Resource

Whole-Body Profiling of Cancer Metastasis with
Single-Cell Resolution
Graphical Abstract

Authors
Shimpei I. Kubota, Kei Takahashi,
Jun Nishida, ..., Kazuki Tainaka,
Kohei Miyazono, Hiroki R. Ueda

Correspondence
miyazono@m.u-tokyo.ac.jp (K.M.),
uedah-tky@umin.ac.jp (H.R.U.)

In Brief
Kubota et al. find that CUBIC-cancer
analysis can profile cancer metastasis ata single-cell level in a whole-animal
context. In addition, CUBIC-cancer
analysis can bridge the resolution gap
between in vivo bioluminescence imaging
and histology. This analytical pipeline
would contribute to the profiling of
biological functions of whole organs.

Highlightsd

RI-optimized CUBIC protocol enables whole-bodyexamination of cancer modelsd

CUBIC is applicable to analysis of micrometastases at singlecell resolutiond

CUBIC analysis bridges the resolution gap between in vivo
imaging and histologyd

CUBIC-cancer analysis is useful for profiling biologicalprocesses in whole organs

Kubota et al., 2017, Cell Reports 20, 236–250
July 5, 2017 ª 2017 The Author(s).
http://dx.doi.org/10.1016/j.celrep.2017.06.010

Cell Reports

Resource
Whole-Body Profiling of Cancer Metastasiswith Single-Cell Resolution
Shimpei I. Kubota,1,5 Kei Takahashi,2,5 Jun Nishida,2 Yasuyuki Morishita,2 Shogo Ehata,2 Kazuki Tainaka,1,3
Kohei Miyazono,2,* and Hiroki R. Ueda1,4,6,*
1Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033,

Japan
2Department of Molecular Pathology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033,
Japan
3Brain Research Institute, Niigata University, 1-757 Asahimachidori, Chuo-ku, Niigata 951-8585, Japan
4Laboratory for Synthetic Biology, RIKEN Quantitative Biology Center, 1-3 Yamadaoka, Suita, Osaka 565-5241, Japan
5These authors contributed equally
6Lead Contact*Correspondence: miyazono@m.u-tokyo.ac.jp (K.M.), uedah-tky@umin.ac.jp (H.R.U.)
http://dx.doi.org/10.1016/j.celrep.2017.06.010

SUMMARY

Stochastic and proliferative events initiated from asingle cell can disrupt homeostatic balance andlead to fatal disease processes such as cancer
metastasis. To overcome metastasis, it is necessary
to detect and quantify sparsely distributed metastatic cells throughout the body at early stages. Here,
we demonstrate that clear, unobstructed brain/body imaging cocktails and computational analysis(CUBIC)-based cancer (CUBIC-cancer) analysis
with a refractive index (RI)-optimized protocol enables comprehensive cancer cell profiling of thewhole body and organs. We applied CUBIC-cancer
analysis to 13 mouse models using nine cancer celllines and spatiotemporal quantification of metastatic
cancer progression at single-cell resolution. CUBICcancer analysis suggests that the epithelial-mesenchymal transition promotes not only extravasation
but also cell survival at metastatic sites. CUBICcancer analysis is also applicable to pharmacotherapeutic profiling of anti-tumor drugs. CUBIC-cancer
analysis is compatible with in vivo bioluminescenceimaging and 2D histology. We suggest that a scalable analytical pipeline with these three modalities
may contribute to addressing currently incurable
metastatic diseases.
INTRODUCTION
Rudolf Virchow, the father of modern pathology, declared thatthe body is a ‘‘cell state, in which every cell is a citizen’’ and disease is ‘‘merely a conflict of citizens in this state brought about
by the action of external forces’’ (Nicholls, 1927). His cell theory
brought the current concept of cellular pathology based on
microscopic histological analysis to the early model of pathol-ogy, which largely relied on clinical symptoms and gross appearances in patients. Thereafter, comprehensive analysis anddecoding of the more than 100 billion cells comprising the
mammalian body have become one of the ultimate goals inbiology and medicine. Although recent advances in a series of
live-imaging systems have provided important breakthroughs
to examine dynamics at the whole-body scale, it is still difficult
to clarify the generation and progression of diseases with stochastic and proliferative processes, such as autoimmune andmalignant neoplastic diseases (Saadatpour et al., 2017; Takahashi et al., 2015). In particular, tumor metastasis can be initiatedby a few cancer cells, and use of current imaging systems is achallenge due to their limited spatial resolution. Additionally,
according to metastasis images from these imaging systems,
3D reconstitution of possibly affected organs by 2D histology
is labor intensive. To bridge the spatial resolution gap between
these approaches, a fundamentally novel analytical platform isrequired. To detect and quantify sparsely distributed metastatic
cells at early stages of cancer metastasis, it is necessary todetect metastatic cells throughout the whole body and visualize
individual tumor microenvironments.
Tissue-clearing-based 3D imaging is a most promising strategy for the visualization of the entire mouse body/organs at
single-cell resolution (Susaki and Ueda, 2016). Recently, various
kinds of tissue-clearing protocols have been developed,
including organic solvent-based methods (Dodt et al., 2007;€rk et al., 2012; Renier et al., 2014), hydrophilic chemicalErtu
based methods (Cuccarese et al., 2017; Hama et al., 2011; Keet al., 2013), and hydrogel-based methods (Chung et al.,2013). Spalteholz (1914) first established the basic concept ofhuman tissue clearing based on organic solvents. Although tissue-clearing protocols have been available for a century, it is still
difficult to comprehensively visualize sparsely distributed pathogenic cells throughout the mouse body using current tissueclearing protocols.
In this study, we first investigate optimal refractive indices (RIs)
ranging from 1.44 to 1.52 against each organ, and we demonstrate that an RI of 1.52 has the highest clearing efficiency amongmajor organs. Then, we describe a highly efficient clearing

236 Cell Reports 20, 236–250, July 5, 2017 ª 2017 The Author(s).
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Figure 1. Whole-Body and Whole-Organ Clearing and Imaging with Optimized RI Solutions(A) Whole-body and whole-organ clearing protocols. The whole-organ clearing protocol (left) and the whole-body clearing protocol (right) are shown.
(B) Bright-field images of whole organs (brain, heart, lung, liver, and kidney) from C57BL/6N mouse (13 weeks old, male) after RI adjustment (RI = 1.44, 1.48, and1.52). Control organs were stocked in PBS buffer (RI = 1.33).
(C) Average parallel transmittance of RI-adjusted brain and lung. Parallel light transmittance around the visible region (450–750 nm) of organs was measured(n = 2). Data represent mean ± SD.

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protocol for the whole adult mouse body using a more efficientdelipidation cocktail, clear unobstructed brain/body imaging
cocktails and computational analysis (CUBIC)-L, and an optimized RI-matching medium, CUBIC-R. The specimens treatedwith this clearing protocol are clear enough to use for 3D imagingof the entire mouse body. The combination of light-sheet fluorescent microscopy (LSFM) and confocal laser-scanning microscopy (CLSM) enables the detection of cancer micrometastases
in various kinds of mouse models. Our CUBIC-cancer analysis iscompatible with the in vivo bioluminescence imaging systemand conventional 2D histology, successfully bridging the spatial
resolution gap between these approaches. CUBIC-cancer analysis is applied here to 13 mouse models using nine cancer celllines, and it distinguishes different hematogenous metastatic
patterns, such as angiogenic growth and co-optive growth.
Moreover, CUBIC-cancer analysis enables the spatiotemporalquantification of metastatic cells even in the early stages ofmetastasis formation. In addition, we investigated the in vivorole of transforming growth factor b (TGF-b) signaling in lungmetastasis, and we applied an in vivo system to evaluate thetherapeutic effect of anti-cancer drugs on cancer metastasis.
CUBIC-cancer analysis provides a fundamental platform forthe analysis of the metastatic growth and progression of cancer
throughout the body.
RESULTS
Optimized RI Cocktail Enables Whole-Body andWhole-Organ Clearing in Adult Mice
Previously, we achieved the whole-body imaging of postnatal
day 1 mice at single-cell resolution by whole-body clearing
based on a ScaleCUBIC-1 reagent (Tainaka et al., 2014). However, it remains difficult to sensitively and comprehensively
detect the distribution of cancer cells that have metastasized
to organs throughout the body of adult mice because of an insufficient delipidation efficiency and the unoptimized RI value of the
ScaleCUBIC-1 reagent. To address these issues, we have performed comprehensive chemical profiling of hydrophilic chemicals to characterize highly effective delipidation reagents andwater-soluble high-RI reagents (T.C. Murakami and K. Tainaka,
unpublished data). Through a further combinatorial analysis,
we identified the optimized cocktail of 10 w%/10 w% N-butyldiethanolamine/Triton X-100 (termed CUBIC-L) for delipidation.
CUBIC-L enabled the complete delipidation of isolated paraformaldehyde (PFA)-fixed organs within 2 to 5 days, depending on
the organ without pre-treatment with CUBIC-perfusion andquenching of proteins (Figure 1A, left). We found that CUBIC-L
could also permeabilize skin-detached body samples of adultmice using the CUBIC-perfusion protocol and subsequent delipidation for 7 days (Figure 1A, right; Supplemental Experimental
Procedures).
As for the RI-matching step, there is an ongoing controversy
about the most appropriate RI for permeabilized tissues (Tainaka
et al., 2016). The RI for each intact biological tissue may dependon the contents and densities of lipids, proteins, and other constituents (Johnsen and Widder, 1999; Tuchin, 2015). Since hydrophilic RI media with an RI over 1.52 were likely to precipitate
at room temperature, we evaluated RI-matching cocktails
composed of antipyrine and nicotinamide with RIs ranging
from 1.44 to 1.52 for clearing permeabilized major organs,considering practical use. After a 2-day treatment, all majororgans appeared to be most efficiently transparentized by the
RI-matching cocktail with an RI of 1.52 (Figures 1B and S1A),
termed CUBIC-R, which is composed of 45 w%/30 w% antipyrine/nicotinamide. Indeed, the transmittance of the chemicallytreated organs increased as the RI value increased (Figures 1C
and S1B). We noted that all organs treated with CUBIC-L and
CUBIC-R showed significantly high transmittance in the 450 to
750 nm range, probably due to the high decoloring ability of Nbutyldiethanolamine in CUBIC-L (Figure S1C). These results
prompted us to render the whole-body clearing of adult miceusing CUBIC-R (RI = 1.52) as a final RI-matching step. The transmission images of adult mice demonstrated that the new protocol markedly transparentized adult mice to visualize caudal
vertebrae in ventral to dorsal (V-D) images (Figure 1D).
The New CUBIC Protocol Enables the Whole-Body andWhole-Organ Imaging of Adult Mice by 3D Nuclear
Counterstaining and Immunohistochemistry
Simultaneous visualization of cancer cells, overall organ structures, and related molecular markers would facilitate our understanding of the underlying molecular mechanisms of cancer
metastasis. For the comprehensive analysis of cancer metastasis, we initially carried out nuclear counterstaining with
RedDot2 or propidium iodide (PI) to acquire whole-body andwhole-organ structural images using LSFM according to our previous staining protocols (Figure 1E) (Susaki et al., 2014; Tatsuki
et al., 2016). The resulting 3D-reconstituted image of each organ
enabled the visualization of anatomical structural images even in
deeper regions (Figure S1D). The new CUBIC protocol allowed
the whole-body imaging of adult mice, clearly visualizing the internal structures of cardiovascular and abdominal organs (Figures 1F and S1E).
Next, we investigated whether the new CUBIC protocol iscompatible with 3D immunohistochemistry. The brains of adultmice were co-stained with RedDot2 and anti-a-smooth muscle(D) Bright-field images of whole body of C57BL/6N mouse (6 weeks old, male) treated with CUBIC-R. After delipidation and decolorization, adult mouse bodies
were treated with PBS buffer (upper) or CUBIC-R (lower).
(E) Whole-body and whole-organ staining protocols. The whole-body staining protocol with PI (left) and the whole-organ staining protocol with RedDot2 or
fluorescently labeled primary antibody (right) are shown.
(F) The 3D-reconstituted (3D) body images of PI-stained adult C57BL/6N mouse (10 weeks old, male). Whole-body imaging (WBI), thoracic organ imaging, andabdominal organ imaging are shown.
(G) The 3D-immunostained organ images. The brain from C57BL/6N mouse (15 weeks old, male) was stained with RedDot2 and fluorescein isothiocyanate(FITC)-conjugated anti-a-SMA antibody. 3D (upper) and x-y plane (XY) (lower) images are shown. The white inset was magnified next to the XY image.
See also Figure S1.

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Figure 2. Whole-Body Imaging of Cancer Metastasis at Single-Cell Resolution(A) Whole-body and whole-organ staining protocols. The whole-body staining protocol with PI (left) and the whole-body staining protocol with RedDot2 (right) are
shown.
(B) Whole-body imaging of the experimental liver metastasis model by splenic injection with Panc-1 cells in BALB/c-nu/nu mice. The bioluminescence image(leftmost), WBI, images around the metastatic sites in the liver (L) and pancreas (P) with CUBIC-cancer analysis (second and third from left), magnified 3D and XY
images (fourth and fifth from left) (Panc-1: GFP, nuclei: PI), and representative images of HE staining after CUBIC-cancer analysis (rightmost) are shown.

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actin (a-SMA) antibody according to the staining protocol (Figures 1E and 1G). Spatial distributions of smooth muscle cells
and lymphatic endothelial cells were simultaneously visualized
in the lung by co-staining of a-SMA and vascular endothelial
growth factor receptor 3 (VEGFR3) (Figure S1F). Hence, our
CUBIC protocol was also applicable to 3D multiple immunohistochemical labeling. These results indicate that the new CUBIC
protocol is a powerful tool for comprehensive pathologicalanalysis.
CUBIC-Cancer Analysis Enables the Whole-Body
Imaging of Cancer Metastasis at Single-Cell Resolution
To bridge the resolution gap between in vivo bioluminescenceimaging and 2D histology with our scalable CUBIC-based cancer (CUBIC-cancer) analysis, we established cancer cells stably expressing firefly luciferase and fluorescence proteins,
and we used them in mouse models (Figure 2A). First, we employed an experimental liver metastasis model by splenic injection of human pancreatic cancer cells (Panc-1). Nine weeks
after injection, bioluminescence signals were detected in theabdomens of the mice (Figure 2B, left panel). We applied the
CUBIC-cancer analysis and PI-staining protocol to this mousemodel, and then we acquired whole-body images with LSFM
(Figure 2A). As a result, fluorescence signals were successfully
visualized in several organs, suggesting that the CUBIC-cancer
analysis is applicable to the fluorescent proteins (Figure 2B,
middle panels). In addition, magnified 3D images of these organs were obtained with CLSM in the CUBIC-cancer analysis.
We noted that the resolution of these 3D images was highenough to discriminate individual cancer cells and comparable
to that of 2D hematoxylin and eosin (HE) slice images, whichwere obtained after CUBIC-cancer analysis (Figure 2B, rightpanels).
As another pathway of cancer metastasis, peritoneal dissemination model mice were generated by the intraperitoneal injection of human pancreatic cancer cells (SUIT-2). Twenty-twodays after injection, mCherry signals from SUIT-2 cells wereobserved in the liver and small intestine by CUBIC-cancer analysis (Figure 2C), roughly corresponding to those of bioluminescence signals from in vivo bioluminescence imaging (Figure S2A,
left panel). Combined with counterstained confocal images, we
succeeded in the direct quantification of the cell numbers of cancer colonies (Figures 2C and 2D). The CUBIC-cancer analysis
also recapitulated cancer dissemination in other mice with
SUIT-2 cells (Figures S2B and S2C), indicating the wide applicability of the CUBIC-cancer analysis protocol.
To prepare a syngeneic tumor model, we orthotopically injected murine renal cancer cells (Renca) into wild-type BALB/cmice. Although the formation of a primary renal tumor was visualized 2 weeks after injection, it was difficult to detect cancer
metastasis to other organs by in vivo bioluminescence imaging(Figure S2A, right panel). In contrast, CUBIC-cancer analysis revealed that numerous metastatic colonies had widely spread tothe lung, liver, pancreas, and mesenteric lymph nodes (Figures
2E and S2D). Using CUBIC-cancer analysis combined with
LSFM and CLSM, we also succeeded in the direct quantificationof the cell numbers of these dispersed metastatic colonies (Figures 2F and S2E). Taken together, CUBIC-cancer analysis
makes it possible to not only detect the dispersion of metastatic
cancer cells throughout the entire body with high sensitivity but
also determine the spatial distribution of individual cancer cells
at single-cell resolution. In addition, CUBIC-cancer analysis iscompatible with in vivo bioluminescence imaging and 2D
histology.
Whole-Organ CUBIC-Cancer Analysis Provides a
Systematic, Robust, Widely Applicable, andImmunolabeling-Compatible Analytical Platform forCancer Metastasis
After the comprehensive detection of cancer metastasis
throughout the body, the whole-organ CUBIC-cancer analysis
would globally delineate the metastatic landscape of eachcancer metastasis model. To this end, whole-organ clearing
and staining were conducted according to Figure 3A. Initially,
we quantified the growth of primary tumors in orthotopic
models with SUIT-2 cells and OS-RC-2 cells (Figure S3A).
CUBIC-cancer analysis also permitted us to visualize metastatic colonies in whole organs, including the lung, liver, andintestine (Figure 3B). The colony number and volume of themetastasis in each organ were calculated (Figure S3B).
We also exemplified CUBIC-cancer analysis with a Panc-1
cell liver metastasis model (Figure S3C). This highly reproducible analysis would overcome enduring issues in our understanding of in vivo metastatic progression in various cancers.
Thus, CUBIC-cancer analysis enabled the complete quantification of metastatic colonies in an individual tumor-bearing
mouse.
To verify CUBIC-cancer analysis as a general purpose platform, several lung metastasis models derived from various typesof cancer cells were examined (Figure 3C). The detected redcolor signals were shown to have originated from mCherry, using
immunohistochemical staining with anti-mCherry antibody (Figures 3D and S3D). In the experimental metastasis models, allmouse cancer cells, i.e., 4T1, B16F10, and Renca cells, tended
to grow to large colonies, while the morphologies of these colonies were all different. In case of experimental metastasis(C) Whole-body imaging of the experimental peritoneal dissemination model by intraperitoneal injection with SUIT-2 cells in BALB/c-nu/nu mice. WBI andabdominal organ images in liver (L) and intestine (I) (leftmost and second from left) and magnified 3D and XY images (third from left and rightmost) are shown(SUIT-2, GFP; nuclei, PI).
(D) Quantification of the cell number and the volume of metastatic colonies. Spot analysis and surface analysis were applied to the magnified images in (C).
(E) Whole-body imaging of the spontaneous metastasis model with orthotopic injection of Renca cells in BALB/c mice. WBI and organ images in lung (Lu),heart (H), liver (L), kidney (K), pancreas (P), and lymph nodes (LN) (leftmost and second from left) and magnified 3D and XY images (third from left and rightmost)are shown (Renca, mCherry; nuclei, RedDot2).
(F) Quantification of the cell number and the volume of metastatic colonies. Spot analysis and surface analysis were applied to the magnified images in (E).
See also Figure S2 and Table S1.

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Figure 3. Whole-Organ Imaging of Various Cancer Metastasis Models(A) Whole-organ staining protocols with RedDot2 or antibody.
(B) Whole-organ imaging of the spontaneous metastasis model with orthotopic injection of SUIT-2 cells in BALB/c-nu/nu mice. The bioluminescence image(leftmost) and primary tumors in the pancreas and the metastatic colonization in the distant organs (second, third, and fourth from left and rightmost) are shown(SUIT-2, mCherry; nuclei, RedDot2).
(C) Whole-lung imaging of several metastasis models. In the experimental lung metastasis model, cancer cells (MDA-MB-231, A549, 4T1, B16F10, and Renca)
were intravenously injected in mice. In case of spontaneous metastasis, Caki-1 cells were orthotopically injected into the kidney (cancer cell, mCherry; nuclei,
RedDot2).
(D) Whole-lung 3D immunohistochemistry with anti-mCherry antibody (cancer cell, mCherry; anti-mCherry, Alexa Fluor 647). White insets were magnified next to
the whole-lung images in (C) and (D).
See also Figure S3.

with human cancer cells, MDA-MB-231 cells also formed largecolonies throughout the lungs compared to A549 cells. In
contrast, in spontaneous lung metastasis with Caki-1 cells,
metastatic colonies were observed throughout the lungs,
but their volumes were small. Overall, we succeeded in applying

CUBIC-cancer analysis to 13 different mouse models with ninecancer cell lines (Table S1).
These results demonstrate that whole-organ CUBIC-cancer
analysis is a systematic, robust, widely applicable, and immunolabeling-compatible analytical platform for cancer metastasis.

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Figure 4. 3D Analysis of the Metastatic Patterns in Experimental Brain Metastasis Models(A) Scheme of the experimental procedure.
(B) Whole-brain imaging of the experimental brain metastasis models with MDA-231-D cells or OS-RC-2 cells in BALB/c-nu/nu mice. The 3D images of thebrain samples are shown (upper, MDA-231-D at day 28; lower, OS-RC-2 at day 40; cancer cell, mCherry; a-SMA, FITC; nuclei, RedDot2). Animal number of eachgroup is n = 6.

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CUBIC-Cancer Analysis Enables Monitoring of the
Different Patterns of Brain Metastasis Formation with
Blood Vessels in 3D
Brain metastasis is frequently observed in melanoma and someother cancers, and it results in a high risk of mortality (Gavrilovic
and Posner, 2005; Nussbaum et al., 1996). Recently, twophoton microscopy-based analytical methods have clarified
the correlation between brain metastasis and the cerebral blood
vasculature (Carbonell et al., 2009; Kienast et al., 2010). Nevertheless, quantitative and statistical analyses of the spatial
distribution of all metastatic colonies throughout the brain are
still challenging. Thus, we investigated whether our CUBIC-cancer analysis can be used to classify the potentially different
types of brain metastasis by quantitatively and statisticallyanalyzing metastatic colonies in the entire organ (Figure 4A).
The brain metastasis model of MDA-231-D cells was comparedwith another model using OS-RC-2 cells. Although there appeared to be no distinct difference in metastatic features between these models by in vivo bioluminescence imaging(Figure S4A), CUBIC-cancer analysis clearly showed that metastatic colonies in these models exhibited different histologicalpatterns (Figure 4B).
Many colonies of MDA-231-D cells were localized with
a-SMA-positive vascular smooth muscle cells. On the other
hand, most colonies of OS-RC-2 cells were distant from
a-SMA-positive vascular smooth muscle cells (Figure 4B). In
addition, these metastatic morphological features were also
identified by the histological analysis based on HE staining
with or without CUBIC-cancer analysis (Figure S4B). To quantify
the morphological features, we used the index ‘‘volume per surface area,’’ which was positively correlated with the roundnessof the colony, and we found a significant difference between
the two metastatic models (Figure 4C). Furthermore, the colonies of MDA-231-D cells tended to be smaller and closer tothe blood vessels than those of OS-RC-2 cells (Figure S4C).
These results suggest that the colony formation of OS-RC-2
cells in the brain may be dependent on cancer cell-mediatedneovascularization (angiogenic growth), whereas MDA-231-D
cells may be able to migrate toward pre-existing vasculaturesand hijack them during the process of metastasis (co-optive
growth).
For elucidating the mechanism of cancer metastasis, it isimportant to determine whether brain metastatic foci are generated by a single cell. To test this, equal numbers of mCherry- or
GFP-expressing OS-RC-2 cells were mixed and used for intracardiac injection into the same mouse. CUBIC-cancer analysis
revealed that most mCherry-positive cells in the brain wereseparate from the colonies of GFP-positive cells (Figure 4D),
thus revealing the metastatic properties of individual cancer
cells.

CUBIC-Cancer Analysis Is Applicable to Statistical
Spatiotemporal Analysis during the Initial Steps ofMetastatic Progression
The statistical spatiotemporal analysis of cancer metastasis in an
entire organ has the potential to be a powerful tool for cancer
research. To visualize time-dependent cancer metastasis, anexperimental lung metastasis model with MDA-231-D cells
was used in combination with CUBIC-cancer analysis. We thencarried out whole-lung imaging of metastatic foci or colonies(Figure 5A). To ensure the non-biased exhaustive detection ofsize-variable metastatic foci, the average volume of nuclei wasadopted as the threshold for focus detection (Figure S5A). Immunodeficient BALB/c-nu/nu mice and immunocompetent BALB/cmice were intravenously injected with MDA-231-D cells, andmetastatic foci were detected by CUBIC-cancer analysis 1 hr
after injection (Figures 5B and 5C). In total, three to four miceat each time point were analyzed (Figures S5B and S5C).
In BALB/c-nu/nu mice, numerous metastatic foci spreadthroughout the lung were drastically eliminated within 1 day after
injection (Figure 5D). Although the number of foci continued todecrease after day 1, the average volume of the foci markedly
increased from day 3, suggesting metastatic colonization. On
the other hand, in BALB/c mice, metastatic foci were thoroughly
excluded without any colonization (Figures 5E and S5C).
Confocal images were used to quantify the emerged metastaticcolonies on day 14 in BALB/c-nu/nu mice and the accumulated
cell aggregations in BALB/c mice on day 3 (Figures 5F and 5G).
Since the mCherry signals were often surrounded by those of thecell aggregations, the aggregations likely reflected the immune
response of the host (Figure 5H). Overall, CUBIC-cancer analysis
enabled not only a statistical time course analysis of cancer
metastasis but also direct observations of the stochastic immune-mediated elimination of metastatic foci.
CUBIC-Cancer Analysis Reveals that the EpithelialMesenchymal Transition Might Promote Not Only the
Extravasation but Also the Survival of Cancer Cells at
Metastatic Sites
The epithelial-mesenchymal transition (EMT) is known to play apivotal role in cancer metastasis. Although roles of EMT in theintravasation of cancer cells have been demonstrated (Nieto
et al., 2016; Tsai and Yang, 2013), it has not been clearly determined whether the EMT is involved in the extravasation ofcancer cells (Nieto et al., 2016; Tsai and Yang, 2013). Thus,
we aimed to elucidate the EMT involvement in the extravasationof cancer metastasis in vivo by CUBIC-cancer analysis. In
accordance with a previous report (Saito et al., 2009), inductionof the EMT in A549 cells by TGF-b stimulation was confirmed
by gene profiling and morphological examination (Figures S6A
and S6B).

(C) Quantification of the morphological features in the two metastatic models. Magnified 3D and XY images of typical metastatic foci are shown. Surface analysis
was applied to the 3D images in (B). As a characteristic indicator of different metastatic patterns, the volume of the foci divided by its surface area was calculated.
Data represent mean ± SEM (***p < 0.001).
(D) Lineage analysis using renal cancer cell lines expressing GFP or mCherry. The 3D images of the brains stained with RedDot2 (left), the frequency of themetastatic colonies consisting of a single color (middle), and the minimal distance between metastatic colonies expressing GFP or mCherry (right) are shown.
Data represent mean ± SD.
See also Figure S4.

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Figure 5. Spatiotemporal Dynamics of Metastatic Progression in the Experimental Lung Metastasis Model(A) Scheme of the experimental procedure.
(B and C) Time course images of the experimental lung metastasis model with MDA-231-D cells in BALB/c-nu/nu mice (B) or BALB/c mice (C). BALB/c-nu/nu
mice or BALB/c mice were intravenously injected with MDA-231-D cells. The 3D images of the lung samples at the indicated time points are shown (MDA-231-D,
mCherry; nuclei, RedDot2).

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To investigate the EMT roles in cancer cell arrest in microvessels of distant organs and extravasation, BALB/c-nu/nu mice
were intravenously injected with A549 cells pre-treated with orwithout TGF-b (Figure 6A). Metastatic foci of A549 cells wereclearly detected throughout the lung, and the colonization of
the metastatic foci gradually progressed over 14 days after the
early intensive elimination (Figure 6B). Confocal images allowed
the visualization of metastatic foci around alveoli 1 hr after injection, and they showed the appearance of spheres regardless of
TGF-b stimulation 14 days after injection. Interestingly, statisticalanalysis revealed that TGF-b stimulation significantly increased
the number of metastatic foci from 1 to 14 days (Figure 6C).
In addition, the histological investigation after CUBIC-cancer
analysis revealed that there was no clear difference between unstimulated and TGF-b-pre-stimulated cancer cells in the expression of E-cadherin in metastatic colonies 14 days after injection(Figure 6D). CUBIC-cancer analysis may thus be of value in elucidating unresolved issues regarding the in vivo role of the EMT intumor metastasis.
CUBIC-Cancer Analysis Enables the
Pharmacotherapeutic Profiling of Anti-tumor Drugs
against Cancer Metastasis
Finally, we attempted to establish a therapeutic evaluation system for metastatic whole-lung cancers by statistically profilingmetastatic cells. To assess chemotherapeutic responses in asyngeneic mouse tumor model, wild-type BALB/c mice were injected with 4T1 cells intravenously and treated with the antitumor drugs doxorubicin (Dox), 5-FU, and cyclophosphamide(CPA) (Figure 7A). Eight days after 4T1 injection, the proliferationof metastatic colonies tended to be suppressed in the drugtreated groups (Figures 7B and 7C). In particular, CPA significantly reduced both the total volume and number of colonies.
The daily administration of 5-FU resulted in the drastic reductionof metastatic colonies (Figure 7D). Importantly, many metastatic
foci were still detected in the lungs of 5-FU-treated mice, indicating the high sensitivity of CUBIC-cancer analysis. Some fociwere composed of just a single cancer cell, which might bedormant or resistant to anti-tumor drugs (Figures 7D and 7E).
Taken together, CUBIC-cancer analysis can be used to evaluate
the in vivo effects of anti-tumor drugs at the single-cell level.
DISCUSSION
RI-Optimized CUBIC Protocol Enables the Scalable
Whole-Body Cell Profiling of Various Cancer Models
The RI values of tissue and cell structures are highly diverse,
ranging from 1.34 to 1.59, because the RI for each intact biological tissue depends on the contents and densities of lipids, pro-teins, and other constituents (Tuchin, 2015). Therefore, optimal
RI values were likely to be dependent on each organ. The results of the transmittance evaluation of major organs mounted
on various RI media indicated that the transparency of the organs tended to increase as the RI values increased (Figures
1C and S1B). Interestingly, this tendency was observed regardless of the organ, suggesting that these organs mostly containthe constituents with relatively similar RI values. We noted that
the RI value-dependent increase in the transparency of mostof the organs did not reach a plateau, even at an RI of 1.52.
It would be very difficult to prepare hydrophilic RI-matching
media with higher RI values (RI > 1.52) using current chemical
candidates, because of their water solubility or low RI value perunit mass. Future development of such RI-matching media(RI > 1.52) may be favorable to make these organs transparent
more efficiently. Another technical challenge is to develop
whole-body-clearing protocols compatible with bone tissue
clearing. Mild, effective, and highly permeable decalcification
methods compatible with our CUBIC protocol would facilitate
our understanding of the mechanisms of cancer metastases,
including bone metastasis. We noted that perfusion-assisted
agent release in situ (PARS) and ultimate 3D imaging of solvent-cleared organs (uDISCO) protocols might be also applicable to whole-body profiling of cancer metastasis with asingle-cell resolution (Pan et al., 2016; Susaki et al., 2015; Tainaka et al., 2014; Yang et al., 2014). Therefore, comparison of
the characteristics of the proposed CUBIC-cancer protocol inthis study with those of these two methods would be importantin the future.
CUBIC-Cancer Analysis Provides a Global Analytical
Pipeline of Cancer Metastasis by Bridging the
Resolution Gap between Conventional In Vivo
Bioluminescence Imaging and 2D Histology
To obtain an overview of cancer metastasis, it would be important to create a global analytical pipeline visualizing the overall
dynamics of metastatic progression, spatiotemporal distributionof metastatic colonies at single-cell resolution, and their surrounding microenvironments. In this study, we attempted tobridge the resolution gap between in vivo bioluminescence imaging and 2D histology by our scalable CUBIC-cancer analysis,using cancer cell lines stably expressing luciferase and fluorescent proteins.
We determined whether fluorescent signals in whole-body andwhole-organ images actually originated from cancer cells orindirectly originated from some other cells, such as phagocytesthat digested labeled cancer cells. To compare the in vivo
bioluminescence imaging and CUBIC-cancer analysis, we performed in vivo bioluminescence imaging before tissue clearing.

(D and E) Quantification of the metastatic foci in BALB/c-nu/nu mice (D) or BALB/c mice (E). The number and the average volume of foci from (B) or (C) are shown.
Animal number at each time point is n = 3–4. Data represent mean ± SD.
(F) Quantification of the cell number of metastatic colonies. Spot analysis and surface analysis were applied to the 3D images in (B). Data represent mean ± SD.
(G) Quantification of the cell number of cell aggregates observed in BALB/c mice. Spot analysis and surface analysis were applied to the 3D images of day 3 in (C).
Data represent mean ± SD.
(H) Representative magnified 3D and XY images of the lungs containing the cell aggregates at day 3 after injection in BALB/c mice (left and middle) (MDA-231-D,
mCherry; nuclei, RedDot2). The typical HE staining pattern after CUBIC-cancer analysis including the cell aggregates (right most) is shown.
See also Figure S5.

Cell Reports 20, 236–250, July 5, 2017 245

(legend on next page)

246 Cell Reports 20, 236–250, July 5, 2017

Bioluminescence images from luciferase activity roughly corresponded to the fluorescent signals (Figures 2B, 3B, S2C, andS3C), despite apparent differences in multiple organ metastases
by Renca cells (Figures 2E and S2A) and brain metastasis by
MDA-231-D cells (Figures 4B and S4A). In the former case, bioluminescence signals in metastatic organs may not have been detected with this dynamic range because the signals were too
intense at the primary lesion. In the latter case, bioluminescencesignals may have been shielded by the skull, and they seemed toleak out to the eyes and neck (Figure S4A). In addition, because
we could perform HE staining of CUBIC-treated organs, we
compared the CUBIC-cancer analysis results with the data obtained by the conventional histological method. We found thatthe basic tissue pathology was well preserved in HE staining,
and we could detect the tumors in the CUBIC-treated samples(Figures 2B and 6B). However, the histology of the metastatic tumors of OS-RC-2 cells, which are clear cell renal carcinoma andcontain high amounts of glycogen, was distinct between the
intact samples and samples after CUBIC-cancer analysis,
possibly because of the delipidation process (Figure S4B). Theseobservations indicated that certain CUBIC-treated samples canbe used for immunohistochemical analysis, although the cell
membrane in the samples may be damaged due to delipidation.
Together, CUBIC-cancer analysis could successfully bridge theresolution gap between conventional in vivo bioluminescenceimaging and 2D histology.
EMT Significantly Promotes the Extravasation orSurvival of Cancer Cells In Vivo
The EMT contributes to the metastatic process of intravasationin cancer metastasis (Nieto et al., 2016; Polyak and Weinberg,2009). Cancer cells that have undergone EMT participate in
extravasation by enhancing the attachment of cancer cells tometastatic sites through the induction of matrix metalloproteinases or formation of filopodia-like protrusions (Labelle et al.,2011; Shibue et al., 2012). In this study, we investigated the relevance of the EMT in lung metastasis by statistical and temporal
analysis of metastatic colonies in an experimental extravasation
model (Figure 6). The nature of the in vivo dynamics of metastatic
extravasation is an ongoing controversy. While the metastatic
extravasation was postulated to proceed over 1–2 days (Tsaiand Yang, 2013), another group demonstrated using an in vitro
extravasation assay that cancer cells could extravasate frommicrovessels within 3 hr (Spiegel et al., 2016). Our results
showed that the number of foci at 1 hr after injection was not
significantly different, suggesting that the EMT appears to be
not involved in the arrest of cells in microvessels. To our surprise,

the number of metastatic foci markedly increased on day 1 after
the injection of cells pre-treated with TGF-b1, and this number
was approximately sustained up to day 14 (Figure 6C). Thesefindings suggest that the fate of cancer cells in metastatic sites
is determined in the very early phase (within a day) after arriving
at distant organs, and that the EMT might be associated with thesurvival of cancer cells at metastatic sites. The EMT is thought toconfer stem cell-like features that allow cells to disseminate andgain resistance to apoptosis or anti-tumor therapies (Polyak andWeinberg, 2009). Hence, the significant promotion of metastasis
by EMT induction may reflect the transformation of differentiatedcells into a stem cell-like phenotype, including the induction of
extracellular matrix production and gaining of the resistance toapoptosis.
Plasticity between the epithelial and mesenchymal phenotypes also plays a pivotal role in the process of metastasis (Yeand Weinberg, 2015). In clinical studies, intravasated cancer
cells circulating in the blood have been shown to exhibit themesenchymal phenotype, while colonized cancer cells in organs
were found to exhibit the epithelial phenotype (Kallergi et al.,2011; Kowalski et al., 2003). Because of the relatively shortterm stimulation of A549 cells by TGF-b1 in the present study,
we assumed that the plasticity of the EMT was maintained inthis experiment. In fact, our results suggested that A549 cells
that colonized the lung were restored to the epithelial phenotype
with the restoration of E-cadherin expression. Taken together,
our CUBIC-cancer analysis clarified the in vivo dynamics of theplasticity between the epithelial and mesenchymal phenotypes
during the progression of cancer metastasis through the statistical analysis of metastatic foci at the whole-organ level.
CUBIC-Cancer Analysis Provides an In Vivo Therapeutic
Evaluation System to Aid in the Treatment of Metastatic
TumorsTo quantitatively evaluate the therapeutic effects of anti-tumor
drugs, it is necessary to establish an ultra-sensitive detection
system that can determine whether metastatic colonies arecompletely eliminated or whether single cancer cells resistantto anti-tumor drugs still remain. This distinction is particularly
important because the emergence of chemoresistant cancer
cells eventually leads to the proliferation of cancer cells andthe development of incurable tumors. It is difficult to detect
metastasized cells at single-cell resolution by in vivo bioluminescence imaging, and visualizing whole-organ images by 2D
histology is labor intensive. Notably, our CUBIC-cancer analysisof the entire lung successfully detected tiny foci consisting ofeven a single cancer cell (Figures 7D and 7E). Therefore, ourFigure 6. In Vivo Role of the EMT Induced by TGF-b Pre-treatment in the Progression of Lung Metastasis(A) Scheme of the experimental procedure.
(B) Time course images of the experimental lung metastasis model with A549 cells. A549 cells pre-treated without (left) or with (right) TGF-b1 were intravenously
injected in BALB/c-nu/nu mice. The 3D images of the lung samples at the indicated time points are shown (A549, mCherry; nuclei, RedDot2). Representative
images of HE staining after CUBIC-cancer analysis are also shown.
(C) Quantification of the volume and the number of foci of metastatic colonies pre-treated with or without TGF-b1. Surface analysis was applied to the 3D imagesin (B). Animal number at each time point is n = 4. Data represent mean ± SD.
(D) Immunohistochemistry of E-cadherin. Samples on day 14 after CUBIC-cancer analysis were subjected to immunohistochemistry with anti-E-cadherin
antibody.
See also Figure S6.

Cell Reports 20, 236–250, July 5, 2017 247

A

B

D

C

E

Figure 7. Quantitative Evaluation of Therapeutic Effects of Anti-tumor Drugs in an Experimental Lung Metastatic Model(A) Scheme of the experimental procedure.
(B) In vivo therapeutic efficacy of anti-tumor drugs. The 3D images of the lung samples are shown (4T1, mCherry; nuclei, RedDot2).
(C) Quantification of pharmacotherapeutic effects of anti-tumor drugs. Animal number of each group is n = 4. Data represent mean ± SD.
(D) In vivo therapeutic evaluation of once daily treatment with 5-FU. The 3D images of the lung samples are shown (4T1, mCherry; nuclei, RedDot2).
(E) Quantification of pharmacotherapeutic effect of once daily treatment with 5-FU. Animal number of each group is n = 4 (5-FU) and n = 5 (saline). Data represent
mean ± SD.

CUBIC-cancer analysis may provide critical information for thedevelopment of a curative treatment for metastasis.
Furthermore, we wish to emphasize another important advantage of CUBIC-cancer analysis: it allows a seamless connection

248 Cell Reports 20, 236–250, July 5, 2017

between in vivo live imaging and 2D histology, complementing
the shortcomings of each modality. The global analytical pipeline consisting of these methods is quite robust and reliable,
because the experimental data could be thoroughly verified

by the three modalities. An in vivo therapeutic evaluation systemis urgently needed not only for malignant neoplastic diseases
but also for autoimmune diseases, which remain a challenge
clinically, and emerging induced pluripotent stem cell (iPSC)based regenerative therapies. This analytical pipeline would
have great potential for becoming a de facto standard for an
in vivo therapeutic evaluation system for complex systemic
diseases.
EXPERIMENTAL PROCEDURES
Details are also supplied in the Supplemental Experimental Procedures.
Preparation of Clearing Solutions
Clearing solutions were composed of four chemicals, which were selectedfrom chemical screening (T.C. Murakami and K. Tainaka, unpublisheddata). CUBIC-L for decolorization and delipidation was prepared as a
mixture of 10 w% polyethylene glycol mono-p-isooctylphenyl ether/Triton
X-100 (12967-45, Nacalai Tesque) and 10 w% N-buthyldiethanolamine(B0725, Tokyo Chemical Industry). CUBIC-R for RI matching was prepared
as a mixture of 45 w% 2,3-dimethyl-1-phenyl-5-pyrazolone/antipyrine(D1876, Tokyo Chemical Industry) and 30 w% nicotinamide (N0078, Tokyo
Chemical Industry) (Table S2). See also the Supplemental Experimental
Procedures.
Microscopy
Whole-body and whole-organ images were acquired with a custom-build
LSFM (developed by Olympus). High-resolution images for cell profilingwere acquired with CLSM (FLUOVIEW FV1200, Olympus). The RI-matchedsample was immersed in a mixture of silicon oil HIVAC-F4 (Shin-Etsu Chemical) and mineral oil (RI = 1.467, M8410, Sigma-Aldrich) during image acquisition. See also the Supplemental Experimental Procedures.
Experimental and Spontaneous Mouse Metastatic Tumor Models
Inbred wild-type BALB/c mice, C57BL/6 mice, and BALB/c-nu/nu mice werepurchased from Sankyo Labo Service. All experiments were approved andcarried out according to the Animal Care and Use Committee of the Graduate
School of Medicine, The University of Tokyo. Each metastasis model is
described briefly. For experimental liver metastasis, BALB/c-nu/nu mice
(5 weeks old, female) were injected with Panc-1 cells by open injection in
the spleen. For peritoneal dissemination, SUIT-2 cells were injected into
pancreas orthotopically or intraperitoneally in BALB/c-nu/nu mice (5 weeks
old female). For experimental lung metastasis by intravenous injection,
BALB/c-nu/nu (5 weeks old, female), BALB/c mice (5 weeks old, female ormale), or C57BL/6 mice (5 weeks old, female) were injected with each cellline (MDA-MB-231, MDA-231-D, A549, 4T1, Renca, and B16F10 cells). For
spontaneous lung metastasis by renal subcapsule injection, BALB/c (5 weeks
old, male) or BALB/c-nu/nu (5 weeks old, male) mice were injected with
Renca, OS-RC-2, or Caki-1 cells orthotopically. For experimental brain
metastasis by intracardiac injection, BALB/c-nu/nu (4 weeks old, female
MDA-231-D, male OS-RC-2) mice were injected with MDA-231-D or OSRC-2 cells by puncture into the left ventricle of heart. For the stimulation
with TGF-b, A549 cells were pre-treated with or without TGF-b1 (5 ng/mL)
for 72 hr in culture and subjected to in vivo experiment. See also the Supplemental Experimental Procedures.
Statistical Analysis
An unpaired t test was used to compare the index of volume per surface area of
brain metastasis and to compare the pharmacotherapeutic effects of 5-FU forstatistical significance. Multiple t tests were used to compare the effect of
TGF-b1 stimulation for statistical significance. Dunnett’s multiple comparisonstest was used to examine the pharmacotherapeutic effects of anti-tumor drugs
for statistical significance. All statistical analyses were performed with
GraphPad Prism6 software.

SUPPLEMENTAL INFORMATION
Supplemental Information includes Supplemental Experimental Procedures,
six figures, and four tables and can be found with this article online at http://dx.doi.org/10.1016/j.celrep.2017.06.010.
AUTHOR CONTRIBUTIONS
H.R.U., K.M., S.I.K., K. Takahashi, K. Tainaka, and S.E. designed the study.
S.I.K. performed most of the imaging and analysis of metastasis model
mice. K. Takahashi and J.N. performed most of the establishment of cancer
cell lines for CUBIC-cancer analyses and preparation of metastasis model
mice. Y.M. contributed to the histological experiments. H.R.U., K.M., S.I.K.,
K. Takahashi, K. Tainaka, and S.E. wrote the manuscript. All authors discussed
the results and commented on the manuscript text.
ACKNOWLEDGMENTS
We thank all lab members at The University of Tokyo and RIKEN QBiC, inparticular, A. Millius for editing, T.C. Murakami and C. Shimizu for chemical
screening for tissue clearing, S. Kitamura and K. Nakamura for developing
optimized delipidation medium, M. Fujiwara for developing RI-optimized medium, E.A. Susaki and A. Kuno for developing 3D staining methods, S. Shoi
for helping with statistical analysis, R. Ohno for housing mice, and Bitplane
for instruction on Imaris 8.1.2. We also thank Y. Katsuno and F. Murai forplasmid preparation and H. Miyoshi (RIKEN) for providing the lentiviral vectors.
This work was supported by grants from AMED-CREST (Japan Agency for
Medical Research and Development [AMED]/Ministry of Education, Culture,
Sports, Science and Technology, Japan [MEXT], H.R.U.); CREST (JST/MEXT, H.R.U.); Brain/MINDS (AMED/MEXT, H.R.U.); Basic Science and Platform Technology Program for Innovative Biological Medicine (AMED/MEXT,
H.R.U.); Grants-in-Aid for Scientific Research from the Japan Society for the
Promotion of Science (JSPS) (KAKENHI grant 14J01180 to S.I.K., grant
17J30007 to S.I.K., grant 13J10991 to K. Takahashi, and grant 16J05993 toJ.N.), Grant-in-Aid for Challenging Exploratory Research (JSPS KAKENHI
grant 16K15124 to K. Tainaka), Grants-in-Aid for Scientific Research (S)(JSPS KAKENHI grants 25221004 to H.R.U. and 15H05774 to K.M.), andGrants-in-Aid for Scientific Research on Innovative Areas (JSPS KAKENHI
grant 23115006 to H.R.U.); and Brain Protein Aging and Dementia Control(17H05688 to K. Tainaka) from MEXT; a grant for Practical Research for Innovative Cancer Control (16ck0106193h0001) from AMED (K.M.); and a SpecificResearch Grant from The Tokyo Society of Medical Sciences (S.E.). T.C. Murakami, K. Tainaka, and H.R.U. have filed a patent application for CUBIC-reagents. A part of this study was done in collaboration with the Olympus
Corporation.
Received: March 24, 2017
Revised: May 2, 2017
Accepted: May 29, 2017
Published: July 5, 2017
REFERENCES
Carbonell, W.S., Ansorge, O., Sibson, N., and Muschel, R. (2009). The vascularbasement membrane as ‘‘soil’’ in brain metastasis. PLoS ONE 4, e5857.
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.
Cuccarese, M.F., Dubach, J.M., Pfirschke, C., Engblom, C., Garris, C., Miller,
M.A., Pittet, M.J., and Weissleder, R. (2017). Heterogeneity of macrophage
infiltration and therapeutic response in lung carcinoma revealed by 3D organ
imaging. Nat. Commun. 8, 14293.
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).

Cell Reports 20, 236–250, July 5, 2017 249

Ultramicroscopy: three-dimensional visualization of neuronal networks in thewhole mouse brain. Nat. Methods 4, 331–336.

(2017). Imaging techniques: new avenues in cancer gene and cell therapy.
Cancer Gene Ther. 24, 1–5.

€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 ofsolvent-cleared organs using 3DISCO. Nat. Protoc. 7, 1983–1995.

Saito, R.A., Watabe, T., Horiguchi, K., Kohyama, T., Saitoh, M., Nagase, T.,
and Miyazono, K. (2009). Thyroid transcription factor-1 inhibits transforming
growth factor-beta-mediated epithelial-to-mesenchymal transition in lungadenocarcinoma cells. Cancer Res. 69, 2783–2791.

Gavrilovic, I.T., and Posner, J.B. (2005). Brain metastases: epidemiology andpathophysiology. J. Neurooncol. 75, 5–14.
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.
Johnsen, S., and Widder, E.A. (1999). The physical basis of transparency inbiological tissue: ultrastructure and the minimization of light scattering.
J. Theor. Biol. 199, 181–198.
Kallergi, G., Papadaki, M.A., Politaki, E., Mavroudis, D., Georgoulias, V., andAgelaki, S. (2011). Epithelial to mesenchymal transition markers expressedin circulating tumour cells of early and metastatic breast cancer patients.
Breast Cancer Res. 13, R59.
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.
Kienast, Y., von Baumgarten, L., Fuhrmann, M., Klinkert, W.E., Goldbrunner,
R., Herms, J., and Winkler, F. (2010). Real-time imaging reveals the single
steps of brain metastasis formation. Nat. Med. 16, 116–122.
Kowalski, P.J., Rubin, M.A., and Kleer, C.G. (2003). E-cadherin expression inprimary carcinomas of the breast and its distant metastases. Breast Cancer
Res. 5, R217–R222.
Labelle, M., Begum, S., and Hynes, R.O. (2011). Direct signaling between
platelets and cancer cells induces an epithelial-mesenchymal-like transitionand promotes metastasis. Cancer Cell 20, 576–590.
Nicholls, A.G. (1927). The fourth chapter in the history of pathology. Can. Med.
Assoc. J. 17, 843–845.
Nieto, M.A., Huang, R.Y., Jackson, R.A., and Thiery, J.P. (2016). EMT: 2016.
Cell 166, 21–45.
Nussbaum, E.S., Djalilian, H.R., Cho, K.H., and Hall, W.A. (1996). Brain metastases. Histology, multiplicity, surgery, and survival. Cancer 78, 1781–1788.
Pan, C., Cai, R., Quacquarelli, F.P., Ghasemigharagoz, A., Lourbopoulos, A.,€rk, A. (2016).
Matryba, P., Plesnila, N., Dichgans, M., Hellal, F., and Ertu
Shrinkage-mediated imaging of entire organs and organisms using uDISCO.
Nat. Methods 13, 859–867.
Polyak, K., and Weinberg, R.A. (2009). Transitions between epithelial andmesenchymal states: acquisition of malignant and stem cell traits. Nat. Rev.
Cancer 9, 265–273.

Shibue, T., Brooks, M.W., Inan, M.F., Reinhardt, F., and Weinberg, R.A. (2012).
The outgrowth of micrometastases is enabled by the formation of filopodiumlike protrusions. Cancer Discov. 2, 706–721.
Spalteholz, W. (1914). Über das Durchsichtigmachen von menschlichen undtierischen Präparaten und seine theoretischen bedingungen, nebst Anhang(Leipzig: S Hierzel), pp. 1–93.
Spiegel, A., Brooks, M.W., Houshyar, S., Reinhardt, F., Ardolino, M., Fessler,
E., Chen, M.B., Krall, J.A., DeCock, J., Zervantonakis, I.K., et al. (2016). Neutrophils suppress intraluminal NK cell-mediated tumor cell clearance andenhance extravasation of disseminated carcinoma cells. Cancer Discov. 6,
630–649.
Susaki, E.A., and Ueda, H.R. (2016). Whole-body and whole-organ clearing
and imaging techniques with single-cell resolution: Toward organism-levelsystems 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.
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.
Takahashi, K., Nagai, N., Ogura, K., Tsuneyama, K., Saiki, I., Irimura, T., andHayakawa, Y. (2015). Mammary tissue microenvironment determines T celldependent breast cancer-associated inflammation. Cancer Sci. 106, 867–874.
Tatsuki, F., Sunagawa, G.A., Shi, S., Susaki, E.A., Yukinaga, H., Perrin, D., Sumiyama, K., Ukai-Tadenuma, M., Fujishima, H., Ohno, R., et al. (2016). Involvement of Ca(2+)-dependent hyperpolarization in sleep duration in mammals.
Neuron 90, 70–85.
Tsai, J.H., and Yang, J. (2013). Epithelial-mesenchymal plasticity in carcinoma
metastasis. Genes Dev. 27, 2192–2206.
Tuchin, V.V. (2015). Tissue optics and photonics: Light-tissue interaction.
J. Biomed. Photonics Eng. 1, 98–135.

Renier, 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.

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.

Saadatpour, Z., Rezaei, A., Ebrahimnejad, H., Baghaei, B., Bjorklund, G.,
Chartrand, M., Sahebkar, A., Morovati, H., Mirzaei, H.R., and Mirzaei, H.

Ye, X., and Weinberg, R.A. (2015). Epithelial-mesenchymal plasticity: A central
regulator of cancer progression. Trends Cell Biol. 25, 675–686.

250 Cell Reports 20, 236–250, July 5, 2017

Cell Reports, Volume 20

Supplemental Information

Whole-Body Proﬁling of Cancer Metastasiswith Single-Cell Resolution
Shimpei I. Kubota, Kei Takahashi, Jun Nishida, Yasuyuki Morishita, Shogo Ehata, Kazuki
Tainaka, Kohei Miyazono, and Hiroki R. Ueda

1

Figure S1. Whole-body and whole-organ clearing and imaging with optimized RI solutions, Related to2

Figure 13(A) Bright-field images of whole organs (pancreas and spleen) from C57BL/6N mouse (13 weeks old, male)4

after RI adjustment (RI=1.44, 1.45, 1.46, 1.47, 1.48, 1.49, 1.50, 1.51, and 1.52). Control organs were stocked in5

PBS buffer (RI=1.33). (B) Averaged parallel transmittance of RI adjusted organs. Averaged parallel

6transmittance around the visible region (450-750 nm) of organs (heart, liver, kidney, pancreas, and spleen) was

7

measured (n=2). Data represent mean ± SD. (C) Transmission curves around the visible region (450-750 nm) of

8

fixed whole organs (brain, heart, lung, liver, kidney, pancreas, and spleen) treated with the new CUBIC protocol

9(CUBIC-L for delipidation and decoloring, and CUBIC-R for RI adjustment). (D) 3D (upper) and XY images

10(lower) of the brain, heart, liver, and kidney from RedDot2-stained adult C57BL/6N mouse (15 week old, male).

11(E) Magnified images in Figure 1F. 3D (middle) and XY images (left and right) of chest, abdomen, pancreas,

12

and intestine are shown. (F) The 3D-immunostained organ images with anti-a-SMA antibody and anti-VEGFR3

13

antibody. The lung from C57BL/6N mouse (13 week old, male) was co-immunostained with FITC-conjugated14

anti-a-SMA antibody and anti-VEGFR3 antibody on the first staining step, and then with Alexa 647-conjugated15

anti-mouse IgG antibody on the second staining step. 3D (upper) and XY images (lower) are shown. The white

16

inset was magnified next to the XY image.

17

A WT adult (C57BL/6N)
1.44

1.45

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SpleenPancreas1.33 (PBS)

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D RI = 1.52, Nuclear staining: RedDot2, LSFM
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Heart

Transmittance

100

Heart

Liver

Kidney

2 mm

2 mm

3 mm

2 mm

Z = 4.0 mm

Z = 3.0 mm

Z = 4.0 mm

Z = 3.0 mm

2 mm

2 mm

3 mm

2 mm

Lung

60

Liver
40

KidneyPancreas

20

Spleen
750

700

650

600

550

500

0
450

Brain

Wave length (nm)E RI = 1.52, Nuclear staining: PI, LSFM

F RI = 1.52 , LSFM,

Z = 5.0 mm

PancreasZ = 3.3 mm

2 mm

2 mm

2 mm

Z = 8.0 mm

Intestine

2 mm

1 mm

Antibody: Anti-α-SMA-FITC, Anti-VEGFR3
Lung

α-SMA

VEGFR3

2 mm

2 mm

2 mm

Z = 2.0 mm

Z = 1.5 mm

α-SMA

VEGFR31 mm

2 mm

200 μm200 μmFigure S1

1

Figure S2. Whole-body imaging of cancer metastasis, Related to Figure 22(A) In vivo bioluminescence imaging of Figures 2C and 2E. (B) Whole-body imaging of the experimental

3

peritoneal dissemination model with SUIT-2 cells according to Figure 2C. WBI (left most), images of liver and4pancreas (LP), and intestine (I) (second left), and magnified images (right most) are shown (SUIT-2: GFP,

5

nuclei: PI). (C) Whole-body imaging of the experimental peritoneal dissemination model by intraperitoneal

6

injection with SUIT-2 cells in BALB/c-nu/nu mice. The bioluminescence image (left most), WBI (second left),

7

images of liver (L) and intestine (I) (third left) and magnified 3D and XY images (fourth left and right most) are8

shown (SUIT-2: mCherry, nuclei: RedDot2). (D) Magnified images of pancreas and lymph nodes as shown in9

Figure 2E. Images of pancreas (P) and lymph nodes (LN) (left most) and magnified 3D and XY images (second

10left to right most) are shown (Renca: mCherry, nuclei: RedDot2). (E) Quantification of the cell number and the11volume of metastatic colonies. Spot analysis and surface analysis were applied to the magnified images in

12

Figure S2D.

13

A Cell line: SUIT-2

B Cell line: SUIT-2, Nuclear Staining: PI

Cell line: Renca
In vivo imaging

LSFM (3D)WBI

22

CLSM (3D)
Pancreas

100 μmI

CLSM (3D)
Intestine

1 cm

2 mm

100 μm

LSFM (3D)L

CLSM (3D)Liver

CLSM (XY)mCh/RedDot2

1 cm

100 μm

100 μm

LSFM (3D)I

2

6

LSFM (3D)LP

LP 4 mm

22

Luciferase activity [105 ph/s]

Luciferase activity [102 ph/s]

60

100

In vivo imaging

C Cell line: SUIT-2, Nuclear staining: RedDot2
LSFM (3D)
WBI

55

Luciferase activity [103 ph/s]

500

In vivo imaging

L

LSFM (3D)

CLSM (3D)

CLSM (XY)

I

Intestine

mCh/RedDot2

1 mm

100 μm

100 μmI

52 cm

D Cell line: Renca, Nuclear staining: RedDot2, Host: BALB/c
LSFM (3D)P

CLSM (3D)
Pancreas

CLSM (XY)
CLSM (XY)mCh/RedDot2 RedDot2

#4
#5
2 mm

100 μm#4#5100 μmE
Pancreas #4
Pancreas #5
LN #6

100 μm

0

2

4

6

8

10

Number (x10 )3

LSFM (3D)LN

CLSM (3D)LN#6CLSM (XY)
CLSM (XY)mCh/RedDot2 RedDot2#6

Pancreas #4
Pancreas #5
LN #6

2 mm

100 μm100μm

100 μm

100 μm

0

5

10

15

20

Volume (x106 μm3)

Figure S2

1

Figure S3. Whole-organ imaging of various cancer metastasis models, Related to Figure 32(A) Quantification of the occupancy of the primary tumor in the primary organ in orthotopic models with

3

SUIT-2 cells and OS-RC-2 cells. Four to five weeks after injection, the volumes of primary tumors and organs

4(SUIT-2: pancreas, OS-RC-2: kidney) were measured. The occupancies of the primary tumors are shown

5(SUIT-2 cells: n=3, OS-RC-2: n=4). Data represent mean ± SD. (B) Quantification of the number and the total

6

volume of metastatic foci in the experimental metastasis model with SUIT-2 cells as shown in Figure 3B.

7

Surface analysis was applied to the whole-organ images. The number and total volume of metastatic foci in the

8organs are shown (SUIT-2 cells: n=3). Data represent mean ± SD. (C) Whole-liver imaging of the experimental

9

liver metastasis model with Panc-1 cells. Images of in vivo bioluminescence and whole liver images by CUBIC

10

are shown (Panc-1: mCherry, nuclei: RedDot2) (n=4). (D) Confirmation of the specific detection of cancer cells

11

in the CUBIC-Cancer analysis by merged images of a red channel from mCherry-expressing cancer cells and12

far-red channel from Alexa Fluor 647-conjugated anti-mCherry antibody staining. The 3D images of lung, brain,

13

and pancreas from the 4T1 metastasis model mouse immunostained with Alexa Fluor 647-conjugated14

anti-mCherry antibody are shown (4T1: mCherry, anti-mCherry: Alexa Fluor 647).

15

0

1
0
Intestine

OS-RC-2

SUIT-2

OS-RC-2

0

5

2

Liver

20

10

3

Lung

0

40

Tot. Vol. (x1010 µm3)

1

60

15

Intestine

2

80

Liver

Occupancy (%)

3

20

Lung

B

100

Number of foci (x102)

4

SUIT-2

Tot. Vol. (x1011 µm3)

A

5

44

200

Luc. activity [103 ph/s]

C Cell line: Panc-1, Nuclear staining: RedDot2, Scale bar: 2 mm, LSFM

D Cell line: 4T1, Antibody: Anti-mCherry-Alexa Fluor 647 antibody, Scale bar: 2 mm, LSFM
Lung
mCherryAnti-mCherry

Brain
mCherryAnti-mCherry

Figure S3

Pancreas
mCherryAnti-mCherry

1Figure S4. 3D analysis of the metastatic patterns in experimental brain metastasis models, Related to2

Figure 43(A) In vivo bioluminescence imaging of brain metastasis. Representative images of the brain metastasis are

4

shown (left: MDA-231-D, right: OS-RC-2). (B) HE staining of the samples after CUBIC-Cancer analysis or5

normal samples. Representative images of the brain metastasis are shown (MDA-231-D: left, OS-RC-2: right,

6

normal sample: upper, sample after CUBIC-Cancer analysis: lower). (C) Whole-brain analysis of the distribution

7of metastatic volume and distance from blood vessel. Surface analysis and spot analysis were applied to the 3D

8images in Figure 4B. Data represent mean ± SD.

9

A Cell line: MDA-231-D, In vivo Imaging

1

2

11

21

100

Luciferase activity [102 ph/s]

Luciferase activity [103 ph/s]

100

1000

Cell line: OS-RC-2, In vivo Imaging

B Cell line: MDA-231-D, Normal tissue

1 mm

Cell line: OS-RC-2, Normal tissue

100 μm

1 mm

Cell line: MDA-231-D, Tissue after CUBIC

1 mm

Cell line: OS-RC-2, Tissue after CUBIC

100 μm

1 mm

100 μmDistance distribution of metastasis

Relative frequency (%)

Volume distribution of metastasis

30

20

10

50

MDA-23140

OS-RC-230
20
10Figure S4> 500

Distance from vessel (μm)

Metastatic volume (μm3)

450

400

350

300

250

200

150

100

50

109.0

108.5

108.0

107.5

107.0

106.5

106.0

105.5

105.0

104.5

104.0

103.5

103.0

0

0

0
102.5

Relative frequency (%)

C

100 μm1

Figure S5. Spatio-temporal dynamics of experimental lung metastasis, Related to Figure 52(A) Determination of the minimum threshold for the metastatic focus count. The volume of individual metastatic

3

foci is postulated to be larger than the nuclear volume. To quantify the pulmonary nuclear volume in the

4

RedDot2-stained lung, surface analysis and spot analysis were applied to the 3D lung images acquired with

5

CLSM. The averaged nuclear volume of six fields (right most) is shown. (B, C) Time course images of the6experimental lung metastasis model with MDA-231-D cells in BALB/c-nu/nu mice (B) and BALB/c mice (C).

7

The three or two additional individual mice were analyzed by CUBIC-Cancer analysis according to Figures 5B8

and 5C. (MDA-231-D: mCherry, nuclei: RedDot2). These images were further analyzed with Imaris software in

9

Figures 5D, 5E, 5F, and 5G.

10

A Average nuclear volume, CLSM
RedDot2

Surface analysis

400

Spot analysis

Volume (μm3)

Merge

200 μm

300
200
100
0

NuclearB Cell line: MDA-231-D, Nuclear staining: RedDot2, Host mouse: BALB/c-nu/nu mouse, LSFM
1 hr

6 hr

12 hr

Day 1

Day 3

Day 7

Day 14

2 mm

2 mm

2 mm

2 mm

2 mm

2 mm

2 mm

1 hr

6 hr

12 hr

Day 1

Day 3

Day 7

Day 14

2 mm

2 mm

2 mm

2 mm

2 mm

2 mm

2 mm

1 hr

6 hr

12 hr

Day 1

Day 3

Day 7

2 mm

2 mm

2 mm

2 mm

2 mm

2 mm

C Cell line: MDA-231-D, Nuclear staining: RedDot2, Host mouse: BALB/c mouse, LSFM
1 hr

6 hr

12 hr

Day 1

Day 3

Day 7

Day 14

2 mm

2 mm

2 mm

2 mm

2 mm

2 mm

2 mm

1 hr

6 hr

12 hr

Day 1

Day 3

Day 7

Day 14

2 mm

2 mm

2 mm

2 mm

2 mm

2 mm

2 mm

1 hr

6 hr

12 hr

Day 1

Day 3

Day 7

2 mm

2 mm

2 mm

2 mm

2 mm

2 mm

Figure S5

1

Figure S6. Role of EMT induced by TGF-b during lung metastasis, Related to Figure 6

2(A) Expression of EMT markers in A549 cells. A549 cells were stimulated with or without TGF-b1 (5 ng/ml) for3

3 days. Relative expression levels of each gene are shown. All data are presented as mean (duplicate). (B)4FITC-conjugated phalloidin staining of A549 cells. A549 cells were stimulated with or without TGF-b1 (55ng/ml) for 3 days. Formation of the F-actin was evaluated by phalloidin staining.

6

A
Relative expression

1.5

CDH1

SNAI1

6

3

1.0

0.5

1.0
2

1

VIM

2.0

Ctrl

TGF- 1
20

TGF- 1Ctrl

SERPINE1

10

2

0.5

5

1

0.0

0

0

B

TGF- 1Ctrl

TGF- 1

0.0

Ctrl

MMP2

25

15
10
5
0
Ctrl

TGF- 1

Ctrl

Control

TGF- 1

DAPI/Phalloidin-FITC

TGF- 1

DAPI/Phalloidin-FITC

100 μmTGF- 1

20

3

1.0

Ctrl

TGF- 1
FN1

4

15

1.5

0.5

0

0
Ctrl

SNAI2

2.0
1.5

4

2

0.0

Relative expression

CDH2

4

100 μm

100 μmFigure S6

100 μm1

Table S1. List of mouse tumor models in the present study, Related to Figure 2

2

9 cancer cell lines and 13 mouse tumor models were used in this study. Source of cancer cells, type of cancer,

3

injection protocol, and organs for metastasis are described.
Cell

Sourcetype

injection

host mousemetastasis

Figure

MDA-MB-231-Luc

humanbreast cancer

intravenous

BALB/c-nu/nu,

lung

3C, 5, S5

2-mCherry/(i.v.)

BALB/c

MDA-231-D-Luc2-(1 hr-6 w)

BALB/c-nu/nu

bone, brain

4, S4

BALB/c-nu/nu

lung

3C, 6

BALB/c-nu/nu

brain, bone

4, S4

BALB/c-nu/nu

lung

S3A

BALB/c-nu/nu

lung

3C, 3D

BALB/c-nu/nu

peritoneum

2C, 2D,

mCherry
MDA-231-D-Luc2-

human

breast cancer

mCherry
A549-Luc2-(i.c.) (4 w)
humanlung cancer

mCherry
OS-RC-2-Luc2-

intracardiac

i.v.
(1 hr-4 w)

human

GFP/

renal

i.c.

carcinoma(5-7 w)

renal

orthotopic (4carcinoma

w)

renal

orthotopic

carcinoma(14 w)

pancreatic

intra-

cancerperitoneal

S2A,(i.p.) (1-3 w)

S2B, S2C

OS-RC-2-Luc2mCherry
OS-RC-2-Luc2-GF

human

P
Caki-1-Luc2-

human

mCherry
SUIT-2-Luc2-GFP/

human

SUIT-2-Luc2mCherry
SUIT-2-Luc2-

human

mCherry
Panc-1-Luc2-GFP/
Panc-1-Luc2-

humanpancreaticorthotopic (5cancerw)

pancreatic

splenic

cancer(4-9 w)

BALB/c-nu/nu

BALB/c-nu/nu

lung, liver,

3B, S3A,

peritoneum

S3B

liver

2B, S3C

mCherry

4T1-mCherrymousebreast cancer

i.v.

BALB/c-nu/nu

lung

(2 w)

3C, S3D,
7

B16F10-mCherrymousemelanomai.v. (2 w)

C57/B6lung

3C, 3D

Renca-Luc2-

mouse

renal

i.v. (3 w)

BALB/c

lung

3C

renal

orthotopic (2BALB/clung,

2E, 2F,

carcinomaw)

peritoneum

S2A,

mCherry
Renca-Luc2mCherrycarcinomamouse

S2D, S2E

1
2

1

Table S2. List of the name of CUBIC solutions

2

We developed four clearing solutions as below.
Name

Key ingredients

References

Sca/eCUBIC-1

Urea, Quadrol, Triton X-100

Susaki et al., 2014, Tainaka et al., 2014,
Susaki et al., 2015

Sca/eCUBIC-2

Urea, Sucrose, Triethanolamine

Susaki et al., 2014, Tainaka et al., 2014,
Susaki et al., 2015

CUBIC-L

N-buthyldiethanolamine, Triton

Present study

X-100
CUBIC-R

3

Nicotinamide, Antipyrine

Present study

1

Table S3. Antibodies used in the present study

2

Indicated antibodies were used for immunostaining in the present study.
Antibody

Catalog number

Company

F3777

Sigma-Aldrich

Anti-VEGFR3/Flt-4 antibody

AF743

R&D Systems

Alexa Fluor 647 AfiiniPure Fab Fragment Bovine

805-607-008

Jackson

FITC-conjugated anti-a-smooth muscle actin (a -SMA)
antibody

Anti-Goat IgG, Fc fragment specific

ImmunoResearchLaboratories

Alexa Fluor 647 conjugated anti-mCherry antibody

M11241(16D7)
Anti-E-cadherin (24E10) antibody

Thermo Fischer
Scientific#3195

Cell Signaling
Technology

3
4

1

Table S4. Primer sequences used in the present study

2

Indicated primers were used for qRT-PCR analysis in the present study.
GeneOrientation

HPRT1

Forward

TTTGCTTTCCTTGGTCAGGC

ReverseGCTTGCGACCTTGACCATCT

Forward

ATTTTTCCCTCGACACCCGAT

Reverse

TCCCAGGCGTAGACCAAGA

Forward

TTCCTTGCTTCTGACAATGG

ReverseATGTCATAATCAAGTGCTG

Forward

ACTGCAGCCGTGCCTTCG

ReverseGTGCTTGTGGAGCAGGGACATForward

GCCTCCAAAAAGCCAAACTACA

ReverseGAGGATCTCTGGTTGTGGTATGACA

ForwardGAGAACTTTGCCGTTGAAGC

ReverseTGGTATTCACGAAGGTGACG

Forward

GGCTGACTTCACGAGTCTTTCA

ReverseATGCGGGCTGAGACTATGACA

ForwardCTGGCCGAAAATACATTGTAAA

ReverseCCACAGTCGGGTCAGGAG

ForwardTGGCGATGGATACCCCTTT

ReverseTTCTCCCAAGGTCCATAGCTCAT

CDH1

CDH2

SNAI1

SNAI2

VIM

SERPINE1

FN1

MMP2

3
4

Sequence (5' to 3')

1

EXTENDED EXPERIMENTAL PROCEDURES

2
3

Preparation of Clearing Solutions

4

Compared with previous CUBIC solutions, the viscosity of CUBIC-L and CUBIC-R is relatively low. Although

5

the water content of CUBIC-R is low to achieve high RI, CUBIC-R is unlikely to precipitate and easy to handle.

6

N-methylnicotinamide (M0374, Tokyo Chemical Industry Co., Ltd.) is an alternative chemical for nicotinamide,7

and used in Figure 1G, S1D, S1F, 3C (MDA-MB-231), 4 (MDA-231-D).

8
9

The CUBIC Protocol for Whole-body and Whole-organ Clearing

10

For the preparation of whole-organ clearing samples, adult mice (C57BL/6N) were sacrificed by an overdose of

11

pentobarbital (> 100 mg/kg, Kyoritsu Seiyaku, Tokyo, Japan) and then perfused with 20 ml of PBS (pH 7.4) and

1230 ml of 4% (w/v) PFA (02890-45, Nacalai Tesque) in PBS via left ventricle of the heart. The excised organs

13

were post-fixed in 4% (w/v) PFA at 4°C for 24 hours. The specimens were washed with PBS for more than 2

14hours three times to remove PFA just before clearing. The fixed organs were immersed in 50% (v/v) CUBIC-L

15(1:1 mixture of water and CUBIC-L) for more than 6 hours and further immersed in CUBIC-L with gentle

16

shaking at 37°C for 2-5 days, depending on the organs (typically 3 days for the heart, lung, intestine, pancreas,17

and spleen, and 5 days for the brain, liver, and kidney). CUBIC-L should be daily refreshed during the procedure.

18

After decolorization and delipidation, the organs were washed with PBS at room temperature for more than 2

19hours three times. The organs were further immersed in 50% (v/v) CUBIC-R (1:1 mixture of water and

20

CUBIC-R) for more than 6 hours, and then in CUBIC-R at room temperature with gentle shaking for at least 1

21

day.

22

For the preparation of whole-body clearing samples, anesthetized mice were perfused with 30 ml of

23

PBS, 150 ml of 4% (w/v) PFA in PBS, 20 ml of PBS, and 100 ml of 50% (v/v) CUBIC-L in this order via left

24

ventricle of the heart. The mice were immersed in 200 ml of 50% (v/v) CUBIC-L at 37°C for more than 6 hours,

25

and further immersed in CUBIC-L at 37°C with gentle shaking for at least 5 days. After decolorization and26

delipidation, mice were washed with PBS at room temperature for 1 day. Mice were further immersed in 50%

27

CUBIC-R for 1 day, and then in CUBIC-R at room temperature with gentle shaking for at least 1 day. Although28bone tissues, gastrointestinal tissues, brown adipose tissues, and eyeball were still difficult to be transparentized

1in this protocol, it was sufficiently clear enough to execute whole-body and whole-organ scanning of cancer

2

metastasis. Expressed proteins are expected to be stably conserved even after clearing procedure, while RNA3

may be fully degraded in alkaline CUBIC-L treatment.

4
5

3D Nuclear Staining of CUBIC Samples

6

For nuclear staining with propidium iodide (PI), whole-body samples were subjected to nuclear staining with 5

7µg/ml PI during decolorization and delipidation at 37°C with shaking for 7 days. For nuclear staining with

8

RedDot2, whole-body/organ samples were subjected to nuclear staining with 1:100 diluted RedDot2 after9

decolorization and delipidation at room temperature with shaking for 3 to 7 days.

10
11

3D Immunostaining of CUBIC Samples

12

Organ samples after decolorization and delipidation were subjected to immunostaining with the 1:100 diluted

13

antibodies in the staining buffer composed of 0.5% (v/v) Triton X-100, 0.25% casein (37528, Thermo Fisher

14

Scientific, Waltham, MA), and 0.01% sodium azide (31208-82, Nacalai Tesque) for 3 to 5 days at room

15

temperature with shaking. The stained samples were washed with PBS three times at room temperature with16

rotation, and then immersed in CUBIC-R. The following antibodies were used for the staining (Table S3):

17

FITC-conjugated anti-a-smooth muscle actin (a-SMA) antibody produced in mouse (F3777, Sigma-Aldrich) for18

the brain, heart, lung, liver, kidney, pancreas, and spleen, mCherry antibody (16D7) Alexa Fluor 647 conjugate

19(M11241, Thermo Fisher Scientific) for the brain, lung, pancreas, Mouse VEGFR3/Flt-4 antibody (AF743, R&D

20

Systems, Minneapolis, MN), and Alexa Fluor 647 AffiniPure Fab Fragment Bovine Anti-Goat IgG, Fc fragment

21

specific (805-607-008, Jackson ImmunoResearch Laboratories, West Grove, PA).

22

The magnified images in Figure 1G and S1F clearly showed the separated punctate signals and luminal

23

signals. Regarding the detected red-color signals in cancer cells, we confirmed their signals by immunostaining

24

with anti-mCherry antibody. In Figure 3D and S3D, the red-colored signals totally matched with the25

immunostaining signals from anti-mCherry antibody.

26

1

Microscopy

2

Whole-body and whole-organ images were acquired with a custom-build LSFM (developed by Olympus, Tokyo,

3

Japan). Images were captured at 0.63 × objective lens (numerical aperture = 0.15, working distance = 87 mm) with4

digital zoom from 1 × to 6.3 × zoom. Lasers of 488 nm, 532 nm, 590 nm, and 639 nm were used for image

5

acquisition. To cover whole-body or whole-organs, the stage was moved both in the lateral direction and axial

6

direction. When the stage was moved to the axial direction, the detection objective lens was synchronically moved

7

to the axial direction to avoid defocusing. High resolution images for cell profiling were acquired with CLSM

8(FLUOVIEW FV1200, Olympus). Images were captured at 25 × objective lens (numerical aperture = 1.0,

9

working distance = 8.0 mm) with digital zoom from 1 × to 2 × zoom. Lasers of 473 nm, 559 nm, and 635 nm were10

used for image acquisition. RI matched sample was immersed in a mixture of silicon oil HIVAC-F4 (RI = 1.555,

11

Shin-Etsu Chemical Co., Ltd., Tokyo, Japan) and mineral oil (RI = 1.467, M8410, Sigma-Aldrich) during image

12

acquisition. 3D-rendered images were visualized, captured and analyzed with Imaris software (version 7.7.1 and

13

8.1.2, Bitplane AG, Zurich, Switzerland).

14
15

RI Matching Cocktails Composed of Antipyrine and Nicotinamide for RI Optimization of Each Organ

16

We prepared RI matching cocktails composed of antipyrine and nicotinamide for the purpose of discovering the17

optimized RI value for each organ. Cocktails with the RI ranging from 1.44 to 1.47 were adjusted only by

18

antipyrine concentration. Cocktails with the RI ranging from 1.48 to 1.49 were adjusted by antipyrine

19

concentration under the 10 wt% concentration of nicotinamide. Cocktails with the RI ranging from 1.50 to 1.51

20

were adjusted by antipyrine concentration under the 20 wt% concentration of nicotinamide. The cocktail with the21

RI of 1.52 was composed of 45 wt%/30 wt% antipyrine/nicotinamide. PFA-fixed organs were decolorized and22

delipidated by CUBIC-L for 5 days. After decolorization and delipidation, the organs were washed with PBS at23

room temperature overnight. Then, the organs were further immersed in 50% CUBIC-R for 6 hours, and in the24

prepared RI matching cocktails or PBS at room temperature with gentle shaking for 2 days.

25
26

Measurement of Light Transmittance27

We measured light transmittance of the brain, heart, lung, liver, kidney, pancreas and spleen samples from 450 to28

750 nm (in Figures 1C, S1B, and S1C) at 5 nm intervals with an integrating sphere (Spectral Haze Meter SH

17000, Nippon Denshoku Industries Co., Ltd., Tokyo, Japan). Every sample was embedded in the gel matrix

2composed of CUBIC-R containing 2% agarose (318-001195, Wako Pure Chemical Industries, Ltd., Osaka,

3

Japan) inside the optical cell. The averaged parallel transmittance was measured. Trends in the increase of4

transmittance against RI were different among organs. Lung and liver displayed a sharp increase of transmittance5

below RI of 1.46, whereas transmittance of the brain, heart, and kidney were gradually increased over the6experimental RI range, suggesting that transparency of the former organs was a major determinant of the RI

7matching step.

8
9

Image Data Processing and Analysis

10

All raw image data were collected in a lossless 16-bit TIFF format. 3D-rendered images were visualized and11captured with Imaris software (version 7.6.4, 7.7.1 and 8.1.2, Bitplane). Brightness, contrast, and gamma of the

12

3D-rendered images were manually adjusted with the software at minimum when visualized. The 3D images

13

were then used for image analysis with Imaris software. For the quantification of cancer metastasis, appropriate

14

threshold of signals from reporter proteins was selected in each experiment and surface analysis was performed

15

with Imaris software. To count cell number, appropriate threshold of signals from nuclear counterstaining was

16

selected in each experiment and spot analysis was performed with Imaris software. To quantify the17morphological features, we introduced the index of “volume per surface area” (the volume of the colony divided

18

by the surface area of the colony), which was positively correlated with the roundness of the colony in Figure 4C.

19

Noise signals such as hair of mouse were manually excluded with the software at minimum when visualized.

20
21

Cancer Cells

22

Human breast cancer cells, MDA-MB-231-5a-D (MDA-231-D), are a highly metastatic clone from

23

MDA-MB-231 (Ehata et al., 2007). Human renal cancer cells, OS-RC-2 and Caki-1, were kindly provided by

24

Prof. Tatsuro Irimura (Juntendo University, Tokyo, Japan) and Dr. Isaiah J Fidler (MD Anderson Cancer Center,

25TX), respectively. SUIT-2 (Japanese Cancer Research Resource Bank, Osaka, Japan), Panc-1 (American Type

26

Culture Collection (ATCC), Manassas, VA), A549, MDA-MB-231, MDA-231-D, and B16F10 (ATCC) cells

27

were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum

28(FBS), 50 U/ml penicillin and 50 µg/ml streptomycin as previously described (Hoshino et al., 2015). Renca

29(ATCC), 4T1 (ATCC), and OS-RC-2 cells were maintained in RPMI1640 containing 10% FBS and1penicillin/streptomycin (Hoshino et al., 2011). Caki-1 cells were maintained in minimum essential medium

2(MEM) containing 10% FBS and penicillin/streptomycin.

3
4

Establishment of Cancer Cells Stably Expressing Luciferase and mCherry/GFP

5

To establish cancer cells stably expressing firefly luciferase and mCherry/GFP under CMV or EF-1 promoter,6

we used a lentiviral expression system (kindly provided by Dr Hiroyuki Miyoshi, RIKEN, Saitama, Japan). To

7

prepare the expression vectors, firefly luciferase gene (originated from pGL4.10, Promega, Madison, WI), GFP

8gene and mCherry gene (originated from pmCherry, Clontech Laboratories, Inc., Mountain View, CA) were9

inserted into the entry vector (pENTR201). Recombination between pENTR201 and destination vector

10(CSII-CMV-RfA, CSII-EF-RfA) was performed with Gateway LR Clonase II enzyme (Thermo Fischer

11

Scientific). As a GFP expression vector, CS-CDF-CG-PRE was also used. 293FT cells were transfected with

12

vector constructs encoding each protein, the VSV-G and Rev expressing construct (pCMV-VSV-G-RSV-Rev),13

and the packaging construct (pCAG-HIVgp). The culture supernatants containing viral particles were collected14

and used as lentivirus vectors.

15
16

Experimental and Spontaneous Mouse Metastatic Tumor Models

17

Each metastasis model is described briefly as follows. For experimental liver metastasis, BALB/c-nu/nu mice (518weeks old, female) were injected with Panc-1 cells (0.5-1 × 106 cells/mouse) by open injection in the spleen.

19

Five minutes after injection, the spleen was surgically resected. For peritoneal dissemination, SUIT-2 cells were20

injected into pancreas orthotopically (5 × 105 cells/mouse) or intraperitoneally (i.p.) (2 × 105 cells/mouse) in

21

BALB/c-nu/nu mice (5 weeks old, female). In the experimental liver metastasis model with Panc-1 cells and the22peritoneal dissemination model with SUIT-2 cells, mice were fed with OpenSource Diets (D10001, ESP

23

EKISHIN Co., Ltd., Tokyo, Japan) to reduce autofluorescence from intestine. For experimental lung metastasis

24by intravenous (i.v.) injection, BALB/c-nu/nu (5 weeks old, female) or BALB/c mice (5 weeks old, female: 4T1,

25

male: Renca) or C57BL/6 mice (5 weeks old, female) were injected with each cell line (MDA-MB-231: 5 × 105

26

cells/mouse, MDA-231-D: 3 × 105 cells/mouse, A549: 1 × 106 cells/mouse, 4T1: 1.5-5 × 105 cells/mouse, Renca:27

1 × 105 cells/mouse, B16F10: 2 × 105 cells/mouse). For spontaneous lung metastasis by renal subcapsule28

injection, BALB/c (5 weeks old, male) or BALB/c-nu/nu mice (5 weeks old, male) were injected with Renca,

29

OS-RC-2 or Caki-1 cells orthotopically (Renca: 1 × 105 cells/mouse, OS-RC-2: 1 × 105 cells/mouse, Caki-1: 1 ×

1

105 cells/mouse). For experimental brain metastasis by intracardiac (i.c.) injection, BALB/c-nu/nu mice (4 weeks

2old, female: MDA-231-D, male: OS-RC-2) were injected with MDA-231-D or OS-RC-2 cells (MDA-231-D: 1-5

3

× 105 cells/mouse, OS-RC-2: 1 × 105 cells /mouse) by puncture into the left ventricle of heart. Mice were4

sacrificed at 5 or 6 weeks after injection. For the stimulation with TGF-b, A549 cells were pre-treated with or5

without TGF-b1 (5 ng/ml) for 72 hours in culture. After the stimulation with TGF-b in vitro, cells were6

harvested and inoculated intravenously (1 × 106 cells /mouse) in BALB/c-nu/nu mice (5 weeks old, female).

78Anti-tumor Drug Administration In Vivo

9

One day after 4T1 i.v. injection (1.5-3 ×105 cells/mouse), mice were administered with an anti-tumor drug in a

10

protocol as described previously (Bao et al., 2011). Briefly, mice were injected with doxorubicin (Dox, 5 mg/kg

11

body weight, Toronto Research Chemicals, Toronto, Canada), 5-FU (35 mg/kg body weight, Nacalai Tesque) or

12cyclophosphamide (CPA, 40 mg/kg body weight, Nacalai Tesque) intraperitoneally every other day (Day 1, 3, 5,

13

and 7). On Day 8, mice were sacrificed. In continuous administration, mice were injected with 5-FU (35 mg/kg

14

body weight) every day (Day 1-10). On Day 11, mice were sacrificed. Each drug was dissolved in saline (Otsuka

15

Pharmaceutical Factory, Inc., Tokushima, Japan). Mice in control groups were injected with saline.

1617

In Vivo Bioluminescence Imaging

18

Mice were anesthetized with avertin and injected intraperitoneally with D-luciferin potassium salt (Promega).

19

Ten to fifteen minutes after injection, luciferase activity was measured using NightOWL II LB983 (Berthold

20

Technologies GmbH & Co. KG, Bad Wildbad, Germany).

21
22

Histological Examination

23

After CUBIC-Cancer analysis, whole liver, brain and lung were washed with PBS and resected. The samples

24

were embedded in paraffin and subjected to HE staining as previously described (Hoshino et al., 2015).

25
26

Immunohistochemistry27

Immunohistochemistry was performed with VECTASTAIN Elite ABC Kit (Vector Laboratories Inc.,

28

Burlingame, CA). Samples after CUBIC-Cancer analysis in PBS were embedded in paraffin. Anti-E-cadherin

29(24E10) antibody was purchased from Cell Signaling Technology (Danvers, MA).

1
2

RNA Isolation and qRT-PCR Analysis

3

Total RNA was extracted with Isogen reagent (Nippon Gene, Toyama, Japan). cDNA was synthesized using

4

PrimeScript II 1st strand cDNA Synthesis Kit (Takara, Otsu, Japan) according to the manufacturer’s protocol.

5Gene expression was analyzed with StepOne Plus Real time-PCR System (Life Technologies, Carlsbad, CA) and6

Fast SYBR Green Master Mix with ROX (Roche Diagnostics, Tokyo, Japan). The expression level of each gene7

was normalized to that of hypoxanthine-guanine phosphoribosyltransferase (HPRT1). Primer sequences are

8

shown in Table S4.

910

Phalloidin Staining

11

A549 cells were stimulated with or without TGF-b1 (5 ng/ml) for 3 days. After the stimulation, cells were fixed

12

with 4% formaldehyde solution and permeabilized with 0.1% Triton-X. Cells were stained with

13

FITC-conjugated phalloidin (P5282, Sigma-Aldrich). Cell nuclei were stained with DAPI (Vector Laboratories

14Inc.). Images were captured with All-in-One fluorescence microscope BZ-X710 (KEYENCE, Osaka, Japan).

15

1

SUPPLEMENTAL REFERENCES

2
3

Bao, L., Haque, A., Jackson, K., Hazari, S., Moroz, K., Jetly, R., and Dash, S. (2011). Increased expression of

4

P-glycoprotein is associated with doxorubicin chemoresistance in the metastatic 4T1 breast cancer model. Am J

5

Pathol 178, 838-852.

6

Ehata, S., Hanyu, A., Fujime, M., Katsuno, Y., Fukunaga, E., Goto, K., Ishikawa, Y., Nomura, K., Yokoo, H.,

7

Shimizu, T., et al. (2007). Ki26894, a novel transforming growth factor-b type I receptor kinase inhibitor,

8

inhibits in vitro invasion and in vivo bone metastasis of a human breast cancer cell line. Cancer Sci 98, 127-133.

9

Hoshino, Y., Katsuno, Y., Ehata, S., and Miyazono, K. (2011). Autocrine TGF-b protects breast cancer cells from10

apoptosis through reduction of BH3-only protein, Bim. J Biochem 149, 55-65.

11

Hoshino, Y., Nishida, J., Katsuno, Y., Koinuma, D., Aoki, T., Kokudo, N., Miyazono, K., and Ehata, S. (2015).

12

Smad4 decreases the population of pancreatic cancer-initiating cells through transcriptional repression of13

ALDH1A1. Am J Pathol 185, 1457-1470.

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