Tissue clearing and its applications in neuroscience

Hiroki R. Ueda

1,2,*

Ali Ertürk

3,4,5

Kwanghun Chung

6,7,8,9,10,11,12

Viviana Gradinaru

Alain Chédotal

Pavel Tomancak

15,16

Philipp J. Keller

Department of Systems Pharmacology, University of Tokyo, Tokyo, Japan

Laboratory for Synthetic Biology, RIKEN BDR, Suita, Japan

Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig–

Maximilian University of Munich, Munich, Germany

Institute of Tissue Engineering and Regenerative Medicine, Helmholtz Zentrum München,

Neuherberg, Germany

Munich Cluster for Systems Neurology (SyNergy), Munich, Germany

Institute for Medical Engineering and Science, Massachusetts Institute of Technology,

Cambridge, MA, USA

Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge,

MA, USA

Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA,

USA

Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge,

MA, USA

uedah-tky@umin.ac.jp.

Author contributions

H.R.U., A.E., K.C., V.G., A.C. and P.J.K. researched data for the article. All the authors contributed to substantial discussion of its

content, wrote the article and reviewed and edited the manuscript before submission.

Competing interests

H.R.U. is a co-inventor on a patent applications covering the CUBIC reagents (PCT/JP2014/070618 (pending), patent applicant 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 and the 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), patent applicant 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) and a co-founder of LifeCanvas Technologies.

V.G. is a co-inventor on patent applications covering PACT and PARS (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-sheet 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.

Peer review information

Nature Reviews Neuroscience

thanks S. Gentleman and the other, anonymous, reviewer(s) for their contribution to the peer review of

this work.

Supplementary information

Supplementary information is available for this paper at

https://doi.org/10.1038/s41583-019-0250-1

HHS Public Access

Author manuscript

Nat Rev Neurosci

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Published in final edited form as:

Nat Rev Neurosci

. 2020 February ; 21(2): 61–79. doi:10.1038/s41583-019-0250-1.

Author Manuscript

Eli & Edythe Broad Institute of MIT and Harvard, Cambridge, MA, USA

Center for NanoMedicine, Institute for Basic Science, Seoul, Republic of Korea

Graduate Program of Nano Biomedical Engineering, Yonsei-IBS Institute, Yonsei University,

Seoul, Republic of Korea

Division of Biology and Biological Engineering, California Institute of Technology, Pasadena,

CA, USA

Institut de la Vision, Sorbonne Université, INSERM, CNRS, Paris, France

Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany

IT4Innovations, Technical University of Ostrava, Ostrava, Czech Republic

Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA

Abstract

State-of-the-art tissue-clearing methods provide subcellular-level optical access to intact tissues

from individual organs and even to some entire mammals. When combined with light-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 of magnitude. 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 cellular and subcellular

information on complex mammalian bodies and large human specimens at an accelerated pace.

The rapid generation of terabyte-scale imaging data furthermore creates a high 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.

Histological techniques have been the standard procedure for investigating tissues for several

decades; however, a complete understanding of biological mechanisms in health and disease

requires an unbiased exploration of the whole organism, not just selected parts of tissue.

This need is 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

even some entire organisms. Indeed, a century-old approach at rendering tissues transparent

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 microscopy

–

Three major tissue-clearing approaches are currently available: hydrophobic, hydrophilic

and hydrogel-based methods

(FIG. 1); hydrophobic tissue-clearing and hydrophilic

tissue-clearing methods are also referred as ‘solvent’ and ‘aqueous’ tissue-clearing methods,

respectively. In general, these tissue-clearing methods, especially in highly efficient

protocols, remove lipids (delipidation), pigments (decolourization) and calcium phosphate

(decalcification) and aim to match the refractive indices (refractive index (RI) matching) of

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the specimens and the imaging media by reaching an almost complete level of transparency

for intact organs and even entire adult rodent bodies. Although hydrophobic methods usually

shrink the tissues and thereby allow the imaging of larger samples, hydrophilic and

hydrogel-based methods, if the 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 that an organ can act as a polymer gel even

in the absence of exogenous polymers

, which was originally predicted by Tanaka and

colleagues via a bottom-up approach

. If an organ acts as a polymer gel, the RI of an

organ (‘polymer gel’) can be described by both the RI and the volume of its components

(‘monomers’) according to a theoretical formula called the Lorentz–Lorenz relation

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 RI of the medium and

this results in minimization of light scattering. In addition, the minimization of light

absorption, which also contributes to transparency of the specimens, can be achieved by

decolourization (removing the pigments) of an organ. Recent comprehensive chemical

profiling of hydrophilic tissue-clearing reagents provides a better understanding of the

chemical principles of how each chemical functional group can contribute to tissue-clearing

processes

In parallel to forging a better understanding of the principles 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 the

development of ex vivo deep-tissue labelling methods for whole mouse brain

–

and

whole mouse body

and of in vivo systematic adeno-associated virus-based labelling of

CNS and peripheral nervous system cells

. Once tissues are labelled and rendered

transparent, light-sheet microscopy provides rapid 3D whole-brain imaging at subcellular

resolution

. Some forms of light-sheet microscopy can already generate isotropic and high-

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 microscopy

Furthermore, the development of powerful machine-learning algorithms will be essential to

optimize the analysis of such large data sets, especially for image segmentation. The

convergence of these diverse disciplines around the tissue-clearing methods will pave the

way to unbiased 3D histological assessments, which should 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 microscopy and data analysis and

management — as well as applications of tissue clearing in neuroscience, especially in

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human studies. We then discuss the challenges and opportunities relating to the integration

of these technologies to generate unbiased insights into human physiology and pathology.

Hydrophobic tissue clearing

Hydrophobic tissue-clearing approaches involve the use of organic solvents and often

provide complete transparency of an intact specimen quickly

. 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 days

. Ethyl cinnamate has also been used in hydrophobic

tissue clearing instead of organic solvents

. 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 circuits

, inflammation

, stem cells

and cancer cells

–

, and in

unsectioned rodent organs and human biopsy specimens

. DISCO-based methods have also

been combined with deep-tissue immunolabelling approaches to study rodent embryos

human embryos

, cancer biopsy specimens

, adult mouse brains

and, more recently,

whole mouse bodies (discussed later)

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 subsequent organic solvent immersion step to extract most of the lipids and

increase the RI to match the average RI of biological tissues (the RI for both organic

solvents and shrunk brain tissue is ~1.56)

. In most of the DISCO approaches (except

immunolabelling-enabled DISCO+ (iDISCO+))

, dehydration leads to a marked shrinkage

of the specimen

. 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 microscopy

. The shrinkage of individual

organs, especially CNS tissue, was isotropic; the bones also shrunk isotropically but to a

lesser degree

. Through the use of uDISCO, the researchers visualized neuronal

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 signal

. Although clearing and

imaging increasingly larger samples are valuable steps in the unbiased 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 uncovered

. iDISCO involves pretreatment of

the specimen with solutions containing H

and methanol to permeabilize mouse brain, a

process that may also purge most of the epitopes for the antibodies

. In the future,

development of new deep-tissue labelling approaches with full epitope preservation will be

critical to broaden the applications of hydrophobic tissue-clearing methods.

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To scale up the immunolabelling to whole adult mouse, Cai et al. recently introduced

vDISCO (the ‘v’ refers to the variable domain of heavy-chain antibodies; that is,

nanobodies), using high-pressure delivery of nanobodies for complete immunolabelling of

the whole body with bright Atto dyes in the far-red region to overcome low signal intensity

and autofluorescence of many tissues in the blue–green region

. 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 cleared mouse 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 in physiological and pathological states

. As vDISCO enormously

amplifies signal contrast even for dissected organs, in the future it will provide high-quality

ground-truth data for machine learning-based algorithms aiming to map transgenically

labelled mammalian brains

Hydrophilic tissue clearing

Hydrophilic tissue-clearing methods use water-soluble reagents for tissue clearing. Although

the tissue-clearing performance of hydrophilic tissue-clearing methods was sometimes

inferior to that of hydrophobic tissue-clearing methods, the former have obvious advantages,

including high levels of biocompatibility, biosafety and preservation of protein function.

Hydrophilic reagents usually form hydrogen bonds with components of tissues such as

proteins as well as surrounding water molecules, which can help to preserve the 3D structure

of tissue components and, thereby, the signal of fluorescent proteins. Moreover, hydrophilic

reagents can be dissolved in water at a high concentration and can be used as an RI-

matching medium to provide a high RI in the medium. Hydrophilic tissue-clearing methods

also have a long history of about a quarter of a century (since 1995; BOX 1).

One of the first applications of hydrophilic tissue clearing to neuroscience was by Chiang

and colleagues. They developed FocusClear, which includes an X-ray contrast agent (for

example, diatrizoate acid) and a detergent (for example, Tween 20), and applied it to

imaging of a whole cockroach brain

. More recently, Miyawaki and colleagues

developed Sca

, which contains urea as a key tissue-clearing component, and can expand

biological samples — for example, whole mouse embryos (embryonic day 13.5), infant

mouse brains (postnatal day 15) and adult mouse brain slices (7–13 weeks old) — by

hyperhydration

and thereby reduce their RIs. In contrast to most hydrophobic tissue-

clearing regents, Scale can more efficiently preserve fluorescent proteins

. This method has

allowed imaging of yellow fluorescent protein-labelled

Thy1

-expressing neurons in infant

and adult mice, green fluorescent protein-labelled neural stem cells in the adult mouse

hippocampus and cell-cycle status of mouse embryos via detection of the Fucci-S/G

/M and

Fucci-G

markers

. The researchers further developed Sca

e by combining urea with

sorbitol

to develop Sca

, which can be applied to adult, old and diseased mouse brain

hemispheres. Imai and colleagues developed the See Deep Brain (SeeDB) protocol

, which

uses fructose

as its key RI-matching component, and used it to trace neural circuits in

mouse olfactory bulbs because it does not expand or shrink biological samples and therefore

preserves their morphology. They further refined this method by using an X-ray contrast

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agent with a higher RI, leading to the development of the SeeDB2 protocol, which has been

applied to super-resolution imaging of neural circuits in mouse brains

Ueda and colleagues took a system-level approach to identifying potent hydrophilic tissue-

clearing reagents by comprehensive chemical profiling. They discovered a series of amino

alcohols that have both delipidation

and decolonization

capabilities and then developed a

series of clear, unobstructed brain or body imaging cocktails and computational analysis

(CUBIC) reagents for both delipidation and decolourization

. Profiling more than 1,600

hydrophilic chemicals, they also discovered a series of aromatic amides that can be used as

potent RI-matching reagents

. By combination of the most advanced delipidation

(CUBIC-L, where ‘L’ stands for ‘delipidation’) and RI-matching (CUBIC-R+, where ‘R’

stands for ‘RI matching’ and ‘+’ stands for a basic condition by adding an amino alcohol,

butyldiethanolamine) reagents, the tissue-clearing performance of hydrophilic methods is

now similar to or can even exceed that of hydrophobic methods without losing its

advantages of biocompatibility, biosafety and preservation of protein function (for example,

fluorescence)

. CUBIC has been applied to whole-brain imaging of immediate early gene

expression induced by light exposure

and drug administration

, whole-brain imaging of

cancer metastasis

, whole-brain imaging of individual neurons

and taste-sensing circuits

in mouse brains

, and 3D brain imaging of glutaminergic synaptic connections

hypothalamic neural subtypes

, layer-specific astrocyte morphology

and visual

projections of retinal ganglion cells

. CUBIC has been also applied to 3D imaging of other

organs, including haematopoietic stem cells in bone marrow

, carcinoma in the lung

and

single-cell lineage tracing in the mammary gland

and development of a heart

. Therefore,

the peripheral nervous systems in these organs can be analysed in detail.

Delipidation in some hydrophilic tissue-clearing methods creates space for large substances

such as antibodies to penetrate more rapidly and deeply into tissues, allowing 3D

immunohistochemistry in large samples. CUBIC has been successfully used to achieve 3D

immunohistochemistry for adult mouse brain, heart, lung, stomach and intestine

Recently, CUBIC-L–CUBIC-R was combined with whole-brain 3D immunohistochemistry

to image blood vessels in entire mouse brains

. CUBIC was also applied to 3D

immunohistochemistry and imaging of neurons expressing vesicular acetylcholine

transporter that innervated islets in a pancreas

. In addition to the enhanced penetration of

antibodies owing to delipidation, weakening the interaction between an antibody and a tissue

seems to accelerate the penetration of an antibody into a tissue. For example, the AbSca

protocol, which uses the protein denaturant urea in antibody staining, achieved 3D

immunohistochemistry of an adult mouse hemisphere

. An original Sca

e protocol uses

urea for tissue clearing not for antibody staining. AbSca

e was used with brain-wide

immunohistochemistry to image amyloid-

plaques, a hallmark pathological feature of

Alzheimer disease, in mice

Hydrophilic methods can be also extended to expansion microscopy

. For example, the

CUBIC-X protocol, which involves the use of an imidazole and an antipyrine as

hyperhydrating reagents, can expand the adult mouse brain 10-fold in volume

(the ‘X’ in

CUBIC-X stands for ‘X-fold expansion). The use of the CUBIC-X protocol allows whole-

brain cell profiling of adult mouse brains and allowed the development of a 3D single-cell-

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resolution mouse brain atlas (CUBIC-Atlas)

, which contains ~10

cells of an entire

brain

(FIG. 2). Such brain atlases could become widely used platforms for mapping cell

activity (for example, the expression of immediate early genes), cell types (for example,

specific subtypes of neurons and glia cells) and neural connectomes (for example, mapping

the neural connections by adeno-associated virus and/or rabies virus).

Hydrogel-based tissue clearing

To widen the applications of tissue-clearing technologies, Deisseroth, Chung and colleagues

introduced a hydrogel-based tissue-clearing method called ‘cleared lipid-extracted acryl-

hybridized rigid immunostaining/in situ hybridization-compatible tissue hydrogel’

(CLARITY), which secures a broad category of biomolecules at their physiological

locations by covalently linking the molecules to an acryl-based hydrogel

(FIG. 3). This

unique hydrogel 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)

. 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 hybrid

. 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 CLARITY

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)

. Weakening gel architecture, however, causes loss of tissue

information. To accelerate molecular labelling without compromising tissue integrity, a new

mode of transporting molecules into tissue was developed, termed ‘stochastic

electrotransport’. Stochastic electrotransport generates electrophoretically driven diffusive

random motion of a broad range of molecules (for example, antibodies, dyes and detergents)

to rapidly deliver them into dense tissue gels and it allows uniform clearing and staining of

intact mouse organs within 2 or 3 days (as opposed to weeks to months in methods that rely

on the passive diffusion of such molecules)

The physicochemical properties of the acryl-based gel can be engineered to add

functionalities to the tissue–gel hybrid. Boyden and colleagues hybridized tissue with an

expandable hydrogel and then digested proteins using a proteinase to expand the construct

isotropically for super-resolution imaging

. Chung and colleagues developed a technique

called ‘magnified analysis of proteome’ (MAP) that eliminated the protein digestion step

described above to preserve the 3D proteome of intact organs while allowing isotropic

expansion

. In MAP, a highly dense hydrogel is synthesized in situ and then protein

complexes are dissociated to achieve isotropic expansion of the 3D proteome at the organ

level. The authors demonstrated its distinct utility for super-resolution imaging and

reconstruction of neural projections. For better RNA detection, a recent study used 1-

ethyl-3-(3-dimethylaminopropyl)carbodiimide chemistry to anchor RNAs to polyacrylamide

gel

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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

in organ-scale transparent tissues. SHIELD uses polyepoxy chemicals to protect the

physicochemical properties (for example, protein fluorescence and antigenicity) of

biomolecules by forming intramolecular and intermolecular crosslinks

. To achieve

uniform and controlled crosslinking in organ-scale tissues, polyepoxy chemicals are first

dispersed in a buffer called ‘system-wide control of interaction time and kinetics of

chemicals (SWITCH)-Off buffer’ that inhibits the fixation reaction

. Once the crosslinker

has been uniformly dispersed, the reaction is turned on globally by moving the sample to a

SWITCH-On buffer that enhances the fixation reaction. This simple strategy allowed

scalable and uniform preservation of protein fluorescence, transcripts and proteins.

SHIELD-preserved and delipidated tissue can withstand harsh antibody destaining

conditions and therefore allows multiple rounds of staining and imaging of the same sample.

Using SHIELD, the study authors demonstrated integrated mapping of neural circuits and

their downstream targets at single-cell resolution as well as 3D molecular phenotyping of

intact needle biopsy samples within only hours

(FIG. 3). In addition, these technologies

have allowed the study of 3D structure of cerebral organoids

and network-specific amyloid

progression

Hydrogel-based methods are further applicable to the whole-body scale. Gradinaru and

colleagues developed perfusion-assisted agent release in situ (PARS), which rendered rodent

bodies transparent, allowing maps of central and peripheral nerves at target organs to be

obtained

. Gradinaru and colleagues realized that the circulatory system (the vasculature)

could be used to deliver clearing agents and labels instead of relying on passive diffusion,

which is prohibitively slow for large organs or whole organisms. CLARITY-based methods

can be adapted for bones

, and for magnified single-cell visualization while retaining

fluorescent markers (see Supplementary Fig. 4 in REF.

Labelling

For CLARITY and other tissue-clearing methods to reach their full potential, it will be

imperative to integrate data from post-mortem samples with markers of functionality. 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 or biochemical changes (for example, Ca

influx) that can be evaluated after death and brain-wide. The history of activity in brain

circuits can also be reported by changes in protein expression. As discussed earlier,

hydrophobic, hydrophilic and hydrogel-based tissue-clearing methods are compatible with

antibody staining of intact organs. The legacy of brain activity is also reported by changes in

RNA transcripts, which can be detected by single-molecule fluorescence in situ

hybridization and are indeed retained in PACT-cleared tissues

. More broadly, preserving

the spatial relationships of tissues while accessing the transcriptome of selected cells is of

crucial interest for many areas of biology and it motivated the development of methods for

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multicolour, multi-RNA imaging in deep tissue

. With use of single-molecule hybridization

chain reaction, tissue hydrogel embedding and clearing by PACT and light-sheet

microscopy, the detection of single-molecule mRNAs in approximately millimetre-thick

brain slices is possible

. With rRNA labelling in PACT-cleared samples, researchers

mapped the identity and growth rate of pathogens in sputum samples from individuals with

cystic fibrosis

Tissue-clearing methods have been particularly useful in tracing long-range projections in

the CNS and the peripheral nervous system. However, to maximize their impact, the clearing

methods need to be complemented by labelling methods that can, for example, highlight the

desired circuits with strong, morphology-filling markers, while preserving their genetic

identity and be easy to use and highly customizable (that is, they minimize the need to

generate or cross transgenic animals)

. This has been nicely applied to study the

topography of dopaminergic projections in the mouse brain

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 barrier

has been beneficial in this respect, and methods are now available to label

individual cells in the brain, peripheral nervous system and other organs with a wide range

of hues using genetically encoded fluorescent proteins expressed by multiple viral vectors.

Viral-assisted spectral tracing is a highly customizable multicolour labelling system with

controlled labelling density that allows detailed studies of 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, this will greatly

enhance our understanding of physiology and pathology

Applications in humans

Our knowledge of the human body is primarily macroscopic and based on classic anatomical

methods that were 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 revolutionize not only the study of experimental

animals but also the study of the anatomy of and disease diagnosis in humans. The surge of

tissue-clearing innovation comes primarily from neurobiologists, who are facing a daunting

task: deciphering the human connectome. The nervous system is by far the most complex

organ, with probably hundreds of different cell types forming intricate circuits and networks,

the organization of which is extremely difficult to understand from 2D slices. The

combination of tissue clearing and light-sheet microscopy has already allowed us to image

entire mouse brains and spinal cords and to better understand the extent of CNS

deterioration in some animal models of neurological diseases

. The CUBIC approach

has been combined with stimulated emission depletion microscopy to better visualize

synaptic contacts onto the dendritic spines of pyramidal neurons in the mouse cortex

. The

next challenge is to translate this knowledge to the analysis of the human nervous system

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and to develop applications that will improve the post-mortem pathological evaluation of

brain disorders.

CLARITY-based clearing procedures have allowed successful imaging of cortical pyramidal

neurons and interneurons

and cerebellar Purkinje cells and granule cells

in healthy

human brain samples. Such procedures have also permitted imaging of pathological human

brain samples, including visualization of tissue from an individual with autism

-synuclein

inclusions, dopaminergic axons and substantia nigra in Parkinson disease

, Purkinje cells,

mitochondria and vasculature in cerebellar ataxia

and amyloid plaques in Alzheimer

disease

. Other brain-clearing techniques, such as iDISCO

and Sca

, 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, with these

techniques also facilitating the detection of diffuse plaques. CUBIC

has been used in

whole-brain immunohistochemistry of blood vessels in cancer metastasis. Moreover,

GABAergic interneurons were imaged in slices from hemimegalencephalic cortex that was

cleared with 2,2

′

-thiodiethanol

, and neurons and vessels were imaged in spinal cord

fragments that were cleared with ACT-PRESTO (active clarity technique–pressure-related

efficient and stable transfer of macromolecules into organs)

. The clearing reagent

OPTIClear

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 micrometres

corresponding at best to about 1/1,500 of the total human brain volume

. Good

transparency sometimes required a significant extension of the clearing time, compared with

a few days in rodents

. 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, and many antibodies failed to work following

tissue-clearing treatment. Using single-chain variable fragments of conventional antibodies

or nanobodies might be a good option to increase penetration into the brain samples

Therefore, although the progress so far is promising, routine post-mortem 3D imaging of the

entire human brain is still currently out of reach and remains a tantalizing challenge.

Scalability of the clearing method will be 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 the diagnosis 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 human embryology and

allows easy access to 3D cytoarchitecture of millimetre-sized brain organoids

. Solvent-

based DISCO clearing methods proved to be perfectly adapted to the transparentization of

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human embryos and fetuses at least up to 3 months of gestation

. Moreover, with use of

this approach, whole-mount immunolabelling of centimetre-sized human specimens was

performed with a large panel of antibodies

(FIG. 4), and indicated how motor and sensory

axons invade the hands, limbs, head and various organs and revealed an unexpected

heterogeneity and stochasticity of sensory nerve branching pattern

. 3D imaging of

DISCO-cleared human embryos also demonstrated that neurons secreting gonadotropin-

releasing hormone migrate in two separate pathways and colonize many brain regions

outside the hypothalamus

. The innervation of the embryonic pancreas was described

following the use of CLARITY

. 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 project

, one component of the

Human Cell Atlas, an international initiative that aims to identify and map all cells of the

human body.

Understanding how tumour cells and viruses proliferate and spread within the body are

major questions in oncology and virology, and they are attracting greater attention

neuroscience because there are tight interactions between nervous systems and the immune

system

. Recent applications of tissue-clearing methods have helped follow at an

unprecedented resolution the dissemination of HIV-infected human T lymphocytes in mouse

lymphoid organs

and human cancer cell lines in whole cleared mice

. These studies have

already demonstrated 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

described

–

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.

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 of these efforts focused on

pushing existing, well-established techniques, such as two-photon microscopy, to their

performance limits. Notably, Chandrashekar, Myers and colleagues 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 resolution

. They achieved

a spatial resolution of 0.45 μm laterally and 1.33 μm axially at a data acquisition rate of 1.6

×10

μm

per second (FIG. 5), which allowed the fully automated acquisition of entire

mouse brains in approximately 1 week. To increase the speed and resolution even further,

ongoing efforts are taking advantage of emerging imaging strategies. Light-sheet

microscopy

101

–

103

is a key approach to this end, as it promises particularly striking

performance advances in both spatial and temporal regimes.

The central idea in light-sheet microscopy is to illuminate the specimen with a thin sheet of

laser light from the side and acquire an image of the illuminated plane with a camera-based

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detection system. In the most common implementations of light-sheet microscopy, the wide-

field detection arm is oriented at a right angle to the light-sheet illumination axis and

separate objectives are used for illumination and detection. Compared with conventional

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 be

achieved 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 of energy 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 regions in the imaging process

(above and below the focal plane). In these latter methods, local light exposure thus

increases linearly with the size of the imaging volume and can even result in complete

bleaching of parts of the specimen 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

colleagues

. 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 and slightly less than 1 μm laterally (FIG. 5). Since this first report, many

other studies have used light-sheet imaging for conceptually similar experiments. Initial

efforts relied exclusively on custom-built systems

104

–

108

, but soon thereafter the first

commercial products followed, such as the LaVision BioTec ultramicroscope

These microscopes were quickly made compatible with a variety of clearing methods and a

wide spectrum of biological specimens, including various organs

, whole rat brains

107

chicken embryos

106

, human embryos

and even entire adult mice

Imaging speed in light-sheet microscopy is generally limited by the frame rate of the

camera, and most approaches use similar optics and similar types of static

101

or scanned

102

light sheets. Therefore, comparable spatial and temporal resolutions have been achieved with

most systems developed and used to date. Importantly, however, in the past few years, new

approaches have allowed fundamental advances in spatial resolution in light-sheet

microscopes designed for live imaging applications. When a relatively large field of view is

being 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 sheets that are generated by conventional beam-shaping techniques. By

contrast, high, spatially isotropic resolution of about a few hundred nm can now be achieved

using Bessel beams

109

or lattice light-sheets

110

, which present an alternative to

conventionally used Gaussian beams 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 imaging

111

–

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

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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 view of the specimen can thus simply be replaced by the much greater lateral

resolution offered by the second, perpendicular view of the same specimen. This concept

ofmultiview imaging, as it has been implemented in diSPIM

111

and IsoView

112

light-sheet

microscopy, is a particularly 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 rates

112

. For example, in IsoView

microscopy, this concept allows an isotropic spatial resolution of 400 nm at a volume

throughput of more than 10

μm

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 and computational techniques for adapting the microscope to 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 the light path both in light-sheet illumination and in

fluorescence detection. To ensure optimal overlap between light sheets and detection focal

planes throughout the specimen volume — and thus optimal resolution, contrast and signal

strength — adaptive imaging and autofocusing techniques have been developed

104

114

–

116

which are conceptually 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 to note that even techniques with a relatively large field of view typically cannot

fit the entire specimen within their native imaging volume. These limitations arise from a

fundamental trade-off between field size and numerical aperture (and thus resolution) in the

design of conventional optics, constraints related to the properties of high-end digital

cameras, and from challenges associated with the creation of sufficiently thin light sheets

over a large field of view. Thus, a typical workaround in existing approaches to high-

resolution imaging of large 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 systems

117

118

. This approach largely decouples specimen

size from spatial resolution, allowing state-of-the-art microscopes to produce images of

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 microscopy

119

and

alternative optical strategies for generating light sheets

120

In the future, we expect to see powerful, new implementations of light-sheet microscopy that

combine these latest breakthroughs in high-speed, high-resolution imaging 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 the

imaging process across large tissue volumes.

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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 that tackle downstream

challenges in large-scale data analysis and management. For example, a raw recording of a

single mouse brain obtained with high-resolution light microscopy typically comprises

thousands of individual 3D subvolumes (also referred to as ‘tiles’) and tens of terabytes of

image data. The tiling of image acquisition is usually necessary owing to the limited field of

view of light microscopes, which, for specimens as large as a mouse brain, can capture only

a small portion of the specimen volume at a time. Thus, translation of either specimen or

optics and sequential acquisition of multiple subvolumes is required to eventually arrive at

an image 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; and visualization 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 to facilitate rapid compression and decompression of large-scale light

microscopy image data. The KLB file format is a 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 other compression algorithms in the block-based

container and requires 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 achieved

121

. Similarly, the BigDataViewer

122

Fiji plugin

leverages the infrastructure of the ImageJ ecosystem

123

, namely the ImgLib2 library

124

, 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 that data are transferred optimally. When coupled with the HDF5 container, for

example, in the context of multiview reconstructions of light-sheet microscopy data

125

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

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

D format offers a complementary approach to high-performance data compression using

graphics processing unit-based compute unified device architecture (CUDA) processing

127

Although B

D does not use a block-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 in storage requirements and a speed-up of computational processing of image

data can also be achieved by using alternative representations of the information encoded in

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an image. For example, the adaptive particle representation adaptively resamples an image,

guided by local information content and using local gain control, and stores the information

as a set of particle cells with associated intensity values

128

. 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 as filtering or

image segmentation. The challenge will be to adapt 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 recorded with the light microscope. BigStitcher is scalable and efficient

software that facilitates such computations for terabyte-scale image data of large specimens,

such as entire mammalian brains or entire invertebrate nervous systems

117

. This tool

leverages the power of ImgLib2 and BigDataViewer and is conveniently available as a

plugin for the widely used image processing package Fiji

129

. Once these initial image

processing steps are complete and a high-quality image volume of the complete specimen

has been reconstructed, downstream analyses and data mining frequently involve mapping

and comparison of the image data with an anatomical reference atlas, image segmentation,

annotation of the image data, and interactive data visualization. For the mapping of image

volumes to each other or to a common anatomical atlas, several tools have been

developed

130

–

132

, but they currently cannot process very large data volumes. Bigwarp

133

allows manual, interactive, landmark-based deformable image alignment on arbitrarily large

images as it uses BigDataViewer for visualization and navigation and a thin plate spline

implemented in Java to build a deformation from point correspondences

134

. For image

segmentation, powerful approaches to image analysis based on machine 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 interface

135

. 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 by labelling a small subset of the image data, thus visually indicating the desired

classification scheme. iLastik then learns 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 to iteratively refine segmentation results and update the respective classifiers.

Other software tools support the investigation of structure–function relationships in the

mouse brain. For example, an open-source framework consisting of the software packages

WholeBrain and OpenBrainMap

136

allows integration of anatomical, molecular and

functional light-microscopy image data. This framework uses information and definitions

from the mouse reference atlas generated by the Allen Institute

137

138

, and it includes tools

for mapping labelled neurons into a versatile, standardized brain atlas, statistical data

analysis and visualization and sharing of data through an interactive Web interface.

CATMAID

139

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 such data through a decentralized Web interface. These design

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principles have been enabling not only for applications in neuroscience, and the field of

connectomics in particular

140

, but also for large-scale projects in other areas of the life

sciences, such as whole-tissue cell tracking in developmental biology

141

. 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 handling

high-resolution images of large, cleared specimens. BigDataViewer

122

and TeraFly

142

allow

3D viewing of large 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 is smoothly integrated in the Fiji ecosystem and plays a central role in

advanced software suites such as BigStitcher, BigWarp and MaMuT for manual

segmentation and tracking of cells in multiview light-sheet microscopy data

143

. 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 plane of 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 located elsewhere. 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 reduce

the cost of histology by several-thousand-fold compared with 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 underlying tissue-clearing

processes. Moreover, we still need more 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 whole organs 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 as organs and bodies of adult

rodents and primates with isotropic subcellular resolution (less than 1 μm); and faster and

more accurate algorithms for stitching, atlas registration and structure recognition in data

sets larger than tens of terabytes. Furthermore, a standard atlas of organs and bodies of

rodent and primates is required to quantitatively compare protein and RNA expression in

different individuals. It would be ideal if new methods such as multiplexed robust

fluorescence in situ hybridization, which allows imaging of dozens of mRNAs on brain

sections, could also be combined with tissue-clearing methods

144

. 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 and proteins 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 in treated versus control

groups, thereby assessing whether drug candidates for given neurological diseases can cross

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the blood-brain barrier and whether they can bind to desired targets in the brain and spinal

cord at the level of 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 the targeting of such drugs at single-cell

resolution in the whole body of preclinical animal models. Therefore, monitoring

biodistributions and actions of drugs at single-cell resolution will be critical to

comprehensively understand underlying cellular mechanisms and thereby design effective

drugs. In this regard, nanotechnology will also greatly facilitate targeted drug delivery in

many fields of biomedical science, including neuroscience. The effects of nanoparticle-

based drugs are currently assessed by coarse imaging methods such as bioluminescence,

which can detect the signal only when they are of millimetre size, significantly jeopardizing

the use of nanoparticles to conduct nanoscale functions. Tissue-clearing methods will be a

key technology in the development of drug-carrying nanoparticles, as such methods will

allow identification of these agents in the whole rodent and primate bodies at single-cell

resolution.

3D histopathology provided by tissue-clearing methods will also significantly scale up the

investigation of human tissues in both preclinical and clinical arenas. For example, labelling

and imaging whole human biopsy samples from patients with cancer (several thousand times

larger than in classical histology) at single-cell resolution will provide much faster and more

accurate diagnosis and staging of the tumours. Finally, clearing, labelling and imaging of

adult human tissues on the order of centimetres will be one of the primary technologies to

reveal the cellular structure of whole organs and eventually map circuits in whole human

brains.

Supplementary Material

Refer to Web version on PubMed Central for supplementary material.

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 grant support 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

and the Deutsche Forschungsgemeinschaft (A.E.), the David and Lucile Packard Foundation (Packard Fellowship),

the McKnight 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, the NIH 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 National de la Recherche within the

Investissements d’Avenir programme under reference ANR-11-IDEX-0004-02 (A.C.), the European 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.).

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