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
13
,
Alain Chédotal
14
,
Pavel Tomancak
15,16
,
Philipp J. Keller
17
1
Department of Systems Pharmacology, University of Tokyo, Tokyo, Japan
2
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 Zentrum München,
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
9
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
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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.
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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
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
1
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
2
–
14
.
Three major tissue-clearing approaches are currently available: hydrophobic, hydrophilic
and hydrogel-based methods
15
,
16
(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
17
, which was originally predicted by Tanaka and
colleagues via a bottom-up approach
18
,
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 volume of its components
(‘monomers’) according to a theoretical formula called the Lorentz–Lorenz relation
20
,
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 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
22
.
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
4
,
23
–
27
and
whole mouse body
6
,
27
,
28
and of in vivo systematic adeno-associated virus-based labelling of
CNS and peripheral nervous system cells
29
,
30
. Once tissues are labelled and rendered
transparent, light-sheet microscopy provides rapid 3D whole-brain imaging at subcellular
resolution
31
. 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
31
.
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
32
. 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
7
,
33
. Ethyl cinnamate has also been used in hydrophobic
tissue clearing instead of organic solvents
11
. 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
7
,
33
, inflammation
34
, stem cells
35
,
36
and cancer cells
37
–
39
, and in
unsectioned rodent organs and human biopsy specimens
40
. DISCO-based methods have also
been combined with deep-tissue immunolabelling approaches to study rodent embryos
4
,
8
,
human embryos
23
, cancer biopsy specimens
40
,
41
, adult mouse brains
4
,
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 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)
15
. In most of the DISCO approaches (except
immunolabelling-enabled DISCO+ (iDISCO+))
34
, dehydration leads to a marked shrinkage
of the specimen
32
. 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
42
. The shrinkage of individual
organs, especially CNS tissue, was isotropic; the bones also shrunk isotropically but to a
lesser degree
42
. 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
28
. 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
26
. iDISCO involves pretreatment of
the specimen with solutions containing H
2
O
2
and methanol to permeabilize mouse brain, a
process that may also purge most of the epitopes for the antibodies
26
. 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
28
. 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
28
,
43
. 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
11
,
44
.
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
45
,
46
. More recently, Miyawaki and colleagues
developed Sca
l
e
12
, 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
47
and thereby reduce their RIs. In contrast to most hydrophobic tissue-
clearing regents, Scale can more efficiently preserve fluorescent proteins
12
. 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
2
/M and
Fucci-G
1
/G
0
markers
12
. The researchers further developed Sca
l
e by combining urea with
sorbitol
48
to develop Sca
l
eS
49
, which can be applied to adult, old and diseased mouse brain
hemispheres. Imai and colleagues developed the See Deep Brain (SeeDB) protocol
13
, which
uses fructose
50
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
51
.
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
5
and decolonization
6
capabilities and then developed a
series of clear, unobstructed brain or body imaging cocktails and computational analysis
(CUBIC) reagents for both delipidation and decolourization
5
,
27
,
52
. Profiling more than 1,600
hydrophilic chemicals, they also discovered a series of aromatic amides that can be used as
potent RI-matching reagents
22
,
27
. 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,
N
-
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)
22
. CUBIC has been applied to whole-brain imaging of immediate early gene
expression induced by light exposure
5
and drug administration
53
, whole-brain imaging of
cancer metastasis
27
, whole-brain imaging of individual neurons
54
and taste-sensing circuits
in mouse brains
55
, and 3D brain imaging of glutaminergic synaptic connections
56
,
hypothalamic neural subtypes
57
, layer-specific astrocyte morphology
58
and visual
projections of retinal ganglion cells
59
. CUBIC has been also applied to 3D imaging of other
organs, including haematopoietic stem cells in bone marrow
60
, carcinoma in the lung
61
and
single-cell lineage tracing in the mammary gland
62
and development of a heart
63
. 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
5
,
6
,
22
,
27
,
52
.
Recently, CUBIC-L–CUBIC-R was combined with whole-brain 3D immunohistochemistry
to image blood vessels in entire mouse brains
27
. CUBIC was also applied to 3D
immunohistochemistry and imaging of neurons expressing vesicular acetylcholine
transporter that innervated islets in a pancreas
64
. 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
l
e
protocol, which uses the protein denaturant urea in antibody staining, achieved 3D
immunohistochemistry of an adult mouse hemisphere
49
. An original Sca
l
e protocol uses
urea for tissue clearing not for antibody staining. AbSca
l
e was used with brain-wide
immunohistochemistry to image amyloid-
β
plaques, a hallmark pathological feature of
Alzheimer disease, in mice
27
,
49
.
Hydrophilic methods can be also extended to expansion microscopy
65
. 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
17
(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)
17
, which contains ~10
8
cells of an entire
brain
17
,
66
(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
2
(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)
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 hybrid
2
. 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
3
,
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 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)
24
.
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
65
. 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
69
. 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
70
.
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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
71
. 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
25
. 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
25
(FIG. 3). In addition, these technologies
have allowed the study of 3D structure of cerebral organoids
72
and network-specific amyloid
progression
73
.
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
3
,
68
. 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
2
can be adapted for bones
74
, and for magnified single-cell visualization while retaining
fluorescent markers (see Supplementary Fig. 4 in REF.
9
).
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
2+
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
3
,
75
. 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
71
. 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
76
. With rRNA labelling in PACT-cleared samples, researchers
mapped the identity and growth rate of pathogens in sputum samples from individuals with
cystic fibrosis
77
.
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)
78
. This has been nicely applied to study the
topography of dopaminergic projections in the mouse brain
79
.
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
29
,
80
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
81
.
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
8
,
26
,
82
,
83
. 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
84
. 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
85
,
86
and cerebellar Purkinje cells and granule cells
87
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
2
,
α
-synuclein
inclusions, dopaminergic axons and substantia nigra in Parkinson disease
88
, Purkinje cells,
mitochondria and vasculature in cerebellar ataxia
87
and amyloid plaques in Alzheimer
disease
85
. Other brain-clearing techniques, such as iDISCO
82
and Sca
l
eS
49
, 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
27
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
10
, 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)
89
. The clearing reagent
OPTIClear
90
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
2
,
25
,
49
,
82
,
85
,
87
,
88
,
90
,
corresponding at best to about 1/1,500 of the total human brain volume
91
. Good
transparency sometimes required a significant extension of the clearing time, compared with
a few days in rodents
10
. 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
92
.
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
92
. 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
23
,
93
. Moreover, with use of
this approach, whole-mount immunolabelling of centimetre-sized human specimens was
performed with a large panel of antibodies
23
(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
23
. 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
93
. The innervation of the embryonic pancreas was described
following the use of CLARITY
94
. 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
95
, 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
96
in
neuroscience because there are tight interactions between nervous systems and the immune
system
97
. 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
96
and human cancer cell lines in whole cleared mice
27
. 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
94
,
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.
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
54
. They achieved
a spatial resolution of 0.45 μm laterally and 1.33 μm axially at a data acquisition rate of 1.6
×10
6
μm
3
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
14
. 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
67
,
104
–
108
, but soon thereafter the first
commercial products followed, such as the LaVision BioTec ultramicroscope
5
,
6
,
8
,
23
,
33
,
42
,
52
.
These microscopes were quickly made compatible with a variety of clearing methods and a
wide spectrum of biological specimens, including various organs
2
,
5
,
7
,
33
, whole rat brains
107
,
chicken embryos
106
, human embryos
23
and even entire adult mice
3
,
6
,
42
.
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
8
μm
3
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
n
D)
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
B
3
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
3
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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