Please cite this article as: Susaki and Ueda, Whole-body and Whole-Organ Clearing and Imaging Techniques with Single-Cell Resolution: Toward
Organism-Level Systems Biology in Mammals, Cell Chemical Biology (2016), http://dx.doi.org/10.1016/j.chembiol.2015.11.009

Cell Chemical Biology

Review
Whole-body and Whole-Organ Clearing and Imaging
Techniques with Single-Cell Resolution: Toward
Organism-Level Systems Biology in Mammals
Etsuo A. Susaki1,2,3,4 and Hiroki R. Ueda1,3,4,*
1Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku,
Tokyo 113-0033, Japan
2PRESTO, Japan Science and Technology Agency (JST), 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan
3Laboratory for Synthetic Biology, RIKEN Quantitative Biology Center (QBiC), 1-3, Yamadaoka, Suita, Osaka 565-0874, Japan
4AMED-CREST, Japan Agency for Medical Research and Development (AMED), 1-7-1, Ohte-machi, Chiyoda-ku, Tokyo, 100-0004, Japan*Correspondence: uedah-tky@umin.ac.jp
http://dx.doi.org/10.1016/j.chembiol.2015.11.009

Organism-level systems biology aims to identify, analyze, control and design cellular circuits in organisms.
Many experimental and computational approaches have been developed over the years to allow us to
conduct these studies. Some of the most powerful methods are based on using optical imaging in combination with fluorescent labeling, and for those one of the long-standing stumbling blocks has been tissue
opacity. Recently, the solutions to this problem have started to emerge based on whole-body and whole-organ clearing techniques that employ innovative tissue-clearing chemistry. Here, we review these advancements and discuss how combining new clearing techniques with high-performing fluorescent proteins or
small molecule tags, rapid volume imaging and efficient image informatics is resulting in comprehensive
and quantitative organ-wide, single-cell resolution experimental data. These technologies are starting toyield information on connectivity and dynamics in cellular circuits at unprecedented resolution, and bring
us closer to system-level understanding of physiology and diseases of complex mammalian systems.
Introduction
The life sciences have been experiencing a revolutionary period
since around the turn of Twenty-first century, when the massive
efforts to determine the genomic sequences of various modelorganisms began to yield fruit. At that time, a fledgling disciplineof systems biology began to emerge as an extension of the traditional molecular biology and was fueled by the large volumesof data and systems level views of cells and organism coming
out of genome sequencing efforts. Further advancements in‘‘omics’’ methods beyond genomics, such as proteomics, transcriptomics, lipidomics, metabolomics, and similar, emerged
and further expanded the scope and reach of systems biology
in the post genome era. Early on, systems biology introduced anumber of interesting concepts and systems-biological approaches to be applied to fundamental questions mainly within
the molecular-to-cellular layer, which over time developed into
four interacting research domains: (1) identification of components of the system, (2) analysis of the system, (3) control ofthe system, and (4) reconstitution and design of the system (Kitano, 2002). Although the development of various technologies
in the molecular-to-cellular level of analysis has been prosperous, systems-biological approaches in the upper-layer ofmulticellular organisms, the cellular-to-organ layer, has been
limited until recently.
Considering that the structure and function of an organism are
an ultimate manifestation of a collective output from all cellular
circuits within that organism, there is an obvious need to develop
technologies that would allow us to comprehensively identify
and quantitatively analyze cellular circuits within an organism
in order to better understand its anatomy and physiology. In

particular, the realization of systems biology in the cellular-to-organism layer (‘‘organism-level systems biology’’) is of special
importance in mammalian research because of its wide range
of applicability to life and health sciences. From this point ofview, the recent developments of whole-body and whole-organ
clearing and imaging technologies with single-cell resolution are
opening the door toward organism-level systems biology. These
technologies have started to provide comprehensive and quantitative experimental data in the cell-to-organism layer. In thisreview, we propose a plausible roadmap for realizing the organism-level systems biology in mammals by reviewing the recent
methodological achievements in whole-body and whole-organ
clearing and imaging, as well as cellular labeling and image
informatics (Figure 1). These technologies constitute the versatile experimental platform for this rapidly developing research
field, which will support further acceleration of life and healthsciences.
Chemical Approach of Tissue Clearing for Whole-Body
and Whole-Organ Imaging
Optical imaging, particularly in combination with fluorescent labeling of cells, is one of the practical ways for collecting singlecell resolution information inside an organ or a body becauseof its good spatial resolution and high signal-to-noise ratio. Tissue opacity, however, is one of the major obstacles in wholebody and whole-organ optical imaging. Tissues consist ofvarious substances with different optical properties (Tuchin,2015). Therefore, when the light goes through tissues, light distribution and intensity become broadened and decayed due tolight scattering and light absorption, respectively, which causes

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Please cite this article as: Susaki and Ueda, Whole-body and Whole-Organ Clearing and Imaging Techniques with Single-Cell Resolution: Toward
Organism-Level Systems Biology in Mammals, Cell Chemical Biology (2016), http://dx.doi.org/10.1016/j.chembiol.2015.11.009

Cell Chemical Biology

Review

Figure 1. Steps for Organism-Level Systems Biology
Overview of the proposed research scheme, aiming at organism-level systems biology in mammal by comprehensive cell detection and analysis of whole-organ/
body. This is achieved by optical tissue clearing and imaging (or alternatively by sectioning tomography without clearing) of properly labeled specimens. Data are
analyzed to extract biological information, for which high-spec PC equipment is generally needed.

blurred and weakened images, especially in deeper regions of
the tissue. Thus, the purpose of tissue clearing is mainly to minimize light scattering and absorption for better optical imaging.
Efficient tissue clearing contributes to improved imaging depth
and contrast (Cicchi et al., 2005; Vargas et al., 2001), which isnecessary to support progress in whole-body and whole-organ
3D imaging. Although tissue clearing has a history that’s morethan 100 years long (Spalteholz, 1914; Zhu et al., 2013), tissueclearing methods that can be implemented on practical timescales of days to weeks with a reasonable clearing efficiency
for optic imaging are a much more recent development (Hamaet al., 2015; Miyawaki, 2015; Ode and Ueda, 2015; Richardsonand Lichtman, 2015). In this section, we will first discuss lightscattering and light absorption, two physical properties of biological specimens that make whole-organ and whole-bodyclearing necessary, highlight basic principles of currently available methods of tissue clearing, and discuss some of theoutstanding obstacles to whole-body and whole-organ imaging.
Physical Properties of Biological Specimens that
Interfere with Whole-Body and Whole-Organ Imaging
One important obstacle for the light transmittance is light scattering. A biological specimen consists of various substances ofdifferent optical properties. In the tissue, biological substancessuch as water, lipids, proteins, and minerals usually exist as aninhomogeneous mixture (Figures 2A and 2B). Their inhomogeneous distributions cause strong lateral scattering and thus lightcannot pass straight through a tissue for the forward direction(Richardson and Lichtman, 2015; Tuchin, 2015) (Figure 2A).
Furthermore, biological substances sometimes form inhomogeneous structures inside and outside cells, which also causes
light scattering and are another reason for tissue opacity (Johnsen and Widder, 1999; Tuchin, 2015) (Figures 2A and 2B). These
include lipid particles and organelles (e.g., mitochondria), large
protein clusters (e.g., collagen fibers), or even whole cell volume(e.g., red blood cells) (Beauvoit et al., 1994; Beuthan et al., 1996;
Mourant et al., 1998; Steinke and Shepherd, 1988; van Staveren
et al., 1991; Wen et al., 2009; Yeh and Hirshburg, 2006). These

particles cause either the so-called Rayleigh scattering (thereason that the sky is blue) or Mie scattering (the reason thatmilk is white), depending on their sizes. Therefore, this point isimportant in considering the various clearing efficiencies ofdifferent organs (Susaki et al., 2015; Tainaka et al., 2014) ordifferent regions within the same organ (e.g., gray matter andwhite matter in the brain) (Ke et al., 2013; Susaki et al., 2014),
because the degree of light scattering is different among
different organs and regions inside an organ (Cheong et al.,1990), probably due to the difference in their components andmicroscopic structures. Furthermore, fixation may affect theclearing efficiency because of differing features or density of
the cross-link reaction. We found out that the pH of paraformaldehyde is a critical factor for the subsequent clearing step with
the CUBIC (clear, unobstructed brain/body imaging cocktails
and computational analysis) protocol, probably attributable
to the difference in pH-dependent fixation efficiency (Susaki
et al., 2015).
Another important obstacle for the light transmittance is lightabsorption by biological substances (Figures 2A and 2C). Lightabsorption is the transformation of light energy to some otherform of energy (e.g., heat), which causes decay of light intensity
during its travel within a tissue (Tuchin, 2015). Intrinsic lightabsorbing substances in tissue, particularly heme and melaninpigments, have high light-absorption ability within the visible
light range (400–600 nm) (Horecker, 1943; Tuchin, 2015; Weissleder, 2001). The removal of these internal light absorbers
therefore contributes to translucency of animals, which wasevidenced by translucent fish models of pigmentation mutants(Wakamatsu et al., 2001; White et al., 2008). Considering all ofthese reasons, strategies for tissue clearing are therefore aimed
at minimization of light scattering and light absorption inside thetissue (Figure 2A).
Minimization of Light Scattering by RI Matching
As discussed above, light scattering inside a tissue is one of
the main reasons of tissue opacity. Tissue clearing thus can beachieved by homogenization of optical property inside the tissue

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Please cite this article as: Susaki and Ueda, Whole-body and Whole-Organ Clearing and Imaging Techniques with Single-Cell Resolution: Toward
Organism-Level Systems Biology in Mammals, Cell Chemical Biology (2016), http://dx.doi.org/10.1016/j.chembiol.2015.11.009

Cell Chemical Biology

Review
Figure 2. Strategies for Optical Tissue
Clearing(A) Two main steps contributing to tissue clearing.
A biological tissue is composed of inhomogeneousmaterials with different optical properties, causing
strong light scattering or absorption. Light scattering can be reduced by optically homogenizing
the volumetric space with an RI matching reagent
and removal of light scatterer (mainly lipid
component). In addition, a major light absorber,heme in the tissue, should be removed to further
tissue clearing.
(B) Light scattering reduction in the tissue. In theactual opaque tissue (e.g., brain), various components with different optical properties (represented
as RI, for example) exist and construct somestructures that work as light scatterers. Clearing
can be achieved by removing water (lower RI)
and lipid (higher RI) and fulfilling the space with
RI-matching reagent (similar RI to the remaining
components).
(C) Reduction of light absorption in the tissue.
Blood has a light absorbing capacity under 600 nm
of wavelength, which partly contributes to the
reduction of light path in the raw (non-perfused)opaque tissue. Heme is the main light absorber inthe blood and can be removed from tissue inseveral ways; heme-removal efficiency and proteinpreservation capability vary among the methods.
The animal experiments were approved by the
Animal Care and Use Committee of the RIKEN
Kobe Institute and The University of Tokyo, and allof the animals were cared in accordance with theInstitutional Guidelines.

sample so that light is transmitted straight in the forward direction. The refractive index (RI) is an index related to the velocity
of light in a substance and reflects how much the speed of lightgets attenuated as it passes through a material as compared tothe speed of light in vacuum. In practical terms, RI is a simple indicator of optic properties and can be used to compare different
systems. Discontinuity of RIs causes light scattering; inside atissue, the light encounters inhomogeneity and areas of different
RIs as well as different microscopic structures, all resulting
in scattering and loss of light intensity in the forward direction(Figures 2A and 2B). Various tissue substances (e.g., proteins and lipids) havedifferent RIs, and they are overall higher(around 1.4 to 1.6) (Johnsen and Widder,
1999; Tuchin, 2015) than surrounding
water (1.33). Therefore, one reason why
tissue optical clearing might work is that
it would enable RI matching inside thesample; for example, when water in thesample is removed and then replacedwith a substance of higher RI matchedwith the tissue’s RI, light scattering is
minimized and the tissue becomes moretransparent. This RI matching concept
was suggested by optics and examinedin earlier studies (Liu et al., 1996; Tuchin,2015; Tuchin et al., 1997). This concept
was also considered in the developmentof most of the known tissue-clearing reagents. Indeed, the RI of these reagents is adjusted to around
1.4–1.5, similar to the gross RI range of several soft tissues (Bolin
et al., 1989; Hama et al., 2015; Richardson and Lichtman, 2015).
However, it is of note that the RI matching concept does not
explain completely the reduction of light scattering observed.
Choi et al. (2005), for example, cryopreserved human skin withtissue-clearing chemicals (a selected group of hydroxy-terminated reagents, organic solvents, and organic acids), andobserved no correlation between RI and optic clearing potential.
Mao et al. (2008) also investigated the clearing ability of alcohols

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Please cite this article as: Susaki and Ueda, Whole-body and Whole-Organ Clearing and Imaging Techniques with Single-Cell Resolution: Toward
Organism-Level Systems Biology in Mammals, Cell Chemical Biology (2016), http://dx.doi.org/10.1016/j.chembiol.2015.11.009

Cell Chemical Biology

Reviewand concluded that the optical clearing effect is related to thenumber of hydroxyl groups rather than RIs. RI adjustment withinorganic ions (e.g., zinc iodide) was also found not to be effective for brain clearing (Ke et al., 2013). Such a discrepancy between the RI matching concept and some of the experimental
observations can be attributed to other mechanisms of lightscattering reduction. One explanation might be that in addition
to affecting RI, clearing solvents also change the structure andsize of biomolecules that scatter light. For example, sugars
and sugar alcohols that are often used as components ofclearing solutions destabilize and dissociate high-order collagenstructures, which results in increased tissue transparency (Yehand Hirshburg, 2006). Dehydration that occurs during clearing
also contributes to the efficiency of the clearing process, beyond
the fluid volume replacement by RI-adjusting reagents (Jiang
and Wang, 2004; Rylander et al., 2006; Yu et al., 2011; Zhu
et al., 2013). This process can make the tissue components
denser, leading to more ordered structures and an increase in
the particle size, which also reduces internal light scattering. A
theoretical study also proposed that the light scattering isdependent on the particle size and shape in addition to the RI(Johnsen and Widder, 1999). Further plausible reasons, suchas the penetration speed of the reagents into tissue, or matchingof other optical properties between the reagents and surrounding tissue substances, can be also important. In conclusion,
although there are solid practical solutions that allow for highclearing efficiency, more intensive studies of the chemical principles that underline tissue clearing are needed for us to fully rationalize empirical observations.
Modern tissue clearing reagents and protocols are summarized in Table S1. A tissue clearing protocol with an organic
chemical cocktail (a mixture of benzyl alcohol and methyl
salicylate) was first reported by a German anatomist Walter
Spalteholtz more than 100 years ago (Spalteholz, 1914), andthis protocol was still in use one century after it was firstdescribed (Steinke and Wolff, 2001). The original protocol was
also modified to BABB (Murray’s clear), a mixture of benzyl
alcohol and benzyl benzoate that was used in whole-mount immunostaining and clearing as early as the 1980s (Dent et al.,1989), and later for whole-body fluorescent imaging of adult fly(McGurk et al., 2007) and mouse embryo with optical projection
tomography (McGurk et al., 2007; Sharpe et al., 2002). The BABB
was also used for the whole-brain fluorescence imaging of
Thy1-YFP transgenic (Tg) mouse with light-sheet microscopy(Dodt et al., 2007). Other organic solvents were further screenedin later studies for the purpose of higher clearing efficiency andfluorescence protein preservation. A protocol with dibenzylether(DBE) was found among these trials (Becker et al., 2012), andthese protocols and imaging applications were summarized
and proposed as 3D imaging of solvent-cleared organs(3DISCO) (Ertürk et al., 2012a) or shown to be compatible towhole-mount immunohistochemistry and following 3D imaging,reported as iDISCO (Renier et al., 2014). These methods wereused for recent whole-organ imaging purposes, including rodent
brain, spinal cord, kidney, lung, or embryo developments (Belleet al., 2014; Ertürk et al., 2012a, 2012b, 2014; Renier et al.,2014; Soderblom et al., 2015). During the clearing procedure,
these organic reagents need complete dehydration to replaceand fill the space in tissue. They immerse into the space, puta-

tively making hydrophobic interactions with surrounding tissue
components.
Biocompatible hydrophilic reagents have also been widelyused; for example, the clearing ability of hydrophilic or watersoluble reagents, such as sugars (e.g., glucose), alcohols (e.g.,
glycerol, polyethylene glycol, butanediol, TMP, sorbitol, xylitol),
DMSO, and oleic acid were tested in skin with the intention ofmedial application (Choi et al., 2005; Jiang and Wang, 2004;
Mao et al., 2008; Vargas et al., 1999, 2001; Yu et al., 2011; Zimmerley et al., 2009). Biocompatible hydrophilic reagents canmake hydrogen bonds with the tissue components together
with the surrounding water, which is a milder interaction for biological materials than is the case with organic reagents. Therefore, various protocols with hydrophilic chemicals have been
recently developed particularly for the purposes of fluorescenceimaging. Generally, the hydrophilic reagents that can be dissolved in water at a very high concentration give a high RI in
the solution and can thus be used for RI-matching reagents.
The sugar group are conventional tissue clearing reagents andhave been adapted to some of the clearing protocols. For
example, the SeeDB protocol uses a high concentration of fructose (115% [w/v]) (Ke et al., 2013). Its RI was adjusted to 1.49,
an efficient value for brain clearing. This protocol maintained aconstant sample volume during the procedure and preserved
cellular morphology. The cleared sample was subjected to
two-photon imaging with an optimized objective lens, covering
the whole mouse brain area. A high concentration (75%[w/v]) of sucrose was also tested for two-photon imaging ofmouse cortex, reaching 1.5 mm in depth (Tsai et al., 2009).
Sucrose is also utilized in the CUBIC clearing protocol we developed, providing a chemical-based delipidation and RI-matching(ScaleCUBIC-2, containing 60% [w/v] of sucrose to increase
the RI to 1.49, according to the evidence in the SeeDB study)(Susaki et al., 2014).
Alcohol group chemicals, such as glycerol, are also conventionally used for the tissue-clearing reagent. Scale, a urea-based
clearing reagent, utilizes this chemical and achieves both
RI-matching and swelling repression (Hama et al., 2011). The
combination of alchohol group chemicals with CLARITY, a gelembedding and SDS-based delipidation protocol, also gave agood clearing result (Epp et al., 2015). Aminoalcohols such as
N,N,N0 ,N’-Tetrakis(2-hydroxypropyl)ethylenediamine and triethanolamine were adopted in CUBIC reagents by replacing glycerol in the original Scale recipe. Their efficient tissue-clearing
ability was disclosed by a non-biased chemical screening inwhich all 7 aminoalcohols out of 20 polyhydric alcohol candidates showed the positive effect, suggesting that the structuralcharacteristic is important (Susaki et al., 2014). A part of its
clearing ability is attributable to RI matching (RI of 100% reagents = 1.48), whereas they can also contribute to the otherclearing processes, such as delipidation, by their weak detergent activity and alkaline-pH buffering ability. 2,20 -thiodiethanol(TDE), which has been shown to be useful as an RI-matching reagent, was shown in imaging of cultured cells (Staudt et al., 2007)
and was recently used in deep 3D imaging and tested withmouse brains (Aoyagi et al., 2015; Costantini et al., 2015).
Approximately sixty percent of TDE (RI = 1.45) gives relatively
good clearing performance with fluorescence preservation. Thischemical was also tested as a final RI-matching reagent for

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Please cite this article as: Susaki and Ueda, Whole-body and Whole-Organ Clearing and Imaging Techniques with Single-Cell Resolution: Toward
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Cell Chemical Biology

Review
CLARITY in place of FocusCrear, a commercially available RI
matching reagent (RI = 1.43–1.45), and supported whole-brain
imaging with light sheet fluorescence microscopy (LSFM)(Costantini et al., 2015).
Sugar alcohols are also used for this purpose. Sorbitol, one of
the conventional clearing reagents, was recently adopted insRIMS (refractive index matching solution) (70% [w/v] Sorbitol,
RI = 1.44 by our measurement) and ScaleS (40% [w/v] in
ScaleS4, RI = 1.44) recipes because of its efficient clearing
ability (Hama et al., 2015; Treweek et al., 2015; Yang et al.,2014). Poly-ether group molecules such as PEG are also usedin the ClearT2 recipe (Kuwajima et al., 2013).
Urea, the main component of Scale reagents, was discovered
as a clearing reagent due to its clearing ability of hydrophobic
polyvinylidene floride (PDVF) membrane (Hama et al., 2011).
However, tissue-clearing ability and PDVF-clearing ability do
not seem correlated. A possible mechanism for the tissueclearing ability of urea might be attributable to its hydration
ability, which accelerates the penetration of water and otherchemicals in the recipe and results in the decrease and subsequent matching of tissue’s gross RI. The original Scale recipe
has relatively low RI (1.38 in ScaleA2), but an updated reagent
obtained by adding sorbitol and DMSO has an increased RI
value (1.44) and improved clearing ability (Hama et al., 2011,2015). Formamide apparently has chemical properties similar
to urea and was applied to ClearT recipes by single use or in combination with polyethylene glycol (Kuwajima et al., 2013).
Finally, some chemicals containing hydrophilic aromatic groups
have been used as one of the clearing cocktail components. RIMS
contains 88% (w/v) of 5-(N-2,3-Dihydroxypropylacetamido)2,4,6-triiodo-N,N’-bis(2,3-dihydroxypropyl)isophthalamide (Histodenz) (RI = 1.46, adjustable to RI = 1.38–1.48). A related chemical,
diatrizoate acid, is applied to FocusClear (Chiang, 2002).
Minimization of Light Scattering by Active Removal
In addition to RI matching, an active removal of potent lightscattering substances is effective for efficient tissue clearing(Figures 2A and 2B). In recent high-performance tissue-clearing
methods, lipids are often removed because they are strong lightscattering substances. Even in earlier studies, lipids werealready suspected to be strong light scattering substances (Beuthan et al., 1996; Chance et al., 1995; Meyer, 1979; van Staveren
et al., 1991). Due to their high RI range (around 1.5) (Beuthan
et al., 1996; Tuchin, 2015) and property of producing granularstructures (causing Rayleigh and Mie scattering), lipids are
considered to be one of the main light scattering substances
inside a tissue. Simple RI matching of lipid droplets and surrounding media was indeed correlated with reduced light scattering of the mixture (Chance et al., 1995; Wen et al., 2009). In
addition, reagents and protocols with an active lipid removal
property, including BABB and 3DISCO, which achieve this byemploying organic solvents, CLARITY and its related protocols,or CUBIC, which uses physical lipid removal by electrophoresisor chemical lipid removal by high-concentration of detergents,
generally result in a strong clearing efficiency (Chung et al.,2013; Costantini et al., 2015; Ertürk et al., 2012a; Hama et al.,2015; Richardson and Lichtman, 2015; Susaki et al., 2014;
Yang et al., 2014).
Another major obstacle, particularly for whole-body imaging,is bone clearing. The mineral nature of the bones endows themwith RI (1.55–1.65) that’s higher than other tissues and surrounding substances (Ascenzi and Fabry, 1959; Ohmi et al., 2000).
Administration of RI-adjusting reagents such as glycerol andpropylene glycol partially reduced light scattering of bone (Genina et al., 2008). RI matching also improved imaging quality inside the bone, which was evidenced by infant brain imaging
through the cranial bone (Tainaka et al., 2014) or application of
BABB and 3DISCO for bisected femur clearing and imaging(Acar et al., 2015). As in the case of delipidation, RI-adjustment
combined with a decalcification step further contributes to efficient clearing of bone-associated tissues. Chelating reagents
such as EDTA or organic acids such as nitric, hydrochloric, formic, acetic, picric, or trichloroacetic acid are used in histologicaldecalcification (Begum et al., 2010; Callis and Sterchi, 1998;
Gomes et al., 2008). Recently, a passive CLARITY protocol(PACT) was combined with EDTA-based decalcification, leading
to an efficient clearing and imaging of mouse tibia (PACT-deCAL
protocol) (Treweek et al., 2015). Overall, integrated clearing protocols with finely tuned chemical compositions will continue tobe developed in future studies to achieve better clearing results
for whole-body imaging.
Minimization of Light Absorption by Active Removal
The process of light absorption is the transformation of light energy to some other form of energy as the light traverses the tissues, which eventually attenuates the intensity of the passing
light (Tuchin, 2015). The light absorbing substances in the tissue
should be thus removed for the subsequent imaging purposes ina process often referred to as decolorization. Such intrinsic lightabsorbing substances include lipid, beta carotene, lycopene, cytochrome c, and bilirubin, together with hemoglobin and melaninplaying the dominant role due to their prevalance and their highlight absorbing capacity. Heme in hemoglobin and myoglobin
works as a major chromophore that absorbs visible light (Horecker, 1943; Weissleder, 2001) and causes reduction of visible
light transmission (corresponding to 0.1 of absorbance) in the
blood (Figure 2C, left graph). This means that removal of hemoglobin is critical for whole-organ imaging of heme-rich tissues(Figure 2C, right panel). Conventionally, heme removal can beachieved using peroxide, a classicmethod for pigment bleachingthat destroys the protein, and using acid-acetone or strong basewash, which leads to heme dissociation (Fagan et al., 1999; Kristinsson and Hultin, 2004; Oliver et al., 1987; Spalteholz, 1914;
Steinke and Wolff, 2001). However, these protocols are too
harsh to preserve cellular proteins, including GFP (Alnuami
et al., 2008; Haupts et al., 1998; Steinke and Wolff, 2001), andare not widely used for fluorescence imaging.
Simple perfusion of buffer and fixative can contribute to thephysical removal of red blood cells, and thus large amounts ofheme, in the vessels; however, this is usually not enough forcomplete heme removal. A recent study showed the unexpected
contribution of clearing reagent aminoalcohols in heme removal(Tainaka et al., 2014). The likely mechanism by which aminoalcohols remove heme is based on their ability to coordinate heme
and replace the coordination point of the heme to globin protein
chain due to its structural similarity. CLARITY and related protocols also have the ability to remove heme with their improved
electrophoresis procedure, resulting in the preparation of almost
completely decolored samples (Epp et al., 2015; Lee et al., 2014;
Treweek et al., 2015).

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Cell Chemical Biology

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Melanin, a pigment derived from tyrosine via a multistep reaction that includes oxidation and polymerization, is also an additional obstacle for tissue decolorization. Melanin granules are
not only strong light absorbers but are also, given their size (inthe range of 0.3–1 mm in the case of isolated human hair melanin
granules [Potsch et al., 1997]) and high RI (1.6–1.7), strong lightscatterers (Tuchin, 2015). The polymer-based structure of thismolecule makes it insoluble in water and organic solvent. So
far, no efficient protocol for melanin removal has been reported,and this remains an outstanding issue that requires further development in future studies.
Currently Available Protocols for Efficient Whole-Body
and Whole-Organ Clearing
Protocols that achieve both lipid and heme removal as well as
using RI matching reagents can make samples highly transparent. Most of tissue-clearing protocols using organic chemicals (BABB and 3DISCO) remove lipid components and maketissue samples clear by matching their RI values closely. Thus,
these protocols can make various organs highly transparent(Belle et al., 2014; Ertürk et al., 2012a, 2012b, 2014; Renier
et al., 2014; Soderblom et al., 2015).
The CLARITY-based protocol PARS (perfusion-assisted reagent release in situ) also seems to have both lipid and heme
removing ability (Treweek et al., 2015; Yang et al., 2014). The
CLARITY protocol includes a delipidation step by electrophoresis or by simple immersion in SDS, after which the sample isembedded in acrylamide gel to keep proteins and nucleic acids
inside (Chung et al., 2013; Tomer et al., 2014; Treweek et al.,2015; Yang et al., 2014). In addition, an improved electrophoresis
protocol can also remove most of the blood color within a tissue(Epp et al., 2015; Lee et al., 2014; Treweek et al., 2015). The delipidated and decolored organs are then immersed in RI-adjusting reagents such as FocusClear or RIMS (Chung et al., 2013;
Treweek et al., 2015; Yang et al., 2014). In their attempt toachieve whole-body clearing of an adult mouse, Treweek et al.
and Yang et al. perfused a body with SDS for up to 2 weeks after
the gel embedding (PARS protocol) (Treweek et al., 2015; Yanget al., 2014). The cleared tissue obtained in this way can be also
used for immunostaining and 3D imaging purposes.
The efficient whole-body clearing and imaging of an adult mouse
was first demonstrated by the CUBIC protocol (Tainaka et al.,2014; Susaki et al., 2015). The CUBIC protocol also has the ability
to delipidate tissue and decolorizeduring its tissue clearing. A nonionic detergent, Triton X-100, and possibly the aminoalcohol andurea as well, contribute to the delipidation. In addition, the aminoalcohol can actively remove heme from the tissue. These properties are important for the efficient whole organ/body clearing
including heme-rich organs (Higashiyama et al., 2015; Susaki
et al., 2015; Tainaka et al., 2014). CUBIC provides a whole-bodyclearing protocol by reagent perfusion and subsequent
immersion, which clears whole infant and adult mouse bodies
for up to 2 weeks and allows whole-organ/body imaging as well.
Preservation of Structural and Molecular Integrity after
Clearing
One of the main issues with all the clearing strategies we
described is that they all, to a certain extent, affect structuraland molecular integrity of the sample under investigation. However, this is a recognized problem and there have been many efforts to provide practical solutions to minimize these artifacts.

Shrinkage or swelling during or at the end of the clearing procedure may cause some artifacts in structural integrity. This point
has been intensively examined in some clearing protocols suchas SeeDB and ScaleS. In general, clearing with organic reagents
causes 20%–25% shrinkage of the sample (Table S2). Some ofthe hydrophilic chemical-based methods, such as ScaleS,
CLARITY, and CUBIC, render temporal expansion of clearing tissue (the size recovered to its original size after RI matching);
however, such temporary swelling itself does not necessarily
affect ultrastructures in EM section images (Hama et al., 2015;
Mikula and Denk, 2015).
Delipidation-combined protocols (e.g., 3DISCO, CLARITY,CUBIC) can still target molecular to subcellular scales in addition
to whole-organ to cellular scales, because subcellular structures(e.g., spines and axons) or molecular localizations (e.g., spine
proteins) seem intact in the original papers (Chung et al., 2013;
Ertürk et al., 2012b; Renier et al., 2014; Susaki et al., 2014). On
the other hand, concerns about subcellular structural fidelity
and molecular integrity were suggested in other studies. A recent
study by Hama et al. examined the integrity of spine structures of
the PACT, CUBIC, or 3DISCO-cleared brains by using electron
microscopy and suggested a possible effect in the structures(Hama et al., 2015) while the original paper of CLARITY
concluded that the ultrastructures were largely preserved(Chung et al., 2013). Because EM detects high electron density(mainly lipid structure) in the tissue, this type of microscope
may not be compatible with such highly delipidated samples,
and thus an optimized preparation and staining process should
be considered (Chung and Deisseroth, 2013; Hama et al., 2015).
On the contrary, sample swelling can be utilized to get to
improve the process of clearing and imaging. Expansion microscopy, a ‘‘reverse-thinking’’ method, oppositely exploited thetissue expansion to overcome the limit of optic resolution(Chen et al., 2015). This was achieved by embedding tissue
into a hydropolymer, which led to a 4.5-fold linear expansion of
the sample. A similar strategy was recently introduced in ePACT(Treweek et al., 2015).
Issues in molecular integrity (e.g., loss of molecules, quenchingof fluorescent proteins, altered antigenicity) sometimes become apractical problem. A large part of proteins and nucleic acid seems
to retain at the practically detected levels even after delipidation,
because they can be still labeled and observed in the following
staining. However, more quantitative evaluations evidenced thatthe delipidation steps indeed affects the amount of protein inside
and the signal intensity of fluorescent proteins (Chung et al.,2013; Hama et al., 2015; Yang et al., 2014). Also, it should be notedthat this process is inappropriate if lipid itself or lipophilic dyes are
the observation target (Ertürk et al., 2012a; Hama et al., 2015) andthus different methods, such as adeno-associated virus (AAV)
tracing, should be considered for a similar purpose.
Quenching of fluorescent proteins has been a particular focus
in the history of tissue clearing and fluorescent imaging. Thisquenching can be caused by loss of the protein and destructionof its structure during the clearing step. Although the organic reagents give a strong clearing efficiency, fluorescent signal loss
has been a main issue. Researchers have tried to ameliorate
the problem by screening other hydration and RI matching chemicals, removal of peroxidase, or a change in the fixation method(Becker et al., 2012; Oldham et al., 2008; Sakhalkar et al., 2007;

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Please cite this article as: Susaki and Ueda, Whole-body and Whole-Organ Clearing and Imaging Techniques with Single-Cell Resolution: Toward
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Cell Chemical Biology

Review
Schwarz et al., 2015b). In addition, usage of hydrophilic reagents
mostly resolved this problem (Chung et al., 2013; Hama et al.,2011; Ke et al., 2013; Kuwajima et al., 2013; Susaki et al.,2014). The quenching is also dependent on treating temperatureand pH—by controlling these parameters, GFP fluorescent signals were further preserved even in the BABB-based protocol(FluoClearBABB protocol) (Schwarz et al., 2015b). We also
improved the ScaleCUBIC-1 recipe based on this study, whichsignificantly enhanced the fluorescent preservation property ofthe protocol (protocol available at http://cubic.riken.jp) Therefore, these parameters seem critical for keeping the molecular
integrity of fluorescent proteins during clearing.
The antigenicity issue also relates to molecular integrity.
Compatibility of various antibodies was examined in many
clearing protocols (Belle et al., 2014; Chung et al., 2013; Keet al., 2013) (Hama et al., 2011, 2015; Hirashima and Adachi,
2015; Renier et al., 2014; Susaki et al., 2014; Tainaka et al.,2014; Yang et al., 2014). Nevertheless, treatments with clearing
reagents may affect the protein structure and alter antigenicityof the antibody target, as with the other histological treatments(fixation, cryopreservation, paraffin-embedding, or antigenretrieval); for example, Hama and colleagues again reported
the concern that some antibodies for synaptic and membraneproteins did not work well in the CUBIC sample (Hama et al.,2015). The opposite case may also exist because suchcompatibility is determined by the combination of antibodies
and sample conditions. As is in the case with conventional immunohistochemical assays, it is important to check the compatibility of the specific antibody to the tissue after clearing
treatments.
Additional Notes and Future Directions of Tissue
Clearing
The proposed reagents and protocols discussed here canpotentially be shuffled and combined to develop better protocols, based on their chemical classification and putative functions in the clearing process (Table S2). The strategy ofcombining and shuffling protocols has been explored, at leastin part, and examples include combinations of TDE, SeeDB or
ScaleCUBIC-2 with CLARITY (Costantini et al., 2015; Susaki
et al., 2014; Unnersjö-Jess et al., 2015), RIMS with CUBIC (Treweek et al., 2015), and SeeDB with Scale (FRUIT method) (Houet al., 2015). Given the relatively small chemical space exploredin tissue clearing studies, we argue that the field needs to invest
effort in the discovery of other chemicals with higher clearing
performance either among organic solvents or hydrophilic reagents. Non-biased comprehensive screening campaigns,
similar to those used in the recently reported studies, are likely
to help discover clearing reagents with different and uniquechemical properties and structures (Kurihara et al., 2015; Susaki
et al., 2014). We expect that hits identified using chemical
screening will lead to further rational development of higher performing tissue-clearing reagents. Finally, in addition to the needto discover and develop new chemical reagents, we need tocontinue to investigate fundamental chemical principles of tissue
clearing in order to place our understanding of how and why tissue clearing happens on stronger mechanistic footing (Cheong
et al., 1990; Johnsen and Widder, 1999; Richardson and Lichtman, 2015; Tuchin, 2015; Wilson and Steven, 1990). Once weunderstand the mechanisms behind tissue clearing, we will beable to use that information to develop even more sophisticatedtechnologies.
Cell Labeling Methods for Whole-Body and Whole-Organ
Imaging
Tissue clearing and subsequent imaging are important aspectsof the research scheme discussed here. One additional important step in this scheme is cell labeling, in which fluorescenttags or proteins are introduced to enable visualization of different
cellular types, components, and activities. Here, we will summarize recent progress in multi-color fluorescent imaging withvarious fluorescent proteins and small molecule tags anddiscuss (Figure 3).
Cell Labeling with Fluorescent Proteins
One of the major ways to accomplish cell labeling is through theuse of fluorescent proteins (Figure 3A). Here, one or more fluorescent proteins are introduced into a genome, usually fusedto the target protein of interest or to its promoter region, and allowed to express. The imaging method is then optimized to measure the fluorescent signal of these proteins. This means that it iscritical to preserve structural integrity of the fluorescent proteins,and several tissue-clearing methods were optimized to preserve
the fluorescent protein signals.
In general, the first step toward the robust fluorescent signal isensuring that fluorescent protein(s) used are expressed at
appropriate levels. Cell-type specific promoters and enhancers
are usually used to dissect a specific, genetically defined population of cells (Huang and Zeng, 2013; Luo et al., 2008). Furthermore, to achieve both specificity and high expression level
simultaneously, a binary expression system (a combination ofdriver, determining the cell specificity, and reporter, turned onby the driver activity and expressed under a strong and ubiquitous promoter) is applied. In experiments with mammals, thebacteriophage Cre recombinase, Yeast flippase (Flp), or bacterial tetracycline-regulated transactivator (tTA) system are widelyused as the drivers. They are applied not only for the simple
driver-reporter combination, but also for more sophisticated genetic labeling methods. For example, combinations of Cre-Flp,
Cre-tTA or split Cre molecules realize the intersection or subtraction of reporter expression (Huang and Zeng, 2013). Brainbow(Cai et al., 2013; Livet et al., 2007) and mosaic analysis with double markers (MADM) (Zong et al., 2005) allow distinct and isolated cell labeling by multiple color variation, which also rely on
Cre recombinase activity. However, whole-organ/body imaging
has not yet been tried for these samples.
Inclusion of subcellular-scale connectivity information in a single whole-organ/body dataset is still challenging, but several genetic tools can be applied for such purposes. For investigating
synaptic connectivity with light microscopy, Kim and colleagues
developed mammalian GFP reconstitution across synaptic partners (mGRASP) (Kim et al., 2012). GRASP is based on functional
complementation between two non-fluorescent split-GFP fragments. When these fragments are tethered to the synaptic cleftin two different, synaptically connected neurons, the fluorescentsignal will be observed only if the two neurons are in a close proximity, which allows the two non-fluorescent GFP fragments tocome together and form the complete and fluorescent GFP. In
this case, a Cre recombination system was used to label preand post-mGRASP cells.

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Cell Chemical Biology

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Figure 3. Strategies for Cell Labeling
Cells should be properly labeled according to their biological context in the system, such as variation, structure, or function. This can be achieved by the following
steps.
(A) Genetic introduction of fluorescent markers or recombinases. These procedures have conventionally taken sigificant time and effort. However, most of themating procedures can be skipped by using recent high-throughput mouse genetics technologies.
(B) In vivo labeling of cells or regions with chemicals (e.g., neural tracer) or viruses.
(C) Whole-mount histochemical procedures with chemical stains and antibodies. For efficient 3D staining, a permeabilization step is apparently needed.

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Please cite this article as: Susaki and Ueda, Whole-body and Whole-Organ Clearing and Imaging Techniques with Single-Cell Resolution: Toward
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Cell Chemical Biology

Review
Functional labeling of cells and their time-series observation
are critical for understanding dynamical properties of the multicellular systems. Promoters and enhancers of immediate-earlygenes (IEGs), a group of genes that are induced immediately
by neuronal activity, have been widely used to achieve thisgoal (Huang and Zeng, 2013; Kawashima et al., 2014). These genetic tools label the integrated neural activities within several minutes to hours, on the timescale that is also proper for a tissue
clearing and imaging scheme. Several related tools are alsoavailable for whole-brain functional labeling at the cellular resolution. In these cases, a few things that should be considered
are promoter and enhancer types, which should be chosen depending on the cell type and desired signal intensity. For these
purposes, the regulatory elements of c-Fos and Arc/Arg3.1 arewidely used. An isolated c-Fos promotor successfully labeled awide range of neurons activated by pentylenetetrazol or kainicacid in vivo (Schilling et al., 1991; Smeyne et al., 1992) andwas later utilized in c-Fos-GFP transgenic animals (Barth et al.,
2004; Cifani et al., 2012). Whole-brain c-Fos-GFP expression
was monitored with serial two-photon tomography, revealing social behavior-induced neural evoking patterns in male and female mice (Kim et al., 2015b). Arc-GFP Knock-in (KI) and Tg,
or Arc-Venus Tg animals were also established for the similar
purpose (Eguchi and Yamaguchi, 2009; Grinevich et al., 2009;
Vousden et al., 2015; Wang et al., 2006). The latter Tg strains
have been evaluated in a whole-brain 3D imaging with CUBIC(Susaki et al., 2014) and serial two-photon tomography (Vousden
et al., 2015). Kawashima and colleagues identified a neuronal
activity–dependent enhancer of an Arc gene promoter, the synaptic activity–responsive element (SARE), and developed a genetic tool for labeling in vivo neural activity by using a synthetic,enhanced SARE (E-SARE) promoter (Kawashima et al., 2009,2013). c-Fos-tTA, Arc-CreER (tamoxifen-induced Cre) Tg, or
the TRAP system were further developed to restrict the time window of functional cell labeling (Denny et al., 2014; Guenthneret al., 2013; Reijmers et al., 2007). In those systems, the fluorescent transgene is only induced after or during the time windowof doxycycline or tamoxifen administration, but once labeled,the expression can be sustained until observation. For shorter
timescales, immunostaining of phosphorylated extracellularsignal-regulated kinase (ERK) has been also used (Cancedda
et al., 2003; Randlett et al., 2015).
Virus vectors are another widely applied way to deliver genetic
tools into animals (Huang and Zeng, 2013; Luo et al., 2008)(Figure 3B). For the specific cell labeling, viruses with a Cre/
Flp/tTA-driven expression cassette are usually injected in thedriver KI or Tg strain (again, high-throughput mouse genetics
are important here!). Furthermore, an engineered tropism, thespecificity of virus for a particular cell or tissue, helps a specific,
cell-type-dependent infection of pseudo-typed virus vectors.
Several engineered viruses, including AAV, lentivirus, retrovirus,
rabies virus (RV), canine adenovirus (CAV), or herpes simplex
virus are in use.
Systematic projection mapping of neurons (or projectome)
was performed with AAV and whole-brain 3D imaging. A largescale mesoscopic connectome project by the Allen Institute isa representative study, performing comprehensive AAV labelingand serial two-photon tomography imaging (see below) (Ohet al., 2014). Furthermore, AAV-based Brainbow tools can beutilized for similar experiments to examine the single-cell level
connectivity (Cai et al., 2013). A recent effort to improve AAV
capsid (PHP.B) supports more efficient expression in global
neural areas (Deverman and Patterson, 2015).
The RV system is also widely used for comprehensive neural
input mapping. RV infects neurons through axon terminals andis spread by retrograde transsynaptic transfer. The RV system,
which was developed by limiting pre-synaptic infections of
the virus to the single trans spreading and engineered byharboring the pseudo-type envelope, provides the cell-type specific infection and direct input mapping of neural circuit or even
input-output mapping with CAV (TRIO method) (Osakada andCallaway, 2013; Schwarz et al., 2015a; Wickersham et al.,2007). The macroscopic to mesoscopic resolution of whole-organ imaging is suitable for detecting the RV-labeled cells in ahigh-throughput and comprehensive manner. Indeed, automated serial sectioning (Velez-Fort et al., 2014) or multiple
whole-brain imaging with tissue clearing (Lerner et al., 2015; Menegas et al., 2015; Niedworok et al., 2012; Schwarz et al., 2015b)
has been recently performed for this purpose.
Several large resource institutes systemically produce andprovide many driver strains and support the use of these genetic
tools by making them available to the community (e.g., GENSAT[Gerfen et al., 2013; Gong et al., 2007] and the Allen Institute[Madisen et al., 2010]). However, it is also critical for eachresearcher to produce new KI or Tg strains in a high-throughput
manner. The recently developed technologies of genome editing
and developmental engineering enable such high-throughput
production of new KI or Tg strains (Figure 3A). The CRISPRCas system, an RNA-guided nuclease technology, is one of
the representatives (Cong et al., 2013). Reporter knocked-in animals can be directly produced at the rate of 45% by injecting
Cas9 RNA, guide RNA, and a targeting construct with a transgene into fertilized eggs (Aida et al., 2015; Yang et al., 2013).
Another example is the ‘‘ES-mouse’’ technology. ES-mouse refers to an F0 generation animal with almost all bodies composedof the injected ES cell-derived cells (the animal is also called
100% chimera mouse). The efficient production of ES-mouse
was achieved by the usage of 8-cell stage embryos (all cells
are still totipotent) and the ground-state ES cells cultured withseveral inhibitors (Kiyonari et al., 2010; Poueymirou et al.,2007; Ying et al., 2008). Both technologies significantly shorten
the period to generate a new KI or Tg strains because a new
mouse strain can be generated directly from genome-edited
fertilized eggs or ES cells within a single generation, without mating. Thus, researchers can complete their experiments with thefirst generation animals from the genome-editing process (typically within 2–4 months). This strategy has already been shownin knockout phenotyping (Poueymirou et al., 2007) and the production of a new knock-in strain harboring a bright red fluorescent protein transgene (Susaki et al., 2014; Tainaka et al., 2014).
Cell Labeling with Small Chemicals and Antibodies
Histological tissue staining with small molecule reagents and antibodies can be used to detect specific molecules, structures,and cell types inside tissue, even for 3D samples (Figure 3C).
For example, whole-mount immunostaining with BABB was performed even in earlier studies by using embryos of frog,
opossum, and mouse, or mouse cerebellum after weaning(Dent et al., 1989; Luque et al., 1998b; Sharpe et al., 2002; Sillitoe

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Cell Chemical Biology

Reviewand Hawkes, 2002). Modified Golgi-Cox and Nissl staining werealso performed for uniform whole-brain staining of rodents (Wuet al., 2014; Zhang et al., 2011). Most of the recent clearing
and imaging technologies are compatible with such wholemount staining or pre-labeling before imaging. However, the procedures still have some difficulties, particularly because of thepenetration step into deep regions of 3D tissue.
Fluorescent small compounds have been widely used inclearing and imaging studies; for example, many nucleic acidstains (e.g., DAPI, propidium iodide, SYTO series dyes, TOPRO3, or DRAQ5) are important for giving anatomical orientations during and after observation (Costantini et al., 2015; Susaki
et al., 2014; Yang et al., 2014). This is even informative for somespecific structures inside the tissue, as evidenced by extraction
or quantitative comparison of coronal vessels in the heart, bronchial tracts in the lung, or ducts and islets of Langerhans in thepancreas (Tainaka et al., 2014). This also gives a global anatomical cue for calculating registration and alignment parameters ofwhole-brain analysis (Susaki et al., 2014, 2015). Fluorescent
Nissl stain reagents (NeuroTrace) (Yang et al., 2014) can bealso used for a similar purpose. Lipophilic tracing dyes (e.g.,
DiI) can be introduced in live animals (Figure 3B) and are compatible with some tissue clearing protocols (Hama et al., 2015; Houet al., 2015; Ke et al., 2013; Kuwajima et al., 2013). However,preservation of lipid contents should be considered in theusage of such chemicals. Indeed, protocols with delipidation(e.g., CUBIC, 3DISCO, PACT) are not apparently amenable tothe use of these lipophilic dyes (Ertürk et al., 2012a; Hamaet al., 2015). Fixable protein tracers such as Phaseolus vulgarism
leucoagglutinin and cholera toxin subunit b or dextran-basetracers (e.g., FluoroRuby) can substitute for the similar tracing
purpose, even in the whole-brain scale (Quinta et al., 2015; Zingg
et al., 2014).
Immunohistochemical analysis with various antibodies andsubsequent 3D imaging is compatible and benefits from many
clearing protocols (Belle et al., 2014; Chung et al., 2013; Hamaet al., 2011, 2015; Hirashima and Adachi, 2015; Ke et al., 2013;
Renier et al., 2014; Susaki et al., 2014; Tainaka et al., 2014;
Yang et al., 2014). The permeabilization step is critical for antibody penetration in the 3D tissue sample. Delipidation by highly
concentrated detergents or electrophoresis substitutes for thisstep and supports staining of several millimeters in depth (Chunget al., 2013; Susaki et al., 2014; Tainaka et al., 2014; Tsai et al.,2009; Yang et al., 2014). These samples were observed after
clearing with high-concentrated sucrose, CLARITY, or CUBIC
protocols. MeOH and/or DMSO with hydrogen peroxide (forpigment bleach and endogenous peroxydase destruction, in
the original paper) or with repeated freeze-thaw procedure arealso conventionally used (Davis et al., 1991; Dent et al., 1989;
Luque et al., 1998a; Sillitoe and Hawkes, 2002). The dehydration-rehydration procedure permeabilize the sample possibly
by removing membrane lipids, which enabled the whole-mountimmunostaining and 3D imaging of mouse hemisphere, whole
mouse embryo and postnatal infants, and adult mouse organs
such as the brain and the kidney (Gleave et al., 2013; Renier
et al., 2014). The samples were cleared with BABB or 3DISCO(proposed as ‘‘iDISCO’’ protocol) for 3D imaging. iDISCO also
provides an alternative permeabilizing procedure with 20%
DMSO and low-concentration (0.1–0.2%) of detergent cocktails

to keep antigenicity. AbScale and ChemScale protocol, recently
reported by Hama and colleagues, is unique in utilizing urea
before and during 3D staining (Hama et al., 2015). In this protocol, the tissue is first permeabilized with several Scale reagents(Scale S0, A2 and B4, containing 4–8 M urea and low concentrations of lipid removal reagents) and then antibodies or chemicals
react in a solution containing urea again.
Large samples including whole adult mouse brain, heart, lung,stomach, or intestine were tried for 3D immunohistochemistry(Chung et al., 2013; Renier et al., 2014; Tainaka et al., 2014).
However, the penetration depth had not been fully evaluated inthese studies. Recently, antibody penetration depth was evaluated in ScaleS study by comparing the staining degree of
NeuN antibody in pre-cut and post-cut samples (Hama et al.,2015), showing uniform staining of mouse hemisphere. To further
improve the whole organ/body staining and imaging, factors
affecting penetration rate should be covered in detail. Studies
suggested some critical factors; for example, Li et al. investigated diffusion of antibodies in a CLARITY-cleared sample andshowed the diffusion is well described by Fick’s law (Li et al.,2015), which suggests that several factors such as particle
radius, concentration of stains, penetration time, or interactions
between stains and the sample are important. Indeed, the diffusion coefficients of IgG, F(ab’)2, Fab, or nanobody are smaller(1/4) than that of the small molecule components of the solution. Furthermore, it takes IgG (150 kDa) about ten times longer
to achieve the same degree of penetration as a nanobody(14 kDa). The importance of a stain’s concentration was examined in the iDISCO study, showing that the 3D staining resultsof kidney were much improved when using more concentrated
antibody preparation (Renier et al., 2014). In addition to factors
on the stain side, physicochemical properties of the tissue
further complicate the issues in 3D staining; for example, lower
PFA concentration, protease K treatment, and the repeatedfreeze-thaw cycle improved the antibody penetration by apparently affecting the mesh size of the sample (Gleave et al., 2013;
Luque et al., 1998a). Solving these issues and developing moresophisticated 3D staining protocol is an active area of research.
For example, a method that employs external electric field enables the active transport of stains and achieves rapid labelingof mouse organs with stains that vary from small molecules toantibodies within 1 day (Kim et al., 2015a; Li et al., 2015).
In addition to small molecule and antibody stains, wholemount FISH protocols were also tried. However, more improvement is needed for its practical use in 3D staining and imaging(Luque et al., 1998a; Yang et al., 2014).
3D Fluorescence Imaging for Whole-Body andWhole-Organ Cell Profiling
Criteria for Whole-Body and Whole-Organ Imaging
with Single-Cell Resolution
Technically speaking, the goal of the imaging step is to takewhole-organ/body images with a reasonable (cellular to subcellular) resolution in a high-throughput manner. Instruments used
for this purpose are summarized in Table S2. There are several
critical points to be considered to ensure high quality performance of the imaging step. The imaging color should beproperly selected according to the imaging purposes. For
example, particularly in the case of deeper 3D imaging, brighter

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Cell Chemical Biology

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Figure 4. Imaging Strategies and Examples of Whole-Brain Imaging(A) Choice of color. Even after optical clearing of tissue, red light can penetrate more than blue to green light, and imaging quality can be better when red
fluorescence is used. Typical fluorescent proteins or dyes are shown for reader’s reference. The graph of percentage of transmittance was re-plotted from thedata in Susaki et al., 2014.
(B) Choice of objective lens. For whole-organ/body imaging with cellular (several microns)-to-subcellular (<1 mm) resolution, an ideal objective lens would have asignificant optical resolution (e.g., Rayleigh resolution is approximately 0.67 mm when N.A. = 0.5, l = 0.55 mm), and a deep working distance (e.g., 20 mm for thehalf-depth of an adult mouse body). In the current microscopy setups (Table S2), longer WD with a lower N.A. lens for wide-field imaging or shorter WD with ahigher N.A. for higher resolution imaging are typically adopted. Lenses in the figure are: XLPLN10XSVMP, XLSLPLN25XGMP, MVPLAPO0.63X, MVPLAPO2X(Olympus); MacroFluo Planapo 0.8 3 and 2 3, HC FLUOTARL 25 3 (Leica); Z.1 detection optics 5 3, LD Plan-Aphochromat 20 3 (Zeiss); and AZ-Plan Apo 4 3(Nikon). Lined and fulfilled stars indicate dry and immersion objectives, respectively.
(C–F) Examples of whole-brain 3D imaging of Thy1-GFP/YFP Tg strains, with various imaging setup. Images were adapted with permission from Chung et al.,2013, for (C), Ertürk et al., 2012a, for (D), and Tomer et al., 2014, for (E), respectively. The image in (F) was recaptured from data in Susaki et al., 2015.
The animal experiments related to (A) and (F) were approved by the Animal Care and Use Committee of the RIKEN Kobe Institute and The University of Tokyo, andall of the animals were cared in accordance with the Institutional Guidelines.

fluorescent signals of red to near infrared wavelength can give
better results due to their higher penetration in the tissue(Figure 4A). Also, the optical resolution of the images should beproperly selected. This is mainly determined by the numericalaperture (N.A.) of the objective lens for x-y, where sub-micrometer range can be achieved when N.A. of the objective isover 0.34 according to the Rayleigh criterion of the optical resolution, 0.61 3 l/N.A., where l is the wavelength of light (l =0.55 mm is typically used). At low magnification range, such asa macro imaging setup, the camera’s pixel size (p) and magnifi-

cation (M) of objective determine the final x-y resolution of theimage, 2 3 p/M. Furthermore, for the 3D imaging purpose of alarge sample, working distance (WD) of the objective is also critical. The need for trade-offs between N.A. and WD in thecurrently available objectives (Figure 4B), the field-of-view size
and magnification of objective, and the pixel size and camera
sensitivity, can be minimized by improvement of devices or sample preparation to allow for better scalable imaging. For example,
a recent idea to combine Expansion microscopy and ePACT canovercome the optical resolution by expanding the sample itself

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Cell Chemical Biology

Review(Chen et al., 2015; Treweek et al., 2015). The z-resolution isdetermined by the thickness of optical or physical sectioning,which is dependent on the instrument (see below). Lastly, thethroughput also depends on the specifications of imaging instruments (e.g., way of illumination, field-of-view size, or speed ofstage movement) and signal intensities from the sample.
Currently available instruments tend to be built for a range ofresolution and throughput according to the experimental purposes (Table S2), from a lower resolution with a wide field-ofview (e.g., a whole mouse horizontal image in a single x-y image)to a higher resolution (e.g., reconstitution of long-range axons in
the volumetric image). Even in the former setup, imaging with asingle cell resolution can be achieved using sparsely labeled
targets such as Arc promoter-driven fluorescent proteins or
RV-labeled neurons (Schwarz et al., 2015b; Susaki et al., 2014;
Susaki et al., 2015). On the other hand, the latter setup requires
image reconstitutions by tiling or stitching. Open-source computational tools like XuvTools (http://www.xuvtools.org [Emmenlauer et al., 2009]) or TeraStitcher (http://abria.github.io/
TeraStitcher/ [Bria and Iannello, 2012]) are available for suchcalculation, while the data size, resolution and image acquisition,and calculation times are also the trade-offs. These instruments
and imaging applications are further discussed in the following
sections.
LSFM for High-Throughput Imaging with Single-Cell
Resolution
Optical sectioning of highly transparentized samples obviates
the need for serial sectioning and reconstitution and provides ahigh-throughput approach for globally mapping cells and cell
populations across the organs and body (Chung and Deisseroth,2013; Luque et al., 1998b). A combination of two old-yet-new
technologies, tissue clearing and LSFM, is fruitful in this aspectand has been rapidly growing within the latest decade (details
were recently reviewed in Keller and Ahrens, 2015). A thin lightsheet is illuminated from the side of a transparent specimen to
obtain optical sectioning without physical sectioning, and excitation signals are acquired from its vertical sides. Because excitation-detection paths are separated, and a single x-y plane imageis acquired in a single sweep (without line scanning), this microscopy instrument enables high volumetric imaging speed, highsignal-to-noise ratio, and low levels of photobleaching andphototoxic effects (Keller and Ahrens, 2015; Keller and Dodt,
2012; Osten and Margrie, 2013; Tomer et al., 2014). Theseadvantages meet the requirements for the systems biology
approach, as they achieve coverage of a wide range of spatial
and temporal scales. Various related experiments in recent years
have utilized this microscopy for whole-organ and whole-organism imaging purposes, including spatiotemporal monitoring ofanimal embryo developments (Keller et al., 2008; Krzic et al.,2012; Tomer et al., 2012; Truong et al., 2011; Wu et al., 2011,2013), functional imaging of whole-brain neural activities in fish
and fly brains (Ahrens et al., 2013; Lemon et al., 2015; Panier
et al., 2013; Vladimirov et al., 2014), whole-organ imaging ofdozens of samples in a realistic timescale (Lerner et al., 2015;
Menegas et al., 2015; Susaki et al., 2014; Tainaka et al., 2014),
or even whole infant mouse imaging at the cellular resolution(Tainaka et al., 2014).
Development of the light sheet microscopy dates back to thework of Siedentopf and Zsigmondy done at the turn of the twen-tieth century (Siedentopf and Zsigmondy, 1903). The first attempts to combine light sheet microscopy with tissue clearing
were reported in early 1990s by Voie and colleagues, whoused rhodamine-immersed guinea pig cochlea and Spalteholz’s
clearing mix (Voie et al., 1993). More recently, Dodt and colleagues used ultramicroscopy, an instrument with macro-zoom
microscopy and a light-sheet illumination unit to take a large
field-of-view image to acquire images of YFP-expressing mousebrain cleared with BABB (Dodt et al., 2007). The related LSFMs
were later used in 3DISCO applications and CUBIC studies (Figures 4D and 4F, Table S2) (Belle et al., 2014; Ertürk et al., 2012a,
2012b, 2014,; Renier et al., 2014; Soderblom et al., 2015; Susaki
et al., 2014; Tainaka et al., 2014). Furthermore, several researchteams developed a custom-build LSFM for whole-organ imaging
of BABB or CLARITY samples (Figure 4E and Table S2) (Costantini et al., 2015; Lerner et al., 2015; Niedworok et al., 2012;
Schwarz et al., 2015b; Tomer et al., 2014). Recently, a commercially available LSFM was also utilized for a large-scale datacollection (77 specimens) of RV-labeled brains (Menegas et al.,2015) (Table S2).
Because the sample should be highly transparentized, someof the recent tissue-clearing protocols (3DISCO, CLARITY, or
CUBIC, so far) are mainly applied to the large volumetric imaging
with LSFM. In addition, some issues related to the optic configuration should be considered: one is the axial resolution, which isdetermined by the minimum thickness of the sheet and its
cross-section shape. The thickness of the illumination beam is
non-uniform and is transverse to the imaging axis, having a‘‘waist’’ at the thinnest part (Leischner et al., 2009; Voie et al.,1993). This is because the illumination beam is focused by a cylindrical lens or an objective lens in the illumination path. Furthermore, the confocal parameter, a value related to the width of lightsheet waist, becomes shorter according to the square of halfsheet thickness, assuming a Gaussian beam. This non-uniformityof the sheet causes different z-resolution throughout the image,
particularly in a large field of view and with a thinner sheet. A
proper range of parameters among the minimum sheet thickness,x-y resolution, and field-of-view size should be thus selected. This
problem can be avoided by merging signals obtained only fromthe beam waist (Buytaert and Dirckx, 2007; Santi et al., 2009),
but this is done at the expense of imaging throughput. Other attempts to improve the axial resolution or image contrasts of
LSFM are focused on developing additional LSFM configurations
for various imaging purposes, exploiting a virtual-slit effect by rolling shutter mode of a sCMOS camera chip (Baumgart and Kubitscheck, 2012; Tomer et al., 2014), multiview deconvolution using
the images of isotropic multiview LSFM (Chhetri et al., 2015),
combinatorial use of confocal system (Silvestri et al., 2012), lightsheet produced by incoherent extended focusing (Dean and
Fiolka, 2014), Bessel-beam illumination (Fahrbach and Rohrbach,2012; Fahrbach et al., 2010; Planchon et al., 2011), two-photon
illumination (Planchon et al., 2011; Truong et al., 2011; Zonget al., 2015), structured-illumination (Kalchmair et al., 2010; Keller
et al., 2010), lattice light sheet (Chen et al., 2014), or stimulated
emission depletion (STED)-integrated light sheet (Friedrichet al., 2011), some of which were already tested for whole-organ/body imaging in a scalable manner (Table S2). Furthermore,deconvolution or adaptive optics was also introduced for
improving image quality (Dodt et al., 2007; Ertürk et al., 2012a;

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Please cite this article as: Susaki and Ueda, Whole-body and Whole-Organ Clearing and Imaging Techniques with Single-Cell Resolution: Toward
Organism-Level Systems Biology in Mammals, Cell Chemical Biology (2016), http://dx.doi.org/10.1016/j.chembiol.2015.11.009

Cell Chemical Biology

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Tainaka et al., 2014; Turaga and Holy, 2010), but should overcome the vast amount of calculation in the current situation.
In summary, LSFM application for cleared tissue imaging provides one of the best opportunities to acquire multiple 3D images
in a high-throughput manner. Some of the recent studies havestarted to use this strategy to address specific biological question. Further development and optimization of the instruments
will provide multi-scale imaging options, from sub-cellular towhole-organ/body scales.
Other Microscopy Setups
Several tissue-clearing methods were also combined with
confocal and multi-photon imaging techniques. Although there
are some drawbacks, such as photobleaching and much longer
acquisition time, these applications provide more opportunities
for large and deep 3D imaging experiments.
While single-photon confocal microscopy approaches arelimited to use near the opaque tissue surface (100 mm) (Helmchen and Denk, 2005), efficient tissue clearing enabled submillimeter to millimeter scale observations (Calve et al., 2015;
Hama et al., 2011, 2015; Ke et al., 2013; Renier et al., 2014; Susaki et al., 2014; Unnersjö-Jess et al., 2015; Yang et al., 2014) andeven whole-brain, whole-lung, and whole-embryo imaging(Chung et al., 2013; Quinta et al., 2015) (Figure 4C and Table
S2). A combinatorial use of wide-field LSFM for whole-organ
observation and confocal microscope with higher N.A. objectives will further allow a scalable observation in a single
specimen, avoiding quenching and longer scanning time. Twophoton microscopy allowed imaging of several hundred microns
to 1 mm in depth in various non-cleared organs by the use of a
far-red pulse laser, which generates signals in a non-linear
fashion (Helmchen and Denk, 2005). Again, by combining twophoton microscopy with tissue clearing, it was possible toconduct an imaging experiment covering whole-brain areas(Ke et al., 2013). In this case, a moderate clearing efficiency,
which preserves the ultrastructures, is sufficient for such twophoton imaging application.
Optic projection tomography (OPT) is another imaging setupthat is used with optic tissue clearing. OPT provides the opportunity for rapid, bright-field, and fluorescent 3D imaging ofeven centimeters cubed in size with a few micron resolution(Wong et al., 2013) (Table S2). When combined with BABB
clearing, large volumetric samples, including developing mouseembryos, xenograft tumor tissues, whole mouse heart, kidney,
brain or hemisphere, and white adipose tissue have all been
examined successfully using OPT (Anderson et al., 2013; Gleave
et al., 2012, 2013; Jeansson et al., 2011; Oldham et al., 2008; Sakhalkar et al., 2007; Sharpe et al., 2002; Vinegoni et al., 2009;
Walls et al., 2008; Zeng et al., 2015).
An alternative and complementary methods of whole-organ/
body imaging can be performed with automated sectioning-imaging instruments (Osten and Margrie, 2013). The proposed
setup allows users to skip the clearing step and yet obtain ahigh-resolution image without optic blurring throughout the sample. There are some possible drawbacks, however, whencompared with clearing-imaging strategy. Because it requires
physical sectioning time, throughput is lower compared to
LSFM imaging. Tissue clearing also enables multi-scale (EM tomacro-zoom imaging) and multi-modal (transgenes and chemical staining) data acquisition in a single sample, while thesectioning-imaging method destroys the sample in a single imaging sequence. A more complicated and expensive setup isalso an obstacle to wider use for this type of instrument. However, these sectioning-imaging instruments have been utilized
in several whole-organ imaging and data collection studies
because of their advantages (Table S2). For example, serial
two-photon (STP) tomography, a high-speed multi photon microscope with integrated vibratome sectioning (Ragan et al.,2012), has been used for RV-tracing, whole-brain c-Fos andArc activity monitoring, and a large-scale mesoscopic connecome project with AAV tracing (Kim et al., 2015b; Oh et al.,2014; Velez-Fort et al., 2014; Vousden et al., 2015). Two-photon
microscopy allows the user to take images of a deeper area fromthe sectioned surface and to obtain undisturbed optical sections. Another technique is micro-optical sectioning tomography(MOST) and the related instruments fluorescence-MOST(fMOST) and 2p-fMOST, which are composed of a microtome,
a light microscope/scanning fluorescence microscope/twophoton microscope, and an image recorder (line-scan CCD or
PMT) (Gong et al., 2013; Li et al., 2010; Wu et al., 2014; Zheng
et al., 2013). Overall, the emerging sectioning and imaging techniques have already yielded interesting results, such as a brainwide dataset at a high (1 mm voxels) resolution, which is enoughfor reconstituting long-distance projections of individual axons in
3D space. In that case, the image collection took hundreds ofhours for acquisition. Furthermore, a transparentized brain was
also subjected to the STP tomography to exploit advantagesof tissue clearing, which could expand its imaging depth (Costantini et al., 2015). As noted above, we do not think that a single
method will offer a solution to all of the issues focused on by
those who are interested in whole organ/body imaging applications; rather, we envision that finding creative ways to combine
different methods, including those not yet developed, will provide greater opportunities.
Image Informatics for System Identification and System
Analysis
One final aspect of whole-organ/body imaging at single-cell resolution that we want to highlight is how system-level biologicalinformation should be extracted from the acquired images. The
omics-type approaches discussed here need processing ofmulti-dimensional data, including three dimensions with multicolor and -orientation images, and with multiple samples ortimepoints. However, such large volumes of data, dozens ofgigabytes to several terabytes for a single dataset, pose significant challenges for software and hardware and demand efficientsolutions and have been often discussed in the scientific community (for example, see discussions in Akil et al., 2011). The
informatics field for large image dataset analysis is in the earlystage of development, facing challenges similar to those facedin the the first stage of the microarray era, because of insufficient
software resources and hardware specs. Below we highlightsome of the current efforts in this arena.
An example of the analysis pipeline is shown in Figure 5. Procedures following this general pipeline have been proposed andapplied in several studies of multiple mammalian, fish, or fly datasets of whole-organ scale (Ahrens et al., 2013; Amat et al.,2015; Helmstaedter and Mitra, 2012; Menegas et al., 2015; Portugues et al., 2014; Susaki et al., 2014, 2015). Such pipelines can

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Please cite this article as: Susaki and Ueda, Whole-body and Whole-Organ Clearing and Imaging Techniques with Single-Cell Resolution: Toward
Organism-Level Systems Biology in Mammals, Cell Chemical Biology (2016), http://dx.doi.org/10.1016/j.chembiol.2015.11.009

Cell Chemical Biology

Review

Figure 5. Possible Analysis Pipeline of Whole-Organ/Body Images
In the scheme of systems biology, multiple samples are generally prepared according to experimental conditions. Signals from labeled cells are comprehensively
collected and subjected to the substantial analysis pipeline. In most cases, the acquired data are too big (gigabyte to terabyte, see Table S2) to be directly
analyzed and thus should be compressed by changing file format and file size. If multi-orientation images are acquired for single specimens, they should be
integrated to a single dataset for image complementation. This pre-processed data can then be used for various analyses to extract system-level biologicalinformation through structural segmentation, registration to atlas or between samples, and calculating differences among samples. These calculations are bettersupported by high-performance PC, such as GPU-based cluster computers. Three-dimensional brain data and reconstituted coronal section images were
captured from the same dataset in Figure 4F. The brain atlas image was obtained from the Allen Institute for Brain Science, Allen Mouse Brain Atlas at http://
mouse.brain-map.org (Lein et al., 2007).

be partly constructed by available software. In addition tocommercially available software for 3D visualization and basic
analysis (e.g., Imaris from Bitplane, Amira from FEI, Vision4D
from arivis, and Image-Pro from MediaCybernetics), many
open-source informatics tools for visualization and analysis
have been developed, such as Fiji/ImageJ and its plugins(http://fiji.sc/Fiji); for example, BigDataViewer, http://fiji.sc/
BigDataViewer (Pietzsch et al., 2015); CellProfiler, http://www.
cellprofiler.org (Carpenter et al., 2006); KNOSSOS, http://www.
knossostool.org (Helmstaedter et al., 2011); Vaa3D, http://
home.penglab.com/proj/vaa3d/home/index.html (Peng et al.,2010); ITK-SNAP, http://www.itksnap.org/pmwiki/pmwiki.php(Yushkevich et al., 2006); Ssecrett and NeuroTrace, http://gvi.
seas.harvard.edu/paper/ssecrett-and-neurotrace-interactivevisualization-and-analysis-tools-largescale-neuroscience-d(Jeong et al., 2010);OMERO, http://www.openmicroscopy.org/
site/products/omero (Allan et al., 2012); goFigure2, http://gofigure2.
sourceforge.net (Megason, 2009); CATMAID, http://catmaid.org/
index.html (Saalfeld et al., 2009); ilastic, http://ilastik.org (Sommer et al., 2011); Icy, http://icy.bioimageanalysis.org (de Chaumont et al., 2012); BioImageXD, http://www.bioimagexd.net(Kankaanpaa et al., 2012); bioView3D, http://biodev.ece.ucsb.
edu/webpages/software/bioView3D/ (Kvilekval et al., 2010); or
VolView,
http://www.kitware.com/opensource/volview.html(Kitware). In general, the experiments should be planned withcare to balance the quality and quantity, because higher resolution images definitely increase the data size (Table S2). Furthermore, customized software and hardware solutions should bealso considered for each experimental purpose in the current situation. For example, a high-spec hardware such as GPU-based
cluster machines can accelerate the process, but issues in memory, algorism, data structure, processing, and storage should be
simultaneously solved (Beyer et al., 2015).
In most cases, image size is reduced first to avoid overflow in
hardware and calculation time. This is simply achieved by

reducing image resolution and changing image format (Susaki
et al., 2014, 2015). Although too much reduction causes loss of
information, this can be avoided by the development of a proper
lossless image format (e.g., Keller Lab Block or the KLB format
that was recently reported in Amat et al., 2015) and softwarethat can handle a larger data size.
If a single specimen is acquired as multiple image sets fromdifferent views or orientations, the image sets need to be merged
again to a single dataset. Registration of different images is thus
applied in this step and performed with ANTS software andnuclei counterstaining as a registration cue (Murphy et al.,2011; Susaki et al., 2014, 2015), with Amira software andautofluorescence image or manually selected landmark structures inside (Gong et al., 2013; Schwarz et al., 2015b), with
Elastix software and an averaged autofluorescence shape (Klein
et al., 2010; Menegas et al., 2015), with the Computational
Morphometry Toolkit and summed stack images of time-lapse
data (in the case of zebrafish larvae) (Portugues et al., 2014;
Rohlfing and Maurer, 2003), or with an in-house image registration software and the averaged background shape (Vousden
et al., 2015). The registration and alignment procedure havealso been applied to further image analysis including atlas
mapping or direct comparison analyses, as described below.
Image fusion process could be performed by substantiallycalculating the degree of image sharpness and compensating
the blurred part of the dataset with the shaper images acquired
from different orientations (Susaki et al., 2014, 2015). In theother case, multi-view fusion was performed by introducing
the assumption of determined acquisition directions (i.e., several
orthogonal views) (Amat et al., 2015). This strategy is advantageous because it allows for throughput, scalability, andflexibility, while there are constraints in imaging setup and orientations. The fused dataset can be utilized for further image
analysis, which possibly includes segmentation, quantification
in atlas space, or direct comparison analysis.

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Please cite this article as: Susaki and Ueda, Whole-body and Whole-Organ Clearing and Imaging Techniques with Single-Cell Resolution: Toward
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Cell Chemical Biology

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Segmentation is a critical step for giving a biological meaning
to the raw image data. Extraction of gross anatomical structures,
like coronal vessels in the heart or the islets of Langerhans in thepancreas (Tainaka et al., 2014), or cellular to subcellular structureslike neurites or amyloid plaques (Chung et al., 2013; Costantini
et al., 2015; Ertürk et al., 2012a, 2012b; Gong et al., 2013; Hamaet al., 2011, 2015; Ke et al., 2013; Schwarz et al., 2015b; Soderblom et al., 2015) were tested in many studies. Furthermore, forthe cellular resolution 3D images, segmentation of the cell bodyor the nuclei is most important and has been tried, for example,
with 3D sphere approximation algorithms (Shimada et al., 2005;
Quan et al., 2013, 2014), combination and expansion of 2D segmentation algorithms (Latorre et al., 2013), determination of cell
centroids (Tsai et al., 2009; Wu et al., 2014), a combined
approach of parametric contour evolution and spatiotemporal
local data association rules (Amat et al., 2014), or even exploitinga machine-learning method in the process (Frasconi et al., 2014;
Menegas et al., 2015; Silvestri et al., 2015) and a GPU machine
workflow (Tracking with Gaussian Mixture Models software, orTGMM) (Amat et al., 2015). These segmentations also simplify
the data contents to the essential pieces and further reducethe data size.
Registration is a critical step to map individual organ/bodydata to an atlas in order to compare multiple organ/body data
samples in different conditions or time points. The registration
to the well-annotated atlas also allows us to analyze organ/
body data, e.g., count cells or quantify signal intensities, in a
specific anatomical region by using annotation information
associated with an atlas. In the neuroscience field, both 2D image- and 3D image-based brain atlas mapping were performed
to reveal whole-brain neural circuitries or activities (Menegas
et al., 2015; Oh et al., 2014; Pollak Dorocic et al., 2014; Schwarz
et al., 2015b; Vousden et al., 2015; Zingg et al., 2014). For
example, Menegas and colleagues recently reported a case of
RV-based dopamine neuron circuit mapping with 77 brains outof eight injection sites. These brains were cleared and imaged
with CLARITY and LSFM, registered across brains, and thelabeled cell numbers extracted according to the anatomical
space (Menegas et al., 2015).
Furthermore, to compare and integrate different experimental
conditions or time points, direct alignment among samples was
also performed. One example was in CUBIC studies that calculated signal subtraction of light-stimulated or unstimulated
Arc-dVenus Tg mouse brains (Susaki et al., 2014, 2015). Not
only the visual cortex, but several association regions werealso identified as the stimulus-associated regions with cellular
resolution. Faster timescale data of whole-brain functions
were also examined in similar procedures; for example, Portugues et al. performed two-photon based time-lapse imagingof zebrafish larvae brains, expressing genetically encoded
calcium indicator GCaMP5 (Portugues et al., 2014). Data from13 brains were combined after registration and alignment,
followed by the production of an averaged activity map andits dissection into functional networks. Such whole-brain
functional imaging and data analysis in mammalian systemappears too difficult in the current situation, but a combinationof global snapshots of fixed brains with focused functional
observations with faster timescale can be a possible substitute.
An informatics tool for the functional brain mapping (both appli-

cable for whole zebrafish brain and local mouse cortex) withcluster computing has also been proposed (Freeman et al.,2014).
All of these procedures contribute to identifying system components (i.e., the responsible cells) and analyzing their anatomical and functional connectivity from the large 3D or 4D imaging
dataset, which will facilitate the systematic understanding ofcomplex organism-level biological functions.
Concluding Remarks
As discussed here, comprehensive and quantitative experimental data in the cell-to-organism layers is now becoming
available in mammalian studies. These technologies primarily
help to comprehensively identify cellular circuits and quantitatively analyze their dynamics, which will lead to system-level understanding of the cellular circuits in the organisms. By using thetechnologies that we discussed here, the field can examine
structure-dynamics relationships in the cellular circuits of interest at an increased depth that will eventually lead toward
improved understanding of basic design principles. We thinkthat this can be achieved if the strategies discussed here are
combined with functional assays using perturbation tools andquantitative phenotyping or with temporal observations using
assays in shorter timescales, such as fMRI, in vivo imaging, or
electrophysiological experiments.
Along with their benefits for basic science, the systems-biological approaches in mammals have the potential to advanceboth the pharmaceutical and clinical fields. For example, highthroughput 3D imaging will provide an advanced drug screening
approach by finding out the systematic responses throughout
the body (Martinez et al., 2015). Three-dimensional analysis ofclinical specimens will expand the ability to find pathological lesions and disease-related cells and will improve the sensitivity
and objectivity of diagnosis and treatment. These are justsome of the ways in which further improvements in whole-organ/body imaging are likely to have a positive impact on not
only the life sciences, but the health sciences as well.
SUPPLEMENTAL INFORMATION
Supplemental Information includes References for Supplemental Tables andtwo tables and can be found with this article online at http://dx.doi.org/
10.1016/j.chembiol.2015.11.009.
ACKNOWLEDGMENTS
We thank the people at The University of Tokyo, in particular T. Murakami,
K. Tainaka, A. Kuno, M. Kaneko, T. Mano, T. Katsumata, K. Miyamichi, andT. Miyawaki for helping with manuscript and figure preparation. This work
was supported by the Program for the Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) and AMED of Japan;
by a Grant-in-Aid for Scientific Research (S) (25221004), a grant for Scientific
Research on Innovative Areas (23115006) and for Young Scientists (A)(15H05650) from MEXT/Japan Society for the Promotion of Science (JSPS);
by a grant from AMED-CREST and the Basic Science and Platform Technology Program for Innovative Biological Medicine from AMED; by PRESTO
from JST; by the strategic programs for R&D (President’s discretionary fund)of RIKEN; by an intramural Grant-in-Aid from the RIKEN QBiC; by a Grantin-Aid from the Japan Foundation for Applied Enzymology; from the Brain
Sciences Project of the Center for Novel Science Initiatives of the National
Institutes of Natural Sciences (BS271005), and from the Tokyo Society of
Medical Science. RIKEN has filed a provisional patent application based on
the CUBIC works.

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