020), it would be important to extend the framework in the future to accommodate the brains of other organisms, such as rats or primates. As the whole-brain data of diverse
organisms are collected, we expect that a data integration platform like CUBIC-Cloud would further increase its value.
STAR+METHODS
Detailed methods are provided in the online version of this paper
and include the following:d KEY RESOURCES TABLE
d RESOURCE AVAILABILITY

B Lead contact
B Materials availability
B Data and code availability

ll
OPEN ACCESS

Articled EXPERIMENTAL MODEL AND SUBJECT DETAILSB Miced METHOD DETAILSB Tissue clearing and whole-brain 3D immunostaining
B LSFM imaging
B Imaging the fluorescent bead embedded in cleared tis-

E.A.S. performed LSFM imaging. C.S., K.K., and H.O. performed tissue
clearing and immunostaining experiments. K.K. performed sleep measurements by using SSS. H.O. assisted in the preparation of AD model mice.
S.S. and R.G.Y. assisted in the deployment of the cloud server. E.A.S., K.T.,
K. Murata, and K. Miyamichi prepared Cre-mice, produced virus, and performed injections. T.M., S.S., and H.R.U. wrote the manuscript with input
from all co-authors.

sue
B Cell detection
B Accuracy evaluation of cell detection
B CUBIC-Atlas
B Brain registration
B Registration

between CUBIC- and iDISCO-cleared
brainsB Architecture of CUBIC-CloudB A web-based whole-brain viewer
B Privacy and data ownership policy of CUBIC-Cloud
B Whole-brain analysis of PV and SST expressing neurons
B Whole-brain analysis of ChAT expressing neurons
B Whole-brain analysis of Th expressing neurons
B Brain nuclei segmentation using alpha shape algorithm
B Whole-brain analysis of Iba1 expressing cells
B Whole-brain analysis of Ab plaques
B Whole-brain analysis of c-Fos profile change induced
by LPS
B Rabies virus injection
B Whole-brain analysis of RV-injected brains
d QUANTIFICATION AND STATISTICAL ANALYSIS
B Statistical analysis
SUPPLEMENTAL INFORMATION
Supplemental information can be found online at https://doi.org/10.1016/j.
crmeth.2021.100038.
ACKNOWLEDGMENTS
The authors would like to thank K. Wilkins and T. Miyawaki for proofreading
the manuscript, T.C. Murakami for discussions on CUBIC-Atlas, M. Kuroda
for technical assistance on LSFM imaging, S.I. Kubota for helping with the
LPS experiments, and R. Tanaka for supporting sample preparations. The
AppNL-G-F/NL-G-F knockin mouse strain (RBRC06344) was provided by RIKEN
BRC (deposited by T. Saito and T.C. Saido) through the National BioResource
Project of the MEXT/AMED, Japan. This work was supported by a Grant-in-Aid
for JSPS Fellows (JSPS KAKENHI, grant 19J13071, to T.M.); an ANRI scholarship (to T.M.); PRESTO (JST, grant JPMJPR15F4, to E.A.S.); a Grant-in-Aid for
Scientific Research (B) (JSPS KAKENHI, grant 19H03413 to E.A.S.); a Grantin-Aid for Scientific Research on Innovative Areas (JSPS KAKENHI, grant
17H06328 to E.A.S.); AMED-PRIME (JP21gm6210027, to E.A.S.); ERATO Touhara Chemosensory Signal Project (JST, grant JPMJER1202, to K.T.); a Grantin-Aid for Scientific Research (S) (JSPS KAKENHI, grant 25221004, to H.R.U.);
Brain/MINDS (AMED/MEXT, grant JP17DM0207049, to H.R.U.); the Scienceand Technology Platform Program for Advanced Biological Medicine(AMED/MEXT to H.R.U.); a Grant-in-Aid from the Human Frontier Science Program (to H.R.U.); and Grants-in-Aid from the Takeda Science Foundation(to E.A.S. and H.R.U.). The graphical user interface of CUBIC-Cloud wasco-developed by CUBICStars and Tecotec, Inc.
AUTHOR CONTRIBUTIONS
T.M. and H.R.U. designed the study. T.M. created and implemented the CUBIC-Cloud software stack. T.M. and K.K. performed data analysis. T.M. andDECLARATION OF INTERESTS
T.M., R.G.Y., and E.A.S are employees of CUBICStars, Inc. H.R.U. is a founderand CTO of CUBICStars, Inc. T.M. and H.R.U. are co-inventors on patent applications filed by CUBICStars, Inc., regarding the CUBIC-Cloud framework.
CUBIC-Cloud web service is provided and maintained by CUBICStars, Inc.
H.R.U. is a co-inventor on patents and patent applications owned or filed by
RIKEN covering the CUBIC reagents.
Received: December 21, 2020
Revised: March 17, 2021
Accepted: May 20, 2021
Published: June 21, 2021
REFERENCES
Adzic, G., and Chatley, R. (2017). Serverless computing: economic and architectural impact. In Proceedings of the 2017 11th Joint Meeting on Foundationsof Software Engineering - ESEC/FSE (ACM Press), pp. 884–889.
Armstrong, D.M., Saper, C.B., Levey, A.I., Wainer, B.H., and Terry, R.D. (1983).
Distribution of cholinergic neurons in rat brain: demonstrated by the immunocytochemical localization of choline acetyltransferase. J. Comp. Neurol. 216,
53–68.
Ascoli, G.A., Donohue, D.E., and Halavi, M. (2007). NeuroMorpho.Org: a central resource for neuronal morphologies. J. Neurosci. 27, 9247–9251.
Avants, B.B., Epstein, C.L., Grossman, M., and Gee, J.C. (2008). Symmetricdiffeomorphic image registration with cross-correlation: evaluating automatedlabeling of elderly and neurodegenerative brain. Med. Image Anal. 12, 26–41.
Bannon, D., Moen, E., Schwartz, M., Borba, E., Cui, S., Huang, K., Camplisson,
I., Koe, N., Kyme, D., Kudo, T., et al. (2019). Dynamic allocation of computational resources for deep learning-enabled cellular image analysis with Kubernetes. bioRxiv, 505032.
Belle, M., Godefroy, D., Couly, G., Malone, S.A., Collier, F., Giacobini, P., andChédotal, A. (2017). Tridimensional visualization and analysis of early humandevelopment. Cell 169, 161–173.e12.
Berg, S., Kutra, D., Kroeger, T., Straehle, C.N., Kausler, B.X., Haubold, C.,
Schiegg, M., Ales, J., Beier, T., Rudy, M., et al. (2019). ilastik: interactive machine learning for (bio)image analysis. Nat. Methods 4522, 3–5.
Cai, R., Pan, C., Ghasemigharagoz, A., Todorov, M.I., Förstera, B., Zhao, S.,
Bhatia, H.S., Parra-Damas, A., Mrowka, L., Theodorou, D., et al. (2019).
Panoptic imaging of transparent mice reveals whole-body neuronal projections and skull–meninges connections. Nat. Neurosci. 22, 317–327.
Celio, M. (1990). Calbindin D-28k and parvalbumin in the rat nervous system.
Neuroscience 35, 375–475.
Chan, K.Y., Jang, M.J., Yoo, B.B., Greenbaum, A., Ravi, N., Wu, W.L., Sánchez-Guardado, L., Lois, C., Mazmanian, S.K., Deverman, B.E., and Gradinaru, V. (2017). Engineered AAVs for efficient noninvasive gene delivery to
the central and peripheral nervous systems. Nat. Neurosci. 20, 1172–1179.
Chen, Z., Jalabi, W., Shpargel, K.B., Farabaugh, K.T., Dutta, R., Yin, X., Kidd,
G.J., Bergmann, C.C., Stohlman, S.A., and Trapp, B.D. (2012). Lipopolysaccharide-induced microglial activation and neuroprotection against experimental brain injury is independent of hematogenous TLR4. J. Neurosci. 32,
11706–11715.
Chen, F., Tillberg, P.W., and Boyden, E.S. (2015). Expansion microscopy. Science 347, 543–548.

Cell Reports Methods 1, 100038, June 21, 2021 13

ll
OPEN ACCESS

Chung, K., Wallace, J., Kim, S.-Y., Kalyanasundaram, S., Andalman, A.S., Davidson, T.J., Mirzabekov, J.J., Zalocusky, K.A., Mattis, J., Denisin, A.K., et al.
(2013). Structural and molecular interrogation of intact biological systems. Nature 497, 332–337.
Dahlström, A., and Fuxe, K. (1964). Evidence for the existence of monoaminecontaining neurons in the central nervous system. I. Demonstration of monoamines in the cell bodies of brain stem neurons. Acta Physiol. Scand. Suppl.
39 (Suppl 232), 1–55.
Dodt, H.-U., Leischner, U., Schierloh, A., Jährling, N., Mauch, C.P., Deininger,
K., Deussing, J.M., Eder, M., Zieglgänsberger, W., and Becker, K. (2007). Ultramicroscopy: three-dimensional visualization of neuronal networks in thewhole mouse brain. Nat. Methods 4, 331–336.
Donato, F., Rompani, S.B., and Caroni, P. (2013). Parvalbumin-expressing
basket-cell network plasticity induced by experience regulates adult learning.
Nature 504, 272–276.
€rk, A., Becker, K., Jährling, N., Mauch, C.P., Hojer, C.D., Egen, J.G., Hellal,
ErtuF., Bradke, F., Sheng, M., and Dodt, H.-U. (2012). Three-dimensional imaging
of solvent-cleared organs using 3DISCO. Nat. Protoc. 7, 1983–1995.
Falk, T., Mai, D., Bensch, R., Çiçek, Ö., Abdulkadir, A., Marrakchi, Y., Böhm,
A., Deubner, J., Jäckel, Z., Seiwald, K., et al. (2019). U-Net: deep learningfor cell counting, detection, and morphometry. Nat. Methods 16, 67–70.
Frasconi, P., Silvestri, L., Soda, P., Cortini, R., Pavone, F.S., and Iannello, G.
(2014). Large-scale automated identification of mouse brain cells in confocal
light sheet microscopy images. Bioinformatics 30, i587–i593.
Fu, Q., Martin, B.L., Matus, D.Q., and Gao, L. (2016). Imaging multicellularspecimens with real-time optimized tiling light-sheet selective plane illumination microscopy. Nat. Commun. 7, 11088.
€rth, D., Vaissière, T., Tzortzi, O., Xuan, Y., Märtin, A., Lazaridis, I., Spigolon,
Fu
G., Fisone, G., Tomer, R., Deisseroth, K., et al. (2018). An interactive framework
for whole-brain maps at cellular resolution. Nat. Neurosci. 21, 139–149.
Furube, E., Kawai, S., Inagaki, H., Takagi, S., and Miyata, S. (2018). Brain region-dependent heterogeneity and dose-dependent difference in transient microglia population increase during lipopolysaccharide-induced inflammation.
Sci. Rep. 8, 1–15.
Gao, L. (2015). Extend the field of view of selective plan illumination microscopy by tiling the excitation light sheet. Opt. Express 23, 6102.
Gao, C., Leng, Y., Ma, J., Rooke, V., Rodriguez-Gonzalez, S., Ramakrishnan,C., Deisseroth, K., and Penzo, M.A. (2020). Two genetically, anatomically andfunctionally distinct cell types segregate across anteroposterior axis of paraventricular thalamus. Nat. Neurosci. 23, 217–228.

ArticleJohansson, O., Hökfelt, T., and Elde, R. (1984). Immunohistochemical distribution of somatostatin-like immunoreactivity in the central nervous system of theadult rat. Neuroscience 13, 265–IN2.
Karolchik, D., Hinrichs, A.S., and Kent, W.J. (2009). The UCSC Genome
Browser. Curr. Protocols Bioinformatics, Chapter 1:Unit1.4.
Ke, M.-T., Fujimoto, S., and Imai, T. (2013). SeeDB: a simple and morphologypreserving optical clearing agent for neuronal circuit reconstruction. Nat. Neurosci. 16, 1154–1161.
Kim, E.J., Jacobs, M.W., Ito-Cole, T., and Callaway, E.M. (2016). Improvedmonosynaptic neural circuit tracing using engineered rabies virus glycoproteins. Cell Rep. 15, 692–699.
Kim, Y., Yang, G.R., Pradhan, K., Venkataraju, K.U., Bota, M., Garcı́a del Molino, L.C., Fitzgerald, G., Ram, K., He, M., Levine, J.M., et al. (2017). Brain-wide
maps reveal stereotyped cell-type-based cortical architecture and subcorticalsexual dimorphism. Cell 171, 456–469.e22.
Kirst, C., Skriabine, S., Vieites-Prado, A., Topilko, T., Bertin, P., Gerschenfeld,
G., Verny, F., Topilko, P., Michalski, N., Tessier-Lavigne, M., and Renier, N.
(2020). Mapping the fine-scale organization and plasticity of the brain vasculature. Cell 180, 780–795.e25.
Ku, T., Swaney, J., Park, J.-Y., Albanese, A., Murray, E., Cho, J.H., Park, Y.-G.,
Mangena, V., Chen, J., and Chung, K. (2016). Multiplexed and scalable superresolution imaging of three-dimensional protein localization in size-adjustabletissues. Nat. Biotechnol. 34, 973–981.
Kubota, S.I., Takahashi, K., Nishida, J., Morishita, Y., Ehata, S., Tainaka, K.,
Miyazono, K., and Ueda, H.R. (2017). Whole-body profiling of cancer metastasis with single-cell resolution. Cell Rep. 20, 236–250.
Li, X., Yu, B., Sun, Q., Zhang, Y., Ren, M., Zhang, X., Li, A., Yuan, J., Madisen,
L., Luo, Q., et al. (2018). Generation of a whole-brain atlas for the cholinergic
system and mesoscopic projectome analysis of basal forebrain cholinergic
neurons. Proc. Natl. Acad. Sci. U SA 115, 415–420.
Mano, T., Albanese, A., Dodt, H.-U., Erturk, A., Gradinaru, V., Treweek, J.B.,
Miyawaki, A., Chung, K., and Ueda, H.R. (2018). Whole-brain analysis of cells
and circuits by tissue clearing and light-sheet microscopy. J. Neurosci. 38,
9330–9337.
Matsumoto, K., Mitani, T.T., Horiguchi, S.A., Kaneshiro, J., Murakami, T.C.,
Mano, T., Fujishima, H., Konno, A., Watanabe, T.M., Hirai, H., and Ueda,
H.R. (2019). Advanced CUBIC tissue clearing for whole-organ cell profiling.
Nat. Protoc. 14, 3506–3537.
McDonald, A., and Betette, R. (2001). Parvalbumin-containing neurons in the
rat basolateral amygdala: morphology and co-localization of CalbindinD28k. Neuroscience 102, 413–425.

Gonchar, Y., Wang, Q., and Burkhalter, A. (2007). Multiple distinct subtypes of
GABAergic neurons in mouse visual cortex identified by triple immunostaining.
Front. Neuroanat. 1, 3.

Miyamichi, K., Shlomai-Fuchs, Y., Shu, M., Weissbourd, B.C., Luo, L., andMizrahi, A. (2013). Dissecting local circuits: parvalbumin Interneurons underlie
broad feedback control of olfactory bulb output. Neuron 80, 1232–1245.

Gradinaru, V., Treweek, J., Overton, K., and Deisseroth, K. (2018). Hydrogeltissue chemistry: principles and applications. Annu. Rev. Biophys. 47,
355–376.

Murakami, T.C., Mano, T., Saikawa, S., Horiguchi, S.A., Shigeta, D., Baba, K.,
Sekiya, H., Shimizu, Y., Tanaka, K.F., Kiyonari, H., et al. (2018). A three-dimensional single-cell-resolution whole-brain atlas using CUBIC-X expansion microscopy and tissue clearing. Nat. Neurosci. 21, 625–637.

Haberl, M.G., Churas, C., Tindall, L., Boassa, D., Phan, S., Bushong, E.A., Madany, M., Akay, R., Deerinck, T.J., Peltier, S.T., and Ellisman, M.H. (2018).
CDeep3M—Plug-and-Play cloud-based deep learning for image segmentation. Nat. Methods 15, 677–680.

Murray, E., Cho, J.H., Goodwin, D., Ku, T., Swaney, J., Kim, S.-Y., Choi, H.,
Park, Y.-G., Park, J.-Y., Hubbert, A., et al. (2015). Simple, scalable Proteomic
imaging for high-dimensional profiling of intact systems. Cell 163, 1500–1514.

Hama, H., Kurokawa, H., Kawano, H., Ando, R., Shimogori, T., Noda, H., Fukami, K., Sakaue-Sawano, A., and Miyawaki, A. (2011). Scale: a chemical
approach for fluorescence imaging and reconstruction of transparent mousebrain. Nat. Neurosci. 14, 1481–1488.

Osakada, F., and Callaway, E.M. (2013). Design and generation of recombinant rabies virus vectors. Nat. Protoc. 8, 1583–1601.

Hama, H., Hioki, H., Namiki, K., Hoshida, T., Kurokawa, H., Ishidate, F., Kaneko, T., Akagi, T., Saito, T., Saido, T., and Miyawaki, A. (2015). ScaleS: an optical clearing palette for biological imaging. Nat. Neurosci. 18, 1518–1529.

Pende, M., Vadiwala, K., Schmidbaur, H., Stockinger, A.W., Murawala, P., Saghafi, S., Dekens, M.P.S., Becker, K., Revilla-i Domingo, R., Papadopoulos,
S.-C., et al. (2020). A versatile depigmentation, clearing, and labeling method
for exploring nervous system diversity. Sci. Adv. 6, eaba0365.

Herbison, A.E. (2018). The Gonadotropin-releasing hormone pulse generator.
Endocrinology 159, 3723–3736.
Ito, D., Imai, Y., Ohsawa, K., Nakajima, K., Fukuuchi, Y., and Kohsaka, S.
(1998). Microglia-specific localisation of a novel calcium binding protein,Iba1. Brain research. Mol. Brain Res. 57, 1–9.

14 Cell Reports Methods 1, 100038, June 21, 2021

Paxinos, G., and Franklin, K. (2012). Paxinos and Franklin’s the Mouse Brain in
Stereotaxic Coordinates, 4th Edition (Academic Press).

Potree, M.S. (2016). Rendering Large Point Clouds in Web Browsers, PhD
thesis.
Power, R.M., and Huisken, J. (2017). A guide to light-sheet fluorescence microscopy for multiscale imaging. Nat. Methods 14, 360–373.

llArticleRenier, N., Wu, Z., Simon, D.J., Yang, J., Ariel, P., and Tessier-Lavigne, M.
(2014). iDISCO: a simple, rapid method to immunolabel large tissue samples
for volume imaging. Cell 159, 896–910.
Renier, N., Adams, E.L., Kirst, C., Wu, Z., Azevedo, R., Kohl, J., Autry, A.E., Kadiri, L., Umadevi Venkataraju, K., Zhou, Y., et al. (2016). Mapping of brain activity by automated volume Analysis of immediate early genes. Cell 165, 1789–
1802.
Saalfeld, S., Cardona, A., Hartenstein, V., and Tomancak, P. (2009). CATMAID:
collaborative annotation toolkit for massive amounts of image data. Bioinformatics (Oxford, England) 25, 1984–1986.
Saito, T., Matsuba, Y., Mihira, N., Takano, J., Nilsson, P., Itohara, S., Iwata, N.,
and Saido, T.C. (2014). Single App knock-in mouse models of Alzheimer’s disease. Nat. Neurosci. 17, 661–663.
Salinas, C.B.G., Lu, T.T.-H., Gabery, S., Marstal, K., Alanentalo, T., Mercer,
A.J., Cornea, A., Conradsen, K., Hecksher-Sørensen, J., Dahl, A.B., et al.
(2018). Integrated brain atlas for unbiased mapping of nervous system effects
following Liraglutide treatment. Scientific Rep. 8, 10310.
Sommer, C., Straehle, C., Kothe, U., and Ilastik, F.A.H. (2011). Interactive
learning and segmentation toolkit. IEEE International Symposium on Biomedical Imaging: From Nano to Macro (IEEE), pp. 230–233.
Sunagawa, G.A., Sumiyama, K., Ukai-Tadenuma, M., Perrin, D., Fujishima, H.,
Ukai, H., Nishimura, O., Shi, S., Ohno, R.-i., Narumi, R., et al. (2016). Mammalian reverse Genetics without crossing reveals Nr3a as a short-sleeper gene.
Cell Rep. 14, 662–677.
Susaki, E.A., and Ueda, H.R. (2016). Whole-body and whole-organ clearing
and imaging techniques with single-cell resolution: toward organism-level systems biology in mammals. Cell Chem. Biol. 23, 137–157.
Susaki, E.A., Tainaka, K., Perrin, D., Kishino, F., Tawara, T., Watanabe, T.M.,
Yokoyama, C., Onoe, H., Eguchi, M., Yamaguchi, S., et al. (2014). Whole-brain
imaging with single-cell resolution using chemical Cocktails and computational analysis. Cell 157, 726–739.
Susaki, E.A., Tainaka, K., Perrin, D., Yukinaga, H., Kuno, A., and Ueda, H.R.
(2015). Advanced CUBIC protocols for whole-brain and whole-body clearing
and imaging. Nat. Protoc. 10, 1709–1727.
Susaki, E.A., Shimizu, C., Kuno, A., Tainaka, K., Li, X., Nishi, K., Morishima, K.,
Ono, H., Ode, K.L., Saeki, Y., et al. (2020). Versatile whole-organ/body staining
and imaging based on electrolyte-gel properties of biological tissues. Nat.
Commun. 11, 1982.
Tainaka, K., Kubota, S.I., Suyama, T.Q., Susaki, E.A., Perrin, D., Ukai-Tadenuma, M., Ukai, H., and Ueda, H.R. (2014). Whole-body imaging with singlecell resolution by tissue Decolorization. Cell 159, 911–924.
Tainaka, K., Kuno, A., Kubota, S.I., Murakami, T., and Ueda, H.R. (2016).
Chemical Principles in tissue clearing and staining protocols for whole-bodycell profiling. Annu. Rev. Cell Dev. Biol. 32, 713–741.
Tainaka, K., Murakami, T.C., Susaki, E.A., Shimizu, C., Saito, R., Takahashi, K.,
Hayashi-Takagi, A., Sekiya, H., Arima, Y., Nojima, S., et al. (2018). Chemical

OPEN ACCESS

landscape for tissue clearing based on hydrophilic reagents. Cell Rep. 24,
2196–2210.e9.
Tatsuki, F., Sunagawa, G.A., Shi, S., Susaki, E.A., Yukinaga, H., Perrin, D., Sumiyama, K., Ukai-Tadenuma, M., Fujishima, H., Ohno, R.-i., et al. (2016).
Involvement of Ca 2+ -dependent Hyperpolarization in sleep duration in mammals. Neuron 90, 70–85.
Tomer, R., Ye, L., Hsueh, B., and Deisseroth, K. (2014). Advanced CLARITY for
rapid and high-resolution imaging of intact tissues. Nat. Protoc. 9, 1682–1697.
Tsukahara, S., and Yamanouchi, K. (2002). Sex difference in septal neurons
projecting axons to midbrain central gray in rats: a combined Double retrograde tracing and ER-immunohistochemical study. Endocrinology 143,
285–294.
Tsukahara, S., Kanaya, M., and Yamanouchi, K. (2014). Neuroanatomy andsex differences of the lordosis-inhibiting system in the lateral septum. Front.
Neurosci. 8, 1–13.
Vogelstein, J.T., Perlman, E., Falk, B., Baden, A., Gray Roncal, W., Chandrashekhar, V., Collman, F., Seshamani, S., Patsolic, J.L., Lillaney, K., et al.
(2018). A community-developed open-source computational ecosystem for
big neuro data. Nat. Methods 15, 846–847.
Wang, Y., Kim, J., Schmit, M.B., Cho, T.S., Fang, C., and Cai, H. (2019). A bed
nucleus of stria terminalis microcircuit regulating inflammation-associatedmodulation of feeding. Nat. Commun. 10, 2769.
Wang, Q., Ding, S.-L., Li, Y., Royall, J., Feng, D., Lesnar, P., Graddis, N.,
Naeemi, M., Facer, B., Ho, A., et al. (2020). The allen mouse brain common coordinate framework: a 3D reference atlas. Cell 181, 936–953.e20.
Wu, J., Silversmith, W.M., Lee, K., and Seung, H.S. (2019). Chunkflow: distributed hybrid cloud processing of large 3d images by convolutional nets. Front.
Neural Circuits 13, 1–9.
Xu, X., Roby, K.D., and Callaway, E.M. (2010). Immunochemical characterization of inhibitory mouse cortical neurons: three chemically distinct classes ofinhibitory cells. J. Comp. Neurol. 518, 389–404.
Yang, B., Treweek, J.B., Kulkarni, R.P., Deverman, B.E., Chen, C.K., Lubeck,
E., Shah, S., Cai, L., and Gradinaru, V. (2014). Single-cell phenotyping withintransparent intact tissue through whole-body clearing. Cell 158, 945–958.
Yeo, S.-H., Kyle, V., Blouet, C., Jones, S., and Colledge, W.H. (2019). Mapping
neuronal inputs to Kiss1 neurons in the arcuate nucleus of the mouse. PLoS
One 14, e0213927.
Yushkevich, P.A., Piven, J., Hazlett, H.C., Smith, R.G., Ho, S., Gee, J.C., andGerig, G. (2006). User-guided 3D active contour segmentation of anatomical
structures: significantly improved efficiency and reliability. NeuroImage 31,
1116–1128.
Zhang, C., Yan, C., Ren, M., Li, A., Quan, T., Gong, H., and Yuan, J. (2017). A
platform for stereological quantitative analysis of the brain-wide distribution of
type-specific neurons. Sci. Rep. 7, 14334.

Cell Reports Methods 1, 100038, June 21, 2021 15

ll
OPEN ACCESS

ArticleSTAR+METHODSKEY RESOURCES TABLEREAGENT or RESOURCESOURCEIDENTIFIER

Mouse anti-PV antibody Dilution 1:50

Swant

Cat# PV235

Rat anti-SST antibody Dilution 1:10

Millipore

Cat# MAB354,
RRID AB_2255365

Rabbit anti-ChAT antibody Dilution 1:200

Abcam

Cat# ab178850,
RRID AB_2721842

Mouse anti-Th antibody Dilution 1:20

Santa Cruz Biotechnology

Cat# sc-25269,
RRID AB_628422

Rabbit anti-Iba1 antibody red
fluorochrome(635)-conjugated Dilution 1:50

Wako

Cat# 013-26471,
RRID AB_2687911

Mouse anti-6E10 antibody Dilution 1:75

BioLegend

Cat# 93049,
RRID AB_2715854

Rabbit anti-c-Fos antibody Dilution 1:100

Cell Signaling Technology

Cat# 2250S,
RRID AB_2247211

Goat anti-Mouse IgG1, Fcg fragment
specific, Cy3, Dilution 1:150

Jackson ImmunoResearch

Cat# 115-167-185 RRID AB_2632514

Goat anti-Rat IgG, Fcg fragment specific,
Alexa Fluor 594, Dilution 1:150

Jackson ImmunoResearch

Cat# 112-587-008, RRID AB_2632490

Goat anti-Rabbit IgG, Fcg fragment
specific, Alexa Fluor 594, Dilution 1:150

Jackson ImmunoResearch

Cat# 111-587-008
RRID AB_2632469

Goat anti-Mouse IgG2a, Fcg fragment
specific, Alexa Fluor 594, Dilution 1:150

Jackson ImmunoResearch

Cat# 115-587-186,
RRID AB_2632540

AAV9-CAG-FLEx-TCb

Miyamichi et al., 2013 (Custom made by
Penn Vector Core)

N/A

AAV9-CAG-FLEx-oG

Kim et al., 2016 (Custom made by Penn
Vector Core)

N/A

Rabies DG-GFP+EnvA

Osakada and Callaway, 2013 (Amplified in
the lab from a virus shared by Dr. Masahiro
Yamaguchi)

N/A

Whole-brain expression analysis of PV(analyzed data)

This study

https://cubic-cloud.com

Whole-brain expression analysis of PV (rawimage)

This study

http://cubic-atlas.riken.jp/

Whole-brain expression analysis of SST(analyzed data)

This study

https://cubic-cloud.com

Whole-brain expression analysis of SST(raw image)This study

http://cubic-atlas.riken.jp/

Whole-brain expression analysis of ChAT
(analyzed data)

This study

https://cubic-cloud.com

Whole-brain expression analysis of ChAT
(raw image)This study

http://cubic-atlas.riken.jp/

Whole-brain expression analysis of TH(analyzed data)

This study

https://cubic-cloud.com

Whole-brain expression analysis of TH (rawimage)

This study

http://cubic-atlas.riken.jp/

Antibodies

Bacterial and virus strains

Deposited data

(Continued on next page)e1 Cell Reports Methods 1, 100038, June 21, 2021

ll
OPEN ACCESS

ArticleContinued
REAGENT or RESOURCESOURCEIDENTIFIER

Whole-brain expression analysis of Iba1
(analyzed data)

This study

https://cubic-cloud.com

Whole-brain expression analysis of Iba1
(raw image)This study

http://cubic-atlas.riken.jp/

Whole-brain antibody staining Ab plaque(6E10) (analyzed data)

This study

https://cubic-cloud.com

Whole-brain antibody staining Ab plaque(6E10) (raw image)This study

http://cubic-atlas.riken.jp/

Whole-brain expression analysis of c-Fos(LPS and control condition) (analyzed data)

This study

https://cubic-cloud.com

Whole-brain expression analysis of c-Fos(LPS and control condition) (raw image)This study

http://cubic-atlas.riken.jp/

Whole-brain circuit analysis of ARHKiss1
using Rabies virus (analyzed data)

This study

https://cubic-cloud.com

Whole-brain circuit analysis of ARHKiss1
using Rabies virus (raw image)This study

http://cubic-atlas.riken.jp/

Mouse strain AppNL-G-F/NL-G-F

RIKEN BioResource Research Center

RBRC No. RBRC06344,
RRID IMSR_RBRC06344

Mouse strain Kiss1tm1.1(cre/EGFP)Stei/J

Jackson Laboratory

Stock No. 017701,
RRID IMSR_JAX:017701

Experimental models: organisms/strains

Software and algorithms
Fiji

NIH

RRID SCR_02285

Python Anaconda distribution

Anaconda

https://www.anaconda.com
RRID SCR_018317

ITK-SNAP

Yushkevich et al., 2006

RRID SCR_002010

ANTS

Avants et al., 2008

RRID SCR_004757

Ecc

This study

https://github.com/DSPsleeporg/ecc

CUBIC-CloudThis study

https://cubic-cloud.com

RESOURCE AVAILABILITY
Lead contact
Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Hiroki R.
Ueda (uedah-tky@umin.ac.jp).
Materials availability
This study did not generate new unique reagents.
Data and code availability
Raw 3D image data analyzed in this study are available at http://cubic-atlas.riken.jp through an interactive image viewer powered by
CATMAID (Saalfeld et al., 2009). The analyzed whole-brain data in point cloud format is deposited on http://cubic-atlas.riken.jp, as
well as CUBIC-Cloud’s public repository. The additional data that support the findings of this study are available from the corresponding author upon reasonable request.
Cell detection program used in this study is available at GitHub code repository (https://github.com/DSPsleeporg/ecc). CUBICCloud computing service is accessible at https://cubic-cloud.com.
CUBIC-Cloud offers both free and paid subscription plans. CUBIC-Cloud is free for use for viewing data. Free plan users can also
try analysis of several brains using the tokens supplied at the initial sign-up. Requests to upload brains or create notebooks over thefree use limit are handled for a fee, to cover and compensate the maintenance cost of the cloud server.

Cell Reports Methods 1, 100038, June 21, 2021 e2

ll
OPEN ACCESS

ArticleEXPERIMENTAL MODEL AND SUBJECT DETAILSMice
All experimental procedures and housing conditions were approved by the Animal Care and Use Committee of The University of Tokyo. AppNL-G-F/NL-G-F mice were provided by RIKEN BioResource Research Center (RBRC No. RBRC06344) (Saito et al., 2014).
Kiss1-Cre mouse was purchased from The Jackson Laboratory (Kiss1-tm1.1(cre/EGFP)Stei/J, stock no. 017701). In all experiments,
male mice were used unless otherwise specified. 8- to 14-weeks-old animals were used for experiments (for details see the STAR
Method section of each experiment).
To sample brains, animals were anesthetized by an overdose of pentobarbital (> 100 mg/kg), then transcardially perfused with
10 mL of PBS (pH 7.4) and 20 mL of 4% paraformaldehyde (PFA). The brains were dissected, post-fixed in 4% PFA overnight at
4 C and stored in PBS.
METHOD DETAILS
Tissue clearing and whole-brain 3D immunostaining
To clear brain tissues, we used second-generation CUBIC protocols (Tainaka et al., 2018; Matsumoto et al., 2019). For delipidation,
we used CUBIC-L (10 wt% N-butyldiethanolamine, 10 wt% Triton X-100), and for RI matching, either CUBIC-R+(N) (45 wt% Antipyrine, 30 wt% Nicotinamide, 0.5 vol% N-butyldiethanolamine) or CUBIC-R+(M) (45 wt% Antipyrine, 30 wt% N-methylnicotinamide,
0.5 vol% N-butyldiethanolamine) was used. CUBIC-R+(M) was used for samples labelled by fluorescent proteins (FPs) becauseof its better FP signal retention, and CUBIC-R+(N) was used otherwise. For all brains, nuclear staining (using either SYTOX-G,
BOBO-1 or RedDo2) was applied. For whole-mount 3D immunostaining, we followed CUBIC-HV protocol (Susaki et al., 2020)).
The details on antibody staining for each experiment can be found in the corresponding sections in STAR Method. Before imaging,
the sample was embedded in a transparent agarose gel so that it could be rigidly mounted on a microscope stage (Murakami et al.,2018; Matsumoto et al., 2019).
LSFM imaging
A custom-built macro-zoom LSFM (named GEMINI system) was used to image cleared brains (the details of this microscope can also
be found in (Susaki et al., 2020)). For illumination, the microscope was equipped with 488, 532, 594 and 642 nm diode or DPSS lasers(SOLE-6, Omicron). The laser sheet was generated by a cylindrical lens and the sheet thickness was adjustable by a mechanical slit.
For detection, the microscope was equipped with 0.63X macro-zoom objective lens (MVPLAPO 0.63X, Olympus) and 0.63-6.3X variable zoom optics (MVX-ZB10, Olympus). After passing a suitable fluorescence filter, the fluorescence signal was captured bysCMOS camera (Zyla 5.5, Andor).
To achieve homogeneous light-sheet thickness throughout the field of view, several rectangular image strips with shifted illumination focus were obtained and digitally stitched together, similar to the approach used in TLS-SPIM (Gao, 2015; Fu et al., 2016). The
width of the rectangular strip was matched with the Rayleigh range of the illumination light-sheet. In our setup, the sheet thickness
was approximately 10 mm and the rectangular strip width was 1500 mm, which required 6 image strips to cover the entire brain.
For RV samples, the image voxel size was (X,Y,Z) = (8.25, 8.25, 9.0) mm with 1.2X intermediate zoom optics. For other experiments,
the voxel size was (X,Y,Z) = (6.45, 6.45, 7.0) mm with 1.6X intermediate zoom optics. It should be noted that the effective resolution
should take into account the moderate tissue expansion (1.5X) caused by CUBIC-R treatment.
For each dye/FP, the following laser and fluorescence filter pair was used: Alexa 594 [Ex: 594 nm, Em: 641/75 nm bandpass (FF02641/75-32, Semrock)], Cy3 [Ex: 532 nm, Em: 585/40 nm bandpass (FF01-585/40-32, Semrock)], SYTOX-G, BOBO-1 and GFP [Ex:488 nm, Em: 520/40 nm bandpass (FF01-520/44-32, Semrock)], RedDot2 [Ex: 642nm, Em: 708/75 nm bandpass (FF01-708/75-32,
Semrock)], mCherry [Ex: 594 nm, Em: 628/32 nm bandpass (FF01-628/32-32, Semrock)].
Imaging the fluorescent bead embedded in cleared tissue
1.0 mm-diameter green-yellow fluorescent beads (Thermo Fisher, #F8765) were diluted in PBS so that the final bead concentration
was 0:93107 particles/ml. This bead-mixed PBS solution was perfused in mice, prior to the PFA perfusion. Because the bead surface
was modified with amine, PFA was able to cross-link and fix the beads within the tissue. After tissue clearing, the whole brain was
imaged using the macro-zoom LSFM with XYZ voxel resolution of 6.45x6.45x7.0 mm (Figure S2A). Then, single and well-isolated
bead particles were manually annotated (n>15 for each brain region) using ITK-SNAP software (Yushkevich et al., 2006). Subsequently, the mean spot profiles were computed and fitted with Gaussian using custom Python code. The fitted sigma values fromsix regions were all within 4.4 to 4.9 mm (lateral) and 5.3 to 6.7 mm (axial) (Figure S2B), validating homogeneous image quality
throughout the entire brain. Given the digital sampling frequency (6.5 mm) of the microscope used, this result was nearly the ideal PSF.
Using the same data, we have also quantified the absolute bread fluorescent intensity of different brain areas (Figure S2C). The
graph indicates that the thalamus, which is most distant from the brain surface, showed relatively lower brightness, reflecting theattenuation of the illumination beam as well as the emitted fluorescence by the tissue. Nonetheless, the differences in magnitude
between regions were less than two-fold, which we concluded is good enough for quantitative analysis.

e3 Cell Reports Methods 1, 100038, June 21, 2021

ll
ArticleOPEN ACCESS

Cell detection
The cell detection workflow used in this study is shown in Figure 1A (’’step 3’’). As the first step, a machine-learning algorithm wasused to classify voxels into labelled cells and other structures. For this step, we used ilastik (Sommer et al., 2011). To train the classifier, for each label type (such as c-Fos), manual annotation images were prepared. In this study, three classes were defined, whichwere (1) signals of interest, i.e., cells labelled by FPs or antibodies (2) bright but false signals, such as non-specific binding of antibodies to vascular structures or neurites extending from cell bodies and (3) background (i.e. void space). Typically, 5,000 to 10,000
voxels were annotated as class 1 per one dataset. To increase the robustness, at least two brains with identical labelling conditions
were annotated. Image annotation was performed using ITK-SNAP software (Yushkevich et al., 2006).
Next, following the ilastik workflow, image feature descriptors were selected to distinguish the three classes defined above. For PV,
Sst, ChAT, Th, Iba1, c-Fos, RV-GFP and AAV-mCherry images, the selected descriptors were Gaussian ( = 0.3, 0.7 voxel), Gaussian
gradient magnitude ( = 0.7 voxel), difference of Gaussian ( = 0.7, 1.0, 1.6, 3.5 voxel) and Hessian of Gaussian eigenvalues ( = 1.0, 1.6,
3.5 voxel). For A images, difference of Gaussian ( = 5.0 voxel) and Hessian of Gaussian eigenvalues ( = 5.0 voxel) were additionally
included, so that the larger spatial context was taken into account. Then, ilastik software trained the voxel classifier using random
forest algorithm. Hyperparameters related to random forest algorithm was automatically configured by ilastik. After the classifier
was trained, it was applied to the test dataset to evaluate accuracy. If obvious error was present, manual annotation data were further
supplemented, so that the classifier became more robust against such errors. Once the classifier achieves satisfactory accuracy, it
could reliably be applied to other images with the same label type.
By applying the voxel classifier trained above to each brain image, a probability image was produced, where the value of each voxel
represents the probability of that voxel being class 1 (Figure S3A). The probability value was given in the range [0, 1]. Using this probability image, a custom Python program isolated individual cells in the following way. First, the probability threshold, Pth = 0:7, wasapplied to make a binarized image. Then, connected voxels were searched and merged together, to find individual objects. If theidentified object volume was larger than a threshold, Vth , it was sent to the object separation routine. The object separation routine
1=3
simply finds local maxima with an exclusion distance rexcl = Vth . Vth was heuristically determined to be Vth = 43 = 64 for PV, Sst, cFos, Iba1, Rabeis-GFP and AAV-mCherry, and Vth = 53 = 125 for ChAT and Th. For A plaque segmentation, Vth = N was used. Hence,
there are two free parameters (Pth and Vth ), both of which can be intuitively determined.
Using 20 CPU cores, cell detection of one 3D image stack (typically 2560x2160x1200 voxels) took about 1 h to complete.
The Python source code and its documentation is available at https://github.com/DSPsleeporg/ecc.
Accuracy evaluation of cell detection
Accuracy of the above explained cell detection procedure was extensively evaluated by comparing automated counting results withmanual cell counting (Figure S3B-H). Manual cell counting was performed by cropping a small cubic image volume (50 or 75 or 100
voxels, depending on the cell density) from brain images which were not used in machine learning training. Well-trained human annotators (n = 2) independently marked all of the cells present in the image and typically yielded 100-200 marked cells. Image annotation was performed using ITK-SNAP software (Yushkevich et al., 2006). Cells annotated by both human and algorithm were regarded as true positives. Cells annotated by human but not by algorithm was regarded as false negative. Cells annotated byalgorithm but not by human were regarded as false positives. Then, true positive rate (TPR, also called sensitivity) and positive predictive value (PPV, also called precision) were evaluated for each human annotator. To quantify the overall performance, F1 score,defined as F1 = 2  ðPPV 3 TPRÞ=ðPPV + TPRÞ, was also evaluated. For most of the label types and brain regions, our cell detection
algorithm robustly demonstrated good F1 scores, with average score being 0.80 (PV), 0.83 (Sst), 0.88 (ChAT), 0.80 (Th), 0.88 (Iba1),
0.83 (c-Fos) and 0.89 (GFP).
Raw 3D image data, along with markers highlighting all detected cells, are available at http://cubic-atlas.riken.jp through an interactive image viewer powered by CATMAID (Saalfeld et al., 2009).
CUBIC-Atlas
CUBIC-Cloud uses CUBIC-Atlas version 1.1 (Murakami et al., 2018) as the reference brain coordinate, to which all individual brain
data were mapped. From the originally published atlas (version 1.0), we added a few minor updates in this study to generate CUBIC-Atlas version 1.1. The updates included (1) slight discontinuity between dorsal and ventral image volume (so-called ’’theta tiledisplacement’’ in Murakami et al. paper) were corrected and (2) region annotation was updated to be compatible with the Allen Brain
Atlas CCFv3 (October 2017). CUBIC-Atlas v1.1 can be downloaded from http://cubic-atlas.riken.jp.
Brain registration
CUBIC-Cloud uses the symmetric image normalization (SyN) algorithm implemented in ANTs library (Avants et al., 2008) to run registration between CUBIC-Atlas (’’fixed’’ brain) and individual brain sample (’’moving’’ brain). First, nuclear staining image of the ’’moving’’ image was downscaled to a voxel size of 50x50x50 mm. Nuclear staining image of CUBIC-Atlas was downscaled to a voxel sizeof 80x80x80 mm. Considering the sample’s physical expansion by clearing treatment (2.2X for CUBIC-Atlas and 1.5X for CUBIC-R+
treated brains), this downscaling operation resulted in an effective voxel size of about 35x35x35 mm in both images. The registration
first computed affine transformation to coarsely align the orientation and size, using mutual information as the optimizer metric.
Subsequently, non-linear warping was computed by SyN algorithm, which optimized the warp field by maximizing the normalized

Cell Reports Methods 1, 100038, June 21, 2021 e4

ll
OPEN ACCESS

Articlecross-correlation (NCC) between the two images under diffeomorphic regularization (Avants et al., 2008). Given image IðxÞ and image
JðxÞ, the NCC value between I and J at the voxel position x is given byNCCðI; J; xÞ =CI; JD
jIjjJj(Equation 1)

where CA; BD represents the inner product taken over a local window with radius R centered at position x. jAj is the L2 norm of thevector computed over a local window with radius R. Here, IðxÞ = IðxÞ  mI ðxÞ means the subtraction of the local mean, where local
mean mI ðxÞ is computed over a local window with radius R centered at position x. R = 4 (voxels) was used throughout our brain
registration.
Other detailed parameters used in the ANTs registration are the following. In the first affine transformation stage, the pseudo ANTs
command below was used.
—transform Affine[0.1] \—metric MI[X,Y,1,128,Regular,0.5] \—convergence [1000x1000x1000,1e-5,15] \—shrink-factors 8x4x2 \—smoothing-sigmas 3x2x1vox
In the second SyN transformation stage, the pseudo ANTs command below was used.
—transform SyN[0.1,3.0,0.0] \—metric CC[X,Y,1,4] \—convergence [500x500x500x50,1e-6,10] \—shrink-factors 8x4x2x1 \—smoothing-sigmas 3x2x1x0vox
In addition, -winsorize-image-intensities [0.05,1.0] and -use-histogram-matching 0 was used throughout.
The representative registration result is visualized in Figure S3I, along with the NCC value heatmap (Figure S3L). To show the reproducibility of the registration, 20 individual brains were mapped onto CUBIC-Atlas with identical registration parameters. The mean
NCC value of each coronal planes were computed and plotted (Figure S3H). 20 independent curves overlap with each other, meaningthat the optimization attempt by the registration reached saturation. NCC value tends to show higher value in the olfactory area andcerebellum, due to the presence of more distinct structural features.
CUBIC-Cloud users can check and validate the registration accuracy of their own brain by looking at the NCC value map and thesummarized plot, which can be downloaded from the ’’Downloads’’ list of the brain detail window.
Registration between CUBIC- and iDISCO-cleared brains
C57BL/6N mouse brain (male, 8-weeks-old, n = 2) were cleared by iDISCO method following the standard protocol (Renier et al.,2014, 2016). The cell nuclei staining was performed by incubating the sample with 2 uM TO-PRO-3 (ThemoFisher #T3605) dissolvedin PTwH solution for 5 days at 37 C. The cleared brain was scanned with LSFM with (X,Y,Z) = (6.5, 6.5, 7.0) mm resolution.
Prior to running registration, the nuclear staining image was downscaled to (X,Y,Z) = (30, 30, 30) mm voxel resolution. Using ANTs,
registration with CUBIC-Atlas was performed using the same parameters as described above. The representative registration resultis shown in Figure S4A. A series of coronal slice images are shown in Figure S4C, along with the corresponding normalized crosscorrelation (NCC) values represented as a heatmap (Figure S4D). The mean of NCC in each coronal slice was plotted in Figure S4B.
As is shown in this plot, the iDISCO-brain registration resulted in similar NCC values as CUBIC-brain registration in most of the brain
areas, indicating that the registration method was able to align the iDISCO-cleared brain with the comparable accuracy. However, theregistration accuracy was poor in the olfactory bulb (Figures S4A–S4C). Indeed, the morphology of olfactory bulb is affected in the
CUBIC clearing process, where the organ is expanded and a gap between left and right bulb is widened. Although we have attempted
to tune the registration parameters to overcome this misalignment, no parameters we tested was able to give accurate alignment in
the olfactory bulb. SyN transformation method implemented in ANTs penalizes the deformation where neighboring deformation vectors are not aligned. This strongly penalizes the deformation where the gap between olfactory bulb is widened or shrunk, because thedeformation vector flips the orientation at the center. We presume that this is the reason why the registration in the olfactory bulb was
not successful, and further research would be needed to overcome this challenge.
Architecture of CUBIC-Cloud
CUBIC-Cloud’s entire application stack is deployed on the cloud computing infrastructures offered by Amazon Web Service (AWS).
The cloud is constructed using the serverless architecture (Adzic and Chatley, 2017). Serverless architecture have zero real instancesthat are always running; instead, the cloud is composed by connecting microservices, which are dynamically invoked by events.
Such cloud design eliminates the cost of idling servers, while allowing to flexibly and instantly scale out the computing power
when the traffic or load to the service increases.

e5 Cell Reports Methods 1, 100038, June 21, 2021

ll
ArticleOPEN ACCESSThe schematic illustration of the cloud architecture is shown in Figure S1. When user accesses the web site, the trafic is firsthandled by CloudFront. CloudFront is responsible for caching static contents, managing SSL/TLS and web application firewall(WAF). Then, static web contents are fetched from S3 bucket and returned to the user. User authentication is handled by Cognito.
Once authenticated, users can access the protected API endpoints securely using json web token (JWT). All REST API requests arerouted by API Gateway and handled by Lambda. Lambda handlers have access to various back-end resources, including the databases and the data buckets. The metadata of the users, brains, notebooks, and studios are stored in DynamoDB. Large data files(such as images and csv tables) are stored in S3.
Once a user uploads the brain data, the upload completion event from S3 triggers a ’’preprocessing’’ task in the Elastic Container
Service (ECS) cluster. Preprocessing includes brain registration, transformation, and data conversion. ECS automatically launches a
new Elastic Compute Cloud (EC2) instance, pulls the Docker container from Elastic Container Registry (ECR), and initiate a new task.
The task execution is orchestrated by StepFunctions. Notebook tasks (i.e. generating plots) are similarly orchestrated by StepFunctions, except that the runtime is either Lambda or Fargate, depending on the required memory size of the task.
Most of the API handlers are written in Python. The cloud resources are managed by AWS CDK library for Python, and the cloud isdeployed using the CloudFormation generated by the CDK library. The user interfaces (UIs) were constructed using HTML/CSS/JavaScript and Vue.js framework.
A web-based whole-brain viewer
CUBIC-Cloud offers a point-cloud based interactive 3D brain viewer, a feature called studio. The viewer is written in JavaScript, andruns in most of the standard web browsers, including Google Chrome and Firefox. It uses WebGL for hardware accelerated 3D
rendering. The core of the point cloud rendering engine was adopted from the open-source project, Potree (2016). Following Potree,
CloudEye uses a specialized point cloud format where the whole point cloud was divided and stored in a multi-resolution hierarchicalstructure (octree structure). This hierarchical data structure enabled the adaptive data querying in response to client’s viewpoint, inwhich a portion of the points near the viewer’s camera was selectively loaded. The octree-formatted point cloud data were automatically generated as a part of the preprocessing task in the CUBIC-Cloud server. Each point can be attached with several attributes,
including the region ID and fluorescent intensity. Points may be colored using these attributes. For example, points may be given
gradient colors based on their fluorescence intensity values.
Users can navigate and explore the whole-brain data by intuitive mouse interactions. Points can be selectively hidden/displayed
based on the region ID. Arbitrary combinations of brains may be overlaid with user-defined colors. Users can also make a slice view,which can be moved or rotated with simple mouse dragging. Users can also grab a point (cell) and query its information, such as
fluorescent intensity.
Privacy and data ownership policy of CUBIC-Cloud
CUBIC-Cloud has rigorously defined the privacy and data ownership policy, which can be viewed at the CUBIC-Cloud’s web site(https://cubic-cloud.com/terms and https://cubic-cloud.com/privacy). In essence, all data stored in the user’s private space solely
belongs to the user and all intellectual properties are protected. When a user decides to publish in the public repository, the depositor
shall grant unrestricted use of the data to all other users in CUBIC-Cloud.
Whole-brain analysis of PV and SST expressing neurons
C57BL/6N wild-type mice brains (8-week-old, n = 4) were cleared, stained, imaged, and analyzed as described in the corresponding sections in STAR Method. Brains were stained with PV antibody (Swant, #PV235; 1/50 dilution; anti-mouse IgG1 secondary Fab fragment conjugated with Cy3 (Jackson ImmunoResearch, #115-167-185)), SST antibody (Millipore, #MAB354; 1/10
dilution; anti-rat IgG secondary Fab fragment conjugated with Alexa 594 (Jackson ImmunoResearch, #112-587-008)) and nuclear staining dye (BOBO-1, Thermo Fisher #B3582). The whole-brain summary of detected PV+ and SST+ cells are provided
in Table S1.
We discussed the PV+ and SST+ cell in the isocortex in the main text. Within the striatum, PV+ cells were almost entirely absent in
the LS and anterior, central, intercalated and medial amygdalar nucleus (AAA, CEA, IA and MEA), as observed previously (McDonald
and Betette, 2001). Distribution in the brain stem faithfully reproduced the previous slice-based immunohistochemical studies (Celio,
1990; Johansson et al., 1984) (Figure 2L). In general, the thalamus contained low numbers of PV+ or SST+ cells, except that dense
PV+ cells were present in the RT and PP. In the hypothalamus, although PV+ cells were sparse, many nuclei contained medium tohigh density of SST+ neurons. Within the midbrain, PV+ cells were particularly abundant in the IC and SNr, while SST+ cells weremost frequently observed in the RAmb. Within the pons and medulla, the NTB, SOC and NLL contained both PV+ and SST+ cellswith relatively high density, while sparsely scattered populations were observed in other areas. In the cerebellum, there were a large
number of PV+ neurons in Purkinje layers. Distinct SST+ cell clusters were found in the NOD and FL.
Whole-brain analysis of ChAT expressing neurons
C57BL/6N wild-type mice brains (8-week-old, n = 4) were cleared, stained, imaged and analyzed as described in the corresponding
sections in STAR Method. Brains were stained with ChAT antibody (abcam, #ab178850; 1/200 dilution; anti-rabbit IgG secondary

Cell Reports Methods 1, 100038, June 21, 2021 e6

ll
OPEN ACCESS

ArticleFab fragment conjugated with Alexa 594 (Jackson ImmunoResearch, #111-587-008)) and nuclear staining (SYTOX-G, Thermo
Fisher, #S7020). The whole-brain summary of detected ChAT+ cells are provided in Table S1.
About half of ChAT+ cells were concentrated in striatum and pallidum (34.5% and 13.8%, respectively). Continuously spreadingfrom these regions, some ChAT+ cells were present in the hypothalamus, including lateral, medial and anteroventral preoptic areas(LPO, MPO, AVP) and SO. ChAT+ neurons were hardly observed in the olfactory area, hippocampus, cortical subplate and thalamus.
These observations were in good agreement with the slice-based immunohistochemical study by Armstrong et al., 1983.
Whole-brain analysis of Th expressing neurons
C57BL/6N wild-type mice brains (8-week-old, n = 4) were cleared, stained, imaged and analyzed as described in the corresponding
sections in STAR Method. Brains were stained with Th antibody (Santa Cruz Biotechnology, #sc-25269; 1/20 dilution; anti-mouse
IgG2a secondary Fab fragment conjugated with Alexa 594 (Jackson ImmunoResearch, #115-587-186)) and nuclear staining (SYTOX-G). The whole-brain summary of detected Th+ cells are provided in Table S1.
The majority of the detected Th neurons were localized in well-known dopaminergic cell groups (A8 to A16) and noradrenergic
cell groups (A1 to A7) (Dahlström and Fuxe, 1964). Dopaminergic cell groups include the RR, SNc, rostral and central linear
nucleus raphe (RL and CLI) and VTA, which form the A8, A9 and A10 in midbrain. Within the hypothalamus, Th neurons
were clustered in the periventricular hypothalamic nucleus, anterior, posterior, intermediate and propotic parts (PVa, PVp,
PVi, PVpo), ARH, ZI and ADP, which form A11-A15 cell groups. Th neurons were numerous in the olfactory area (A16), selectively localized in the glomerular layer. Noradrenergic cell groups formed distinct bands crossing several nuclei in the medulla
and pons, which included the LRN, NTS and DMX, which form A1 and A2. In the pons, a particularly high density was observedin and around LC, which forms A6. No significant population of Th+ cells were observed in the isocortex, hippocampus, corticalsubplate, striatum and pallidum.
Brain nuclei segmentation using alpha shape algorithm
A densely aggregated ChAT expressing cells were found in the LDT and its neighboring regions. To segment this brain region automatically, a rectangular region containing these cells were manually cropped. Then, to remove the isolated cells that was not part ofthe continuous body of the nuclei, the following filter was applied: for each cell in the ensemble, the number of neighboring cells within
the radius 100 mm were counted, and if the count is less than 2, the cell was removed from the ensemble. After this filtering, the polygonal surface enclosing the points was constructed using alpha shape algorithm with alpha radius parameter 300 mm, implemented in
MATLAB. This method was applied to n = 4 brains aligned with CUBIC-Atlas, obtaining four independent boundary surfaces. Using
the same method, cluster of TH expressing cells around LC were segmented (n = 4).
Lastly, the overlap between ChAT-defined boundaries were evaluated by computing the Dice’s coefficient,c = ð2  VV1XV2 Þ=ðV1 + V2 Þ where VV1XV2 is the volume of the overlap between the two polygons whose volumes are V1 and V2 , 
4respectively. The Dice’s coefficient of all possible pairs,= 6, were evaluated. The same analysis was applied to TH-defined2boundaries. Lastly, the overlaps between ChAT and TH boundaries were evaluated by picking all possible pairs, 42 = 16.
Whole-brain analysis of Iba1 expressing cells
C57BL/6N wild-type mice (8-week-old, n = 7 for LPS-administered and control group, respectively) were administered with either
1 mg/kg of LPS or saline via i.p. injection. 24 h after injection, brains were sampled. Subsequently, brains were cleared, stained,
imaged and analyzed as described in the corresponding sections in STAR Method. Brains were stained with Iba1 antibody(Wako, #013-26471; 1/50 dilution; directly conjugated with red dye) and nuclear staining (SYTOX-G). The whole-brain summary ofdetected Iba1+ cells are provided in Table S2.
Whole-brain analysis of Ab plaques
AppNL-G-F/NL-G-F mice (9- to 10-month-old, n = 4) were cleared, stained, imaged and analyzed as described in the corresponding
sections in STAR Method. Brains were stained with -Amyloid (6E10) antibody (Biolegend, #93049; 1/100 dilution; anti-mouse IgG1
secondary Fab fragment conjugated with Alexa 594 (Jackson ImmunoResearch, #115-587-185)) and nuclear staining (SYTOX-G).
Whole-brain analysis of c-Fos profile change induced by LPS
To monitor the sleep duration of mice in an non-invasive manner, we used a respiration-based sleep staging method, SSS (Sunagawa
et al., 2016). C57BL/6N wild-type mice (8-week-old, n = 4 each for LPS-administered and control group) were housed in a SSS chamber for 3 days (basal measurement; LD cycle) prior to the injection. On the fouth day at ZT = 14, mice were administered 150 mg/kg
LPS from Escherichia coli (Sigma-Aldrich, #L2990) via i.p. injection, while control mice were administered saline (Figure S7D). Afterinjection, mice were housed in the SSS chamber for another 24 h to monitor sleep.
Brains for whole-brain imaging were collected in a replicate experiment with the identical conditions as above, except that mice
were housed in DD cycle and that brains were sampled at CT = 16-17 after i.p. injection (n = 4 in total for each group, obtained in 1
batch). Collected samples were cleared, stained, imaged and analyzed as described in corresponding sections in STAR Method.

e7 Cell Reports Methods 1, 100038, June 21, 2021

ll
OPEN ACCESS

ArticleBrains were stained by c-Fos antibody (CST, #2250S; 1/75 dilution; anti-rabbit IgG secondary Fab fragment conjugated with Alexa
594 (Jackson ImmunoResearch, #111-587-008)) and nuclear staining dye (SYTOX-G).
Rabies virus injection
The following AAV vectors were generated de novo by PENN vector core using the corresponding plasmids. AAV serotype 9 CAGFLEx-TCb (1:531013 gp/ml) was made using the plasmid described previously (Miyamichi et al., 2013). Here TCb stands for TVAmCherry expression cassette optimized to increase mCherry brightness. To generate AAV serotype 9 CAG-FLEx-oG (4:53 1013
gp/ml), engineered and optimized glycoprotein (oG) (Kim et al., 2016) sequence was ligated to pAAV-FLEX sequence from pAAVFLEX-GFP (Addgene).
Preparation of rabies virus was conducted by using the RVG-GFP, B7GG and BHK-EnvA cells as previously described (Osakada
and Callaway, 2013). The EnvA-pseudotyped RVG-GFP+EnvA titer was estimated to be 1:03109 infectious particles/ml based onserial dilutions of the virus stock followed by infection of the HEK293-TVA800 cell line (a gift from Dr. Edward Callaway at Salk
Institute).
For trans-synaptic tracing using rabies virus, about 20 nL of mixture of AAV9 CAG-FLEx-TCb and CAG-FLEx-oG (diluted to 1:53
1012 gp/ml each) was injected into the ARH of Kiss1-Cre mice. The first AAV transduced a TVA receptor (fused with mCherry) for
EnvA. The second AAV transduced RV glycoprotein (oG) playing a predominant role in the trans-synaptic transport of RV. The injection coordinate was P1.1, L0.2, V5.9 (distance in mm from the Bregma for the posterior [P], and lateral left [L] positions and from thebrain surface for the ventral [V] position). Three weeks later, 30 nL of Rabies G-GFP+EnvA was injected into the same brain region toinitiate trans-synaptic tracing. Because there is no cognate receptor for EnvA in the mouse brain, RVG+EnvA only infects TVA-expressing cells. oG expression from the second AAV complements the RVG, allowing retrograde monosynaptic tracing from Cre-expressing cells. Seven days later, brains were sampled for CUBIC treatment.
Whole-brain analysis of RV-injected brains
After AAV and RV injection, Kiss1-Cre mice (13- or 14-week-old at the time of brain sampling) were cleared, stained, imaged andanalyzed as described in the corresponding sections in STAR method. Brains were stained with nuclear staining (RedDot2, Biotium,#40061).
As a negative control experiment, AAV and RV injection was performed using BALB/c wild-type mice brain (n = 3). After clearing,
the whole-brain image was obtained by LSFM. No GFP or mCherry signals were observed by manual inspection, confirming theabsence of Cre-indepentent leakage of AAV vectors and specificity of the virus delivery.
Starter cells were searched by identifying dual positive (mCherry+ and GFP+) cells. For each channel, cell counting was independently performed, and the center of the mass of the detected cell was obtained. For each mCherry+ cells, if a GFP+ cell was present
within a distance of 24 mm, the cell was counted as starter. Note that because the cleared tissue was expanded by a factor of  1.5,
this was roughly 16 mm in untreated tissue. Occasionally, slight voxel shift (typically no more than 4 voxels) occurred between GFP
and mCherry channels, which was presumably caused by slight misalignment between the 488 nm and 594 nm illumination laser or
re-focusing of the microscope. To correct this, small 3D volumes with distinct features (typically (X,Y,Z) = (50,50,20) voxel volume, n =4 or n = 3) from mCherry and GFP channels were cropped, and the voxel shift was computed by registering two images using ANTs,
where transformation was restricted to only translation. Then, the cell coordinates were corrected by the mean of the computed shift.
To carry out statistical analysis of input cell numbers between male and female brains, we used the normalized cell count, nnorm;i ,where i represents the ID of the brain region. If we let the raw cell number of each brain region be nraw;i , nnorm;i is simply expressed asP
nnorm;i = nraw;i =nraw;i .
i

QUANTIFICATION AND STATISTICAL ANALYSIS
Statistical analysis
Statistical analysis used in each data are described in detail in the corresponding sections in the main text, supplementary materials
or the figure legends. n (the number of samples) and p-values are indicated in the corresponding sections in the main text, supplementary materials or the figure legends.

Cell Reports Methods 1, 100038, June 21, 2021 e8

Cell Reports Methods, Volume 1

Supplemental information

CUBIC-Cloud provides an integrative
computational framework toward
community-driven whole-mouse-brain mapping
Tomoyuki Mano, Ken Murata, Kazuhiro Kon, Chika Shimizu, Hiroaki Ono, Shoi
Shi, Rikuhiro G. Yamada, Kazunari Miyamichi, Etsuo A. Susaki, Kazushige
Touhara, and Hiroki R. Ueda

Mano et al., 2020

Figure S1. Architecture of CUBIC-Cloud1

Schematic diagram showing the architecture of CUBIC-Cloud (see STAR Method), Related to Figure 1

1

Mano et al., 2020

Figure S2. Imaging 1.0 µm-diameter Fluorescent Beads Embedded in Cleared Tissue, Related to Figure 1
12

1.0 µm-diameter fluorescent beads embedded in cleared tissue were imaged using macro-zoom LSFM (1X magnification, 6.5 µm voxel size) fromthe ventral side (see STAR Method).

7(A) Representative sagittal slice (virtually reconstructed from horizontal-major 3D image stack) showing the fluorescent beads embedded in tissue.
(B) Bead spot profiles measured in six brain regions. Lateral and axial profiles were fitted with Gaussian, respectively, and the fitted curves areshown along with the raw data points (see STAR Method). The fitted sigma values (with 95% confidence interval) of the Gaussian are also shown.
The number of particles used to average are shown in the graph.
(C) Mean bead fluorescence intensity measured in six brain regions. Error bar represents the standard deviation.

8

Brain region acronyms follow the ontology defined by Allen Brain Atlas.

3
4
5
6

2

Mano et al., 2020

Figure S3. Validation of Cell Counting and Registration Methods, Related to Figure 1
1
2
3
4
5
6
7
8
9

(A) The cell detection workflow overview (see STAR Method).
(B-H) Accuracy evaluation of the cell counting algorithm. True positive rate (TPR), positive predictive value (PPV) and F1 score were evaluated forseven label types in more than five brain regions. Ground truth was prepared by two independent annotators (see STAR Method).
(I) Normalized cross-correlation (NCC) value between two brains after registration. Mean NCC value of each coronal slice are plotted. 20 brainsfrom different mice were independently registered onto CUBIC-Atlas. Individual profiles (thin lines with light colors) as well as the mean (thick greenline) are shown.
(J) Representative brain registration result. CUBIC-Atlas (cyan) and registered brain (magenta) are overlaid.
(K) Voxel-wise NCC value map computed for the images shown in (J).
Brain region acronyms follow the ontology defined by Allen Brain Atlas.

3

Mano et al., 2020

Figure S4. Registration of iDISCO-cleared brain and CUBIC-cleared brain, Related to Figure 1
1
2
3
4
5
6

(A) Representative images of after registration between iDISCO- and CUBIC-cleared brains. The brains were stained with nuclear staining dyes.
(B) Normalized cross-correlation (NCC) value between two brains after registration. Mean NCC values of each coronal slice are plotted. Shown ingreen colors are the results of iDISCO-cleared brains (n = 2). Shown in pink is the value of CUBIC-cleared brains (mean of n = 7 brains).
(C) Representative brain registration result. iDISCO (magenta) and CUBIC(cyan) are overlaid. Pointed by arrowheads are the region where thealignment was not accurate.
(D) Voxel-wise NCC value map computed for the images shown in (C).

4

Mano et al., 2020

Figure S5. Whole-brain analysis of PV, SST, ChAT, TH and Iba1 Expressing Cells, Related to Figure 2
5

Mano et al., 2020

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

(A) Heatmap showing PV expressing cell density.
(B) Layer-wise average density of PV expressing cells.
(C) Heatmap showing SST expressing cell density.
(D) Layer-wise average density of SST expressing cells.
(E) Heatmap showing ChAT expressing cell density.
(F) Layer-wise average density of ChAT expressing cells.
(G) Heatmap showing Iba1 expressing cell density.
(H) Layer-wise average density of Iba1 expressing cells.
(I) The distribution of PV expression levels per cell in the isocortex. Each column (i.e. the region) was normalized so that the peak equals to 1.0.
(J) The values of SSp-m, VISp, ILA and ECT were extracted from (I) and replotted as a distribution curve.
(K-N) Raw PV immunostaining images in SSp-m (K), VISp (L), ILA (M), and ECT (N).
(O) The distribution of SST expression levels per cell in the isocortex. Each column (i.e. the region) was normalized so that the peak equals to 1.0.
(P) The values of SSp-m, VISp, ILA and ECT were extracted from (O) and replotted as a distribution curve.
(Q-T) Raw SST immunostaining images in SSp-m (Q), VISp (R), ILA (S) and ECT (T).
Brain region acronyms follow the ontology defined by the Allen Brain Atlas.

6

Mano et al., 2020

Figure S6. Iba1 expression level changes upon LPS administration, Related to Figure 23(A) Mean Iba1+ cell density in the isocortex. Saline (gray) and LPS (blue) groups are compared. Data are shown as mean ± STD (n = 7 for eachgroup).
(B) Mean Iba1 expression level per cell in the isocortex.

4(C) Heatmap showing mean Iba1 expression level per cell in all brain regions outside the isocortex, comparing saline and LPS groups.

12

7

Mano et al., 2020

Figure S7. Whole-brain analysis of c-Fos expression under LPS administration, Related to Figure 4
12
34(D) Voxel-wise p-value heatmap showing the affected regions by LPS. P-values of individual voxels were computed by c-Fos+ cell count between
saline- and LPS-administered groups. The color lookup table is log scaled (base 10), where red color represents the regions that were activated(i.e. more c-Fos+ cells) by LPS, and blue represents the repressed regions. Voxels with no significance (p > 0.05) were uncolored. Background is
nuclear staining image of CUBIC-Atlas for navigation. Step between consecutive slices is 0.34 mm.

8Mano et al., 2020

Figure S8. Additional Data of Rabies Virus Tracer Experiment, Related to Figure 5
1
2
3
4
5
6
7
8
9
10
11
12

(A) P-value heatmap where the number of input cells were compared between male and female brains. The color lookup table is log scaled (base10), where red color represents the regions where more input cells were found in female brains, and blue represents the inverse. Regions with nostatistical significance (p > 0.05) were assigned a gray color.
(B) Raw GFP (black) and nuclear staining (RedDot2, purple) images around lateral septal nucleus (LS) and medial prepoptic area (MPO). The
images are digitally reconstructed sagittal sections. Maximum intensity project (MIP) spanning 300 µm thickness.
(C-H) The plot shows the normalized input cell count in regions where sexual dimorphisms were suggested.
Of note, using RV injection and slice-based observation, Wang et al., 2015 investigated the input cell population of pro-opiomelanocortin (POMC)
neurons and agouti-related peptide (AgRP) neurons in the ARH, another dominant cell types in the ARH. The brain areas containing the input cells
to ARHKiss1+ neurons largely overlaped with those of POMC neurons and AgRP neurons. In some areas, however, interesting differences wereobserved. For example, in ventral tegmental nucleus (VTA) and nucleus incertus (NI), no input cells were detected for ARHKiss1+ , while some input
cells were reported to exist for POMC and AgRP. On the other hand, basomedial amygdalar nucleus (BMA), posterior amygdalar nucleus (PA),18

septofimbrial nucleus (SF), central amygdalar nucleus (CEA), substantia innominate (SI), triangular nucleus of septum (TRS), posterior intralaminarthalamic nucleus (PIL), midbrain reticular nucleus (MRN) and parabrachial nucleus (PB) contained small number of input cells to ARHKiss1+ neurons(Figure 5E,F,G), while they were not reported for POMC and AgRP neurons. This absence of input cells may reflect the actual biological differences,or it may reflect the superior sensitivity of our experimental methods to detect sparse populations. Further investigations are needed to draw
conclusions.
*p < 0.05, **p < 0.01; Welch’s t-test.

19

Brain region acronyms follow the ontology defined by the Allen Brain Atlas.

13
14
15
16
17

9