REVIEWS
Tissue clearing and its applications
in neuroscience
Hiroki R. Ueda
Alain Chédotal*, Ali Ertürk 3,4,5, Kwanghun Chung 6,7,8,9,10,11,12, Viviana Gradinaru, Pavel Tomancak15,16 and Philipp J. Keller 17

1,2

,

13

14

Abstract | State-​of-the-​art tissue-​clearing methods provide subcellular-​level optical access tointact tissues from individual organs and even to some entire mammals. When combined withlight-​sheet microscopy and automated approaches to image analysis, existing tissue-​clearing
methods can speed up and may reduce the cost of conventional histology by several orders ofmagnitude. In addition, tissue-​clearing chemistry allows whole-​organ antibody labelling, which
can be applied even to thick human tissues. By combining the most powerful labelling, clearing,
imaging and data-​analysis tools, scientists are extracting structural and functional cellularand subcellular information on complex mammalian bodies and large human specimens at anaccelerated pace. The rapid generation of terabyte-​scale imaging data furthermore creates ahigh demand for efficient computational approaches that tackle challenges in large-​scale data
analysis and management. In this Review, we discuss how tissue-​clearing methods could provide
an unbiased, system-​level view of mammalian bodies and human specimens and discuss future
opportunities for the use of these methods in human neuroscience.
Tissue clearing
A method to make a biological
specimen transparent byminimization of light scatteringand light absorption by the
biological specimen.

Hydrophobic tissue clearing
One of three major tissue-​clearing methods; it useshydrophobic (water-​immiscible)
reagents. It is also referred toas ‘solvent tissue clearing’.

Hydrophilic tissue clearing
One of three major tissue-​clearing methods; it useshydrophilic (water-​miscible)
reagents. It is also referred toas ‘aqueous tissue clearing’ butdoes not always involve the useof water.

Refractive index(RI). The ratio of the speed oflight in a vacuum to its speed ina specified medium. The RI of avacuum is 1 by definition,
whereas the RI of water is~1.33.
*e-​mail: uedah-​tky@umin.ac.jp
https://doi.org/10.1038/
s41583-019-0250-1

Histological techniques have been the standard procedure for investigating tissues for several decades; however,a complete understanding of biological mechanisms inhealth and disease requires an unbiased exploration of thewhole organism, not just selected parts of tissue. This needis particularly evident in the context of the nervous system,
which can be found throughout the body. Tissue-​clearing
methods now allow 3D imaging of intact tissues and evensome entire organisms. Indeed, a century-​old approach atrendering tissues transparent1 has been almost reinvented
with recent developments in tissue-​clearing reagents(Table 1) and protocols (Supplementary Table 1), efficient
fluorescent labelling and rapid volumetric imaging by
light-​sheet microscopy2–14.
Three major tissue-​clearing approaches are currently available: hydrophobic, hydrophilic and hydrogel-​based methods15,16 (Fig. 1); hydrophobic tissue-​clearing andhydrophilic tissue-​clearing methods are also referred as ‘solvent’ and ‘aqueous’ tissue-​clearing methods, respectively.
In general, these tissue-​clearing methods, especially inhighly efficient protocols, remove lipids (delipidation),
pigments (decolourization) and calcium phosphate(decalcification) and aim to match the refractive indices (refractive index (RI) matching) of the specimens andthe imaging media by reaching an almost complete levelof transparency for intact organs and even entire adultrodent bodies. Although hydrophobic methods usuallyshrink the tissues and thereby allow the imaging of larger
samples, hydrophilic and hydrogel-​based methods, if the

Nature Reviews | Neuroscience

reagents have low osmolarity, can expand the specimens(expansion), which further increases the transparency
and effective resolution (Fig. 1).
The physical principles of these tissue-​clearing
methods, and how each tissue-​clearing process (that is,delipid­ation, decolourization, decalcification, RI matching and expansion) contributes to tissue transparency,
are beginning to be elucidated by considering the physical properties of organs. A recent study discovered thatan organ can act as a polymer gel even in the absence ofexogenous polymers17, which was originally predicted by
Tanaka and colleagues via a bottom-​up approach18,19. If
an organ acts as a polymer gel, the RI of an organ (‘polymer gel’) can be described by both the RI and the volumeof its components (‘monomers’) according to a theoretical formula called the Lorentz–Lorenz relation20,21.
Therefore, delipidation and decalcification change the
RI of an organ’s components and hence change the RI of
the organ itself, whereas expansion can increase the volume of an organ’s components, also eventually reducing
the RI of the organ. In either case, these processes allow the
RI of an organ to be more easily matched with the RIof the medium and this results in minimization of lightscattering. In addition, the minimization of light absorption, which also contributes to transparency of the specimens, can be achieved by decolourization (removing thepigments) of an organ. Recent comprehensive chemical
profiling of hydrophilic tissue-​clearing reagents provides a better understanding of the chemical principles

Reviews
Light-​sheet microscopy
A technique that allows fast,high-​resolution imaging of large
biological specimens with low
light exposure by rapidly
acquiring images of thin opticalsections illuminated by laser
light sheets.

of how each chemical functional group can contribute totissue-​clearing processes22.
In parallel to forging a better understanding of theprinciples underlying tissue-​clearing methods, researchers are continuing to improve labelling and data-​analysis
tools to broaden the applications of tissue-​clearing methods. Indeed, labelling options have expanded with thedevelopment of ex vivo deep-​tissue labelling methods
for whole mouse brain4,23–27 and whole mouse body6,27,28
and of in vivo systematic adeno-​associated virus-​basedlabelling of CNS and peripheral nervous system cells29,30.
Once tissues are labelled and rendered transparent,
light-​sheet microscopy provides rapid 3D whole-​brain
imaging at subcellular resolution31. Some forms of light-​
sheet microscopy can already generate isotropic andhigh-​resolution images with a substantially higher
signal-​to-noise ratio and a markedly higher resolution
than for the images that can be generated by two-​photon
microscopy31. Furthermore, the development of powerful machine-​learning algorithms will be essential to optimize the analysis of such large data sets, especially forimage segmentation. The convergence of these diverse
disciplines around the tissue-​clearing methods will pave
the way to unbiased 3D histological assessments, whichshould drastically accelerate the discovery of novel developmental, physiological and pathological mechanisms
impacting whole organisms.
In this Review, we cover the landscape of rapidly
emerging tissue-​clearing methods and their related
technologies — including labelling, light-​sheet micro­
scopy and data analysis and management — as well as
applications of tissue clearing in neuroscience, especially in human studies. We then discuss the challenges
and opportunities relating to the integration of thesetechnologies to generate unbiased insights into humanphysiology and pathology.

Author addresses
Department of systems Pharmacology, university of tokyo, tokyo, Japan.
Laboratory for synthetic Biology, riKeN BDr, suita, Japan.
3
institute for stroke and Dementia research, Klinikum der universität München,
Ludwig–Maximilian university of Munich, Munich, Germany.
4
institute of tissue engineering and regenerative Medicine, Helmholtz ZentrumMünchen, Neuherberg, Germany.
5
Munich Cluster for systems Neurology (syNergy), Munich, Germany.
6
institute for Medical engineering and science, Massachusetts institute of technology,
Cambridge, Ma, usa.
7
Picower institute for Learning and Memory, Massachusetts institute of technology,
Cambridge, Ma, usa.
8
Department of Chemical engineering, Massachusetts institute of technology,
Cambridge, Ma, usa.
9Department of Brain and Cognitive sciences, Massachusetts institute of technology,
Cambridge, Ma, usa.
10
eli & edythe Broad institute of Mit and Harvard, Cambridge, Ma, usa.
11
Center for NanoMedicine, institute for Basic science, seoul, republic of Korea.
12
Graduate Program of Nano Biomedical engineering, Yonsei-​iBs institute, Yonsei
university, seoul, republic of Korea.
13
Division of Biology and Biological engineering, California institute of technology,
Pasadena, Ca, usa.
14
Institut de la Vision, Sorbonne Université, INSERM, CNRS, Paris, France.
15
Max Planck institute of Molecular Cell Biology and Genetics, Dresden, Germany.
16
it4innovations, technical university of Ostrava, Ostrava, Czech republic.
17
Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
1
2

Hydrophobic tissue clearing
Hydrophobic tissue-​clearing approaches involve the useof organic solvents and often provide complete transparency of an intact specimen quickly32. For example,
3D imaging of solvent-​cleared organs (3DISCO), developed by Ertürk and colleagues, can fully clear a whole
adult mouse brain in 1–2 days7,33. Ethyl cinnamate has
also been used in hydrophobic tissue clearing insteadof organic solvents11. Because hydrophobic tissue-​clearing methods are straightforward, only needing
the sequential incubation of the specimen in different
solutions, 3DISCO and its variants have already been
widely used in imaging studies of neuronal circuits7,33,
inflammation34, stem cells35,36 and cancer cells37–39, andin unsectioned rodent organs and human biopsy speci­
mens40. DISCO-​based methods have also been combined with deep-​tissue immunolabelling approaches
to study rodent embryos4,8, human embryos23, cancer
biopsy specimens40,41, adult mouse brains4,26 and, more
recently, whole mouse bodies (discussed later)28.
DISCO-​based methods consist of an initial dehydration step to remove water — the major light scatterer in tissues (the RI of water is 1.33, whereas the
RI of soft tissues is 1.44–1.56) — and a subsequentorganic solvent immersion step to extract most of thelipids and increase the RI to match the average RI of
biological tissues (the RI for both organic solvents andshrunk brain tissue is ~1.56)15. In most of the DISCO
approaches (except immunolabelling-​enabled DISCO+(iDISCO+))34, dehydration leads to a marked shrinkage of the specimen32. Pan et al. developed the ultimate
DISCO (uDISCO) method and shrank mouse bodies
to about one third of their original size, which facilitated imaging of the entire bodies at cellular resolution
by light-​sheet microscopy42. The shrinkage of individual organs, especially CNS tissue, was isotropic; thebones also shrunk isotropically but to a lesser degree42.
Through the use of uDISCO, the researchers visualizedneuronal connectivity in the intact CNS of mice (that is,in the brain and spinal cord).
An important advantage of organic solvent-​based
clearing is the permanent preservation of the specimens, owing to the hardening of the cleared tissues.
This allows multiple imaging sessions and long-​term
reanalysis of the samples, especially by immunolabelling
methods, which can permanently stabilize the endogenous fluorescent signal28. Although clearing and imaging increasingly larger samples are valuable steps in theunbiased analysis of tissues, thorough labelling of large
tissues with specific dyes and antibodies remains a challenge. Towards this goal, first, Tessier-​Lavigne, Renier
and colleagues developed iDISCO, which achieved
immunolabelling of the adult mouse brain and allowed
brain regions differentially activated during parenting
behaviour to be uncovered26. iDISCO involves pretreatment of the specimen with solutions containing H2O2
and methanol to permeabilize mouse brain, a processthat may also purge most of the epitopes for the antibodies26. In the future, development of new deep-​tissuelabelling approaches with full epitope preservation willbe critical to broaden the applications of hydrophobic
tissue-​clearing methods.
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Reviews
combining urea with sorbitol48 to develop ScaleS49, which
can be applied to adult, old and diseased mouse brain
hemispheres. Imai and colleagues developed the See
Deep Brain (SeeDB) protocol13, which uses fructose50
as its key RI-​matching component, and used it to traceneural circuits in mouse olfactory bulbs because it does
not expand or shrink biological samples and therefore
preserves their morphology. They further refined thismethod by using an X-​ray contrast agent with a higher
RI, leading to the development of the SeeDB2 protocol,
which has been applied to super-​resolution imaging ofneural circuits in mouse brains51.
Ueda and colleagues took a system-​level approach toidentifying potent hydrophilic tissue-​clearing reagentsby comprehensive chemical profiling. They discovereda series of amino alcohols that have both delipidation5
and decolourization6 capabilities and then developed
a series of clear, unobstructed brain or body imaging cocktails and computational analysis (CUBIC)
reagents for both delipidation and decolourization5,27,52.
Profiling more than 1,600 hydrophilic chemicals, theyalso discovered a series of aromatic amides that canbe used as potent RI-​matching reagents22,27. By combiHydrophilic tissue clearing
nation of the most advanced delipidation (CUBIC-​L,
Hydrophilic tissue-​clearing methods use water-​soluble where ‘L’ stands for ‘delipidation’) and RI-​matching
reagents for tissue clearing. Although the tissue-​clearing (CUBIC-​R+, where ‘R’ stands for ‘RI matching’ and ‘+’
performance of hydrophilic tissue-​clearing methods stands for a basic condition by adding an amino alcohol,
was sometimes inferior to that of hydrophobic tissue-​ N-butyldiethanolamine) reagents, the tissue-​clearing perclearing methods, the former have obvious advantages, formance of hydrophilic methods is now similar to or can
including high levels of biocompatibility, biosafety and even exceed that of hydrophobic methods without losing
preservation of protein function. Hydrophilic reagents its advantages of biocompatibility, biosafety and preserusually form hydrogen bonds with components of vation of protein function (for example, fluorescence)22.
tissues such as proteins as well as surrounding water CUBIC has been applied to whole-​brain imaging ofmolecules, which can help to preserve the 3D structure immediate early gene expression induced by light expoof tissue components and, thereby, the signal of fluo- sure5 and drug administration53, whole-​brain imaging ofrescent proteins. Moreover, hydrophilic reagents can cancer metastasis27, whole-​brain imaging of individual
be dissolved in water at a high concentration and can be neurons54 and taste-​sensing circuits in mouse brains55,
used as an RI-​matching medium to provide a high RI and 3D brain imaging of glutaminergic synaptic connecin the medium. Hydrophilic tissue-​clearing methods tions56, hypothalamic neural subtypes57, layer-​specificalso have a long history of about a quarter of a century astrocyte morphology58 and visual projections of retinal
ganglion cells59. CUBIC has been also applied to 3D imag(since 1995; Box 1).
One of the first applications of hydrophilic tissue ing of other organs, including haematopoietic stem cells
clearing to neuroscience was by Chiang and colleagues. in bone marrow60, carcinoma in the lung61 and single-​
They developed FocusClear, which includes an X-​ray cell lineage tracing in the mammary gland62 and develcontrast agent (for example, diatrizoate acid) and a opment of a heart63. Therefore, the peripheral nervous
detergent (for example, Tween 20), and applied it to systems in these organs can be analysed in detail.
Delipidation in some hydrophilic tissue-​clearing
imaging of a whole cockroach brain45,46. More recently,
Miyawaki and colleagues developed Scale12, which methods creates space for large substances such as
contains urea as a key tissue-​clearing component, and antibodies to penetrate more rapidly and deeply into
can expand biological samples — for example, whole tissues, allowing 3D immunohistochemistry in large
mouse embryos (embryonic day 13.5), infant mouse samples. CUBIC has been successfully used to achieve
brains (postnatal day 15) and adult mouse brain slices 3D immunohistochemistry for adult mouse brain, heart,(7–13 weeks old) — by hyperhydration47 and thereby lung, stomach and intestine5,6,22,27,52. Recently, CUBIC-​L–
reduce their RIs. In contrast to most hydrophobic tissue-​ CUBIC-​R was combined with whole-​brain 3D immunoclearing regents, Scale can more efficiently preserve histochemistry to image blood vessels in entire mouse
fluor­escent proteins12. This method has allowed imaging brains27. CUBIC was also applied to 3D immunohistoof yellow fluorescent protein-​labelled Thy1-expressing chemistry and imaging of neurons expressing vesicular
neurons in infant and adult mice, green fluorescent acetylcholine transporter that innervated islets in a panprotein-​labelled neural stem cells in the adult mouse creas64. In addition to the enhanced penetration of antihippocampus and cell-​cycle status of mouse embryos bodies owing to delipidation, weakening the interaction
via detection of the Fucci-​S/G2/M and Fucci-​G1/G0 between an antibody and a tissue seems to accelerate themarkers12. The researchers further developed Scale by penetration of an antibody into a tissue. For example,
To scale up the immunolabelling to whole adultmouse, Cai et al. recently introduced vDISCO (the ‘v’refers to the variable domain of heavy-​chain antibodies; that is, nanobodies), using high-​pressure deliveryof nanobodies for complete immunolabelling of thewhole body with bright Atto dyes in the far-​red region
to overcome low signal intensity and autofluorescenceof many tissues in the blue–green region28. This method
amplified the fluorescent signal by two orders of magnitude and thereby allowed imaging of subcellular details
and quantification of single cells deep in intact clearedmouse bodies through bones and muscles. vDISCO
allowed the generation of the first whole-​body neuronal
projectome of adult mice, the study of neurodegeneration and inflammation throughout the body after CNS
lesions and the co-​discovery of skull–meninges connections, which seem to be critical for brain functions inphysiological and pathological states28,43. As vDISCO
enormously amplifies signal contrast even for dissectedorgans, in the future it will provide high-​quality ground-​truth data for machine learning-​based algorithms aiming
to map transgenically labelled mammalian brains11,44.

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Table 1 | Tissue-​clearing reagents used for whole-​organ or body 3D imaging
chemicalGrouprole in tissue clearing

cocktail or protocol

refs

MethanolAlcohol

Dehydration and permabilization

iDISCO, iDISCO+

4,8,26,36

Ethanol

Alcohol

Dehydration

BABB

tert-​Butanol

Alcohol

Dehydration

FluoClearBABB, uDISCO, PEGASOS

Tetrahydrofuran

Ether

Dehydration

3DISCO, iDISCO, vDISCO

Benzyl alcohol

Aromatic alcohol

RI matching and delipidation(putative)

BABB, vDISCO

Benzyl benzoate

Aromatic ester

RI matching and delipidation(putative)

BABB, PEGASOS, vDISCO

Ethyl-3-phenylprop-2enoate (ethyl cinnamate)

Aromatic ester

RI matching

Ethyl cinnamate method

Dibenzyl ether

Aromatic ether

RI matching and delipidation (putative)

3DISCO, iDISCO, iDISCO+

Diphenyl ether

Aromatic ether

RI matching and delipidation (putative) uDISCO

42

HexaneAlkaneDehydration and delipidation?

BABB

14

Dichloromethane

Alkyl halide

Delipidation and dehydration

3DISCO, uDISCO, iDISCO, iDISCO+

Glycerol

Polyol

RI matching

Scale reagents, CLARITY, LUCID

2,2′-Thiodiethanol

Thioalcohol

RI matching

Single treatment, CLARITY, LUCID,
TOMEI

SucroseOligosaccharide

RI matching

ScaleCUBIC2, UbasM-2d(−)-Fructose

Monosaccharide

RI matching

SeeDB, FRUIT

13,165d(−)-Sorbitol

Monosaccharide

RI matchingsRIMS, ScaleS, STP tomography

3,49,54

Xylitol

Monosaccharide

Dehydration and RI matching

ClearSee

182

Methyl-​β-cyclodextrin

Oligosaccharides

Delipidation (extracting cholesterol)

ScaleS, vDISCO

28,49

γ-​Cyclodextrin

Oligosaccharides

Delipidation (extracting cholesterol)

ScaleS

Polyethylene glycol

Polyether

RI matching

ClearT2, PEGASOS

Dimethyl sulfoxide

Sulfoxide

RI matching

ScaleS, STP tomography

Urea

Urea

Hydration (putatively contributes toexpansion and hence RI matching)and chemical penetration

Scale reagents, ScaleCUBIC reagents
UbasM, FRUIT, ClearSee, vDISCO

1,3-Dimethyl-2imidazolidinone

Urea

Hydration (putatively contributes toexpansion and hence RI matching)and chemical penetration

UbasM-1–UbasM-2

181

Formamide

Amide

Putatively similar mechanism to urea

ClearT, ClearT2, RTF

162,184

N-​Methylacetamide

Amide

RI matching

Ce3D

Nicotinamide

Aromatic amide

RI matching

CUBIC-​R, CUBIC-​R+

22,27

N-​Methylnicotinamide

Aromatic amide

RI matching

CUBIC-​R, CUBIC-​R+

22,27

Antipyrine

Phenyl pyrazolone

RI matching and expansion

CUBIC-​X, CUBIC-​R, CUBIC-​R+

17,22,275-(N-2,3Contrast reagent
Dihydroxypropylacetamido)2,4,6-triiodo-​N,N′bis(2,3-dihydroxypropyl)
isophthalamide (iohexol)

RI matching

RIMS–SeeDB2–Ce3D

3,52,185

Diatrizoate acidContrast agent

RI matching

RI matching reagent in SWITCH
protocol, FocusClear

2,25,47

Iodixanol

Contrast reagent

RI matching

RI matching reagent in SWITCH
protocol25

N-​Acetyl-l-​hydroxyproline(trans-1-acetyl-4-hydroxy-​l-proline)

Amino acid

Relaxing collagen structure

ScaleS, vDISCO

N,N,N′,N′-Tetrakis(2-hydroxypropyl)
ethylenediamine (Quadrol)

Amino alcohol

Delipidation, decolorization and RI
matching

ScaleCUBIC-1, PACT-​deCAL–Bone
CLARITY, PEGASOS, vDISCO

Triethanolamine

Amino alcohol

Delipidation, decolorization and RI
matching

ScaleCUBIC-2, RTF

14
42,169,170
4,8,28,33,36,171
14,28,172

14,28,170,172

11

4,7,8,26,33,36,171,173

4,8,26,33,36,42
12,174–179
10,161,177,178,180

5,6,52,181

49
163,170
49,50
5,6,12,28,52,165,175,
176,179,181–183

185

28,49

5,6,28,52,74,170

5,6,52,184

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Table 1 (cont.) | Tissue-​clearing reagents used for whole-​organ or body 3D imaging
chemicalGrouprole in tissue clearing

cocktail or protocol

refs

N-​Butyldiethanolamine

Amino alcohol

Delipidation, decolorization and pH
adjustment

CUBIC-​L, CUBIC-​R+

22,27

Meglumine or
N-​methyl-d-​glucamine

Amino alcohol

Delipidation and RI matching

UbasM-1, RI matching reagent in
SWITCH protocol

25,181

1,3-Bis(aminomethyl)
cyclohexane

Aliphatic amine

Delipidation, decolorization and pH
adjustment

CUBIC-​HL

Imidazole

Imidazole

Expansion and decalcification(with EDTA)

CUBIC-​X, CUBIC-​B

1-Methylimidazole

Imidazole

Decolorization

CUBIC-​P

Sodium dodecyl sulfate

Ionic detergent

Delipidation

CLARITY and related protocols(PACT, PARS, ACT, FACT, FASTClearand simplified CLARITY), SWITCH

Sodiumdodecylbenzenesulfonate

Ionic detergent

Delipidation

CUBIC-​HL

22

Sodium deoxycholate

Ionic detergent

Delipidation

ClearSee

182

Triton X-100

Non-​ionicdetergent

Delipidation

Scale reagents, ScaleCUBIC-1,
UbasM-1 (0.2%), Ce3D (0.1%), SUT,
CUBIC-​L, CUBIC-​HL, vDISCO

Tween 20

Non-​ionicdetergent

Delipidation

FocusClear

EDTA

EDTA

Decalcification

PACT-​deCal, Bone CLARITY,CUBIC-​B, PEGASOS, vDISCO

22

17,22

22
2,3,9,10,22,25,67,74,
89,174,183,186–188

5,6,12,22,28,52,175,
176,181,183,185

47

9,22,28,74,170

BABB, benzyl alcohol and benzyl benzoate; Ce3D, clearing-​enhanced 3D; EDTA , ethylenediaminetetraacetic acid; FACT, fast free-​of-acrylamide clearing tissue;
LUCID, illuminate cleared organs to identify target molecules; PEGASOS, polyethylene glycol-​associated solvent system; RTF, rapid clearing method based ontriethanolamine and formamide; RI, refractive index; RIMS, refractive index-​matching solution; STP tomography , serial two-​photon tomography ; TOMEI,transparent plant organ method for imaging; UbasM, urea-​based amino-​sugar mixture.

the AbScale protocol, which uses the protein denaturant
urea in antibody staining, achieved 3D immunohistochemistry of an adult mouse hemisphere49. An original Scale protocol uses urea for tissue clearing not for
antibody staining. AbScale was used with brain-​wide
immunohistochemistry to image amyloid-​β plaques,
a hallmark pathological feature of Alzheimer disease,in mice27,49.
Hydrophilic methods can be also extended to expansion microscopy65. For example, the CUBIC-​X protocol,
which involves the use of an imidazole and an antipyrine
as hyperhydrating reagents, can expand the adult mousebrain 10-fold in volume17 (the ‘X’ in CUBIC-​X stands
for ‘X-​fold expansion’). The use of the CUBIC-​X protocol allows whole-​brain cell profiling of adult mousebrains and allowed the development of a 3D single-​cellresolution mouse brain atlas (CUBIC-​Atlas)17, whichcontains ~108 cells of an entire brain17,66 (Fig. 2). Suchbrain atlases could become widely used platforms formapping cell activity (for example, the expression of
immediate early genes), cell types (for example, specificsubtypes of neurons and glia cells) and neural connectomes (for example, mapping the neural connections byadeno-​associated virus and/or rabies virus).

Passive CLARITY technique(PACT). A technique that allows
flexible hydrogel formulationand clearing without the need
to use electrophoresis.

physiological locations by covalently linking the molecules to an acryl-​based hydrogel2 (Fig. 3). This uniquehydrogel reinforcement allows complete and uniform
removal of lipids from the tissue while minimizing structural damage and loss of biomolecules (10% protein loss
in CLARITY versus 70% protein loss in formaldehyde-​
only fixed tissues)2. In CLARITY, both electrophoresis-​
driven and simple passive clearing can effectively remove
lipids, which drastically increases the optical transparency
and macromolecule permeability of the hydrogel–tissue
hybrid2. The cleared intact organs can be imaged by fluorescence microscopy without loss of resolution. Small tissues can be readily stained with molecular probes (such as
antibodies); however, the staining of large tissues requires
longer incubation times as in all the tissue-​processing
methods that rely on passive diffusion.
Variants of CLARITY3,9,67,68 have been developed
to increase tissue permeability and probe penetration
by decreasing gel density and the degree of crosslinking, such as the passive CLARITY technique (PACT)3,67.
Weakening gel architecture, however, causes loss oftissue information. To accelerate molecular labellingwithout compromising tissue integrity, a new modeof transporting molecules into tissue was developed,
termed ‘stochastic electrotransport’. Stochastic electroHydrogel-​based tissue clearing
transport generates electrophoretically driven diffusive
To widen the applications of tissue-​clearing technologies, random motion of a broad range of molecules (for examDeisseroth, Chung and colleagues introduced a hydro- ple, antibodies, dyes and detergents) to rapidly delivergel-based tissue-clearing method called ‘cleared lipid-​ them into dense tissue gels and it allows uniform clearing
extracted acryl-​hybridized rigid immunostaining/in situ and staining of intact mouse organs within 2 or 3 days
hybridization-​compatible tissue hydrogel’ (CLARITY), (as opposed to weeks to months in methods that rely on
which secures a broad category of biomolecules at their the passive diffusion of such molecules)24.

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a Hydrophobic method(Optional)
Harsh permeabilization
and antibody staining

Antibody

Dehydration

Delipidation

Key features+ Fast and robust clearing+ Solid, easy to image and store+/– May shrink tissue– May bleach ﬂuorescentproteins or require nanobody
boosting

RI matching

Nanobody
Lipid

Water
(Optional)
Mild permeabilizationand nanobody staining

Water-miscible
solvent

Shrinkage of size

Lipid-miscible
solventb Hydrophilic method
RI matching(Optional)
Decolourization

Delipidation(Optional)
Staining
Original size

Pigment
removalKey features+ High biocompatibility+ High biosafety+/– May expand tissue– May require longer incubation

Expansion and
RI matching

Mild detergents

Hyperhydration
reagents

Mild expansion of sizec Hydrogel-based method
RI matching
Synthetic gel

Monomer
Initiator

Delipidation

Original size

Polyepoxide

Reinforced
tissue gel(Optional)
Staining(Optional)
Synthetic gel
Active or passive
harsh detergents

Key features+ RNA imaging possible+ Multiplexed labelling+ High protein/RNA/DNA
retention+/– May expand tissue– May require longer incubation
or equipment for fast
processing

Expansion
Crosslinked
hydrogel

Expansion of size

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Reviews◀ Fig. 1 | Major tissue-​clearing methods and their key features. a | Hydrophobic methods

demonstrated its distinct utility for super-​resolution
imaging and reconstruction of neural projections. For
better RNA detection, a recent study used 1-ethyl3-(3-dimethylaminopropyl)carbodiimide chemistry
to anchor RNAs to polyacrylamide gel70.
These technological advances have allowed the brain-​wide spatial mapping of biomolecules at subcellular
resolution. However, the harsh chemical processing
used in many tissue-​clearing methods, such as detergent
and organic solvent treatments, sometimes cause tissue
damage. To address this limitation, a method termed‘stabilization to harsh conditions via intramolecular
epoxide linkages to prevent degradation’ (SHIELD) was
developed to preserve the fluorescence of proteins, protein antigenicity, transcripts and tissue architecture inThephysicochemical
properties
oftheacryl-​
organ-​scale transparent tissues. SHIELD uses polyepoxy
Refractive index-​matchingbased gel can be engineered to add functionalities to chemicals to protect the physicochemical properties (forsolution
A solution that is compatible
the tissue–gel hybrid. Boyden and colleagues hybrid- example, protein fluorescence and antigenicity) of biowith the passive CLARITY
ized tissue with an expandable hydrogel and then molecules by forming intramolecular and inter­moleculartechnique, perfusion-​assisted
digested proteins using a proteinase to expand the crosslinks71. To achieve uniform and controlled crosslinkagent release in situ andconstruct isotropically for super-​resolution imag- ing in organ-​scale tissues, polyepoxy chemicals are first
CLARITY. It provides high-​
ing65. Chung and colleagues developed a technique dispersed in a buffer called ‘system-​wide control ofresolution imaging at depth byfurther reducing light scattering
called ‘magnified analysis of proteome’ (MAP) that interaction time and kinetics of chemicals (SWITCH)in both cleared and uncleared
eliminated the protein digestion step described above Off buffer’ that inhibits the fixation reaction25. Once thesamples.
to preserve the 3D proteome of intact organs while crosslinker has been uniformly dispersed, the reaction isallowing isotropic expansion69. In MAP, a highly dense turned on globally by moving the sample to a SWITCH-​
hydrogel is synthesized in situ and then protein com- On buffer that enhances the fixation reaction. This
plexes are dissociated to achieve isotropic expansion simple strategy allowed scalable and uniform preserof the 3D proteome at the organ level. The authors vation of protein fluorescence, transcripts and proteins.
SHIELD-​preserved and delipidated tissue can withstandharsh antibody destaining conditions and therefore
Box 1 | History and recent development of hydrophilic tissue-​clearing methods
allows multiple rounds of staining and imaging of thesame sample. Using SHIELD, the study authors demonBeginning in 1995, the russian physicist tuchin and his colleagues conductedstrated integrated mapping of neural circuits and theirpioneering studies in hydrophilic tissue clearing. they tested a series of chemicals,
downstream targets at single-​cell resolution as well as 3D
including X-​ray contrast agents (for example, trazograph), alcohols (for example,
molecular phenotyping of intact needle biopsy samples
polyethylene glycol, glycerol and propylene glycol), sugars (for example, glucose andwithin only hours25 (Fig. 3). In addition, these technolodextrans) and dimethyl sulfoxide (DMsO) to see if they could increase the refractive
index of a refractive index-​matching medium for biological samples such as humangies have allowed the study of 3D structure of cerebral
sclera145–152. starting at a similar time, Chance and colleagues independently tested theorganoids72 and network-​specific amyloid progression73.
effects of sugars (for example, mannitol, fructose, sucrose and glucose) and alcohols
Hydrogel-​based methods are further applicable to(for example, propanediol and methanol) on the optical properties of biologicalthewhole-​body scale. Gradinaru and colleagues develmaterials such as cells and tissues50,153 when they developed systems to measure theoped perfusion-​assisted agent release in situ (PARS), which
concentration of blood and the saturation of haemoglobin in tissues. Following theserendered rodent bodies transparent, allowing maps ofpioneering studies, Vargas and colleagues applied hydrophilic tissue-​clearing methods
central and peripheral nerves at target organs to be
154,155, and wang andinvolving glycerol, DMsO or glucose to hamster and rat skins
obtained3,68. Gradinaru and colleagues realized that thecolleagues applied glycerol, propylene glycol, ethylene glycol, DMsO and oleic acid to156–158
circulatory system (the vasculature) could be used to. in 2005, Choi and colleagues compared the potentials of various
skin tissuesdeliver clearing agents and labels instead of relying on
reagents — including glycerol, trimethylolpropan, 1,3-butanediol, 1,4-butanediol,
ethylene glycol, 2-methyl-1,3-propanediol, DMsO, linoleic acid, oleic acid and P-0062
passive diffusion, which is prohibitively slow for large— with optically clear human skin159. Yeh and colleagues used sorbitol as a refractive
organs or whole organisms. CLARITY-​based methods2
index-​matching component48, and Hell and colleagues discovered thiodiethanol as an
can be adapted for bones74, and for magnified single-​cellrefractive index-​matching component160. Subsequently, Pavone et al. and Nemoto et al.
visualization
while retaining fluorescent markers (seedeveloped tissue-​clearing protocols10,161 that used thiodiethanol as a key refractive
Supplementary
Fig. 4 in ref.9).
trely on a complete dehydration of the tissue, followed by lipid extraction and refractive
index (RI) matching using organic solvents. Hydrophobic methods are generally fast andcan clear tissues fully. However, some hydrophobic methods can bleach the signal offluorescent proteins rapidly. b | Hydrophilic methods are based on water-​soluble solutions
and are usually associated with higher biosafety and compatibility than hydrophobic
methods. A potent hydrophilic method such as CUBIC starts with decolourization, which
is followed by delipidation, RI matching and (optionally) expansion; some hydrophilic
methods need longer incubation times for intact organs. c | Hydrogel embedding forms
the third major category of tissue clearing. Hydrogel-​based methods use either monomer
and initiator molecules to make a synthetic gel or polyepoxide to make a reinforced
tissue gel. Hydrogel-​based methods can allow retainment of enough RNAs for assays
such as fluorescence in situ hybridization and can be used to expand tissues severalfold
owing to the hydrogel mesh that glues the tissues. Some hydrogel-​based methods needlonger incubation times for intact organs.

index-​matching component. Mason and colleagues developed Clear , which contains
formamide as a key component162. Klein and colleagues developed a tissue-​clearing
protocol for adult mouse brain163 that uses sucrose as a key refractive index-​matchingcomponent50. Jiang et al combined Scale and SeeDB protocols to construct FRUIT164,
which uses urea12 and fructose13,50 as key tissue-​clearing components. a series of X-​ray
contrast agents have frequently been used as refractive index-​matching components
since their introduction145. For example, the X-​ray contrast agent iohexol was used as a
key refractive index-​matching component in a refractive index-​matching solution3,
whereas another X-​ray contrast agent, iodixanol, was used in a switCH study25. see
also the main text for the development and applications of FocusClear, Scale, seeDB
and CuBiC.

Nature Reviews | Neuroscience

Labelling
For CLARITY and other tissue-​clearing methods toreach their full potential, it will be imperative to integrate data from post-​mortem samples with markers offunctionality. We cannot remove the lipids while keeping
the brain alive, but we can at least store a short-​term‘memory of neuronal activity’ via transcriptional orbiochemical changes (for example, Ca2+ influx) that canbe evaluated after death and brain-​wide. The history ofReviewsaClearing and staining

3D imaging

Cell detection

Registration

Analysis and sharing

Brain inagarosegel

Illumination
objective

CUBICAtlas

Detection
objectivebc

1 Cerebral cortex

3D view

2D slice

Sagittal

Whole-brain view

1
50 μm

50 μm

4
2 Hippocampus

Horizontal

2
3

4 Thalamus

Coronal3 Cerebellum

CT22

e

Cluster 1

Cluster 2

Cluster 3

Cluster 4

Section 1

CT16

Section 2

CT10

Coronal sections

CT4

Section 3

MK-801(–)

MK-801(+)

d

www.nature.com/nrn

Reviews◀ Fig. 2 | Whole-​brain single-​cell-resolution imaging and analysis. a | Tissue-​clearing

methods allow whole-​brain profiling of cells. The brain data obtained can be registered inthe 3D single-​cell-resolution mouse brain atlas (CUBIC-​Atlas) and shared by a worldwide
research community via the Internet17. First, tissue clearing (and fluorescent staining if
applicable) is applied to the specimen. Cleared brains are imaged using high-​resolution
light-​sheet microscopy. A graphics processing unit-​based high-​speed cell counting
program identifies all cells from the acquired images, rendering the whole tissue into an
ensemble of cellular points (that is, a point cloud). Then, individual brains are registeredonto the common brain coordinates to allow quantitative comparison and analysis.
Finally, these data should be shared among researchers to allow collaborative and large-​
scale analysis. b | Images of an adult mouse brain obtained by the hydrophilic tissue-​clearing and expansion protocol CUBIC-​X and custom-​made high-​resolution light-​sheet
microscopy17. Nuclear staining using propidium iodide was applied to the cleared brain
tissue. Magnified views of some of the representative brain regions, including cerebral
cortex (view 1), hippocampus (view 2), cerebellum (view 3) and thalamus (view 4),
are presented, along with the whole-​brain overview. Scale bars indicate 50 μm after
normalization of the sample expansion. c | The 3D single-​cell-resolution mouse brain
atlas CUBIC-​Atlas. With use of nuclear staining images such as the ones shown in part b,
all cell nuclei in a whole mouse brain were identified, totalling ~108 cells17,66. The colour ofeach cell represents anatomical annotations of each brain region obtained from the
Allen Brain Atlas. d | The whole-​brain neuronal activity profile with or without the long-​
term administration of a NMDA receptor inhibitor (MK-801) was quantified by imaging
the destabilized fluorescent protein dVenus under the control of the Arc gene promoter
by using the Arc–dVenus transgenic mouse53. Each Arc–dVenus mouse brain at different
circadian times (CT) was mapped onto CUBIC-​Atlas in a probabilistic manner to virtuallyreconstruct the time series17. Arc–dVenus-​expressing cells are shown as dots, with theircolours representing their anatomical areas. e | By clustering analysis of the data shownin part d, four distinct populations of cells were identified, exhibiting different activity
patterns on MK-801 administration. Localization of each cluster is shown in coronal
sections, revealing an inhomogeneous cellular population in the lower and upper
dentate gyrus. Parts b–e were adapted from ref.17, Springer Nature Limited.

Perfusion-​assisted agent
release in situ
(PARS). A method that allows
whole-​rodent clearing andlabelling. It uses the intrinsic
circulatory system (thevasculature) to deliver clearing
agents and labels instead of
relying on passive diffusion,
which can be prohibitively slow
for large organs or wholeorganisms.

Hydrogel-​based tissue
clearing
One of three major tissue-​clearing methods; it crosslinks
biological specimens to makea synthetic hydrogel.

As morphology reconstruction is difficult in data
sets from densely labelled neurons, it would be desirable for the fraction of cells labelled to be easily controlled. Recruiting the power of recently engineered
systemic adeno-​associated viruses that can cross the
blood–brain barrier29,80 has been beneficial in thisrespect, and methods are now available to label individual cells in the brain, peripheral nervous system andother organs with a wide range of hues using genetically encoded fluorescent proteins expressed by multiple viral vectors. Viral-​assisted spectral tracing is ahighly customizable multicolour labelling system with
controlled labelling density that allows detailed studiesof the 3D morphology of cells in intact, thick, cleared
tissue samples (Fig. 3). The unique power of a labelling
approach based on gene delivery is that in addition to it
providing morphology markers, one can introduce state-​
altering genes and evaluate the effects on morphology
of state-​modified and state-​unmodified cells within
the same subject; coupled with tissue clearing, thiswill greatly enhance our understanding of physiology
and pathology81.

Applications in humans
Our knowledge of the human body is primarily macro­scopic and based on classic anatomical methods thatwere established centuries ago and that require the dissection and slicing of individual organs such as the brain.
However, recent studies have showed that tissue clearing and light-​sheet microscopy are starting to revoluactivity in brain circuits can also be reported by changes tionize not only the study of experimental animals but
in protein expression. As discussed earlier, hydropho- also the study of the anatomy of and disease diagnosis
bic, hydrophilic and hydrogel-​based tissue-​clearing meth- in humans. The surge of tissue-​clearing innovation
ods are compatible with antibody staining of intact comes primarily from neurobiologists, who are facingorgans. The legacy of brain activity is also reported by a daunting task: deciphering the human connectome.
changes in RNA transcripts, which can be detected The nervous system is by far the most complex organ,
by single-​molecule fluorescence in situ hybridization with probably hundreds of different cell types formand are indeed retained in PACT-​cleared tissues3,75. ing intricate circuits and networks, the organization
More broadly, preserving the spatial relationships of of which is extremely difficult to understand from 2Dtissues while accessing the transcriptome of selected slices. The combination of tissue clearing and light-​
cells is of crucial interest for many areas of biology and sheet microscopy has already allowed us to image entire
it motivated the development of methods for multicol- mouse brains and spinal cords and to better understandour, multi-​RNA imaging in deep tissue71. With use of the extent of CNS deterioration in some animal models
single-​molecule hybridization chain reaction, tissue of neurological diseases8,26,82,83. The CUBIC approachhydrogel embedding and clearing by PACT and light-​ has been combined with stimulated emission depletion
sheet microscopy, the detection of single-​molecule microscopy to better visualize synaptic contacts onto
mRNAs in approximately millimetre-​thick brain slices the dendritic spines of pyramidal neurons in the mouseis possible76. With rRNA labelling in PACT-​cleared sam- cortex84. The next challenge is to translate this knowlples, researchers mapped the identity and growth rate edge to the analysis of the human nervous system and toof pathogens in sputum samples from individuals with develop applications that will improve the post-​mortem
cystic fibrosis77.
pathological evaluation of brain disorders.
Tissue-​clearing methods have been particularly useCLARITY-​based clearing procedures have allowed
ful in tracing long-​range projections in the CNS and the successful imaging of cortical pyramidal neurons andperipheral nervous system. However, to maximize their interneurons85,86 and cerebellar Purkinje cells and granule
impact, the clearing methods need to be complemented cells87 in healthy human brain samples. Such proceby labelling methods that can, for example, highlight the dures have also permitted imaging of pathologicaldesired circuits with strong, morphology-​filling markers, human brain samples, including visualization of tissue
while preserving their genetic identity and be easy to use from an individual with autism2, α-​synuclein incluand highly customizable (that is, they minimize the need sions, dopaminergic axons and substantia nigra into generate or cross transgenic animals)78. This has been Parkinson disease88, Purkinje cells, mitochondria andnicely applied to study the topography of dopaminergic vasculature in cerebellar ataxia87 and amyloid plaquesin Alzheimer disease85. Other brain-​clearing techniques,projections in the mouse brain79.

Nature Reviews | Neuroscience

ReviewsaAVV–ﬂex–mRuby2–
SYP–EGFP
HO
O
+ H2N
R' R
R

Immunostaining
or FISH

HN-R'
Clearing

SHIELD–MAP expansion
Imaging

PV–Cremouseb

SHIELD
preservation

Light-sheet
imaging

FISH

GPe

IHC

Super-resolution imaging

1-mm section
Delabelling

Expansion

Data integration

mRuby2SYP–eGFPnRTd Cell D

SNr

STN

Axon collateral

c
nRT

GPe

STN

1

SNr

Compilation

3

2
3
PV–mRuby2+

3

2

8

6
7

19
2

GPi

7
GAD1

2

14

10

7
14

8
3
GAD1+; CR–; mRuby2–e

GAD1–; CR–; mRuby2–

Two-component AAV-based
multicolour sparse labelling system

GAD1+; CR–; mRuby2+f

Projection image

GAD1+; CR+; mRuby2–

Presynaptic
boutonTraced morphology

Overlay

Variable-dose
inducer virus
hSyn1

Inducible

High-dose
three-colour
cocktail
Inducible

WPRE

Inducible

WPRE

Inducible

WPRE

www.nature.com/nrn

Reviews◀ Fig. 3 | The sHieLD–MAP and AAV-​based labelling system. SHIELD (system-​wide

control of interaction time and kinetics of chemicals) combined with MAP (magnified
analysis of proteome) allows integrated circuit mapping at single-​cell resolution.
a | The SHIELD–MAP pipeline. SHIELD allows fully integrated multiscale imaging offluorescent protein-​labelled circuits, mRNAs and proteins within the same mouse brain
by simultaneously protecting the molecular and structural information within cleared
tissues. b | The image shows a 3D rendering of fluorescent protein-​labelled neuronal
circuitry of parvalbumin (PV)-positive neurons in the globus pallidus externa (GPe) withan overlaid axon trace of a single labelled neuron. The inset shows example images
from multiround staining and multiscale imaging. Scale bar 50 µm for the insets.
c | Reconstructed axon arborization of the neuron and its downstream targets. Eachcircle represents a neuron. The number of putative axosomatic boutons is marked
inside each circle. The colour of each circle provides molecular details for each neuron.
d | Reconstructed putative axosomatic connectivity for ‘cell D’ (the cell highlighted
in orange in part c). Ramified axons (grey) and enhanced green fluorescent protein(eGFP)-positive presynaptic boutons (blue) are segmented71. Scale bars 20 µm. e | Viral-​assisted spectral tracing (VAST) can be used to label and visualize the 3D morphology
and connectivity of cells in thick, cleared tissue blocks. Here, the schematic shows thetwo-​component VAST labelling system30,80,122. A high dose of a three-​vector cocktail(individually expressing a red, green or blue fluorescent protein) is coadministered alongwith a variable dose of an inducer vector that is required to turn on expression of thethree proteins. To label specific cell populations, the expression of the fluorescentproteins can also be made Cre dependent. f | Projection image of the olfactory bulb
where mitral cells are labelled by VAST (left). Sparse labelling of these cells allows tracingof their dendritic arbors (middle). An overlay of the traces and the projection image isshown in right-​hand image. AAV, adeno-​associated virus; CR, calretinin; FISH,
fluorescence in situ hybridization; hSyn1, human synapsin 1 gene promoter;
IHC, immunohistochemistry; GPi, globus pallidus interna; nRT, nucleus reticularis thalami;SNr, substantia nigra pars reticulata; STN, subthalamic nucleus; WPRE, Woodchuck
hepatitis post-transcriptional regulatory element. Parts a–d are adapted from ref.71,
Springer Nature Limited. Parts e and f are adapted from ref.80, Springer Nature Limited.

such as iDISCO82 and ScaleS49, have also been tried on
Alzheimer disease brain samples; although confirmatory, these studies provided more direct evidence for an
association between microglia and amyloid plaques, withthese techniques also facilitating the detection of diffuse
plaques. CUBIC27 has been used in whole-​brain immuno­
histochemistry of blood vessels in cancer metastasis.
Moreover, GABAergic interneurons were imaged inslices from hemimegalencephalic cortex that was clearedwith 2,2′-thiodiethanol10, and neurons and vessels wereimaged in spinal cord fragments that were cleared with
ACT-​PRESTO (active clarity technique–pressure-​related
efficient and stable transfer of macromolecules into
organs)89. The clearing reagent OPTIClear90 is compatible with lipophilic dyes and has been used to label mossy
fibre axons in adult human cerebellum.
Together, the studies mentioned above provide proof
of concept for and support the feasibility of human brain
clearing, but these and other studies illustrate the current technical limitations of this approach in humans.
First, clearing was efficient on relatively thin brain slices
or blocks of no more than a few hundred cubic micro­
met­res2,25,49,82,85,87,88,90, corresponding at best to about
1/1,500 of the total human brain volume91. Good transparency sometimes required a significant extension of
the clearing time, compared with a few days in rodents10.
Second, formalin-​fixed human brain tissue is highly
auto­fluorescent, is rich in blood and contains lipofuscin-​
type pigments and neuromelanin, which are very difficult to clear. Third, immunostaining was successful only
on samples thinner than a few hundred micrometres, andmany antibodies failed to work following tissue-​clearing
Nature Reviews | Neuroscience

treatment. Using single-​chain vari­able fragments of
conventional antibodies or nanobodies might be a good
option to increase penetration into the brain samples92.
Therefore, although the progress so far is promising,routine post-​mortem 3D imaging of the entire humanbrain is still currently out of reach and remains a tantalizing challenge. Scalability of the clearing method willbe essential, and there is also a need to develop light-​
sheet microscopes with much longer working distances
and a larger field of view. Solvent-​based clearing methods, which can drastically reduce the sample volume,might then be preferred. When optimized, human brain
clearing will be a unique tool for correlating and validating in vivo 3D data obtained by MRI and diffusion
tensor imaging. This will be essential to improve thediagnosis of disease.
Although 3D imaging of transparent adult human
CNS is still in its infancy, tissue clearing has already provided a wealth of new information in the field of humanembryology and allows easy access to 3D cytoarchitecture of millimetre-​sized brain organoids92. Solvent-​based
DISCO clearing methods proved to be perfectly adapted
to the transparentization of human embryos and fetuses
at least up to 3 months of gestation23,93. Moreover, withuse of this approach, whole-​mount immunolabellingof centimetre-​sized human specimens was performed
with a large panel of antibodies23 (Fig. 4), and indicatedhow motor and sensory axons invade the hands, limbs,
head and various organs and revealed an unexpected
heterogeneity and stochasticity of sensory nerve branching pattern23. 3D imaging of DISCO-​cleared humanembryos also demonstrated that neurons secreting
gonadotropin-​releasing hormone migrate in two separate pathways and colonize many brain regions outside
the hypothalamus93. The innervation of the embryonicpancreas was described following the use of CLARITY94.
One can now revisit embryology and envisage building
a comprehensive 3D cartography of human development. Indeed, tissue clearing will be extremely valuable
to the Human Developmental Cell Atlas project95, one
component of the Human Cell Atlas, an international
initiative that aims to identify and map all cells of thehuman body.
Understanding how tumour cells and viruses proliferate and spread within the body are major questions inoncology and virology, and they are attracting greaterattention96 in neuroscience because there are tight interactions between nervous systems and the immune system97. Recent applications of tissue-​clearing methods
have helped follow at an unprecedented resolution thedissemination of HIV-​infected human T lymphocytes inmouse lymphoid organs96 and human cancer cell lines
in whole cleared mice27. These studies have already demon­
strated that the cellular resolution in cleared organs
significantly outperforms what is achieved with classic
tumour detection methods such as bioluminescence. The
cellular composition of cleared human tumour biopsy
samples has also been described94,98–100. Therefore, once
adopted by histopathologists, tissue clearing is expected
to rapidly improve the analysis of tumour cell niches
in their native context, diagnosis, staging of tumours
and the validation of anticancer and antiviral therapies.

Reviews
sCMOS detectors
Scientific-​grade CMOS-​based
cameras that offer a large
sensor area, high pixel count,
low noise, high frame rate, highdynamic range and highquantum yield, all of which arehighly desirable properties for
detectors used in widefield
fluorescence light microscopy.

Light-​sheet microscopy
To take full advantage of the clearing methods discussed
so far and systematically extract structural information
at the subcellular level, clearing methods require microscopes that are capable of rapid, high-​resolution imaging of large volumes. Several powerful imaging methods
have been developed or adapted for this purpose. Some ofthese efforts focused on pushing existing, well-​establishedtechniques, such as two-​photon microscopy, to theirperformance limits. Notably, Chandrashekar, Myers andcolleagues developed an entire imaging framework that
integrates a fast, resonant-​scanning two-​photon microscope with a tissue vibratome to facilitate whole-​brain
imaging at high spatial resolution54. They achieved a spatial resolution of 0.45 µm laterally and 1.33 µm axially at
a data acquisition rate of 1.6 ×106 µm3 per second (Fig. 5),
which allowed the fully automated acquisition of entire
mouse brains in approximately 1 week. To increase thespeed and resolution even further, ongoing efforts aretaking advantage of emerging imaging strategies. Light-​
sheet microscopy101–103 is a key approach to this end, as it
promises particularly striking performance advances inboth spatial and temporal regimes.
The central idea in light-​sheet microscopy is to illuminate the specimen with a thin sheet of laser light fromthe side and acquire an image of the illuminated planewith a camera-​based detection system. In the most common implementations of light-​sheet microscopy, the

a

b

c

d

e

f

Fig. 4 | Towards a 3D developmental human cell atlas. Solvent-​based tissue clearing isparticularly suitable for the analysis of human embryos as it allows visualizationand mapping of immunolabelled cells in large and intact specimens. This provides
unprecedented views of developing organs and human cells. a–d | Peripheral nerves(part a), Müllerian and Wolffian ducts and kidney in the urogenital system (part b),
muscles in the back, arm and head (part c) and the vasculature in the hand (part d) of ahuman embryo at 8 weeks of gestation. e,f | The three sensory nerves of the hand (part e)and lung epithelial tubules (part f) in a fetus at 9.5 weeks of gestation. Parts a,b,d–f are
adapted with permission from ref.23, Elsevier. Part c is adapted with permission from
ref.165, Company of Biologists doi:10.1242/dev.180349.

wide-​field detection arm is oriented at a right angle tothe light-​sheet illumination axis and separate objectives
are used for illumination and detection. Compared withconventional microscopy, this approach offers two primary advantages for the imaging of large tissue volumes.
First, the imaging speed is limited only by the acquisition
rate of the camera and thus exceptionally high data rates
of up to several hundred million voxels per second can beachieved with state-​of-the-​art sCMOS detectors. Second,
photobleaching and phototoxic effects are kept to a minimum, since only the plane in the focus of the detection
system is illuminated with laser light. The amount ofenergy any part of the sample is exposed to is thus constant and independent of the total size of the sample or
imaging volume. By contrast, conventional or confocal
microscopes, which use the same objective for illumination and detection, illuminate also out-​of-focus regionsin the imaging process (above and below the focal
plane). In these latter methods, local light exposure thusincreases linearly with the size of the imaging volume
and can even result in complete bleaching of parts of thespecimen before volume acquisition is complete.
Shortly after its modern reincarnation at the beginning of this century, light-​sheet microscopy was first
applied to the imaging of cleared tissues in 2007 by Dodt
and colleagues14. To rapidly acquire volumetric image
data from fluorescently labelled, excised hippocampi
and even whole embryonic mouse brains, they developed a light-​sheet microscope with low-​magnification,
long-​working-distance detection optics and two opposing illumination arms. This approach offered a spatial resolution on the order of several μm axially andslightly less than 1 μm laterally (Fig. 5). Since this firstreport, many other studies have used light-​sheet imaging for conceptually similar experiments. Initial efforts
relied exclusively on custom-​built systems67,104–108, but
soon thereafter the first commercial products followed,
such as the LaVision BioTec ultramicroscope5,6,8,23,33,42,52.
These microscopes were quickly made compatible with avariety of clearing methods and a wide spectrum of biological specimens, including various organs2,5,7,33, whole
rat brains107, chicken embryos106, human embryos23 andeven entire adult mice3,6,42.
Imaging speed in light-​sheet microscopy is generally limited by the frame rate of the camera, and mostapproaches use similar optics and similar types of static101
or scanned102 light sheets. Therefore, comparable spatial and temporal resolutions have been achieved withmost systems developed and used to date. Importantly,
however, in the past few years, new approaches haveallowed fundamental advances in spatial resolution
in light-​sheet microscopes designed for live imaging
applications. When a relatively large field of view isbeing imaged, the resolution in standard light-​sheet
micro­scopy is comparably low along the direction of
the detection axis — typically several μm at best. This
limitation arises from the relatively thick light sheetsthat are generated by conventional beam-​shaping techniques. By contrast, high, spatially isotropic resolutionof about a few hundred nm can now be achieved using
Bessel beams109 or lattice light-​sheets110, which present
an alternative to conventionally used Gaussian beams
www.nature.com/nrn

Reviewsa10Fig. 5 | resolution and speed of custom and commercial
light-​sheet microscopes. a | Lateral versus axial system
resolution of custom14,105,110–112 (red) and commercial166,167(green) light-​sheet microscopes and state-​of-the-​art two-​
photon microscopes54 (blue). System resolution values168
represent the optical configurations and light-​sheet
properties reported in each study for the demonstration
experiments shown in part b. The quantification disregards
spatial sampling limitations inherent to the choice ofdetector. Resolution values are provided as the full widthat half maximum (FWHM) size of the system point spreadfunction. Lattice light-​sheet (LLS) microscopy uses two
different acquisition modes (dithered light sheet versus
structured illumination (SI)), which affect the speed andresolution. b | Sampling-​limited 3D resolution versus
volume throughput for commercial light-​sheet
microscopes (green) and imaging experiments performed
with custom light-​sheet (black) and two-​photon (blue)2
microscopes. 3D resolution is defined as dlat
dax, where dlat
is the sampling-​limited lateral FWHM size and dax is the
sampling-​limited axial FWHM size of the point spreadfunction. Lateral sampling can be adjusted by changing themagnification of the detection system, which typicallyaffects both volume throughput and effective resolution(for examples, see ×1.26 versus ×12.6 magnification
settings for the LaVision BioTec ultramicroscope or ×16
versus ×32 magnification settings for IsoView). Data points
for custom light-​sheet and two-​photon microscopy refer toimaging of hippocampal dendrites14, Caenorhabditis
elegans development111, cellular dynamics110, zebrafish
nervous system105,112 and mouse brains54. Data points forcommercial microscopes reflect the technicalspecifications provided by the manufacturer165,166.
All techniques included in the plot have been successfully
applied to the imaging of cleared tissues. c | Working
distance of the detection systems for the methods shownin parts a and b. The design by Economo et al.54 does not
require long-​working-distance optics for imaging mousebrains since their microscope is integrated with a tissue
vibratome. Chhetri et al.112 surrounded the specimen withfour identical objectives for illumination and fluorescence
detection, which allows an increase in the bidirectional
working distance by a factor of 2 (compared with thenative working distance of a single objective) in the special
case of imaging transparent, cleared tissues. The maximum
supported specimen size can in principle also be doubled
in other light-​sheet techniques at the expense of temporal
resolution by rotating the specimen by 180° and acquiring
volumetric data from this opposite view. Tomer et al.105
proposed using the majority of the detection objective’s
working distance to introduce a block of high-​
refractive-index material in the detection path. The
effective working distance (distance between the blockand the in-​focus region of the specimen) is thus typicallynot identical to the native working distance of theobjective. For this reason, the latter technique is notincluded in this plot.

and allow the illumination of exceptionally thin sections of a specimen (Fig. 5). Moreover, high resolution
on the order of 300–400 nm in all spatial directions has
also been realized by combining light-​sheet microscopy
with the concept of multiview imaging111–113. The key idea
behind this latter approach is that the directions along
which resolution is low and high, respectively, can be permuted by imaging the same specimen twice from two

orthogonal views. When the complementary information from these views is combined by image processing,
the low axial resolution observed in one given viewof the specimen can thus simply be replaced by the muchgreater lateral resolution offered by the second, perpendicular view of the same specimen. This concept of multi­
view imaging, as it has been implemented in diSPIM111
and IsoView112 light-​sheet microscopy, is a particularly

LLS SI(Chen et al. (2014))

0.2

Axial system resolution (µm)

diSPIM(Wu et al. (2013))
IsoView(Chhetri et al.
(2015))
1.0

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Two-photon(Economo et al.
(2016))
Ultramicroscopy(Dodt et al. (2007))

LaVision BioTec
ultramicroscope
SPED (Tomer et al. (2015))

10.0

1.0

b

LLS dithered(Chen et al. (2014))

Lateral system resolution (µm)

0.1

1011

LaVision BioTec
ultramicroscope (× 1.26)

Volume throughput (µm3 s–1)

1010

SPED
(Tomer et al. (2015))

109

IsoView(Chhetri et al.
((2015)) (× 16)

LaVision BioTec
ultramicroscope(× 12.6)
IsoView (Chhetri et al.
(2015)) (× 32)
Zeiss Lightsheet Z.1
Two-photon(Economo et al. (2016))
diSPIM
Ultramicroscopy(Wu et al. (2013))(Dodt et al. (2007))
LLS dithered(Chen et al. (2014))

108
107
106
105
104
103

102

101

100

10–1

10–2

Sampling-limited 3D resolution (µm3)

cTwo-photon(Economo et al. (2016))
LLS(Chen et al. (2014))
diSPIM(Wu et al. (2013))
Zeiss Lightsheet Z.1
IsoView(Chhetri et al. (2015))
Ultramicroscopy(Dodt et al. (2007))
LaVision BioTec
ultramicroscope0

Nature Reviews | Neuroscience

5

Working distance (mm)

Reviews

Hierarchical data format
version 5(HDF5). A hierarchical data
format and versatile data
model to manage andrepresent extremely large andcomplex data objects.

Compute unified device
architecture(CUDA). A parallel computing
platform and programming
model developed by Nvidia forgeneral computing on its own
graphics processing units.

promising candidate for high-​resolution imaging of large
volumes, since it can be implemented in a way that offers
high spatial resolution over a large field of view at exceptionally high volume acquisition rates112. For example, in
IsoView microscopy, this concept allows an isotropic spatial resolution of 400 nm at a volume throughput of morethan 108 µm3 per second, while offering the long working distance needed for imaging large specimens without
physical sectioning (Fig. 5). Irrespective of the choice of
microscope implementation, the imaging of large samples furthermore benefits from automation strategies andcomputational techniques for adapting the microscopeto the specimen’s optical properties. Although cleared
specimens are exceptionally transparent, remaining spatial variability in the RI or small mismatches in the RIs of
the specimen and the imaging medium can perturb thelight path both in light-​sheet illumination and in fluorescence detection. To ensure optimal overlap between lightsheets and detection focal planes throughout the specimen volume — and thus optimal resolution, contrast
and signal strength — adaptive imaging and autofocusing techniques have been developed104,114–116, which areconceptually compatible with most existing light-​sheet
microscope designs.
When one is imaging very large specimens in the
millimetre or even centimetre range, it is important tonote that even techniques with a relatively large field ofview typically cannot fit the entire specimen within theirnative imaging volume. These limitations arise from afundamental trade-​off between field size and numerical aperture (and thus resolution) in the design of conventional optics, constraints related to the properties ofhigh-​end digital cameras, and from challenges associated with the creation of sufficiently thin light sheets
over a large field of view. Thus, a typical workaroundin existing approaches to high-​resolution imaging oflarge samples, such as lattice light-​sheet microscopy or
IsoView light-​sheet microscopy, is to rely on tiling strategies for imaging specimens as large as entire cleared
and/or expanded nervous systems117,118. This approachlargely decouples specimen size from spatial resolution,
allowing state-​of-the-​art microscopes to produce imagesof optimal quality irrespective of whether the instrument
is used to image single cells or an entire brain (assuming good performance of the tissue-​clearing method).
The two downsides of this approach are the limited data
throughout, owing to the sequential nature of optical tiling, and the need for sophisticated computational methods for image processing (see the next section). Ongoing
research efforts are therefore investigating other possible solutions to these bottlenecks, including the development of axially swept light-​sheet micro­scopy119 andalternative optical strategies for generating light sheets120.
In the future, we expect to see powerful, new implementations of light-​sheet microscopy that combine theselatest breakthroughs in high-​speed, high-​resolution imag­
ing with new large-​aperture, long-​working-distance detection optics developed by several major optics companies
specifically for imaging large cleared tissues. Moreover,further advances in imaging speed will become possible
by pursuing multiplexing strategies that parallelize theimaging process across large tissue volumes.

Data analysis and management
With the advent of powerful microscopes that allow
high-​speed, high-​resolution imaging of large specimens,
there is a need for new computational approaches thattackle downstream challenges in large-​scale data analysis and management. For example, a raw recording of asingle mouse brain obtained with high-​resolution lightmicroscopy typically comprises thousands of individual
3D subvolumes (also referred to as ‘tiles’) and tens ofterabytes of image data. The tiling of image acquisition
is usually necessary owing to the limited field of viewof light microscopes, which, for specimens as large asa mouse brain, can capture only a small portion of thespecimen volume at a time. Thus, translation of either
specimen or optics and sequential acquisition of multiple subvolumes is required to eventually arrive at animage of the entire specimen. To efficiently store, process and extract biological meaning from the resulting
image data, powerful computational methods are needed
to facilitate the following: large-​scale data management;
multitile image registration and fusion; image analysis
and mapping of image data to existing brain atlases; andvisualization and interactive viewing of the 3D image
data. Each of these challenges is the focus of ongoing
efforts in the field, and a range of useful computational
tools are now available.
In the realm of data management, recent efforts have
led to the creation of several file formats designed tofacilitate rapid compression and decompression of large-​
scale light microscopy image data. The KLB file format isa block-​based, lossless compression format that provides
JPEG2000-like compression factors while increasing write
and read speeds fivefold (up to 500 and 1,000 MB per second, respectively)121. The block-​based implementation
furthermore offers fast access to local regions in large multidimensional images. KLB supports integration of othercompression algorithms in the block-​based container andrequires only a CPU, thus allowing effective deployment
on inexpensive, lower-​end computers. By combining
KLB with background masking, a 30-fold to 500-fold
reduction in data size was achieved121. Similarly, theBigDataViewer122 Fiji plugin leverages the infrastructureof the ImageJ ecosystem123, namely the ImgLib2 library124,
to provide seamless access to various block-​based data
formats, including KLB, hierarchical data format version 5(HDF5), Imaris and CATMAID (collaborative annotation
toolkit for massive amounts of image data). In addition,
it provides a powerful caching scheme that ensures thatdata are transferred optimally. When coupled with the
HDF5 container, for example, in the context of multiview reconstructions of light-​sheet microscopy data125,126,
BigDataViewer allows straightforward combination with
arbitrary compression schemes. The reliance on ImgLib2,
which is designed to write efficient image analysis algorithms in Java independent of the data type (8-bit, 16-bit,
RGB and so on), dimensionality (1D to nD) and storage
strategy (memory, local or remote file systems), makes
it possible to deploy through BigDataViewer complex
image processing pipelines on virtually limitless data.
The B3D format offers a complementary approach tohigh-​performance data compression using graphics
processing unit-​based compute unified device architecturewww.nature.com/nrn

Reviews
Adaptive particle
representation
A content-​adaptive
representation of fluorescencemicroscopy images that
overcomes storage andprocessing bottlenecks in big
microscopy image data.

(CUDA) processing127. Although B3D does not use ablock-​based architecture, it offers excellent write/read
speeds on the order of 1,000 MB per second and includes
lossless and lossy compression schemes. A reduction instorage requirements and a speed-​up of computational
processing of image data can also be achieved by using
alternative representations of the information encoded in
an image. For example, the adaptive particle representation
adaptively resamples an image, guided by local information content and using local gain control, and storesthe information as a set of particle cells with associatedintensity values128. The developers of the adaptive particle representation showed that such a representation not
only facilitates data compaction but also has the potential
to speed up subsequent image processing steps, such asfiltering or image segmentation. The challenge will be toadapt existing image processing solutions to this new data
representation paradigm.
Transforming the raw image data of a large, cleared
specimen into a spatially coherent, high-​resolution image
volume that is suitable for data visualization and analysis involves several critical steps. The individual image
tiles need to be precisely aligned and fused, and an additional multiview deconvolution step may be needed if
complementary views of the specimen were recordedwith the light microscope. BigStitcher is scalable andefficient software that facilitates such computations forterabyte-​scale image data of large specimens, such as
entire mammalian brains or entire invertebrate nervous
systems117. This tool leverages the power of ImgLib2 andBigDataViewer and is conveniently available as a plugin
for the widely used image processing package Fiji129.
Once these initial image processing steps are complete
and a high-​quality image volume of the complete specimen has been reconstructed, downstream analyses anddata mining frequently involve mapping and comparison of the image data with an anatomical reference atlas,
image segmentation, annotation of the image data, andinteractive data visualization. For the mapping of image
volumes to each other or to a common anatomical atlas,several tools have been developed130–132, but they currently cannot process very large data volumes. Bigwarp133
allows manual, interactive, landmark-​based deformableimage alignment on arbitrarily large images as it uses
BigDataViewer for visualization and navigation and athin plate spline implemented in Java to build a deformation from point correspondences134. For image segmentation, powerful approaches to image analysis based onmachine learning are quickly gaining momentum. The
iLastik toolkit has been widely adopted as a user-​friendly
framework for interactive image classification and segmentation with an intuitive graphical user interface135.
Familiarity with machine learning algorithms or expertise in image processing is not required to use this toolkit,
as the user communicates with the software simply bylabelling a small subset of the image data, thus visuallyindicating the desired classification scheme. iLastik thenlearns from these user-​provided labels to build classifiers
that are suitable for automated processing of large data
sets. Real-​time feedback furthermore allows the user toiteratively refine segmentation results and update therespective classifiers.

Nature Reviews | Neuroscience

Other software tools support the investigation of
structure–function relationships in the mouse brain. For
example, an open-​source framework consisting of thesoftware packages WholeBrain and OpenBrainMap136
allows integration of anatomical, molecular and functional light-​microscopy image data. This frameworkuses information and definitions from the mouse reference atlas generated by the Allen Institute137,138, and it
includes tools for mapping labelled neurons into a versatile, standardized brain atlas, statistical data analysis andvisualization and sharing of data through an interactive
Web interface. CATMAID139 is another useful software
framework that provides efficient access to large-​scale
electron and light microscopy image data sets as well as
an infrastructure for collaborative annotation of suchdata through a decentralized Web interface. These designprinciples have been enabling not only for applications inneuroscience, and the field of connectomics in particular140, but also for large-​scale projects in other areas of thelife sciences, such as whole-​tissue cell tracking in developmental biology141. Finally, powerful methods designed
to specifically address the need for interactive 3D visualization of large images contribute another essential
facet to the spectrum of open-​source tools for handlinghigh-​resolution images of large, cleared specimens.
BigDataViewer122 and TeraFly142 allow 3D viewing oflarge image stacks and are designed to be very responsive
to user manipulation of the specimen’s coordinate system,
such as translation, rotation and zoom. BigDataViewer issmoothly integrated in the Fiji ecosystem and plays a central role in advanced software suites such as BigStitcher,
BigWarp and MaMuT for manual segmentation andtracking of cells in multiview light-​sheet microscopy
data143. TeraFly is designed in a particularly memory-​
efficient way and provides true 3D rendering capabilities, such as real-​time alpha blending, as well as a ‘Virtual
Finger’ feature that maps user input from the 2D planeof the computer screen to the 3D location in the data set
corresponding to the selected biological structure.

Future objectives
Classical histology is typically performed on only a few
selected thin slices. However, analysis of selected tissue
sections is prone to inevitable biases because one may
miss important biomedical information that is locatedelsewhere. By contrast, 3D histology on intact transparent specimens provides a much greater amount of information and thus a greater level of insight into anatomy
and pathology. Moreover, it can speed up and reducethe cost of histology by several-​thousand-fold comparedwith classical histology approaches.
Although the efficacy of tissue-​clearing methods has
imp­roved considerably, we still lack a rigorous understanding of the physical and chemical principles underly­
ing tissue-​clearing processes. Moreover, we still needmore robust labelling, imaging and data analysis tools
to broaden the applications of tissue-​clearing methods.
In particular, we need the following: rapid and homogeneous protein and RNA labelling methods for wholeorgans of rodents and primates, large human tissues and,even, whole adult rodent and primate bodies; light-​sheet
microscopes that are capable of imaging tissues as large

Reviews
as organs and bodies of adult rodents and primateswith isotropic subcellular resolution (less than 1 μm);
and faster and more accurate algorithms for stitching,atlas registration and structure recognition in data setslarger than tens of terabytes. Furthermore, a standard
atlas of organs and bodies of rodent and primates isrequired to quantitatively compare protein and RNA
expression in different individuals. It would be ideal if
new methods such as multiplexed robust fluorescencein situ hybridization, which allows imaging of dozens ofmRNAs on brain sections, could also be combined withtissue-​clearing methods144. We also expect that tissue-​clearing methods will be combined with other prominent technologies, including single-​cell RNA sequencing
and mass spectrometry, to obtain spatial and temporal
information on the quality and quantity of RNAs andproteins over entire organs and bodies.
In the future, tissue-​clearing methods will significantly impact the drug development process by introducing unbiased readouts of entire organs and bodies intreated versus control groups, thereby assessing whether
drug candidates for given neurological diseases can crossthe blood–brain barrier and whether they can bind todesired targets in the brain and spinal cord at the levelof single cells. Until now, targeting the amyloid peptides
in the plaques by specific antibodies has been a major
endeavour in an effort to develop an effective treatment
for Alzheimer disease, but clinical trials of this approach
have failed to show any efficacy. We speculate that one of
the major contributing reasons for the failure in translational research is the lack of technologies that truly assess
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Acknowledgements
The authors thank E. A. Susaki for help in compiling Table 1
and Supplementary Table 1 for current tissue-​clearing protocols and reagents, K. Matsumoto and Y. Shinohara for drawing the chemical structures in the supplementary information,
T. Mano for contributing to the CUBIC figure, R. Cai and
C. Pan for contributing to the uDISCO figure, S. R. Kumar, G. M.
Coughlin, R. Challis and C. Challis for contributing to the viral-​assisted spectral tracing figure and Y.-G. Park, C. H. Sohn,
T. Ku, V. Lilascharoen and B. K. Lim for contributing to the
SHIELD figure. The authors also gratefully acknowledge grantsupport from Brain/MINDS, the Basic Science and Platform
Technology Program for Innovative Biological Medicine(AMED/MEXT), the Japan Society for the Promotion of
Science (Grant-​in-Aid for Scientific Research (S)) and the
Human Frontier Science Program Research Grant Program(HFSP RGP0019/2018) (H.R.U.), the Munich Cluster for
Systems Neurology (SyNergy), the Fritz Thyssen Stiftung andthe Deutsche Forschungsgemeinschaft (A.E.), the David
and Lucile Packard Foundation (Packard Fellowship), theMcKnight Foundation, the US National Institutes of Health(NIH) (1-DP2-ES027992; U01MH117072), the NCSOFT
Cultural Foundation and the Koreaan Institute for Basic
Science (IBS-​R026-D1) (K.C.), the NIH BRAIN Initiative, theNIH Office of the Director and the US National Science
Foundation (NeuroNex) (V.G.), LABEX LIFESENSES (reference
ANR-10-LABX-65) managed by the French Agence Nationalde la Recherche within the Investissements d’Avenir programme under reference ANR-11-IDEX-0004-02 (A.C.), theEuropean Regional Development Fund in the framework of
the Czech IT4Innovations National Supercomputing Center
path to exascale project, project number CZ.02.1.01/0.0/0.0/
16_013/0001791, within the Czech Research, Development
and Education Operational Programme (P.T.) and the Howard
Hughes Medical Institute (P.J.K.).

of its content, wrote the article and reviewed and edited themanuscript before submission.

Competing interests
H.R.U. is a co-​inventor on a patent applications covering the
CUBIC reagents (PCT/JP2014/070618 (pending), patentapplicant is RIKEN, other co-​inventors are E. A. Susaki and
K. Tainaka; PCT/JP2017/016410 (pending), patent applicant
is RIKEN, other co-​inventors are K. Tainaka and T. Murakami)
and a co-​founder of CUBICStars Inc. A.E. is the applicant andthe inventor on a patent application for technologies relating
to vDISCO clearing (PCT/EP2018/063098 (pending)). K.C.
is the inventor or a co-​inventor on patents and patent applications for CLARITY (PCT/US2013/031066 (active), patentapplicant is Stanford University, co-​inventor is K. A. Deisseroth),stochastic electrotransport (PCT/US2015/024297 (active),patent applicant is MIT), SHIELD (PCT/US2016/064538(pending), applicant is Massachusetts Institute of Technology(MIT), other co-​inventors are E. Murray and J. H. Cho),
SWITCH (PCT/US2016/064538 (pending), applicant is MIT,other co-​inventors are E. Murray and J. H. Cho) and MAP(PCT/US2017/030285 (pending), applicant is MIT, other
co-​inventors are T. Ku, J. M. Swaney and J. Y. Park) anda co-​founder of LifeCanvas Technologies. V.G. is a co-​inventor
o n p a t e n t a p p l i c a t i o n s c ove r i n g PACT a n d PA R S(PCT/US2014/048985 (active), applicant is California
Institute of Technology, other co-​inventors are V. Gradinaru
and B. Yang) and adeno-​associated virus (US14/485,024(active), applicant is California Institute of Technology,
other co-​inventors are B. E. Deverman, P. H. Patterson and
V. Gradinaru) technologies. P.J.K. is an inventor or co-​inventor
on patents and patent applications covering multiview imaging (US14/049,470 (active), applicant is Howard Hughes
Medical Institute) and adaptive light-​s heet microscopy(PCT/US2017/038970 (pending), applicant is Howard Hughes
Medical Institute, other co-​inventors are R. K. Chhetri and
L. A. Royer). P.T. and A.C. declare no competing interests.

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Supplementary information

Author contributions

Supplementary information is available for this paper athttps://doi.org/10.1038/s41583-019-0250-1.

H.R.U., A.E., K.C., V.G., A.C. and P.J.K. researched data for thearticle. All the authors contributed to substantial discussion© Springer Nature Limited 2020