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Review

Whole-Brain Profiling of Cells and Circuitsin Mammals by Tissue Clearingand Light-Sheet Microscopy
Hiroki R. Ueda,1,2,* Hans-Ulrich Dodt,3,4 Pavel Osten,5 Michael N. Economo,6 Jayaram Chandrashekar,7
and Philipp J. Keller7
1Department of Systems Pharmacology, The University of Tokyo, Tokyo 113-0033, Japan
2Laboratory for Synthetic Biology, RIKEN BDR, Suita, Osaka 565-0871, Japan
3Department of Bioelectronics, FKE, Vienna University of Technology-TU Wien, Vienna, Austria
4Section of Bioelectronics, Center for Brain Research, Medical University of Vienna, Vienna, Austria
5Cold Spring Harbor Laboratories, Cold Spring Harbor, NY 11724, USA
6Department of Biomedical Engineering, Boston University, Boston, MA, USA
7Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA*Correspondence: uedah-tky@umin.ac.jp
https://doi.org/10.1016/j.neuron.2020.03.004

Tissueclearingandlight-sheetmicroscopyhavea100-year-plushistory,yetthesefieldshavebeencombinedonly
recentlytofacilitatenovelexperimentsandmeasurementsinneuroscience.Sincetissue-clearingmethodswerefirstcombinedwithmodernizedlight-sheetmicroscopyadecadeago,theperformanceofbothtechnologieshasrapidly
improved,broadeningtheirapplications.Here,wereviewthestateoftheartoftissue-clearingmethodsandlight-sheet
microscopy and discuss applications of these techniques in profiling cells and circuits in mice. We examine
outstandingchallengesandfutureopportunitiesforexpandingthesetechniquestoachievebrain-wideprofilingof
cellsandcircuitsinprimatesandhumans.Suchintegrationwillhelpprovideasystems-levelunderstandingofthephysiologyandpathologyofourcentralnervoussystem.
The beginning of the 20th century saw the birth of two technologies: tissue clearing and light-sheet microscopy. The earliest
report on tissue clearing of opaque biomedical samples, whichappeared in 1911 in Leipzig, was by the German anatomist Walter Spalteholz. He tried to make human hearts transparent tostudy their vascular system (Spalteholz, 1911). Using hydropho€nöl) such as methyl
bic tissue-clearing reagents (Wintergru
salicylate and benzyl benzoate on dehydrated specimens, hesucceeded in visualizing macroscopic structures in transparent
samples for the first time. However, without a technology likelight-sheet microscopy, it was not possible to quantify his findings. So even this key advance brought only some qualitative
insights into human anatomy. Not far from Leipzig, Austrianchemist Richard A. Zsigmondy and German physicist Henry Siedentopf, working in Jena, developed the first light-sheet microscope, the Ultramicroscope (Siedentopf and Zsigmondy,
1902). Unlike Spalteholz, Zsigmondy was looking for very smallthings (Ultramikronen), colloidal particles in solution, which hetried to quantify. In principle, it would have been possible to integrate these two technologies more than 100 years ago, but atthat time, it would not have led far. Light sheets traversing
cleared specimens might create optical sections if one looks at
the specimen at the correct angle, but these images still had tobe recorded. Most importantly, a three-dimensional (3D) modelof the specimen must be reconstructed digitally. Without concurrent inventions of electronic cameras and computers, evena hypothetical encounter of Spalteholz and Zsigmondy would
not have produced the same impact that these methods haverecently achieved.

Approximately 90 years after these seminal works, the first
relevant step toward integration of tissue clearing and light-sheet
microscopy was made by Arno Voie and colleagues (Voie et al.,1993). They designed a modern version of light-sheet microscopy (orthogonal-plane fluorescence optical sectioning microscopy [OPFOS]), based on lasers and digital camera technology.
After clearing the bony structure of the inner ear using EDTA and
Spalteholz’s hydrophobic tissue-clearing reagents (methyl salicylate and benzyl benzoate), Voie and colleagues performed thefirst fluorescence optical imaging of a tissue-cleared biological
specimen, an excised guinea-pig cochlea labeled with fluorescein (Voie et al., 1993). They recorded the images with acharge-coupled device (CCD) camera and successfully reconstructed the spiral in the inner ear with rudimentary homemade
3D reconstruction software. The approach unfortunately remained largely unnoticed by the broader scientific community
for nearly two decades. Light-sheet microscopy rapidly gained
momentum in biological imaging in the early 21st century, withapplications in diverse fields including microbial oceanography,
developmental biology, and neuroscience (Dodt et al., 2007;
Fuchs et al., 2002; Holekamp et al., 2008; Huisken et al., 2004).
In particular, the work by Ernst Stelzer and colleagues sparked
a renewed interest in light-sheet microscopy (Huisken et al.,2004). They reconstructed largely transparent living biologicalsamples, including Medaka fish embryos. Despite the successof this approach for developmental biology, it remained
restricted to naturally transparent samples. The first use oflight-sheet imaging on neural tissues artificially rendered transparent was made by Hans-Ulrich Dodt and colleagues (Dodt

Neuron 106, May 6, 2020 ª 2020 Elsevier Inc. 369

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Review
et al., 2007). They took advantage of hydrophobic tissueclearing reagents, benzyl alcohol and benzyl benzoate (BABB),
that were originally developed by Andrew Murray and Marc
Kirschner around 1989 and first applied to fluorescentand peroxidase-based whole-mount immunocytochemistry of
Xenopus oocytes and embryos (Dent et al., 1989). Combining
this clearing method with ultramicroscopy and image processing
enabled the visualization of neuronal networks at the resolutionof neural dendrites in whole mouse brains (Dodt et al., 2007). A
different set of organic solvents—DBE (dibenzyl ether) and/or
THF (tetrahydrofuran) and dichloromethane (DCM)—were combined in 3D imaging of solvent-cleared organs (3DISCO) to better
preserve the fluorescence of GFP during the clearing process€rk et al., 2012, 2014). Since(Becker et al., 2012, 2014; Ertu
then, various other hydrophobic tissue-clearing reagents withdifferent attributes have been developed to support various applications, including ultimate DISCO (uDISCO) (Pan et al., 2016),
immunolabeling-enabled DISCO (iDISCO) (Belle et al., 2014; Renier et al., 2014), iDISCO+ (Renier et al., 2016), FluoClearBABB(Schwarz et al., 2015), ethyl cinnamate (ECi) (Klingberg
et al., 2017), free-of-acrylamide SDS-based tissue clearing(FASTClear) (Perbellini et al., 2017), polyethylene glycol (PEG)associated solvent system (PEGASOS) (Jing et al., 2018), variable domain of heavy chain antibodies (nanobodies) DISCO(vDISCO) (Cai et al., 2018), multiscale architectonic staining of
human cortex (MASH) (Hildebrand et al., 2018), and stabilized
DISCO (sDISCO) (Hahn et al., 2019).
Clearing methods that use water-soluble reagents (hydrophilic
tissue-clearing methods) are better at preserving the fluorescence of fluorescent proteins and are less toxic compared withapproaches that use organic reagents. Pioneering works on hydrophilic tissue-clearing methods were conducted by Russian
physicist Valery V. Tuchin and colleagues (V.V. Bakutkin et al.,
1995, Int. Soc. Optic. Photon., conference; Tuchin, 2007). They
found that aqueous solutions of various hydrophilic chemicals
had high refractive indices (RIs), a property believed to be important for tissue clarity. The RI of a certain material is defined as theratio of the speed of light in a vacuum to that in the material.
These high-RI solutions included X-ray contrast agents (Trazograph), a series of alcohols (polyethylene glycol, glycerol, andpropylene glycol), sugars (glucose and dextran), and dimethyl
sulfoxide (DMSO) (V.V. Bakutkin et al., 1995, Int. Soc. Optic.
Photon., conference; A.N. Bashkatov et al., 1999, Int. Soc. Optic.
Photon., conference; V.V. Tuchin et al., 1999, Proc. SPIE, conference; Tuchin et al., 1997, 2002; Xu et al., 2003; Zimnyakov et al.,
1996, Int. Soc. Optic. Photon., conference). Around the sametime, Chance and colleagues discovered the RI matching effectof a series of sugars (mannitol, fructose, sucrose, and glucose)and alcohols (propanediol and methanol) (Chance et al., 1995;
Liu et al., 1996). A different cocktail—FocusClear, which contains another X-ray contrast agent (diatrizoate acid) and adetergent (Tween 20)—was used by Chiang and colleagues forwhole-brain imaging of cockroach using a confocal microscope(Chiang et al., 2001). In 2011, Atsushi Miyawaki and colleagues
developed the hydrophilic tissue-clearing method Scale, which
hyperhydrates and delipidates mouse brains with urea-based reagents and a detergent, respectively, resulting in semi-transparent mouse brains (Hama et al., 2011). This clearing approach

370 Neuron 106, May 6, 2020

substantially enhanced the effective imaging depth of twophoton microscopy in adult brain tissue but provided insufficient
clearing for effective light-sheet microscopy. This problem
was then addressed in 2014 by Hiroki Ueda and colleagues,
who developed clear, unobstructed brain/body imaging cocktails and computational analysis (CUBIC), an efficient hydrophilic
tissue-clearing method (Susaki et al., 2014). They used a systematic chemical screening strategy and found a series of amino
alcohols, which can be used to delipidate and decolorize
mammalian brains. CUBIC protocols for whole-body clearing
also seem to permit light-sheet imaging of heart, lung, liver, kidney, pancreas, and other organs (Susaki et al., 2014, 2015; Tainaka et al., 2014), although whole-body imaging still has notbeen rigorously demonstrated because of the lack of an objective definition of whole-body imaging. One of the possible definitions for whole-body or -organ imaging would be the successful
detection of cells or cellular nuclei in a whole body or a wholeorgan with more than a certain level of accuracy (e.g., 95% accuracy).
Further chemical screening and profiling led to the development of a series of CUBIC reagents (CUBIC-L or CUBIC-HL for
delipidation, CUBIC-R+ for RI matching, CUBIC-B for decalcification of bone, CUBIC-P for decolorization, and CUBIC-X fortissue expansion) (Kubota et al., 2017; Murakami et al., 2018;
Tainaka et al., 2018). Parallel efforts revealed the chemical principles of each tissue-clearing process (Susaki and Ueda, 2016;
Tainaka et al., 2016, 2018). CUBIC-based clearing was extended
to the fruit fly D. melanogaster. With this approach (FlyClear),endogenous fluorescence in whole undissected animals waspreserved across developmental stages from larva to adult fly(Pende et al., 2018). In addition to these CUBIC-related reagents,
other hydrophilic tissue-clearing reagents with different purposes have been independently developed, including thiodiethanol (TDE) immersion (Aoyagi et al., 2015; Costantini et al.,2015; Hasegawa et al., 2016; Staudt et al., 2007); see deep brain(SeeDB) (Ke et al., 2013); ScaleS (sorbitol-based scale) (Hamaet al., 2015); FRUIT (a cocktail of fructose and urea) (Hou et al.,2015); urea-based amino-sugar mixture (UBasM) (Chen et al.,2017); clearing-enhanced 3D (Ce3D) (Li et al., 2017); see deepbrain 2 (SeeDB2) (Ke et al., 2016); ClearT (formamide) /ClearT2(a cocktail of formamide and polyethylene glycol) (Kuwajima
et al., 2013); Warner’s method (Warner et al., 2014); ClearSee(Kurihara et al., 2015); rapid clearing method based on triethanolamine and formamide (RTF) (Yu et al., 2018); illuminate clearedorgans to identify target molecules (LUCID) (Mizutani et al.,2018); a series of sugars, sorbitol (Hirshburg et al., 2007) and sucrose (Chance et al., 1995; Tsai et al., 2009); and a series of X-ray
contrast, Histodenz in refractive index matched solution (RIMS)
protocol (Yang et al., 2014) and iodixanol in system-wide control
of interaction time and kinetics of chemicals (SWITCH) protocol(Murray et al., 2015).
In parallel with the development of hydrophobic and hydrophilic tissue-clearing methods, a hydrogel-based tissue-clearing
method called clear lipid-exchanged acrylamide-hybridized rigid
imaging/immunostaining/in situ-hybridization-compatible tissuehydrogel (CLARITY) was also developed in 2013 by Karl
Deisseroth and Kwanghun Chung. In this process, lipids are
removed by perfusion of sodium dodecyl sulfate (SDS), a strong

llReview
detergent, and tissue is subsequently transformed into a clear
acrylamide gel retaining biological elements (Chung et al.,2013). CLARITY employs an electrophoresis step to accelerate
tissue clearing and is therefore more complex than many otherhydrophobic and hydrophilic tissue-clearing methods that relyon passive diffusion. Following SDS perfusion, proteins and nucleic acids are retained, presumably because of their stabilization by the exogenous acrylamide gel. Using CLARITY, it may
be possible to visualize endogenous fluorescent proteins andto label epitopes with fluorescent antibodies (Chung et al.,2013). Although reliable fluorescence preservation and immunolabeling remain challenging, intact CLARITY-processed brains
have been successfully imaged at high resolution (numericalaperture [NA] = 1.0) with light-sheet microscopy (Tomer et al.,2014). Raju Tomer and colleagues developed the passive
CLARITY technique (PACT) by decreasing gel density to improve
tissue permeability and probe penetration (Tomer et al., 2014).
Viviana Gradinaru and colleagues combined PACT with PARS
(perfusion-assisted agent release in situ) to render rodent bodies
transparent (Treweek et al., 2015; Yang et al., 2014). Other variations of CLARITY have been applied to bones (Greenbaum
et al., 2017a) and for the detection of RNA (Greenbaum et al.,2017b; Yang et al., 2014). Sensitive methods for fluorescencein situ hybridization have been used to effectively visualize single
RNA molecules within tissue (Shah et al., 2016).
Hydrogel-based methods have been further exploited toachieve super-resolution imaging. Expansion microscopy,
developed by Ed Boyden and colleagues, uses a swellable hydrogel to isotropically expand tissues. Expanded structures
within tissue may enable imaging at an effective resolution higher
than that achievable using conventional microscopy (Chen et al.,2015). Kwanghun Chung and colleagues developed another hydrogel-based expansion microscopy method, magnified analysis of proteome (MAP), in which they omitted the protein digestion process to preserve proteins in the expanded brain (Ku et al.,2016), and recently introduced stabilization under harsh conditions via intramolecular epoxide linkages to prevent degradation(SHIELD), in which they used polyepoxy chemicals to protect
florescence, antigenicity, transcripts, and tissue architecture(Park et al., 2018). Similar to hydrophobic and hydrophilic tissue-clearing methods, variations of hydrogel-based tissueclearing reagents with different purposes have been developed,
including optimized CLARITY (Lee et al., 2014), CLARITY-TDE(Costantini et al., 2015), plant-enzyme-assisted (PEA)-CLARITY(Palmer et al., 2015), simplified CLARITY (Lai et al., 2016), active
clarity technique-pressure related efficient and stable transfer ofmacromolecules into organs (ACT-PREST) (Lee et al., 2016), andfast free-of-acrylamide clearing tissue (FACT) (Xu et al., 2017)
Since the earliest combined use of hydrophobic (Dodt et al.,2007), hydrophilic (Susaki et al., 2014), or hydrogel-based (Tomeret al., 2014) tissue-clearing methods and modernized light-sheet
microscopy, the performance of tissue-clearing approaches andlight-sheet microscopy has been continuously improved, broadening their utility (Ariel, 2017; Azaripour et al., 2016; Chakraborty
et al., 2019; Fu et al., 2016; Gradinaru et al., 2018; Hörl et al.,2018; Migliori et al., 2018; Richardson and Lichtman, 2015; Silvestri et al., 2016; Susaki and Ueda, 2016; Tainaka et al., 2016; Voigt
et al., 2019) (Figure 1). Indeed, with advanced tissue-labelingand -staining techniques, tissue-clearing methods, and lightsheet microscopy, whole-brain profiling of cells can be performed
comprehensively and efficiently (Belle et al., 2014; Murakami
et al., 2018; Renier et al., 2014, 2016; Susaki et al., 2014; Ye
et al., 2016). Integration of tissue-clearing and light-sheet microscopy also promises to transform methods for interrogating thestructure and connectivity of neural circuits spanning the brain(Ogawa and Watabe-Uchida, 2018; Economo et al., 2016, 2018;
Winnubst et al., 2019).
The Present State of the Art and Challenges in Tissue
Clearing
Tissue-clearing methods provide a powerful approach for opticalimaging deep within biological specimens. Methods for tissue
clearing are under active development so that they may be
applied to larger samples and to improve their compatibility
with an expanded set of techniques for labeling and visualizing
structural and biochemical features of tissues. The physicaland chemical principles that underlie tissue-clearing processes
are also of great interest (Figure 2), and enhanced understandingof these processes continues to drive the development of new
tissue-clearing methods.
Improvements in tissue-clearing methods have relied both on
identification and application of more effective tissue-clearing
chemicals (Pan et al., 2016; Tainaka et al., 2014, 2018) and on
enhanced fixation procedures that permit the use of harsher reagents and procedures (e.g., strong detergents and high temperature) (Murray et al., 2015; Park et al., 2018; Yang et al.,2014). The identified potent tissue-clearing chemicals or conditions have been applied not only to entire bodies of rodents(Pan et al., 2016; Tainaka et al., 2014; Yang et al., 2014) but
also to entire human organs, including a lymph node (Nojima
et al., 2017), a kidney (Tainaka et al., 2018; Zhao et al., 2019),
an eyeball, and even a human brain (Zhao et al., 2019).
Tissue-clearing methods, whether hydrophobic, hydrophilic,or hydrogel based, attempt to minimize light scattering—especially Mie scattering (Tuchin, 2015)—caused by mismatches in
the RIs of different components of biological materials (e.g.,
lipids and proteins) with the RI of the medium (e.g., water)(Figure 2A, light scattering). Among biological materials, water
usually exhibits the lowest RI (1.33), dried lipids exhibit intermediate RIs (1.46–1.48) (Kienle et al., 2014; Pusterla et al.,2017), dried proteins and DNA exhibit higher RIs (1.540–1.598
for dried proteins, Bashkatov et al., 2018; 1.58 for dried DNA,
Inagaki et al., 1974), and hydroxyapatite (HAp) extracted frombone tissue exhibits the highest RI (1.600–1.604) (Antonio,
1949; Ascenzi and Fabry, 1959). In addition to reducing lightscattering caused by inhomogeneity of RIs, tissue-clearing
methods minimize light absorption caused by pigments suchas heme (Figure 2A, light absorption).
Practically, most tissue-clearing methods aim to preserve proteins and/or nucleic acids while removing other components oftissue. Effective tissue-clearing methods usually achieve thefollowing five chemical processes: (1) removing lipids (delipidation) and (2) removing HAp (decalcification) to reduce the inhomogeneity of RIs in biological samples; (3) removing pigments(decolorization) to reduce light absorption; (4) embedding in ahydrophobic solvent or aqueous medium with an RI matching

Neuron 106, May 6, 2020 371

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Review
Hydrophobic reagentsHydrophilic reagentsHydrogel-based(including some ExM protocols)

1914

Figure 1. Timeline of Advances in TissueClearing Methods

Spalteholtz’ s reagent
Benzyl alcohol / Methyl salicylate

1989

BABB for frog
EtOH / Hexane
Benzyl alcohol / Benzyl benzoate

study has revealed that fixed and delipidated natural organs behave as an elecSugars, Alcohols, DMSO, 2,4,6-triiodobenzene acid etc.
trolyte polymer gel even without exogenous polymers (Murakami et al., 2018). If
2007 BABB for whole mouse brain
a biological tissue acts as a polymer gel,
FocusClearTM
2010
the average RI (Rp) of the biological tissue
Diatrizoate(polymer gel) could be described by the
1st Scale
ClearT2011
Triton X-100
PEG / Formamide
average RI (Rm) and volume Vm of its comUrea
Glycerolponents(monomer) according to the Lor2012 3DISCO
Tetrahydrofuran / Dichloromethaneentz-Lorenz equation (Figure 2C, light
Dibenzylether
scattering) (Lorenz, 1880; Lorentz, 1880),
SeeDB
CLARITY
2013
SDS
Fructose
Diatrizoate / Glycerol
where the average RI of biological tissue
ScaleCUBIC
PACT/PARS/RIMS
2014 iDISCO
Rp is an increasing function of Rm/Vm.
Triton X-100 / Quadrol
SDS
MeOH / Dichloromethane
Quadrol
Histodenz / Sorbitol
Dibenzylether
Therefore, the expansion and shrinkage
Urea
Sucrose / Triethanolamineof biological tissues lead to the decrease
ExM
ScaleS
ClearSee
PACT-deCal
2015 FluoClearBABB
and increase of the average RIs, respecSDS
Deoxycholate
Triton X-100
tert-ButanolePACT
Quadrol
Urea
Urea
Benzyl alcohol / Benzyl benzoate
EDTA
Xylitol
Glycerol / Sorbitol / DMSO
tively, which should be matched by RIHistodenz / Sorbitol
TDE immersion
FLUIT
matching media (Figure 2B, RI matching).
2,2'-thiodiethanol
Urea
Fructose
As described earlier, the average RI of
SeeDB2LUCID
SWITCH
MAP
2016 uDISCO
biologicaltissues can be matched by an
Saponin
2,2'-thiodiethanol / Glycerol
SDS
tert-Butanol / Dichloromethane
Iohexol (Histodenz )Diatrizoate / Iodixanol
Benzyl alcohol / Benzyl benzoate / Diphenyl ether
RI-matching medium. However, a remainMeglumine (N-methyl-D-glucamine)
ing issue in delipidated, decalcificated,2017 ECi
CUBIC-L/R
Bone-CLARITY
Triton X-100 / N-Buthyldiethanolamine
EtOH(modified PACT-deCal)
N-Buthyldiethanolamine
ECi (Ethyl cinnamate)and decolorized biological samples (i.e.,
Antipyrine / Nicotinamide or N-methylnicotinamide
cross-linked gels of natural polymers
UBasM
Triton X-100 / Meglumine (N-methyl-D-glucamine)such as proteins and nucleic acids) is the
Urea
Sucrose / 1,3-Dimethyl-2-imidazolidinone /Meglumine (N-methyl-D-glucamine)
localized deviation of the RIs of these polyCe3D
mers from the average RI of the sample
Triton X-100
N-methylacetamide / Histodenz(and hence from the RI-matching me2018 vDISCO
RTF
SUT
dium). One possible solution to address
EDTA
Triethanolamine / Formamide
SDS / Triton X-100
Quadrol
Urea
tert-Butanol / Dichloromethane
this RI-deviation problem would be RI mixCUBIC-X
CUBIC-HL
Benzyl alcohol / Benzyl benzoate / Diphenyl ether
Antipyrine
1,3-bis(aminomethyl)cyclohexane /Imidazole
Sodium dodecylbenzenesulfonate
ing to dissolve such light-scattering matePEGASOSsDISCOCUBIC-P
EDTA
Tetrahydrofuran
rials (i.e., materials of deviation of RIs from
Quadrol
Triton X-100 / N-Buthyldiethanolamine
Propyl gallate (stabilizer)
tert-Butanol / PEG
N-Buthyldiethanolamine / 1-Methylimidazole
Dibenzyl ether
Benzyl benzoate / PEG
the average RI) into the RI-matching meCUBIC-B
EDTA / Imidazole
dium, because light scattering (Mie scatDehydration
tering) depends not only on RI mismatch
Delipidation
Decolorization
but also on the size of the light-scattering
Decalcification
RI matchingmaterials (Figure 2B, RI mixing). If theHydration
Swelling
effective size of the light-scattering materials (e.g., proteins with a micrometerscale structure) could become sufficiently
the remaining components of the tissue; and (5) sometimes ex- smaller than the wavelength of visible lights (0.38–0.75 mm) bypanding biological samples and thus contributing to RI matching dissolving them into the medium, the RIs of materials can(Figure 2B, RI matching). The RIs of RI-matching media differ be mixed with those of medium and/or other dissolved light-scatacross hydrophilic (1.52 for CUBIC-R+), hydrogel-based (1.45– tering materials nearby within the wavelength of visible lights.
1.49 for RIMS), and hydrophobic (1.56 for 3DISCO) methods, Hence, this RI-mixing process would effectively cancel out RI delikely reflecting commensurate sample expansion and shrinkage viation of different light-scattering materials. Importantly, pursuits of RI-matching reagents have already identified a couple(Kubota et al., 2017; Tainaka et al., 2016, 2018).
The optimal RIs of different tissue-clearing methods have of chemicals that might have this RI-mixing property. For
been explained by considering a physical property of natural bio- example, chemical screening of more than 1,600 hydrophilic
logical tissue (Murakami et al., 2018). As predicted from a chemicals identified effective RI-matching reagents (Tainaka
pioneering study on cross-linked gels of extracted natural poly- et al., 2018), some of which (e.g., imidazole and antipyrine) also
mers such as DNA (polynucleotide), protein (polypeptide), and exhibit strong interaction with cross-linked natural polymers
agarose (polysaccharide) by Amiya and Tanaka (1987), a recent such as a gelatin gel (Murakami et al., 2018). Interestingly, known
1990s

Medical applications
by Tuchin’ s group

TM

TM

TM

TM

372 Neuron 106, May 6, 2020

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Review
Figure 2. Physical and Chemical Principlesof Tissue Clearing

Physical Principles of Tissue Clearing

A

Light Absorption

Light Scattering

Materials of Different RIs(Refractive Indices)

Homogenize
Different RIs

Various Pigments（e.g. Heme）

Removal of Pigments
Minimizes Light Absorption

Matching of Different RIs
Minimizes Light Scattering

Adjustable RI matching (average of RI) and RI mixing (deviation of RI)

B

RI matching

RI mixing

Mie scattering
depends on
RI mismatch

Expansion Shrinkage
NormalDistribution

Distribution

ΔRI↓
Low

RI

Mie scattering
depends on
size of material
Size↑

ΔRI↑

Size↓
RI

High

“Average” RI depends on tissue volume,
which should be matched with medium’s RICRemovePigments

RI “deviation” could be minimized bydissolving into RI-matching medium

Chemical Principles of Tissue Clearing
Light Scattering

Lorentz-Lorenz Equation (1880)

Rp
Polymer Refractive
Index (Rp)

RI-matching
Aromatic
amide

Rm

Rm

V

V

Delipidation

Decalcification

Salf-free amine
Ca2+ chelation
PO43- Protonation
High LogP

ProxymalHistidine

Monomer
Volume (V)Monomer Refractive
Index (Rm)

Light Absorption(A) Classical physical principles of tissue clearing
proposed in 1911 by Spalteholz. Light scattering
can be minimized by homogenization of RIs ofmaterials, whereas light absorption can be minimized by removing pigments.
(B) Adjustable RI matching (matching the tissuesize-dependent average RI of biological samples
with RI-matching medium) and RI mixing (dissolving light-scattering materials into RI-matching
medium to minimize RI deviation) proposed in thisreview. The average RI of biological samples depends on the size of tissues, which often expands
in hydrophilic and hydrogel-based tissue-clearing
methods, shrinks in hydrophobic tissue-clearing
methods, and should be matched by RI-matching
medium, because Mie scattering depends on the
RI mismatch between those of light-scattering
materials and medium. For the adjustable setpointof average RI, see also the Lorentz-Lorenz equation in (C). The RI-deviation problem could be
solved by RI mixing by dissolving light-scattering
materials (i.e., materials of deviation of RIs from theaverage RI) into RI-matching medium, becauselight scattering or Mie scattering depends also on
the size of the light-scattering materials.
(C) Chemical principles of tissue clearing. Delipidation, decalcification, and expansion processes
contribute to the composite RI of biological samples (calculated by the Lorentz-Lorenz equation),
which should be matched by RI-matching medium.
Decolorization of heme can be achieved bycompetitive binding of 1-methylimidazole or otheramines to iron-containing heme instead of histidine
in globin. Each tissue-clearing chemical process is
associated with the characteristic chemical natureof the tissue-clearing chemicals.

Expansion

Hyperhydration
e.g. Imidazole, Antipyrine

RI-matching reagents (e.g., DMSO) can interact strongly withcross-linked natural polymers even without water, implying that
this RI-mixing concept might be applicable to hydrophobic
reagents. A unified understanding of the physical properties of
biological materials and chemical properties of RI-matching
and RI-mixing reagents will lead to the efficient engineering of
average and deviation of RIs (RI engineering) in biological tissues
to achieve optimal transparency of larger samples. In addition,
the RI-mixing concept built upon Mie scattering theory and ourmodel of RI matching through the Lorentz-Lorenz equation,
both of which are proposed in this review, might represent substantial conceptual advancements in the physical principles oftissue clearing since Spalteholz first proposed the RI-matching
concept in 1911.

In addition to physical principles of RI
matching and RI mixing in biological tissues, chemical understanding of other tissue-clearing processes such as delipidation, decolorization, and decalcification
1-mehylimidazole
have advanced (Figure 2C, light scatDecolorization
tering). Delipidation removes lipids to
His competition
reduce the inhomogeneity of RIs in biologe.g. N-Alkylimidazol
ical samples and allows other reagentsto diffuse deep into biological samples.
Therefore, delipidation has alreadybecome a common component of hydrophobic, hydrophilic,and hydrogel-based tissue-clearing methods. In hydrophobic tissue-clearing methods such as BABB, 3DISCO, uDISCO,iDISCO+, and sDISCO, polar solvents including THF (3DISCO)€rk et al., 2012, 2014; Hahn et al.,(Becker et al., 2012, 2014; Ertu2019) and alcohols such as ethanol (BABB) (Dent et al., 1989),
tert-butanol (FluoBABB and uDISCO) (Pan et al., 2016; Schwarz
et al., 2015), and methanol (iDISCO+) (Belle et al., 2014; Renier
et al., 2014, 2016) are used as the first step to remove waterand partially remove lipids from biological samples. As a second
step, hexane (BABB) (Dent et al., 1989) and DCM (3DISCO,€rk et al.,uDISCO, and iDISCO+) (Becker et al., 2012, 2014; Ertu
2012, 2014) are used to extensively remove lipids. In hydrophilic
tissue-clearing methods, the nonionic detergent Triton X-100
Protein(globin)

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Review(Scale and CUBIC-1) (Hama et al., 2011; Susaki et al., 2014;
Susaki and Ueda, 2016) and amino alcohols, including Quadrol(CUBIC-1) (Susaki et al., 2014; Susaki and Ueda, 2016), triethanolamine (CUBIC-2) (Susaki et al., 2014; Susaki and Ueda,
2016), and N-butyldiethanolamine (CUBIC-L) (Kubota et al.,2017; Tainaka et al., 2018), are used as potent delipidation chemicals. In hydrogel-based tissue-clearing methods such as
CLARITY and PACT, the ionic detergent SDS (Chung et al.,2013; Treweek et al., 2015; Yang et al., 2014) is mostly used fordelipidation. Improved delipidation procedures have led to thedevelopment of more efficient tissue-clearing protocols. The
ionic detergent sodium dodecylbenzenesulfonate (CUBIC-HL)(Tainaka et al., 2018), non-ionic detergent 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate (CHAPS) (Zhaoet al., 2019), aliphatic amines such as 1,3-bis(aminomethyl)cyclohexane (CUBIC-HL) (Tainaka et al., 2018), and hexanediol (Inoueet al., 2019; Tainaka et al., 2018) were shown to be effective reagents for delipidation of human tissue. Because there is atrade-off between the strength of tissue clearing, especially delipidation, and the preservation of molecular and cellular organization in tissues, we should carefully choose the stringency of tissue
clearing according to the required transparency for the imaging of
the samples.
Decolorization removes pigments to reduce light absorption in
biological samples. Heme in red blood cells can be eluted byamino alcohols such as Quadrol in mild chemical conditions (Tainaka et al., 2014). The hypothesis for decolorization of heme wasproposed such that a basic nitrogen in amino alcohols maycompetitively bind to iron-containing heme instead of histidine
in globin (Figure 2C, light absorption) (Tainaka et al., 2014,2016). This competitive binding hypothesis was strongly supported by the identification of a more potent decolorization compound, 1-methylimidazole, from more than 1,600 chemicals(Tainaka et al., 2018), which almost completely mimics the functional residue of histidine. In addition, SDS in CLARITY and PACT
successfully decolorizes heme, likely as a result of the denaturation of the heme-hemoglobin holoenzyme by SDS (Epp et al.,2015; Lee et al., 2014; Treweek et al., 2015).
Decalcification removes calcium phosphate to reduce lightscattering in biological samples containing bone. The inorganicbone mineral, HAp, was used for chemical screening of 1,600 reagents by measuring the optical density 600 (OD600) of a chemically treated HAp suspension. In addition to EDTA, which has
been known as a strong calcium chelator, potent decalcification-enhancing compounds were identified. Imidazole wasfound to facilitate EDTA-based decalcification, perhaps becauseof the protonation of phosphate ions (Tainaka et al., 2018). Thisenhanced decalcification may be the result of both chelation of
calcium ions by EDTA and protonation of phosphate ions by
imidazole (Tainaka et al., 2018).
In addition to the empirical findings of efficient tissue-clearing
reagents and/or conditions, a more unified understanding of tissue-clearing chemistry has been gradually obtained by chemical
profiling of potent reagents (Tainaka et al., 2014, 2018). In particular, the chemical profiling of more than 1,600 chemicals has
been conducted for delipidation, decolorization, decalcification,
tissue expansion, and RI matching (Murakami et al., 2018; Susaki and Ueda, 2016; Tainaka et al., 2016, 2018). Based on these374 Neuron 106, May 6, 2020

screens, we have hypothesized that delipidation is enhanced bysalt-free amines with high logP (octanol-water partition coefficient). Similarly, decolorization appears to be associated withcompetitive binding to iron-containing heme against histidine
by N-alkylimidazole and decalcification as a result of chelationof Ca2+ by EDTA and protonating of PO42 by an organic base.
Tissue expansion is facilitated by hyperhydration and RI matching by electron enrichment of an RI-matching medium by anaromatic amide (Figure 2C, light scattering and light absorption)(Murakami et al., 2018; Tainaka et al., 2018). Altogether, ouradvancing understanding of the principles underlying tissue
clearing provide a foundation for new approaches suitable for
specific biological applications.
Another important challenge in tissue clearing is labeling of tissues with antibody. Since the 1980s, tissue clearing followed bywhole-mount antibody staining was attempted for comprehensive imaging of whole organs and body, especially for nervous
systems of insects and shrimp and the Xenopus embryo (Beltz
and Kravitz, 1983; Bishop and O’Shea, 1982; Dent et al.,1989). Recently, tissue clearing and staining was applied to 3D
observation of murine and human embryos (Belle et al., 2017;
Hsueh et al., 2017; Renier et al., 2014), various animal organs
and bodies (Cai et al., 2018; Chung et al., 2013; Coutu et al.,2018; Gleave et al., 2013; Hama et al., 2015; Hasegawa
et al., 2019; Kubota et al., 2017; Kumar et al., 2010; Renier
et al., 2016; Sillitoe and Hawkes, 2002; Susaki et al., 2014; Tainaka et al., 2014; Yang et al., 2014), and pathological samplesof human tissues (Chung et al., 2013; Hildebrand et al., 2018;
Lai et al., 2018; Liu et al., 2016; Nojima et al., 2017; Tanaka
et al., 2017; Zhao et al., 2019). To improve the penetration speedof antibodies in a large tissue sample, several approaches havebeen adopted. First, stringent permeabilization by several
methods has been attempted to increase the pore size of fixed
specimens and hence enhance the penetration of antibodies.
These methods include (1) delipidation (Chung et al., 2013;
Duve et al., 1983; Hama et al., 2015; Lai et al., 2018; Susaki
et al., 2014; Yang et al., 2014; Zhao et al., 2019); (2) dehydration(Belle et al., 2014; Dent et al., 1989; Duve et al., 1983; Gleave
et al., 2013; Renier et al., 2014, 2016; Sillitoe and Hawkes,
2002; Zhao et al., 2019); (3) weaker fixation (Gleave et al.,2013); and (4) partial protein digestion with proteases (Gleave
et al., 2013; Kumar et al., 2010; Sillitoe and Hawkes, 2002). Second, weakening non-specific interactions between antibodies
and fixed tissues using chemicals such as urea (Hama et al.,2015) or SDS (Murray et al., 2015) has been also attempted toinhibit trapping of antibodies and hence accelerate the diffusionof antibodies deep into the tissues. Third, active transports byelectrophoresis (Kim et al., 2015a; Lee et al., 2016; Wanget al., 2018) or pressurization such as trans-cardinal perfusion(Cai et al., 2018; Yang et al., 2014) were also tested on tissue
samples to enhance the penetration of antibodies. Finally,
increasing the amounts of antibodies by their iterative supplies(Chung et al., 2013; Lai et al., 2018) or reducing the size of antibodies by using nanobodies (Cai et al., 2019) was also effective
to increase the penetration rate of antibodies. At present, theseattempts to increase the penetration rate of antibody can successfully label relatively small and thin tissues, partiallydissected tissues, or embryonic tissues with the lesser

llReview
Isotropic multi-view light-sheet microscopy(Wu ‘13, see also Chhetri ‘15)

First application in functional neuroscience(Holekamp ‘08, see also Ahrens ’13,
Panier ‘13, Wolf ’15)

First light-sheet microscope: Ultramicroscopy(Siedentopf and Zsigmondy 1903)
Modern laser-light sheet microscopy: OPFOS(Voie 1993)
First application in microbial oceanography(Fuchs ‘02)
1903

1993

2002

2004

Confocal light-sheet microscopy(Fahrbach ‘12, Silvestri ’12, Baumgart ‘12,see also Yang ‘15, de Medeiros ’15)

Single-lens light-sheet microscopy(Dunsby ‘08, see also Bouchard ’15)

Multi-view light-sheet microscopy(Tomer ‘12, Krzic ’12, see also Swoger ‘07,
Schmid ‘13, Lemon ’15)

Scanned light-sheet microscopy(Keller ‘08)

Light-sheet microscopy with adaptive optics(Jorand ‘12, see also Wilding ‘16, Liu ’18)

2007

2008

2010

2011

2012

2013

Inverted light-sheet microscopy(Wu ‘11, see also McGorty ‘15, Strnad ’15)

First application in developmental biology(Huisken ‘04)
First application in tissue clearing(Dodt ‘07)

2014

2015

2016

Spatiotemporally sample-adaptive
light-sheet microscopy(Royer ‘16, see also McDole ’18)

Superresolution light-sheet microscopy(Cella Zanacchi ‘11, see also Friedrich ’11,
Gao ‘12, Galland ’15, Hoyer ‘16)

Structured illumination
light-sheet microscopy(Breuninger ‘07, see also Keller ’10,
Mertz and Kim ’10)
Light-sheet based fluorescence correlation spectroscopy(Wohland ‘10, see also Capoulade ’11)
Bessel beam light-sheet microscopy(Fahrbach ‘10, see also Planchon ’11)
Two-photon light-sheet microscopy(Palero ‘10, see also Truong ’11, Mahou ‘14)

Airy beam light-sheet microscopy(Vettenburg ‘14)
Aspheric light-sheet microscopy(Saghafi ‘14)
Lattice light-sheet microscopy(Chen ‘14)
Hyperspectral light-sheet microscopy(Jahr ’15)
Extended depth-of-fieldlight-sheet microscopy(Olarte ‘15, see also Tomer ‘15, Quirin ’16)

Figure 3. Timeline of Advances in Light-Sheet Microscopy

extracellular matrix. These attempts can also successfully stain
an adult mouse brain or a dissected human specimen with relatively sparse epitopes such as c-Fos, amyloid plaques, or a microglia marker (Belle et al., 2014; Liebmann et al., 2016; Renier
et al., 2014, 2016; Zhao et al., 2019). However, homogeneous
antibody staining of adult mouse brains against high-density epitopes such as NeuN and neurofilament has not yet beenadequately demonstrated and therefore remains a challenge intissue clearing.
The Present State of the Art and Challenges in LightSheet Fluorescence Microscopy
Light-sheet fluorescence microscopy is a powerful technique for
rapid and minimally phototoxic volumetric imaging of biological
specimens. The high imaging speed, high signal-to-noise ratio,
and high light efficiency of this method are the direct result of
the central design principle in light-sheet imaging: a thin volume
section of the specimen is selectively illuminated by a sheet oflaser light, and fluorescence emitted from within this volume section is imaged onto a camera positioned at a right angle to thelight sheet. The illuminated volume section and the detection
focal plane are coplanar; thus, (1) no out-of-focus regions are
exposed to laser light and (2) an image of the entire thin volume
section can be acquired simultaneously. The first feature ensures that the specimen’s photon budget is used efficiently
and dramatically reduces photo-bleaching and photo-toxic effects compared with conventional and confocal fluorescencemicroscopy. The second feature provides high imaging speeds
limited only by camera performance. This design facilitates rapid
3D imaging simply by moving the light sheet and detection focal
plane through the specimen and acquiring a series of images
across the volume. Because of these strengths, light-sheet microscopy has found widespread use throughout the life sciences
over the course of the past decade and is employed routinely inthe fields of developmental biology (Huisken et al., 2004; Keller
et al., 2008; McDole et al., 2018; Rozbicki et al., 2015), cellbiology (Chen et al., 2014; Liu et al., 2018; Nixon-Abell et al.,2016; Reichmann et al., 2018; Valm et al., 2017), and neuroscience (Ahrens et al., 2013; Chhetri et al., 2015; Dodt et al.,2007; Holekamp et al., 2008; Kawashima et al., 2016; Lemonet al., 2015; Panier et al., 2013; Wolf et al., 2015; Wanet al., 2019).
Although even the most basic implementations of light-sheet
microscopy already offer key benefits, advances in microscope
design have added strengths and synergies (Figure 3). These improvements have allowed microscopists to address many
commonly encountered challenges in biomedical imaging,including fundamental limitations in spatial resolution, limitationsin image quality arising from light scattering and aberrations, andlimitations in the rapid imaging of large specimens.
Light scattering is a common problem in biological specimens
with limited transparency, which includes most living, multicellular organisms. By introducing structured illumination (Breuninger et al., 2007; Keller et al., 2010) or line-confocal detection(Baumgart and Kubitscheck, 2012; de Medeiros et al., 2015;
Fahrbach and Rohrbach, 2012; Silvestri et al., 2012) in lightsheet microscopy, the contribution of scattered light to image
formation can be greatly reduced, thus improving contrast andresolution in deeper regions of the specimen. A complementarystrategy for improving depth penetration (and reducing scattering in biological tissues) involves the use of longer-wavelength
light for fluorescence excitation and relies on the principle ofmulti-photon excitation to allow a molecule to enter an excitedstate through concurrent absorption of multiple lower-energy
photons. The use of this concept in light-sheet microscopy
was first demonstrated for static light sheets (Palero et al.,2010) and then improved through integration in beam-scanninglight-sheet microscopes (Mahou et al., 2014; Truong et al.,Neuron 106, May 6, 2020 375

ll
Review2011). However, even with structured illumination, confocal
detection, and multi-photon excitation, many biological specimens are too large to be imaged in high resolution in their entirety, primarily because of light scattering and absorption. To
address this issue, light-sheet microscopy has been enhancedby multi-view imaging capabilities (Krzic et al., 2012; Lemonet al., 2015; Pende et al., 2018; Schmid et al., 2013; Swoger
et al., 2007; Tomer et al., 2012). In such implementations, thespecimen is illuminated by more than one light sheet (typically
two light sheets from opposite directions) (Huisken and Stainier,2007) and imaged by more than one camera (typically two cameras from opposing views), which improves physical coverage oflarge specimens without compromising temporal resolution or
temporal continuity across the volume. Finally, scaling up lightsheet imaging to very large transparent specimens is feasible
through the use of modified objective arrangements, as well as
tiling strategies, in which sample translation can help overcome
the limited field of view of the camera, and detection optics (Hörl
et al., 2018; Migliori et al., 2018; Voigt et al., 2019).
Improvements in spatial resolution have become possible byusing beam shaping to create thinner light sheets (Chen et al.,2014; Fahrbach and Rohrbach, 2010; Pende et al., 2018;
Planchon et al., 2011; Saghafi et al., 2014; Sheppard, 2013;
Vettenburg et al., 2014), as well as by using orthogonal multiview imaging (Chhetri et al., 2015; Wu et al., 2013). In conventional
light-sheet microscopes, axial resolution is typically substantiallylower than lateral resolution—often by up to a factor of 5–10 when
imaging a large field of view. Because axial resolution in thedetection system can only be increased up to a limit imposedby the numerical aperture of the detection objective, one option
for improving overall system resolution is the reduction of lightsheet thickness. Light sheets constructed from Bessel beams
and optical lattices (Chen et al., 2014; Fahrbach and Rohrbach,2010; Planchon et al., 2011) can be made thin enough to improveoverall axial resolution to around 300–400 nm, which is comparable to the lateral resolution limit. However, the field of view of suchmicroscopes is typically limited to around 100 mm along the illumination axis; thus, other solutions are needed for rapid, high-resolution imaging of larger specimens. By acquiring orthogonal viewsof a sample, followed by registration and multi-view deconvolution of these multiple views, images with near-isotropic spatial
resolution on the order of 300–400 nm can be obtained without
the need for constructing thin light sheets (and thus without theneed to limit imaging to a small field of view) (Chhetri et al.,2015; Wu et al., 2013). In addition, complementary efforts are under way to investigate the design of light sheets that maintain anarrow beam waist over long spatial distances (Pende et al.,2018; Saghafi et al., 2014). Alternatively, the relationship between
size of field of view and light-sheet-waist thickness can be decoupled by tiling or scanning the light-sheet waist across the fieldof view (Dean et al., 2015; Fu et al., 2016) or translating the sample
relative to the light-sheet waist (Migliori et al., 2018), which provides more uniform illumination across the field of view.
Although the techniques described earlier offer high system
resolution, the effective spatial resolution in a real biological sample can be substantially lower as a result of optical aberrations.
Unfortunately, most biological specimens, even those that are
relatively transparent, introduce significant aberrations that alter

376 Neuron 106, May 6, 2020

the path and shape of the light sheet and distort the shape ofthe detection focal plane. When limiting the observation to sufficiently small regions in the sample, thus ensuring that wavefront
errors are relatively uniform across the small field of view, lightsheet microscopy can be combined with adaptive optics to efficiently compensate for most, if not all, of these distortions (Jorand
et al., 2012; Liu et al., 2018; Wilding et al., 2016). However, conventional adaptive optics are typically unsuited to light-sheet
applications that require rapid live imaging of large, optically heterogeneous specimens with a field of view exceeding 100 mm. In
this latter scenario, geometrical mismatches between light sheets
and detection focal planes can be rapidly mapped across the
specimen (and over time) by real-time image processing andapproximately corrected for through optical defocus and lightsheet tip and tilt manipulations (McDole et al., 2018; Royeret al., 2016). With this complementary approach, diffractionlimited performance of the light-sheet microscope can be at least
partially recovered in specimens as large and complex aszebrafish, fruit fly, or mouse embryos, which typically provides a2- to 5-fold improvement in spatial resolution.
Temporal resolution in light-sheet microscopy is generally
limited by camera speed. To preserve the high frame rates
offered, for example, by state-of-the-art scientific complementary metal–oxide–semiconductor (sCMOS) cameras in a volumetric imaging setting, the microscope’s detection objective
can be moved in synchrony with the light sheet by fast piezo positioners (Ahrens et al., 2013; Lemon et al., 2015). For example,
high-performance piezos are capable of moving detection objectives at rates of several tens to hundreds of hertz for travel
ranges of up to several hundred micrometers (Greer and Holy,
2019; Piezosystems_Jena, 2019; Wan et al., 2019). The advantage of this approach is that it ensures optimal image quality
by always acquiring images at the native focal plane of the objective; however, the cost of a good piezo is not insignificant. Less
expensive alternative solutions include the use of remote
focusing with electric tunable lenses (Fahrbach et al., 2013) orextended depth-of-field detection (Olarte et al., 2015; Quirin
et al., 2016; Tomer et al., 2015). The reduced cost and ability
to keep the objective stationary come at the expense of a reduction in resolution and image quality, which result from imaging
away from the native focal plane and, in the latter case, fromthe use of an elongated detection point-spread function.
Research in recent years has also produced a range of useful,alternative designs of light-sheet microscopy, such as implementations that are compatible with biological preparations oncoverslips and other horizontal surfaces (McGorty et al., 2015;
Strnad et al., 2016; Wu et al., 2011) or designs that use a single
objective for illumination and detection (Bouchard et al., 2015;
Dunsby, 2008). The latter concept sacrifices some optical performance by reducing the effective numerical aperture andacquiring images away from the native focal plane, but it also enables light-sheet imaging of samples with limited optical accessand supports applications that constrain the placement andorientation of microscope optics.
Whole-Brain Profiling of Cells
Developments in microscopy have helped to bring about amethod-driven renaissance in neuroanatomy that is distinguishedllReview
by a focus on large-scale projects generating unprecedented
amounts of anatomical data. Quantitative, whole-brain profilingof the spatial distribution of cells, their molecular features, andtheir connectivity represents a powerful application of modernized tissue-clearing and light-sheet microscopy in neuroscience.
Comprehensively mapping the distributions of neuronal and glial
types across the brain allows brain regions to be delineated with
unprecedented precision and their components to be defined,leading to an enhanced understanding of brain structure and facilitating comparisons across individuals and across species.
Until recent advances in genetic labeling, tissue processing,and light microscopy, comprehensively mapping cells throughout
the brain has only been possible in simple organisms, such as thenematode Caenorhabditis elegans (White et al., 1986). The sizeand complexity of the mammalian brain present barriers to progress in understanding the organization of the nervous system ofhigher organisms. Accordingly, the classical anatomical literatureof the mammalian brain has been piecemeal, with a given study
typically examining only one or a few brain regions and one or a
few cell types at a time, typically in the male brain. For example,
the use of classical stereological methods has led to quantificationof interneuron distribution in sensory cortices (Gonchar et al.,2008; Pfeffer et al., 2013; Prönneke et al., 2015; Rudy et al.,2011; Xu et al., 2010), but this approach has proven to be too laborious to be applied to whole-brain cell-type analyses (Glaser andGlaser, 2000; Schmitz and Hof, 2005; Williams and Rakic, 1988).
A simple method, called isotropic fractionator, was developed
for counting cell nuclei in suspension by flow cytometry after tissue dissociation and was successfully applied to comparative
studies of neuronal and glial cell counts in the mouse brain andacross a range of species (Herculano-Houzel et al., 2015a,
2015b, 2013; Kverková et al., 2018; Marhounová et al., 2019;
Olkowicz et al., 2016). However, because the mammalian brain
comprises many brain areas (e.g., mouse brain has >400 uniqueareas) that would need to be precisely dissected, this method is
not appropriate for comprehensive analyses or easily adaptable
for analysis of specific cell types.
Automated block-face imaging methods combining top-view
light microscopy and integrated tissue sectioning enable highquality, high-resolution brain-wide imaging. This approach substantially improved upon histological methods in which thintissue sections are manually cut, mounted on slides, and imaged
individually. The enhanced throughput of automated block-face
imaging represents a critical factor for large-scale neuroanatomical projects. Imaging in a block-face configuration also provides
enhanced reliability and minimal distortion, producing complete
datasets that may be registered to standardized anatomicalreference atlases—critical for integrating anatomical data frommultiple experiments. Several block-face imaging methods
have been developed. Serial two-photon tomography (STPT)
takes advantage of two-photon excitation to provide opticalsectioning in standard paraformaldehyde-fixed tissues. STPT
can be used to generate a series of two-dimensional images
spanning all regions of the brain (Kim et al., 2017; Osten andMargrie, 2013; Ragan et al., 2012) or full 3D image volumes spanning the brain (Economo et al., 2016). Knife-edge scanningmicroscopy (KESM) (Mayerich et al., 2008) and micro-opticalsectioning tomography (fluorescence micro-optical sectioning

tomography [fMOST]) (Gong et al., 2016; Li et al., 2010; Zheng
et al., 2013) can also generate complete, 3D whole-brain imagine
volumes using micron-scale sectioning and single-photon imaging of resin-embedded tissue. Block-face serial microscopy tomography (FAST) (Seiriki et al., 2017) uses a spinning-disk
confocal microscope to achieve high imaging rates in a blockface configuration.
One application of these methods is brain-wide mapping ofgenetically defined cell types. For example, Osten and colleagues developed an automated, quantitative brain-wide cell
profiling platform (qBrain) that can be used to map the brainwide distribution of genetically defined cell types across regionsin the mouse brain. qBrain combines recombinase-based
knockin driver mouse lines that label genetically defined cell
types (Huang, 2014; Huang and Zeng, 2013; Madisen et al.,2012), automated brain imaging at single-cell resolution by
STPT, and computational analyses that include cell detection
by convolutional neural networks (Kim et al., 2015b, 2017; Raganet al., 2012). The application of this platform to cell-type anatomy
has immediately demonstrated the power of unbiased, wholebrain anatomical mapping in uncovering novel principles ofmammalian brain organization. First a comparison of quantitative distribution of three major inhibitory neuron cell types, thoseexpressing somatostatin (SST), parvalbumin (PV), and vasoactive intestinal peptide (VIP), across the mouse brain isocortex
revealed hierarchical organization of the neocortex, because
sensory-motor areas were found to be dominated by outputmodulating PV-positive interneurons, whereas association areas
were composed of large numbers of input-modulating SST-positive interneurons. Another perhaps even more surprising finding
from this study was the identification of nine brain regions withdifferent distributions of these cell types between male and female mouse brains, with eight regions containing more VIP+ or
SST+ neurons in the female brain despite the female brain being
smaller overall than the male brain. Most regions with sexuallydimorphic cell-type distributions were from the structures ofthe vomeronasal system, which regulate reproductive and social
behaviors. This finding provides direct, quantitative evidence
that anatomical differences underlie sexually dimorphic behaviors (Bayless and Shah, 2016; Simerly, 2002). Given the unexpected results derived from analyses of only 3 cell types using
STPT, it seems likely that further quantitative atlasing of celltype distributions will provide additional insights into how thecellular composition of different brain areas may contribute totheir functions.
Block-face imaging methods remain attractive because of
their high resolution and compatibility with large tissue samples.
However, due to many of the technological advances reviewed
here, large-volume imaging using tissue clearing and light-sheet
microscopy are closing the gap and provide several key advantages over block-face methods. Block-face imaging requires
specialized instruments that may not be readily accessible tomany researchers, while light-sheet microscopes are becoming
increasingly common fixtures in microscope facilities. Furthermore, light-sheet imaging can achieve acquisition rates for volumetric data that are several orders of magnitude faster thanblock-face imaging methods. Still, to realize whole-organ cell
profiling, clearing methods must achieve high transparency of

Neuron 106, May 6, 2020 377

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ReviewA

B

C

D

Figure 4. Whole-Brain Profiling of Cells by Light-Sheet Fluorescent Microscopy(A) Volume-rendered and single-plane images of a brain transduced with AAV-PHP.eB:NSE-H2B-mCherry (mCherry, green) and counterstained by RD2 (red),which is cleared by CUBIC-L/R+. Overlapped signals are shown in yellow. A volume-rendered image is shown in the center. Single-plane and magnified images
are shown for cerebral cortex, hippocampus, olfactory bulb, and striatum. Both horizontal (x-y) and coronal (x-z) views are also shown. Scale bars, 200 mm (singleplane image) and 25 mm (magnified image).
(B) 3D and cross-section images of the positive-cell-number ratio map of whole mouse brain infected by AAV-PHP.eB (NSE-H2B-mCherry). Voxel size, 80 mm.
Scale bars, 2 mm and 50 mm.
(legend continued on next page)378 Neuron 106, May 6, 2020

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Review
samples so that the light sheet is not degraded by tissueinduced scattering, aberration, and absorption. In addition,light-sheet imaging methods must maintain consistent quality
across large fields of view. Increasingly, these constraints canbe met by state-of-the-art tissue-clearing methods and lightsheet fluorescent microscopy. As a result, this approach is
becoming an appealing strategy for whole-brain profiling of cells
in intact mammalian brains (Liebmann et al., 2016; Murakami
et al., 2018; Renier et al., 2014, 2016; Susaki et al., 2014,2015; Sylwestrak et al., 2016; Tatsuki et al., 2016; Tomer et al.,2014). For example, a tissue-clearing and expansion method,
CUBIC-X, in combination with custom-made light-sheet fluorescent microscopy with a 103 objective lens (NA = 0.6, working
distance = 8 mm), allowed 1.3 million images covering the entire
mouse brain to be successfully obtained.
Whole-brain datasets generated by these instruments range
from 100 GB to 30 TB of data per single mouse brain, necessitating the development of new computational tools for analyses. 3D reconstructions, anatomical registration, and signal
detection and quantification, for example, have been demonstrated using supervised machine learning algorithms trainedon expert-annotated ground truth data (Hawrylycz et al., 2011;
Kim et al., 2015b; Kuan et al., 2015; Ng et al., 2007; Raganet al., 2012). In datasets collected using CUBIC-X, GPU-based
image analysis was used to extract the 3D coordinates of all cells
in the adult mouse brain. Leveraging existing anatomical segmentations of the mouse brain (Dong, 2008; Lein et al., 2007),
this approach yielded a whole-brain atlas with single-cell resolution (CUBIC-Atlas) (Murakami et al., 2018). Further developmentof the cell detection algorithm improved the accuracy and speedof this analysis pipeline so that more than 90% of cells in themouse brain could be identified in several hours (up to 2 TB/h)(Matsumoto et al., 2019). Using this cell-nucleus detection algorithm, the updated CUBIC-Atlas 1.2 of 8-week-old C57BL/6J
mouse brain was constructed that contains the spatial coordinates and brain regions associated with more than 108 cells(Matsumoto et al., 2019). The improved throughput offered bythese protocols allows the analysis of numerous (>100) samples,
providing a highly versatile platform for biomedical research,
including comparative analyses across a range of mammalianand vertebrate species, and opening a new and exciting frontierin neuroanatomy (Figure 4).
The molecular features of cells in the brain—their transcriptomes and proteomes—provide information extending beyond
their morphology—both about their type and about the cellular
processes related to function. Classic approaches for immunofluorescence and fluorescence in situ hybridization for labelingproteins and mRNAs, respectively, have been most frequently
applied within thin tissue sections through which fluorophoreconjugated macromolecules can quickly diffuse. In addition to
reducing the scattering and absorption of light, processes
such as delipidation and hydrogel embedding increase thediffusivity of proteins and oligonucleotides within tissue. Therefore, there is a natural synergy between techniques for clearing
tissue samples and for labeling the mRNAs and proteins withinthem. Nevertheless, devising methods that permit the penetration of antibodies (for immunofluorescence) and oligonucleotides (for in situ hybridization) into tissue samples as large as
intact organs has remained challenging. Penetration of macromolecules into tissues cleared using various clearing techniques has been demonstrated, for example, by CLARITY(Chung et al., 2013), iDISCO+ (Renier et al., 2016), vDISCO(Cai et al., 2018), CUBIC-L/R+, and CUBIC-X (Matsumoto
et al., 2019; Murakami et al., 2018). Nevertheless, uniformly
staining thick samples—particularly intact, adult mammalianbrains—may require protocol and/or probe optimization toeffectively label different molecular targets. In addition, molecular labeling may be time consuming, requiring several weeks
or months for probes to thoroughly penetrate large samples
even following delipidation and/or hydrogel embedding. Active
processes, such as stochastic electrotransport, may acceleratemacromolecule diffusion (Kim et al., 2015a; Lee et al., 2016).
Ongoing methodological development aims to increase thespeed, reliability, and set of molecular targets that may belabeled in cleared tissue.
One powerful application of whole-brain tissue clearing andmolecular labeling is efficient, brain-wide identification of cells
expressing immediate early genes (IEGs). The expression of
IEGs such as c-fos, Arc, Egr-1, FosB, and Npas4 marks transcriptionally activated neurons and that can be used as an indirect measure of cells that have been recently active. Whole-brain
mapping of IEG-expressing neurons using tissue clearing andlight-sheet imaging has been demonstrated using iDisco+ (Renier et al., 2016) to delineate the brain-wide set of neural circuits
engaged by parenting behavior. Identifying neural circuits using
this approach allows the neural circuits engaged by neural processes to be mapped efficiently and comprehensively acrossthe brain.
Whole-Brain Profiling of Circuits
The recent proliferation of neuroanatomical methods has alsoenabled whole-brain profiling of connectivity in the brain. Connectivity determines how information flows through neural circuits, giving rise to the diversity of mammalian behaviors fromthe simple startle response of defensive behaviors to thecomplex neuronal computations during cognitive and emotive
processing. Mesoscale connectivity, which describes thelong-range projections of neural populations, controls whichbrain areas are connected (Mitra, 2014). At the microscale,mapping the brain-wide connectivity of single neurons provides
a fine-scale description of how signals are routed between
brain areas. Mapping connectivity using light microscopy atboth the mesoscale and the microscale involves more stringent
requirements for resolution and contrast than mapping cells,(C) Average cell number of all anatomical regions in three 8-week-old C57BL/6N mouse brains. Only the edge regions (i.e., having no child region) are shown. OLF,
olfactory areas; HPF, hippocampal formation; CNU, cerebral nuclei; HY, hypothalamus; MB, midbrain; P, pons; MY, medulla; CB, cerebellum; FT, fiber tract; VS,ventricular system.
(D) Positive-cell-number ratio of each anatomical region in mouse brain infected by AAV-PHP.eB (NSE-H2B-mCherry). The data and figure are adopted from
Matsumoto et al. (2019).

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A Anterograde tracing

Single-cell reconstruction

Connectivity structure

Source region

Source region

Source region

A

B

A

B

A

E

C

E

C

C

D

Target regions

D

Target regionsBbecause axonal processes with diameters much smaller thanneuronal somata must be detected with high fidelity. Therefore,
block-face imaging methods have remained the dominant
approach and have been deployed successfully for mapping
connectivity. At the mesoscale, STPT has been used to map
the long-range connectivity of thalamic projections, connections between the cortex and the striatum, and perhaps mostnotably, region-to-region connectivity as part of the Allen
Institute for Brain Science Mouse Connectivity Project. This
project has assayed the brain-wide projections of genetically
identified populations of cells in hundreds of brain regions (Ohet al., 2014).
Although mapping mesoscale connectivity effectively identifies
the set of brain regions to which a neural population projects,
microscale connectivity controls how those connections are
structured at the cellular level. This information is crucial for identifying cell types and defining how information is represented andcommunicated between brain areas (Figure 5). To determine
long-range connectivity in the brain with single-neuron resolution,
Economo and colleagues extended STPT to full, volumetric imaging of the mouse brain by increasing the acquisition rate by
20-fold and combining it with tissue clearing (Economo et al.,2016) and sparse neuronal labeling. In doing so, they were able
to reconstruct the axonal projections of single neurons in their

380 Neuron 106, May 6, 2020

B

D

E

Target regions

Figure 5. Whole-Brain Profiling of Circuits
Single-neuron reconstruction reveals structured
connectivity patterns.
(A) Left, anterograde tracing of a population ofneurons in a source brain region can reveal regionsof the brain to which the source connects (i.e.,
regions A, B, and D, but not C and E). Middle,
single-cell axonal reconstruction reveals whereindividual neurons connect. Right, from singleneuron reconstructions, classes of neurons withsimilar connectivity can be identified and structured patterns of connectivity across a population
can be determined.
(B) Top left, single-neuron reconstructions of projection neurons in the subiculum (boxed region).
Top right, axonal reconstructions reveal distinct
brain-wide patterns of connectivity (color coded)(Cembrowski et al., 2018; Winnubst et al., 2019).
Bottom left, single-neuron reconstructions ofpyramidal tract neurons in the motor cortex (boxedregion). Bottom right, axonal reconstructions
reveal two distinct types of pyramidal tract neurons based on their brain-wide connectivity (green,
magenta) (Economo et al., 2018).

entirety. Using whole-brain imaging todetermine axonal connectivity increased
the efficiency of this process by several orders of magnitude compared with existing
histological approaches (Economo et al.,2019). Efficient, single-neuron reconstruction has also been demonstrated using
fMOST (Li et al., 2018). These methods
have enabled the brain-wide projectionsof more than 1,000 neurons to be reconstructed in their entirety (Winnubst et al.,2019). Mapping connectivity at the microscale has been instrumental for revealing the heterogeneity of
projection neurons and the structure of the projection pathways
they comprise in motor (Economo et al., 2018), somatosensory(Hooks et al., 2018), and visual (Han et al., 2018) cortices; hippocampus (Cembrowski et al., 2018); and claustrum (Wang et al.,2019). Despite the success of projects using block-face imaging
to map long-range connectivity in the brain, instruments designed
for this purpose are not widely available to most researchers.
Advances in light-sheet microscopy (Chhetri et al., 2015; Keller
and Dodt, 2012; Verveer et al., 2007) promise to substantiallyreduce the time necessary for brain-wide imaging with sufficient
resolution to resolve and reconstruct axons, increasing the efficiency of this process and enabling comprehensive atlasing of
long-range connectivity patterns in the mammalian brain (Economo et al., 2019).
Future Perspective
Seamless integration of tissue-clearing methods and light-sheet
microscopy will continue to enable key applications in neuroscience, including whole-brain profiling of cells and circuits inrodents. In the near future, improvement of clearing, staining, imaging, and image informatics will expand the spectrum of applications to whole-brain profiling of cells in primates and humans, oneof the grand challenges in neuroscience. Isotropic imaging ofllReview
cleared and 5-fold expanded entire mouse brains at 1.3 3 1.3 3
1.3 mm resolution gives rise to 2.5 TB of data per brain (0.5 TB
3 5-fold volume expansion) and is estimated to be sufficient for
extracting cell-nucleus information and constructing 3D singlecell-resolution whole-brain atlases. Because marmoset brains(7.6 g) and human brains (1,330 g) are approximately 15and 2,660-fold larger, respectively, than mouse brains (0.5 g)(Stephan et al., 1981), whole-brain profiling of marmoset and human brains will give rise to 38 TB and 6.65 PB of data, respectively, and provide single-cell-resolution whole-brain atlases
comprising 1.5 3 109 and 2.7 3 1011 cells, respectively. To
achieve whole-brain profiling of cells in primates and humans,
several remaining issues need to be addressed. One of the remaining issues toward this goal is more stringent delipidation.
Although the chemistry of the delipidation process in tissue
clearing has been greatly advanced by chemical screening andprofiling (Inoue et al., 2019; Tainaka et al., 2018; Zhao et al.,2019) and by developing stringent fixation protocols (Park et al.,2018), delipidation of the lipid-rich brains of marmosets and humans may require improvements in delipidation chemistry, inparticular for the white matter of the brain. Another remaining
issue is auto-fluorescence, in particular human brain specimens.
Because the human brain is usually fixed in formaldehyde for along period, there is a need for minimizing auto-fluorescencearising from long-term fixation. The third issue on the path toward
whole-brain profiling of cells in such large brains is the need fordevelopment of optimal microscopy for marmoset and humanbrains. Because a marmoset brain is 15 times larger than a mousebrain, the development of new, large-aperture optics with the long
working distance required to cover the entire marmoset brain will
be necessary. As far as human brains are concerned, an imaging
strategy based on physical sectioning of the brain will likely present the most promising approach. Even in this case, advancements in microscopy will be essential for rapid, high-resolution
imaging of the resulting thick slices (e.g., 5 mm thickness) witha cross-section area of 100 cm2. Especially for such large tissue
samples that may even comprise human tumors (Dodt et al.,2015), light sheets with an extended Rayleigh range (Saghafi
et al., 2014) could be particularly helpful. In addition, more powerful image informatics are needed to accurately and rapidly extract
information from terabyte- and petabyte-scale datasets. Althoughthese challenges may seem daunting at present, we believe thatthey will likely be solved in the near future, enabling the construction of 3D single-cell-resolution whole-brain atlases that serve asa foundation upon which to integrate additional information, suchas cell types, cell states (e.g., c-Fos expression), and cellular connectivity.
In the realm of structural connectivity, whole-brain profiling of
circuits at the single-neuron level presents another grand challenge for mammalian neuroscience. As discussed earlier, a
recent study characterized the long-range axonal projectionsof more than 1,000 neurons from a few key areas in the mousebrain (Winnubst et al., 2019). However, a detailed brain-wideclassification of neurons into morphological cell types in themouse brain would likely require a sampling of at least 100,000
neurons (0.1% of all cells in the mouse brain, because it is estimated that there are 1,000 brain regions in the mouse brain andlikely more than 10 cell types per region) (Winnubst et al., 2019).

Furthermore, a more complete description of neuronal cell types
would require establishing correspondences of morphology,
gene expression, and function at the single-neuron level. Faster
imaging afforded by novel light-sheet approaches, together withincreased throughput in the reconstruction process (a 10- to
20-fold improvement over the current state of the art) becauseof further automation, could make this goal achievable in a few
years. Obtaining such comprehensive information on the structural organization of the brain will provide foundational information necessary for functional insights into the mammalian brain
in health and disease.
ACKNOWLEDGMENTS
We thank E.A. Susaki for helping to construct Figure 1 for advanced tissueclearing methods, K. Matsumoto for helping to construct Figure 4 and Johan
Winnubst for help with renderings in Figure 5. We also gratefully acknowledge
grant support from Brain/MINDS JP20dm0207049, Science and Technology
Platform Program for Advanced Biological Medicine JP20am0401011,
AMED-CREST JP20gm0610006 (AMED/MEXT) (to H.R.U.), Grant-in-Aid for
Scientific Research (S) JP25221004 (JSPS KAKENHI) (to H.R.U.), and HFSP
Research Grant Program (HFSP RGP0019/2018) (to H.R.U.) as well as funding
from the Austrian Science Fund (FWF) and the Vienna Science Fund (WWTF)(to H.-U.D.), the Howard Hughes Medical Institute (to P.J.K., M.N.E., andJ.C.), and NIMH U01 MH105971 and U01 MH114824 (to P.O.).
AUTHOR CONTRIBUTIONS
H.R.U., H.-U.D., and P.J.K. wrote the introduction section; H.R.U. wrote thetissue-clearing section; P.J.K. and H.-U.D. wrote the light-sheet microscopy
section; P.O., M.N.E., and H.R.U. wrote the whole-brain cell profiling section;
M.N.E. and J.C. wrote the whole-brain circuit profiling section; and H.R.U. and
P.J.K. wrote the future perspective section. All authors discussed and commented on the manuscript text.
DECLARATION OF INTERESTS
H.R.U. is a co-inventor on patent applications covering the CUBIC reagentsand a cofounder of CUBICStars Inc. P.J.K. is a co-inventor on patents and patent applications covering multi-view and adaptive light-sheet microscopy.
P.O. is a cofounder of Certerra Inc. and Certego Therapeutics Inc.
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