CELL SCIENCE AT A GLANCE
SUBJECT COLLECTION: IMAGING
The cell biologist’s guide to super-resolution microscopy
Guillaume Jacquemet1,2,*, Alexandre F. Carisey3,*, Hellyeh Hamidi1, Ricardo Henriques4,5,* and
Christophe Leterrier6,*
ABSTRACT
Fluorescence microscopy has become a ubiquitous method to
observe the location of specific molecular components within cells.
However, the resolution of light microscopy is limited by the laws of
diffraction to a few hundred nanometers, blurring most cellular details.
Over the last two decades, several techniques – grouped under
the ‘super-resolution microscopy’ moniker – have been designed to
bypass this limitation, revealing the cellular organization down to the
nanoscale. The number and variety of these techniques have steadily
increased, to the point that it has become difficult for cell biologists
and seasoned microscopists alike to identify the specific technique
best suited to their needs. Available techniques include image
processing strategies that generate super-resolved images, optical
imaging schemes that overcome the diffraction limit and sample
manipulations that expand the size of the biological sample. In this
Cell Science at a Glance article and the accompanying poster, we
provide key pointers to help users navigate through the various super-
resolution methods by briefly summarizing the principles behind each
technique, highlighting both critical strengths and weaknesses, as
well as providing example images.
KEY WORDS: Super-resolution microscopy, Live-cell imaging,
Labeling, Sample preparation
Introduction
Fluorescence microscopy allows biologists to selectively label
and observe cellular components with high sensitivity in fixed
and living samples (Combs and Shroff, 2017; Kremers et al.,
2010; Masters, 2010), and is one of the leading technologies used
to drive discoveries in life sciences. However, classical light
1Turku Bioscience Centre, University of Turku and Åbo Akademi University,
FI-20520 Turku, Finland. 2Faculty of Science and Engineering, Cell Biology,
Åbo Akademi University, 20520 Turku, Finland. 3William T. Shearer Center for
Human Immunobiology, Baylor College of Medicine and Texas Children’s Hospital,
1102 Bates Street, Houston 77030 TX, USA. 4University College London,
London WC1E 6BT, UK. 5The Francis Crick Institute, London NW1 1AT, UK. 6Aix
Marseille Université, CNRS, INP UMR7051, NeuroCyto, Marseille 13015, France.
*Authors for correspondence (christophe.leterrier@univ-amu.fr;
r.henriques@ucl.ac.uk; Alexandre.Carisey@bcm.edu; guillaume.jacquemet@abo.fi)
G.J., 0000-0002-9286-920X; A.F.C., 0000-0003-1326-2205; H.H., 0000-0003-
4841-8927; R.H., 0000-0002-2043-5234; C.L., 0000-0002-2957-2032
1
© 2020. Published by The Company of Biologists Ltd | Journal of Cell Science (2020) 133, jcs240713. doi:10.1242/jcs.240713
Journal of Cell Science

----!@#$NewPage!@#$----
microscopy is limited by the diffraction of light, which precludes
the visualization of details below ∼200 nm (Vangindertael et al.,
2018). Since the early 2000s, super-resolution microscopy
(SRM)
approaches
have
been
devised
that
bypass
this
diffraction limit to resolve biological objects down to a few
tens of nanometers (Sahl et al., 2017; Schermelleh et al., 2019;
Sigal et al., 2018). A variety of techniques are now available as
either commercial or open, do-it-yourself, instruments and
software (Thorley et al., 2014), and cell biologists interested in
applying SRM to their question of interest can feel lost among the
number of options available. In particular, the different SRM
techniques vary greatly in the resolution they attain, the
constraints they place on sample type, and their general
versatility and ease of use (Bachmann et al., 2015; Wegel et al.,
2016). In this Cell Science at a Glance article and accompanying
poster, we focus on SRM methods that we consider accessible to
researchers, by being commercially available or that are easy to
implement by non-experts. In Box 1, we highlight the general
elements to be considered when preparing samples for SRM. We
briefly explain the principles behind each SRM technique, then
summarize
its
performance
for
relevant
cellular
imaging
parameters using an indicative scoring system represented by radar
plots on the poster (using a ‘1 to 5’ scale of less adapted to more
adapted; see ‘Techniques and Principles’ on poster). The advice we
propose is based on our collective experience of applying various
SRM techniques to different cell biological questions (Almada et al.,
2019; Carisey et al., 2018; Gustafsson et al., 2016; Jacquemet et al.,
2019; Stubb et al., 2019; Vassilopoulos et al., 2019). The aim of this
Cell Science at a Glance article and the accompanying poster is to
provide a short overview that will foster further reading or discussion
with imaging facility specialists and help put these powerful
techniques in the hands of diverse users.
Fluctuation-based super resolution microscopy
Principle
When excited with continuous light, the emitted light of every
fluorophore randomly varies over time. This is due to transitions
between the non-fluorescent states of the fluorophore (van de Linde
and Sauer, 2014) or their interactions with the surrounding
environment (Bagshaw and Cherny, 2006). After capturing these
oscillations over a sequence of tens to hundreds of images,
algorithms such as super-resolution optical fluctuation imaging
(SOFI) (Dertinger et al., 2009) and super-resolution radial
fluctuations (SRRF) (Gustafsson et al., 2016) use the temporal
correlations in these oscillations to predict the presence and location
of fluorophores at improved resolution (see ‘Techniques and
Principles’ and ‘Representative Images’ on poster). The accuracy
and resolution will considerably improve when samples are
decorated with highly fluctuating probes, such as reversibly
photoswitchable fluorescent proteins (Zhang et al., 2016) or
organic dyes in a photoswitch-inducing buffer (van de Linde
et al., 2011).
Versatility – 4/5
Fluctuation-based methods are purely analytical approaches that
are compatible with most microscopes and do not depend on
hardware modifications. They are thus easy to implement, and
free software versions exist, but these require knowledge to
properly tune the processing (Dedecker et al., 2012; Gustafsson
et al., 2016). Moreover, the need to acquire a fast sequence of
images (typically hundreds) can make multidimensional protocols
complex.
XY resolution – 2/5
The resolution improvement considerably depends on the capacity
of the algorithm used to detect fluctuations in fluorophores. It is thus
Box 1. Sample preparation for optimized imaging
Experimental design
• The same pitfalls as in conventional microscopy apply to SRM and
must be known to the experimentalist to avoid bias during image
acquisition, analysis and data validation (Jost and Waters, 2019).
• For multicolor imaging, online tools should be used to predict the
compatibility between the different fluorophores or fluorescent proteins
(Lambert, 2019) and the microscope to be used.
• The speed of acquisition needs to be taken into consideration when
choosing the most appropriate SRM technique. Slow acquisition
strategies may lead to motion blur and/or artifacts during live imaging.
• Sample size should be computed ahead of the experiment using a
pilot dataset.
• Consistency is key for preparation of fluorescent samples for SRM, but
this does not eliminate the need for biological and technical replicates.
Sample labeling
• Fixation methods need to be carefully optimized as each fixative has
specific advantages, drawbacks and dye compatibility. Live-cell
imaging can be used to validate that no fixation artifacts have been
introduced (Pereira et al., 2019).
• Reagents should be carefully validated, including antibodies and
dyes.
• Aldehyde-based fixatives should be quenched using inert amine-
containing molecules (glycine or sodium borohydride).
• The nonspecific adsorption of fluorescent compounds should be
reduced by adding detergent and sera in the washing buffer.
Ultimately, the ratio between signal of interest and background is
more important than the overall brightness of the staining.
• Thick samples can be cleared to remove light-scattering and light-
absorbing components of a tissue (Kolesova et al., 2016; Qi et al.,
2019), allowing access to deeper structures.
Choice of vessel
• Glass coverslips should be used rather than plastic, as plastics
introduce optical aberrations and are not compatible with immersion
oils for long-term imaging.
• The thickness of the glass must match the expected value for the
microscope
used;
170 µm-thick
cover
glass
(#1.5H)
is
often
recommended. To ensure reliable performance, high quality, high
tolerance cover glass should be chosen.
• Positioning of the sample must be designed to reduce the distance
between the objective and the region of interest. This will help improve
the resolution by reducing light dampening and distortion.
• Most techniques can accommodate a slide–cover-glass sandwich
format, but some require access to the sample. In these cases, glass-
bottom imaging dishes and multiwell slides are recommended.
Choice of the imaging medium
• For fixed samples, multiple mounting media are available, all designed
to bring the refractive index (RI) of your sample close to the RI of the
cover glass (RI=1.52).
• Curing mounting media allow for longer conservation and have an RI
that is better for image quality, but lead to a slight shrinkage of cellular
structures.
• Non-curing mounting media may be preferred due to convenience of
use, and preservation of sample structure, despite the compromise
in RI.
• For live-cell imaging, the priority of the medium is to ensure the survival
of the biological sample.
• Imaging media need to be nutrient rich and provide a pH-controlled
environment. For instance, adding 25 mM HEPES in imaging media
may help to ensure cell survival when CO2 access is non-optimal
during imaging (Frigault et al., 2009).
• Consider removing any non-essential compounds from the medium
that are autofluorescent (e.g. Phenol Red, flavins, nicotinamide
adenine dinucleotide and lipofuscin) (Surre et al., 2018).
2
CELL SCIENCE AT A GLANCE
Journal of Cell Science (2020) 133, jcs240713. doi:10.1242/jcs.240713
Journal of Cell Science

----!@#$NewPage!@#$----
difficult to predict the resolution that will be achieved prior to actual
imaging, but an enhancement of two- to three-fold can typically be
expected (Culley et al., 2018a).
Z resolution – 1/5
Resolution improvement in the Z-axis cannot yet be directly
achieved by the algorithms, but specialized multiplane-imaging
setups are being developed to tackle this limitation.
Thick-sample friendliness – 4/5
The resolution and quality of the data reconstructed will depend on
the density of fluorophores captured in each image. As such, optical
sectioning techniques, such as total internal reflection fluorescence
(TIRF), confocal or light-sheet considerably improve the data
generated and enable these methods to super-resolve samples with a
thickness of tens of micrometers.
Live-cell friendliness – 4/5
Fluctuation-based SRM is one of the least phototoxic methods in
existence;
enhanced
resolution
can
be
achieved
using
an
illumination intensity that is similar to conventional fluorescence
imaging (mW/cm2 magnitude). Imaging from minutes to hours
without significant photobleaching or apparent light-induced cell
stress has been demonstrated (Movie 1, Culley et al., 2018a), but
slow speed can be an issue with moving samples (see below).
Image fidelity – 3/5
Intensity in the generated images weakly relates to the local
stoichiometry of fluorophores. While images will represent the
structure (with some degree of unwanted defects), care needs to be
taken when employing further analysis routines that take pixel
brightness into account (Culley et al., 2018b).
Multicolor – 4/5
Multicolor imaging is possible and straightforward since fluctuation
imaging is compatible with most fluorophores.
Temporal resolution – 2/5
A temporal stream of a few hundred images needs to be collected to
capture a single channel, generally taking one to five seconds.
Availability
Free implementations can be found for both SOFI and SRRF
(Dedecker et al., 2012; Gustafsson et al., 2016), and SRRF has been
implemented as onboard processing in some electron multiplying
charge coupled device (EMCCD) cameras (Cooper et al., 2019).
Pixel reassignment super resolution microscopy
Principle
In this technique, single or multiple focal spots are used to scan the
sample, as in confocal microscopy. However, the fluorescence
signal is not captured by a single-point detector, such as a
photomultiplier tube, but by an array detector (camera, concentric
detector array or single-photon detector array) (Ströhl and
Kaminski, 2016; Wu and Shroff, 2018). The signal detected in
each element of the detector array is reassigned in space to achieve a
smaller point-spread-function and thus higher resolution. After data
processing, a 1.4-fold improvement in resolution can be achieved
laterally with a minor axial improvement (Vangindertael et al.,
2018). A number of variants exist for pixel reassignment, such as
image scanning microscopy (ISM) (Müller and Enderlein, 2010),
Zeiss Airyscan (Huff, 2015), rescan confocal microscopy (RCM)
(Luca
et
al.,
2013)
and
multifocal
structured
illumination
microscopy (MSIM) (York et al., 2012). Pixel reassignment has
been adapted to spinning disc and swept-field confocal microscopy
(Azuma and Kei, 2015; Hayashi and Okada, 2015; York et al.,
2013). There is an expectation that the majority of confocal systems
will, in the future, integrate these concepts to improve resolution.
Versatility – 4/5
A lateral resolution improvement of 1.4-fold can be achieved purely
through optics without the need for an additional analytical step.
This means researchers can observe the resolution-enhanced sample
in real-time. MSIM implementations (York et al., 2012), however,
require the digital analysis of the images being collected.
XY resolution – 2/5
The 1.4-fold lateral-resolution improvement can be increased to
two-fold through additional and careful deconvolution of the
collected data.
Z resolution – 2/5
Minor (∼25%) axial resolution improvement has been demonstrated
(York et al., 2013), and further improvement necessitates additional
deconvolution of the collected data.
Thick-sample friendliness – 4/5
Reassignment systems are expected to generate images of similar
fidelity to those collected in laser scanning confocal or spinning disc
microscopy for thick samples.
Live-cell friendliness – 5/5
Pixel reassignment allows single-shot super-resolution imaging
at low-illumination (Movie 2). Reassignment implies additional
magnification, which requires the use of sensitive detection systems.
Image fidelity – 4/5
The pure-optical resolution enhancement does not generate
additional image defects, such as those commonly occurring in
super-resolution techniques that require data processing to generate
a final image. However, the additional use of deconvolution can
lead to image artefacts.
Multicolor – 5/5
The technique does not rely on specific fluorophores and can be
used for multicolor imaging. It has the same multicolor capacity as
laser scanning confocal or spinning disc microscopy.
Temporal resolution – 5/5
Owing to the mostly optical nature of the resolution improvement
process, high-speed imaging is possible and expected to be similar
to that of laser scanning confocal or spinning disc microscopes.
Availability
Commercial offerings include dedicated setups, and add-ons to a
widefield or spinning disc microscope. Most commercial systems
are relatively recent, so they are not yet widely available in imaging
facilities.
Structured illumination microscopy
Principle
In the structured illumination microscopy (SIM) technique, the
sample is illuminated using a patterned light. For each focus plane,
multiple images are taken using a different pattern and are then
3
CELL SCIENCE AT A GLANCE
Journal of Cell Science (2020) 133, jcs240713. doi:10.1242/jcs.240713
Journal of Cell Science

----!@#$NewPage!@#$----
combined by a computer algorithm to reconstruct a super-resolved
image (Schermelleh et al., 2019). The most common patterns are
parallel lines, but hexagonal or even random patterns can be used
(Heintzmann and Huser, 2017). In the case of parallel lines, multiple
shifted and rotated patterns are obtained using a grid or a spatial light
modulator: 9 images for a 2D image (Gustafsson, 2000) and 15
images per plane for a 3D volume (Gustafsson et al., 2008).
Versatility – 4/5
SIM is generally easy to use, suitable for a wide variety of biological
samples and is compatible with most fluorophores with the
conditions that they are relatively resistant to photobleaching and
non-blinking (Demmerle et al., 2017). SIM performances are
affected by both the sample and the imaging conditions, and
therefore dedicated training and optimizations are required to
achieve good results.
XY and Z resolution – 3/5
Linear SIM typically doubles the spatial resolution in all three
dimensions (Gustafsson et al., 2008). Non-linear SIM approaches
that bypass this resolution use fluorophore saturation, but are not yet
commercially available (Li et al., 2015; Rego et al., 2012).
Thick-sample friendliness – 3/5
The SIM design is often based on widefield microscopy. In this
case, SIM performance can be strongly affected by the sample
thickness, as well as by the presence of out of focus light. ‘Grazing
incidence’ illumination (Guo et al., 2018) or TIRF (Kner et al.,
2009; Li et al., 2015) can be used to image objects that are close to
the coverslip with a better signal. In addition, lattice light-sheet
(Chen et al., 2014) or slit-confocal (Schropp et al., 2017)
arrangements can be combined with SIM to image deeper into cells.
Live-cell friendliness – 4/5
SIM is commonly used for live-cell imaging (Burnette et al., 2014;
Carisey et al., 2018; Fiolka et al., 2012). Traditional 3D SIM often
requires the acquisition of hundreds of images per time point
(depending on the volume imaged) and can be phototoxic. Therefore,
SIM-TIRF or grazing-incidence SIM are better suited for live-cell
imaging (Movie 3). In addition, improvement in reconstruction
algorithms are enabling the imaging of biological samples using low
laser power over hour-long time lapses (Huang et al., 2018).
Image fidelity – 4/5
When using linear SIM, the fluorescence intensities in reconstructed
images are not directly transposed from the intensities of the raw
images (Heintzmann and Huser, 2017). The intensity of the
reconstructed image typically correlates well with the brightness of
the original structures, but SIM can attenuate constant signals and is
thus not suited to image and quantify diffuse (cytoplasmic) staining.
Multicolor – 4/5
As SIM is compatible with most fluorophores, its setup can typically
accommodate three to four different color channels (Jacquemet
et al., 2019; Vietri et al., 2015). However, optimal resolution
requires a precise tuning of the oil refractive index that can be
slightly different for distinct channels (Demmerle et al., 2017).
Temporal resolution – 4/5
The temporal resolution of SIM widely depends on the setup used.
Traditional 3D SIM requires the acquisition of hundreds of images
per time point (depending on the volume imaged) and is relatively
slow (several seconds per time point) (Fiolka et al., 2012). Other
SIM implementations such as SIM-TIRF offer a faster acquisition
speed of 11 Hz (Kner et al., 2009). Recent developments such as
instant TIRF-SIM allow acquisitions as fast as 100 Hz, with the use
of a sliding window over the varying patterns (Guo et al., 2018;
Huang et al., 2018).
Availability
SIM microscopy requires a dedicated microscope and therefore can be
expensive to implement. Multiple commercial systems are available.
Stimulated emission depletion microscopy
Principle
Stimulated emission depletion (STED) microscopy is based on laser
scanning confocal microscopy where a second hollow beam (a donut-
shaped STED beam) is overlaid on top of the excitation laser beam
(Heine et al., 2017; Hell and Wichmann, 1994; Klar et al., 2000). In
the zone of overlap between the two beams, the STED beam depletes
the fluorophores before fluorescence takes place, thinning the
emission area to a sub-diffraction sized spot. The donut-shaped
STED beam is obtained using a vortex phase mask and the most
recent systems are combining femtosecond lasers and detector time
gating to improve the signal-to-noise ratio and significantly reduce
the background (Hernández et al., 2015; Moffitt et al., 2011).
Versatility – 3/5
STED microscopy is very similar to, and has the same sample
requirement as, classical confocal microscopy. STED does not need
specific buffers, but special care should be taken during sample
preparation (e.g. fixation) to ensure maximal preservation of cellular
structures at the STED resolution (Blom and Widengren, 2017).
XY resolution – 4/5
In practice, in biological systems, the best resolution achieved are
∼40 nm in living tissue and cells (Bottanelli et al., 2016; Willig et al.,
2006), and 20 nm in fixed samples (Göttfert et al., 2013), and atypical
∼60 nm resolution can be obtained in core facility settings. Recently,
1 nmresolution was achievedbyusing a hybridmicroscopy technique
combining STED and single-molecule localization microscopy
(SMLM) (Balzarotti et al., 2017; Gwosch et al., 2020).
Z resolution – 4/5
In 3D STED, it is possible to also thin the emission spot along the Z-
axis using a second phase mask, which provides up to a four-fold
improvement in Z resolution when compared to point scanning
confocal (Klar et al., 2000). For instance, one study achieved 90 nm
in the Z dimension, while maintaining 35 nm in the XY dimensions
(Osseforth et al., 2014).
Thick-sample friendliness – 2/5
Both the intensity and the geometry of the excitation and depletion
beams are differently affected while traveling across the biological
sample, leading to difficulties when imaging deep into tissues. This
can be partially improved by using multiphoton excitation lasers,
which allow for a deeper penetration imaging capability.
Live-cell friendliness – 3/5
Owing to the requirement for high-intensity illumination by the
STED beam, this technique remains a challenge for live-cell
imaging. From the various configurations available, depletion using
a 775 nm laser line is the most live-cell friendly and has been
shown to work for a panel of fluorescent proteins and probes
4
CELL SCIENCE AT A GLANCE
Journal of Cell Science (2020) 133, jcs240713. doi:10.1242/jcs.240713
Journal of Cell Science

----!@#$NewPage!@#$----
(D’Este et al., 2015). Gentler approaches called RESOLFT, based
on photoswitchable probes, allow resolution enhancement with a
lower light dose (Grotjohann et al., 2011; Masullo et al., 2018).
Image fidelity – 5/5
STED microscopy does not require post-processing of the images,
which strongly limits the risk for artifact generation. Image quality
and signal-to-noise ratio can however be further improved by
photon reassignment using computational deconvolution.
Multicolor – 2/5
The number of fluorophores compatible with STED remains limited
and many commonly used dyes are irreversibly bleached by the high
intensity STED beam. In addition, in multicolor experiments,
special care must be taken when selecting fluorophores to ensure
that no overlap exists between their excitation spectrum and the
depletion laser to improve the resolution of the other channels.
Sequential scanning can be used as a workaround but acquisitions
are limited to a single time point and focal plane.
Temporal resolution – 3/5
STED microscopy allows the user to directly visualize the object of
interest in super resolution without the need for offline computation
or post-processing of multiple images. It is therefore suitable for the
imaging of fast cellular events, such as organelle dynamics and
protein trafficking (Bottanelli et al., 2016).
Availability
STED is commercially available from two companies as complete
setups, with a range of costs that makes them more suited to shared
facilities.
Single-molecule localization microscopy
Principle
During
single-molecule
localization
microscopy
(SMLM)
experiments, the emission of individual fluorophores are recorded
using a camera. The resulting images are composed of diffraction-
limited spots (typically ∼200 nm in width) that are then fitted to
precisely pinpoint the fluorophore position (typically within ∼10–
15 nm) (Diezmann et al., 2017). In practice, tens of thousands of
images of blinking fluorophores are acquired in rapid succession
(Jimenez et al., 2019). A processing software is then used to fit the
blinking events and create a super-resolved image (Baddeley and
Bewersdorf, 2018). To detect single molecules from densely labeled
samples, only a small fraction of the fluorophores can be emitting
photons
at
any one
time
(Li
and
Vaughan,
2018).
This
can be achieved by using sparsely activated photoactivatable/
photo-convertible fluorescent proteins in (fluorescence-) photo-
activated localization microscopy [(F)PALM] (Betzig et al., 2006;
Hess et al., 2006). Organic dyes can also be induced to blink using
specific
buffers
in
(direct)
stochastic
optical
reconstruction
microscopy [(d)STORM] (Heilemann et al., 2008; Rust et al.,
2006) or ground-state depletion (GSD) microscopy (Fölling et al.,
2008). Alternatively, blinking can be generated by the transient
interaction between two short DNA sequences, one labeled and one
unlabeled, in so-called DNA point-accumulation in nanoscale
topography (DNA-PAINT) (Jungmann et al., 2014).
Versatility – 2/5
Owing to its high spatial resolution, SMLM requires specific
care during sample preparation to ensure optimal ultrastructural
preservation (Jimenez et al., 2019; Whelan and Bell, 2015). In
addition, as single molecules are recorded, the density of labeling
must be high enough to delineate the final structure of interest
(Patterson et al., 2010).
XY resolution – 5/5
SMLM reconstructs images at 10 to 15 nm resolution. The final
resolution achieved depends on the brightness of the fluorophores
detected, their labeling density and the capacity to accurately detect
individual fluorophores (Culley et al., 2018b). Importantly, at this
scale, the size of the probe (antibody or fusion protein) can start to
degrade the precision of the imaging (Magrassi et al., 2019).
Z resolution – 5/5
A common way of retrieving the Z coordinate of fluorophores is to
deform the point-spread function into an ellipse using a cylindrical
lens (Huang et al., 2008). This provides a typical Z localization
precision of 20–30 nm (Diezmann et al., 2017).
Thick-sample friendliness – 2/5
SMLM is sensitive to light diffusion and spherical aberrations when
imaging structures that are more than a few microns above the
coverslip. More complex setups using light-sheet illumination
schemes or adaptive optics allow to reach deeper in cells and tissue,
but they are not yet broadly available (Liu et al., 2018).
Live-cell friendliness – 2/5
The time necessary to accumulate enough localizations and the high
illumination intensities needed to visualize single molecules makes
SMLM challenging to use on live samples (Tosheva et al., 2020;
Wäldchen et al., 2015), although it has been performed (Huang
et al., 2013; Jones et al., 2011).
Image fidelity – 2/5
The
complexity
of
the
required
post-acquisition
analysis
(localization and image reconstruction) and the high precision
attained by SMLM makes it prone to artifacts. Care must be taken to
ensure proper quenching of fluorophores and to limit the density of
their blinking.
Multicolor – 3/5
Multicolor remains a challenge for most SMLM strategies –
photoactivatable and/or convertible proteins rapidly occupy all
available channels in PALM (Shroff et al., 2007), and the
photophysics of organic fluorophores makes it difficult to identify
those that are spectrally distinct and have good blinking properties
(Dempsey et al., 2011; Lehmann et al., 2015). However, DNA-
PAINT allows for virtually unlimited sequential imaging of distinct
targets, and is easily used for imaging of three to four colors
(Jimenez et al., 2019; Jungmann et al., 2014).
Temporal resolution – 1/5
The necessity to acquire thousands of images for a single
reconstruction is a strong impediment to fast acquisition in
SMLM. Higher laser intensities and fast cameras can be used (Lin
et al., 2015). Furthermore, the use of artificial intelligence shows
promise in the ability to infer structure from a limited number of
acquired images (Ouyang et al., 2018).
Availability
A regular epifluorescence microscope equipped with lasers is all
that is needed to perform SMLM, with high-power lasers required
5
CELL SCIENCE AT A GLANCE
Journal of Cell Science (2020) 133, jcs240713. doi:10.1242/jcs.240713
Journal of Cell Science

----!@#$NewPage!@#$----
for STORM; several free options exist to process and analyze
SMLM data (Jimenez et al., 2019; van de Linde, 2019).
Expansion microscopy
Principle
Expansion microscopy (ExM) is a sample preparation technique that
physically increases the size of the specimen (Chen et al., 2015).
Multiple variations of the ExM protocol have been described, but they
all share a common workflow. After fixation, the sample is embedded
and cross-linked to a swellable gel that is then expanded using water
(Wassie et al., 2019). The resulting enlarged specimen can then be
imaged using classical microscopy techniques (Chen et al., 2015;
Chozinski et al., 2016; Ku et al., 2016; Tillberg et al., 2016).
Versatility – 3/5
ExM has been successfully applied to a wide variety of samples,
including cells (Chen et al., 2015), tissue sections (Zhao et al.,
2017) and model organisms (Drosophila and zebrafish embryo)
(Cahoon et al., 2017; Freifeld et al., 2017), as well as whole intact
organs (Gao et al., 2019; Ku et al., 2016). ExM has also been used to
observe RNA (Chen et al., 2016) and lipids (Karagiannis et al., 2019
preprint). Each new application of ExM needs careful and specific
optimization.
XY and Z resolution – 4/5
The final ‘resolution’ of the image depends on the final size of the
expended sample, as well as on the microscopy strategy used to
acquire the images. ExM protocols typically lead to a 4.5-fold
expansion in all dimensions (an expected resolution of ∼70 nm), but
others attain a 10-fold expansion (an expected resolution of 25–
30 nm) (Truckenbrodt et al., 2018). Samples can also be expanded
sequentially (an expected resolution of ∼25 nm) (Chang et al.,
2017). ExM is also compatible with other super-resolution
modalities, including STED (Gao et al., 2018), SMLM (Shi et al.,
2019 preprint) and SIM (Cahoon et al., 2017; Halpern et al., 2017).
Thick-sample friendliness – 5/5
ExM can be used on thick samples, such as small model organisms or
whole mouse organs (Cahoon et al., 2017; Freifeld et al., 2017; Gao
et al., 2019; Ku et al., 2016). However, it is important to note that
post-expansion, samples will be at least 100-fold more voluminous
and, therefore, they can be challenging to image at high magnification
using classical microscopes. Light-sheet microscopy is especially
well suited for imaging such samples (Gao et al., 2019).
Live-cell friendliness and temporal resolution – 0/5
ExM is only compatible with fixed samples.
Image fidelity – 3/5
ExM leads to an isotropic expansion and has been shown to preserve
the structural integrity of the various cellular structures imaged. In
addition, using the nuclear pore as a reporter, ExM was found to
have a uniform accuracy in the range of 20 nm (Pesce et al., 2019).
Nevertheless, one concern that remains is whether all the structures
of interest within a sample expand at the same rate or to the
same extent. For instance, in bacteria, the expansion efficacy
varies across species (Lim et al., 2019). Moreover, the digestion
procedure typically attenuates the fluorescence of organic or
protein-based fluorophores, and the expansion itself dilutes the
fluorescent signal spatially (a 4-fold expansion results in 64 times
less fluorescence per volume unit), requiring sensitive microscopes
for ExM sample imaging.
Multicolor – 5/5
ExM is compatible with standard dyes and fluorescent proteins and
is therefore well adapted to multicolor experiments.
Availability
ExM sample preparation only requires commercially available
reagents and is therefore readily available; it is also relatively
inexpensive to implement (Asano et al., 2018).
Conclusions – going above and beyond
Regardless of the technology used to acquire the fluorescent signal,
powerful signal post-processing methods, such as deconvolution or
image restoration, can be used to increase the resolution of the final
image. Various algorithms have been implemented directly in the
acquisition software commercially available, while others, for
instance SACD (Zhao et al., 2018 preprint), can be run
independently of the pipeline. Another promising development is
the use of artificial intelligence to improve the quality of the final
image; here, large datasets of high-resolution images are used to
train neural networks that can then be applied to low-resolution and/
or noisy datasets datasets (Belthangady and Royer, 2019; Moen
et al., 2019; von Chamier et al., 2019; von Chamier et al., 2020
preprint). The resulting image gains a dramatic resolution
improvement thanks to the prior knowledge of the structure
obtained from high-resolution images.
Acknowledgements
We thank Dr Aki Stubb for providing the human induced pluripotent stem cell
samples, Dr Ilkka Paatero for the zebrafish samples and Dr Emilia Peuhu for the
breast tumor tissue sections. We thank Elena Tcarenkova for help with the STED
imaging. We thank Ghislaine Caillol and Angélique Jimenez for the preparation of
SMLM samples and the NCIS imaging facility (INP, CNRS Aix-Marseille University)
for providing imaging resources. The Cell Imaging and Cytometry core facility (Turku
Bioscience, University of Turku, Åbo Akademi University and Biocenter Finland) and
Turku Bioimaging are acknowledged for services, instrumentation and expertise.
This work was made possible in part through use of the UT Southwest Live Cell
Imaging Core Facility in Dallas (Drs Kate Phelps and Dorothy Mundy) and the
equipment of the Texas Children’s Hospital W.T. Shearer Center for Human
Immunobiology.
Funding
Our work in this area has been supported by the Academy of Finland (G.J.) the Sigrid
Juselius Foundation (Sigrid Juséliuksen Sä ä tiö ) (G.J.) and by the Centre National de
la Recherche Scientifique (CNRS; ATIP AO2016) (C.L.). R.H was funded by grants
from the UK Biotechnology and Biological Sciences Research Council (BB/
S507532/1, BB/R021805/1), the UK Medical Research Council (MR/K015826/1),
the Wellcome Trust (203276/Z/16/Z) and core funding by the MRC Laboratory for
Molecular Cell Biology, University College London (MC_UU12018/7).
Supplementary information
Supplementary information available online at
http://jcs.biologists.org/lookup/doi/10.1242/jcs.240713.supplemental
Cell science at a glance
A high-resolution version of the poster and individual poster panels are available for
downloading at http://jcs.biologists.org/lookup/doi/10.1242/jcs.240713.
supplemental
References
Almada, P., Pereira, P. M., Culley, S., Caillol, G., Boroni-Rueda, F., Dix, C. L.,
Charras, G., Baum, B., Laine, R. F., Leterrier, C. et al. (2019). Automating
multimodal microscopy with NanoJ-Fluidics. Nat. Commun. 10, 1-9. doi:10.1038/
s41467-019-09231-9
Asano, S. M., Gao, R., Wassie, A. T., Tillberg, P. W., Chen, F. and Boyden, E. S.
(2018). Expansion microscopy: protocols for imaging proteins and RNA in cells
and tissues. Curr. Protoc. Cell Biol. 80, e56. doi:10.1002/cpcb.56
Azuma, T. and Kei, T. (2015). Super-resolution spinning-disk confocal microscopy
using optical photon reassignment. Opt. Express 23, 15003. doi:10.1364/OE.23.
015003
6
CELL SCIENCE AT A GLANCE
Journal of Cell Science (2020) 133, jcs240713. doi:10.1242/jcs.240713
Journal of Cell Science

----!@#$NewPage!@#$----
Bachmann, M., Fiederling, F. and Bastmeyer, M. (2015). Practical limitations of
superresolution imaging due to conventional sample preparation revealed by a
direct comparison of CLSM, SIM and dSTORM. J. Microsc 262, 306-315. doi:10.
1111/jmi.12365
Baddeley, D. and Bewersdorf, J. (2018). Biological insight from super-resolution
microscopy: what we can learn from localization-based images. Annu. Rev.
Biochem. 87, 965-989. doi:10.1146/annurev-biochem-060815-014801
Bagshaw, C. R. and Cherny, D. (2006). Blinking fluorophores: what do they tell us
about protein dynamics? Biochem. Soc. Trans. 34, 979-982. doi:10.1042/
BST0340979
Balzarotti, F., Eilers, Y., Gwosch, K. C., Gynnå, A. H., Westphal, V., Stefani, F. D.,
Elf, J. and Hell, S. W. (2017). Nanometer resolution imaging and tracking of
fluorescent molecules with minimal photon fluxes. Sci. New York N Y 355,
606-612. doi:10.1126/science.aak9913
Belthangady, C. and Royer, L. A. (2019). Applications, promises, and pitfalls of
deep learning for fluorescence image
reconstruction. Nat. Methods
16,
1215-1225. doi:10.1038/s41592-019-0458-z
Betzig, E., Patterson, G. H., Sougrat, R., Lindwasser, O. W., Olenych, S.,
Bonifacino, J. S., Davidson, M. W., Lippincott-Schwartz, J. and Hess, H. F.
(2006). Imaging intracellular fluorescent proteins at nanometer resolution.
Science 313, 1642-1645. doi:10.1126/science.1127344
Blom, H. and Widengren, J. (2017). Stimulated emission depletion microscopy.
Chem. Rev. 117, 7377-7427. doi:10.1021/acs.chemrev.6b00653
Bottanelli, F., Kromann, E. B., Allgeyer, E. S., Erdmann, R. S., Baguley, S. W.,
Sirinakis, G., Schepartz, A., Baddeley, D., Toomre, D. K., Rothman, J. E. et al.
(2016). Two-colour live-cell nanoscale imaging of intracellular targets. Nat.
Commun. 7, 10778. doi:10.1038/ncomms10778
Burnette, D. T., Shao, L., Ott, C., Pasapera, A. M., Fischer, R. S., Baird, M. A., Der
Loughian, C., Delanoe-Ayari, H., Paszek, M. J., Davidson, M. W. et al. (2014).
A contractile and counterbalancing adhesion system controls the 3D shape of
crawling cells. J. Cell Biol. 205, 83-96. doi:10.1083/jcb.201311104
Cahoon, C. K., Yu, Z., Wang, Y., Guo, F., Unruh, J. R., Slaughter, B. D. and
Hawley, R. S. (2017). Superresolution expansion microscopy reveals the three-
dimensional organization of the Drosophila synaptonemal complex. Proc. Natl.
Acad. Sci. USA 114, E6857-E6866. doi:10.1073/pnas.1705623114
Carisey, A. F., Mace, E. M., Saeed, M. B., Davis, D. M. and Orange, J. S. (2018).
Nanoscale dynamism of actin enables secretory function in cytolytic cells. Curr.
Biol. 28, 489-502.e9. doi:10.1016/j.cub.2017.12.044
Chamier, Lv., Jukkala, J., Spahn, C., Lerche, M., Hernández-pérez, S., Mattila,
P.,
Karinou, E.,
Holden, S.,
Solak, A.C.,
Krull, A. et al. (2020).
ZeroCostDL4Mic: an open platform to simplify access and use of Deep-
Learning in Microscopy. bioRxiv 2020.03.20.000133. doi:10.1101/2020.03.20.
000133
Chang, J.-B., Chen, F., Yoon, Y.-G., Jung, E. E., Babcock, H., Kang, J. S., Asano,
S., Suk, H.-J., Pak, N., Tillberg, P. W. et al. (2017). Iterative expansion
microscopy. Nat. Methods 14, 593-599. doi:10.1038/nmeth.4261
Chen, B.-C., Legant, W. R., Wang, K., Shao, L., Milkie, D. E., Davidson, M. W.,
Janetopoulos, C., Wu, X. S., Hammer, J. A., Liu, J. et al. (2014). Lattice light-
sheet microscopy: Imaging molecules to embryos at high spatiotemporal
resolution. Science 346, 1257998. doi:10.1126/science.1257998
Chen, F., Tillberg, P. W. and Boyden, E. S. (2015). Expansion microscopy.
Science 347, 543-548. doi:10.1126/science.1260088
Chen, F., Wassie, A. T., Cote, A. J., Sinha, A., Alon, S., Asano, S., Daugharthy,
E. R., Chang, J.-B., Marblestone, A., Church, G. M. et al. (2016). Nanoscale
imaging of RNA with expansion microscopy. Nat. Methods 13, 679-684. doi:10.
1038/nmeth.3899
Chozinski, T. J., Halpern, A. R., Okawa, H., Kim, H.-J., Tremel, G. J., Wong,
R. O. L. and Vaughan, J. C. (2016). Expansion microscopy with conventional
antibodies and fluorescent proteins. Nat. Methods 13, 485-488. doi:10.1038/
nmeth.3833
Combs and Shroff (2017). Fluorescence microscopy: a concise guide to current
imaging methods. Curr. Protoc. Neurosci. 79, 2.1.1-2.1.25. doi:10.1002/cpns.29
Cooper, J., Browne, M., Gribben, H., Catney, M., Coates, C., Mullan, A., Wilde,
G. and Henriques, R. (2019). Real time multi-modal super-resolution microscopy
through
Super-Resolution
Radial
Fluctuations
(SRRF-Stream).
In
Single
Molecule Spectroscopy and Superresolution Imaging XII (ed. Z. K. Gryczynski,
I. Gregor, F. Koberling), p. 1088418. International Society for Optics and
Photonics.
Culley, S., Tosheva, K. L., Pereira, P. M. and Henriques, R. (2018a). SRRF:
Universal live-cell super-resolution microscopy. Int. J. Biochem. Cell Biol. 101,
74-79. doi:10.1016/j.biocel.2018.05.014
Culley, S., Albrecht, D., Jacobs, C., Pereira, P. M., Leterrier, C., Mercer, J. and
Henriques, R. (2018b). Quantitative mapping and minimization of super-
resolution optical imaging artifacts. Nat. Methods 15, 263-266. doi:10.1038/
nmeth.4605
Dedecker, P., Duwé, S., Neely, R. K. and Zhang, J. (2012). Localizer: fast,
accurate, open-source, and modular software package for superresolution
microscopy. J. Biomed. Opt. 17, 126008. doi:10.1117/1.JBO.17.12.126008
Demmerle, J., Innocent, C., North, A. J., Ball, G., Mü ller, M., Miron, E., Matsuda,
A., Dobbie, I. M., Markaki, Y. and Schermelleh, L. (2017). Strategic and practical
guidelines for successful structured illumination microscopy. Nat. Protoc. 12,
988-1010. doi:10.1038/nprot.2017.019
Dempsey, G. T., Vaughan, J. C., Chen, K. H., Bates, M. and Zhuang, X. (2011).
Evaluation of fluorophores for optimal performance in localization-based super-
resolution imaging. Nat. Methods 8, 1027-1036. doi:10.1038/nmeth.1768
Dertinger, T., Colyer, R., Iyer, G., Weiss, S. and Enderlein, J. (2009). Fast,
background-free, 3D super-resolution optical fluctuation imaging (SOFI). Proc.
Natl. Acad. Sci. USA 106, 22287-22292. doi:10.1073/pnas.0907866106
D’Este, E., Kamin, D., Gö ttfert, F., El-Hady, A. and Hell, S. W. (2015). STED
nanoscopy reveals the ubiquity of subcortical cytoskeleton periodicity in living
neurons. Cell Reports 10, 1246-1251. doi:10.1016/j.celrep.2015.02.007
Diezmann, A. von, Shechtman, Y. and Moerner, W. E. (2017). Three-dimensional
localization of single molecules for super-resolution imaging and single-particle
tracking. Chem. Rev. 117, 7244-7275. doi:10.1021/acs.chemrev.6b00629
Fiolka, R., Shao, L., Rego, E. H., Davidson, M. W. and Gustafsson, M. G. L.
(2012). Time-lapse two-color 3D imaging of live cells with doubled resolution
using structured illumination. Proc. Natl. Acad. Sci. USA 109, 5311-5315. doi:10.
1073/pnas.1119262109
Fö lling, J., Bossi, M., Bock, H., Medda, R., Wurm, C. A., Hein, B., Jakobs, S.,
Eggeling, C. and Hell, S. W. (2008). Fluorescence nanoscopy by ground-state
depletion and single-molecule return. Nat. Methods 5, 943-945. doi:10.1038/
nmeth.1257
Freifeld, L., Odstrcil, I., Fö rster, D., Ramirez, A., Gagnon, J. A., Randlett, O.,
Costa, E. K., Asano, S., Celiker, O. T., Gao, R. et al. (2017). Expansion
microscopy of zebrafish for neuroscience and developmental biology studies.
Proc. Natl. Acad. Sci. USA 114, E10799-E10808. doi:10.1073/pnas.1706281114
Frigault, M. M., Lacoste, J., Swift, J. L. and Brown, C. M. (2009). Live-cell
microscopy – tips and tools. J. Cell Sci. 122, 753-767. doi:10.1242/jcs.033837
Gao, M., Maraspini, R., Beutel, O., Zehtabian, A., Eickholt, B., Honigmann, A.
and Ewers, H. (2018). Expansion stimulated emission depletion microscopy
(ExSTED). Acs Nano 12, 4178-4185. doi:10.1021/acsnano.8b00776
Gao, R., Asano, S. M., Upadhyayula, S., Pisarev, I., Milkie, D. E., Liu, T.-L.,
Singh, V., Graves, A., Huynh, G. H., Zhao, Y. et al. (2019). Cortical column and
whole-brain imaging with molecular contrast and nanoscale resolution. Science
363, eaau8302. doi:10.1126/science.aau8302
Gö ttfert, F., Wurm, C. A., Mueller, V., Berning, S., Cordes, V. C., Honigmann, A.
and Hell, S. W. (2013). Coaligned dual-channel STED nanoscopy and molecular
diffusion analysis at 20 nm resolution. Biophys. J. 105, L01-L03. doi:10.1016/j.bpj.
2013.05.029
Grotjohann, T., Testa, I., Leutenegger, M., Bock, H., Urban, N. T., Lavoie-
Cardinal, F., Willig, K. I., Eggeling, C., Jakobs, S. and Hell, S. W. (2011).
Diffraction-unlimited all-optical imaging and writing with a photochromic GFP.
Nature 478, 204-208. doi:10.1038/nature10497
Guo, Y., Li, D., Zhang, S., Yang, Y., Liu, J.-J., Wang, X., Liu, C., Milkie, D. E.,
Regan, P. Moore,
et al. (2018). Visualizing intracellular organelle and
cytoskeletal interactions at nanoscale resolution on millisecond timescales. Cell
175, 1430-1442.e17. doi:10.1016/j.cell.2018.09.057
Gustafsson, M. G. L. (2000). Surpassing the lateral resolution limit by a factor of two
using structured illumination microscopy. SHORT COMMUNICATION. J. Microsc
198, 82-87. doi:10.1046/j.1365-2818.2000.00710.x
Gustafsson, M. G. L., Shao, L., Carlton, P. M., Wang, C. J. R., Golubovskaya,
I. N., Cande, W. Z., Agard, D. A. and Sedat, J. W. (2008). Three-dimensional
resolution
doubling
in
wide-field
fluorescence
microscopy
by
structured
illumination. Biophys. J. 94, 4957-4970. doi:10.1529/biophysj.107.120345
Gustafsson, N., Culley, S., Ashdown, G., Owen, D. M., Pereira, P. M. and
Henriques, R. (2016). Fast live-cell conventional fluorophore nanoscopy with
ImageJ through super-resolution radial fluctuations. Nat. Commun. 7, 12471.
doi:10.1038/ncomms12471
Gwosch, K. C., Pape, J. K., Balzarotti, F., Hoess, P., Ellenberg, J., Ries, J. and
Hell, S. W. (2020). MINFLUX nanoscopy delivers 3D multicolor nanometer
resolution in cells. Nat. Methods 17, 217-224. doi:10.1038/s41592-019-0688-0
Halpern, A. R., Alas, G. C. M., Chozinski, T. J., Paredez, A. R. and Vaughan, J. C.
(2017). Hybrid structured illumination expansion microscopy reveals microbial
cytoskeleton organization. Acs Nano 11, 12677-12686. doi:10.1021/acsnano.
7b07200
Hayashi, S. and Okada, Y. (2015). Ultrafast superresolution fluorescence imaging
with spinning disk confocal microscope optics. Mol. Biol. Cell 26, 1743-1751.
doi:10.1091/mbc.E14-08-1287
Heilemann, M., van de Linde, S., Schü ttpelz, M., Kasper, R., Seefeldt, B.,
Mukherjee, A., Tinnefeld, P. and Sauer, M. (2008). Subdiffraction-resolution
fluorescence imaging with conventional fluorescent probes. Angewandte Chemie
Int. Ed. Engl. 47, 6172-6176. doi:10.1002/anie.200802376
Heine, J., Reuss, M., Harke, B., D’Este, E., Sahl, S. J. and Hell, S. W. (2017).
Adaptive-illumination STED nanoscopy. Proc. Natl. Acad. Sci. USA 114,
9797-9802. doi:10.1073/pnas.1708304114
Heintzmann, R. and Huser, T. (2017). Super-resolution structured illumination
microscopy. Chem. Rev. 117, 13890-13908. doi:10.1021/acs.chemrev.7b00218
Hell, S. W. and Wichmann, J. (1994). Breaking the diffraction resolution limit by
stimulated emission: stimulated-emission-depletion fluorescence microscopy.
Opt. Lett. 19, 780. doi:10.1364/OL.19.000780
7
CELL SCIENCE AT A GLANCE
Journal of Cell Science (2020) 133, jcs240713. doi:10.1242/jcs.240713
Journal of Cell Science

----!@#$NewPage!@#$----
Hernández, I. C., Buttafava, M., Boso, G., Diaspro, A., Tosi, A. and Vicidomini,
G. (2015). Gated STED microscopy with time-gated single-photon avalanche
diode. Biomed. Opt. Express 6, 2258. doi:10.1364/BOE.6.002258
Hess, S. T., Girirajan, T. P. K. and Mason, M. D. (2006). Ultra-high resolution
imaging by fluorescence photoactivation localization microscopy. Biophys. J. 91,
4258-4272. doi:10.1529/biophysj.106.091116
Huang, B., Wang, W., Bates, M. and Zhuang, X. (2008). Three-dimensional super-
resolution imaging by stochastic optical reconstruction microscopy. Sci. New York
N Y 319, 810-813. doi:10.1126/science.1153529
Huang, F., Hartwich, T. M. P., Rivera-Molina, F. E., Lin, Y., Duim, W. C., Long,
J. J., Uchil, P. D., Myers, J. R., Baird, M. A., Mothes, W. et al. (2013). Video-rate
nanoscopy
using
sCMOS
camera-specific
single-molecule
localization
algorithms. Nat. Methods 10, 653-658. doi:10.1038/nmeth.2488
Huang, X., Fan, J., Li, L., Liu, H., Wu, R., Wu, Y., Wei, L., Mao, H., Lal, A., Xi, P.
et al. (2018). Fast, long-term, super-resolution imaging with Hessian structured
illumination microscopy. Nat. Biotechnol. 36, 451-459. doi:10.1038/nbt.4115
Huff, J. (2015). The Airyscan detector from ZEISS: confocal imaging with improved
signal-to-noise ratio and super-resolution. Nat. Methods 12, i-ii. doi:10.1038/
nmeth.f.388
Jacquemet, G., Stubb, A., Saup, R., Miihkinen, M., Kremneva, E., Hamidi, H.
and
Ivaska,
J.
(2019).
Filopodome
mapping
identifies
p130Cas
as
a
mechanosensitive regulator of filopodia stability. Curr. Biol. Cb 29, 202-216.e7.
doi:10.1016/j.cub.2018.11.053
Jimenez, A., Friedl, K. and Leterrier, C. (2019). About samples, giving examples:
optimized single molecule localization microscopy. Methods 174, 100-114.
doi:10.1016/j.ymeth.2019.05.008
Jones, S. A., Shim, S.-H., He, J. and Zhuang, X. (2011). Fast, three-dimensional
super-resolution imaging of live cells. Nat. Methods 8, 499-508. doi:10.1038/
nmeth.1605
Jost, A. P.-T. and Waters, J. C. (2019). Designing a rigorous microscopy
experiment: validating methods and avoiding bias. J. Cell Biol. 218, 1452-1466.
doi:10.1083/jcb.201812109
Jungmann, R., Avendaño, M. S., Woehrstein, J. B., Dai, M., Shih, W. M. and Yin,
P. (2014). Multiplexed 3D cellular super-resolution imaging with DNA-PAINT and
Exchange-PAINT. Nat. Methods 11, 313-318. doi:10.1038/nmeth.2835
Karagiannis, E. D., Kang, J. S., Shin, T. W., Emenari, A., Asano, S., Lin, L.,
Costa, E. K., Consortium, I. G. C., Marblestone, A. H., Kasthuri, N. et al.
(2019). Expansion microscopy of lipid membranes. Biorxiv 829903. doi:10.1101/
829903
Klar, T. A., Jakobs, S., Dyba, M., Egner, A. and Hell, S. W. (2000). Fluorescence
microscopy with diffraction resolution barrier broken by stimulated emission. Proc.
Natl. Acad. Sci. USA 97, 8206-8210. doi:10.1073/pnas.97.15.8206
Kner, P., Chhun, B. B., Griffis, E. R., Winoto, L. and Gustafsson, M. G. L. (2009).
Super-resolution video microscopy of live cells by structured illumination. Nat.
Methods 6, 339-342. doi:10.1038/nmeth.1324
Kolesova, H., Capek, M., Radochova, B., Janacek, J. and Sedmera, D. (2016).
Visualization of GFP mouse embryos and embryonic hearts using various tissue
clearing methods and 3D imaging modalities. In European Microscopy Congress
2016: Proceedings, pp. 256-257. American Cancer Society.
Kremers, G.-J., Gilbert, S. G., Cranfill, P. J., Davidson, M. W. and Piston, D. W.
(2010). Fluorescent proteins at a glance. J. Cell Sci. 124, 157-160. doi:10.1242/
jcs.072744
Ku, T., Swaney, J., Park, J.-Y., Albanese, A., Murray, E., Cho, J. H., Park, Y.-G.,
Mangena, V., Chen, J. and Chung, K. (2016). Multiplexed and scalable super-
resolution imaging of three-dimensional protein localization in size-adjustable
tissues. Nat. Biotechnol. 34, 973-981. doi:10.1038/nbt.3641
Lambert, T. J. (2019). FPbase: a community-editable fluorescent protein database.
Nat. Methods 16, 277-278. doi:10.1038/s41592-019-0352-8
Lehmann, M., Lichtner, G., Klenz, H. and Schmoranzer, J. (2015). Novel
organic dyes for multicolor localization-based super-resolution microscopy.
J. Biophotonics 9, 161-170. doi:10.1002/jbio.201500119
Li, H. and Vaughan, J. C. (2018). Switchable fluorophores for single-molecule
localization microscopy. Chem. Rev. 118, 9412-9454. doi:10.1021/acs.chemrev.
7b00767
Li, D., Shao, L., Chen, B.-C., Zhang, X., Zhang, M., Moses, B., Milkie, D. E.,
Beach, J. R., Hammer, J. A., Pasham, M. et al. (2015). Extended-resolution
structured illumination imaging of endocytic and cytoskeletal dynamics. Science
349, aab3500-aab3500. doi:10.1126/science.aab3500
Lim, Y., Shiver, A. L., Khariton, M., Lane, K. M., Ng, K. M., Bray, S. R., Qin, J.,
Huang, K. C. and Wang, B. (2019). Mechanically resolved imaging of bacteria
using expansion microscopy. PLoS Biol. 17, e3000268. doi:10.1371/journal.pbio.
3000268
Lin, Y., Long, J. J., Huang, F., Duim, W. C., Kirschbaum, S., Zhang, Y.,
Schroeder, L. K., Rebane, A. A., Velasco, M. G. M., Virrueta, A. et al. (2015).
Quantifying and optimizing single-molecule switching nanoscopy at high speeds.
PLoS ONE 10, e0128135. doi:10.1371/journal.pone.0128135
Liu, W., Toussaint, K. C., Okoro, C., Zhu, D., Chen, Y., Kuang, C. and Liu, X.
(2018). Breaking the axial diffraction limit: a guide to axial super-resolution
fluorescence microscopy. Laser Photonics Rev. 12, 1700333. doi:10.1002/lpor.
201700333
Luca, G. M. R. D., Breedijk, R. M. P., Brandt, R. A. J., Zeelenberg, C. H. C., de
Jong, B. E., Timmermans, W., Azar, L. N., Hoebe, R. A., Stallinga, S. and Erik,
M. M. et al. 2013). Re-scan confocal microscopy: scanning twice for better
resolution. Biomed. Opt. Express 4, 2644-2656. doi:10.1364/BOE.4.002644
Magrassi, R., Scalisi, S. and Cella Zanacchi, F. (2019). Single-molecule
localization
to
study
cytoskeletal
structures,
membrane
complexes,
and
mechanosensors. Biophys. Rev. 11, 745-756. doi:10.1007/s12551-019-00595-2
Masters, B. R. (2010). The development of fluorescence microscopy. In:
Encyclopedia of Life Sciences (ELS). John Wiley & Sons, Ltd, Chichester.
doi:10.1002/9780470015902.a0022093
Masullo, L. A., Bodén, A., Pennacchietti, F., Coceano, G., Ratz, M. and Testa, I.
(2018). Enhanced photon collection enables four dimensional fluorescence
nanoscopy of living systems. Nat. Commun. 9, 1-9. doi:10.1038/s41467-018-
05799-w
Moen, E., Bannon, D., Kudo, T., Graf, W., Covert, M. and Van Valen, D. (2019).
Deep learning for cellular image analysis. Nat. Methods 16, 1233-1246. doi:10.
1038/s41592-019-0403-1
Moffitt, J. R., Osseforth, C. and Michaelis, J. (2011). Time-gating improves the
spatial resolution of STED microscopy. Opt. Express 19, 4242-4254. doi:10.1364/
OE.19.004242
Mü ller, C. B. and Enderlein, J. (2010). Image scanning microscopy. Phys. Rev.
Lett. 104, 198101. doi:10.1103/PhysRevLett.104.198101
Osseforth, C., Moffitt, J. R., Schermelleh, L. and Michaelis, J. (2014).
Simultaneous dual-color 3D STED microscopy. Opt. Express 22, 7028. doi:10.
1364/OE.22.007028
Ouyang, W., Aristov, A., Lelek, M., Hao, X. and Zimmer, C. (2018). Deep learning
massively accelerates super-resolution localization microscopy. Nat. Biotechnol.
36, 460. doi:10.1038/nbt.4106
Patterson, G., Davidson, M., Manley, S. and Lippincott-Schwartz, J. (2010).
Superresolution imaging using single-molecule localization. Annu. Rev. Phys.
Chem. 61, 345-367. doi:10.1146/annurev.physchem.012809.103444
Pereira, P. M., Albrecht, D., Culley, S., Jacobs, C., Marsh, M., Mercer, J. and
Henriques, R. (2019). Fix your membrane receptor imaging: actin cytoskeleton
and CD4 membrane organization disruption by chemical fixation. Front. Immunol.
10, 675. doi:10.3389/fimmu.2019.00675
Pesce, L., Cozzolino, M., Lanzanò, L., Diaspro, A. and Bianchini, P. (2019).
Measuring expansion from macro- to nanoscale using NPC as intrinsic reporter.
J. Biophotonics 12, e201900018. doi:10.1002/jbio.201900018
Qi, Y., Yu, T., Xu, J., Wan, P., Ma, Y., Zhu, J., Li, Y., Gong, H., Luo, Q. and Zhu, D.
(2019). FDISCO: Advanced solvent-based clearing method for imaging whole
organs. Sci. Adv. 5, eaau8355. doi:10.1126/sciadv.aau8355
Rego, E. H., Shao, L., Macklin, J. J., Winoto, L., Johansson, G. A., Kamps-
Hughes, N., Davidson, M. W. and Gustafsson, M. G. L. (2012). Nonlinear
structured-illumination microscopy with a photoswitchable protein reveals cellular
structures at 50-nm resolution. Proc. Natl. Acad. Sci. USA 109, E135-E143.
doi:10.1073/pnas.1107547108
Rust, M. J., Bates, M. and Zhuang, X. (2006). Sub-diffraction-limit imaging by
stochastic optical reconstruction microscopy (STORM). Nat. Methods 3, 793-796.
doi:10.1038/nmeth929
Sahl, S. J., Hell, S. W. and Jakobs, S. (2017). Fluorescence nanoscopy in cell
biology. Nat. Rev. Mol. Cell Biol. 18, 685-701. doi:10.1038/nrm.2017.71
Schermelleh, L., Ferrand, A., Huser, T., Eggeling, C., Sauer, M., Biehlmaier, O.
and Drummen, G. P. C. (2019). Super-resolution microscopy demystified. Nat.
Cell Biol. 21, 72-84. doi:10.1038/s41556-018-0251-8
Schropp,
M.,
Seebacher,
C.
and
Uhl,
R.
(2017).
XL-SIM:
extending
superresolution
into
deeper
layers.
Photonics
4,
33.
doi:10.3390/
photonics4020033
Shi, X., Li, Q., Dai, Z., Tran, A. A., Feng, S., Ramirez, A. D., Lin, Z., Wang, X.,
Chow, T. T., Seiple, I. B. et al. (2019). Label-retention expansion microscopy.
Biorxiv 687954. doi:10.1101/687954
Shroff, H., Galbraith, C. G., Galbraith, J. A., White, H., Gillette, J., Olenych, S.,
Davidson, M. W. and Betzig, E. (2007). Dual-color superresolution imaging of
genetically expressed probes within individual adhesion complexes. Proc. Natl.
Acad. Sci. USA 104, 20308-20313. doi:10.1073/pnas.0710517105
Sigal, Y. M., Zhou, R. and Zhuang, X. (2018). Visualizing and discovering cellular
structures with super-resolution microscopy. Science 361, 880-887. doi:10.1126/
science.aau1044
Strö hl, F. and Kaminski, C. F. (2016). Frontiers in structured illumination
microscopy. Optica 3, 667. doi:10.1364/OPTICA.3.000667
Stubb, A., Guzmán, C., Nä rvä , E., Aaron, J., Chew, T.-L., Saari, M., Miihkinen,
M., Jacquemet, G. and Ivaska, J. (2019). Superresolution architecture of
cornerstone focal adhesions in human pluripotent stem cells. Nat. Commun. 10,
1-15. doi:10.1038/s41467-019-12611-w
Surre, J., Saint-Ruf, C., Collin, V., Orenga, S., Ramjeet, M. and Matic, I. (2018).
Strong increase in the autofluorescence of cells signals struggle for survival. Sci.
Rep. 8, 12088. doi:10.1038/s41598-018-30623-2
Thorley, J. A., Pike, J., Rappoport, J. Z., Cornea, A. and Conn, P. M. (2014).
Super-resolution microscopy: a comparison of commercially available options. In
Fluorescence Microscopy: Super-Resolution and Other Novel Techniques, (ed. A.
8
CELL SCIENCE AT A GLANCE
Journal of Cell Science (2020) 133, jcs240713. doi:10.1242/jcs.240713
Journal of Cell Science

----!@#$NewPage!@#$----
Cornea and P. M. Conn), pp. 199–212. Academic Press. doi:10.1016/B978-0-12-
409513-7.00014-2
Tillberg, P. W., Chen, F., Piatkevich, K. D., Zhao, Y., Yu, C.-C. J., English, B. P.,
Gao, L., Martorell, A., Suk, H.-J., Yoshida, F. et al. (2016). Protein-retention
expansion microscopy of cells and tissues labeled using standard fluorescent
proteins and antibodies. Nat. Biotechnol. 34, 987-992. doi:10.1038/nbt.3625
Tosheva, K. L., Yuan, Y., Pereira, P. M., Culley, S. and Henriques, R. (2020).
Between life and death: strategies to reduce phototoxicity in super-resolution
microscopy. J. Phys. D: Appl. Phys. 53, 163001. doi:10.1088/1361-6463/ab6b95
Truckenbrodt, S., Maidorn, M., Crzan, D., Wildhagen, H., Kabatas, S. and
Rizzoli, S. O. (2018). X10 expansion microscopy enables 25-nm resolution on
conventional
microscopes.
EMBO
Rep.
19,
e45836.
doi:10.15252/embr.
201845836
van de Linde S. (2019). Single-molecule localization microscopy analysis with
ImageJ. J. Phys. D Appl. Phys. 52, 203002. doi:10.1088/1361-6463/ab092f
van de Linde, S. and Sauer, M. (2014). How to switch a fluorophore: from undesired
blinking to controlled photoswitching. Chem. Soc. Rev. 43, 1076-1087. doi:10.
1039/C3CS60195A
van de Linde, S., Lö schberger, A., Klein, T., Heidbreder, M., Wolter, S.,
Heilemann, M. and Sauer, M. (2011). Direct stochastic optical reconstruction
microscopy with standard fluorescent probes. Nat. Protoc. 6, 991-1009. doi:10.
1038/nprot.2011.336
Vangindertael, J., Camacho, R., Sempels, W., Mizuno, H., Dedecker, P. and
Janssen, K. P. F. (2018). An introduction to optical super-resolution microscopy
for the adventurous biologist. Methods Appl. Fluores 6, 022003. doi:10.1088/
2050-6120/aaae0c
Vassilopoulos, S., Gibaud, S., Jimenez, A., Caillol, G. and Leterrier, C. (2019).
Ultrastructure of the axonal periodic scaffold reveals a braid-like organization of
actin rings. Nat. Commun. 10, 1-13. doi:10.1038/s41467-019-13835-6
Vietri, M., Schink, K. O., Campsteijn, C., Wegner, C. S., Schultz, S. W., Christ, L.,
Thoresen, S. B., Brech, A., Raiborg, C. and Stenmark, H. (2015). Spastin and
ESCRT-III coordinate mitotic spindle disassembly and nuclear envelope sealing.
Nature 522, 231-235. doi:10.1038/nature14408
von Chamier, L., Laine, R. F. and Henriques, R. (2019). Artificial intelligence for
microscopy: what you should know. Biochem. Soc. Trans. 47, 1029-1040. doi:10.
1042/BST20180391
von Chamier, L., Jukkala, J., Spahn, C., Lerche, M., Hernández-pérez, S.,
Mattila, P.,
Karinou, E.,
Holden, S.,
Solak, A.C.,
Krull, A. et al. (2020).
ZeroCostDL4Mic: an open platform to simplify access and use of Deep-Learning
in Microscopy. bioRxiv 2020.03.20.000133. doi:10.1101/2020.03.20.000133
Wä ldchen, S., Lehmann, J., Klein, T., van de Linde, S. and Sauer, M. (2015).
Light-induced cell damage in live-cell super-resolution microscopy. Sci. Rep. 5,
15348. doi:10.1038/srep15348
Wassie, A. T., Zhao, Y. and Boyden, E. S. (2019). Expansion microscopy:
principles and uses in biological research. Nat. Methods 16, 33-41. doi:10.1038/
s41592-018-0219-4
Wegel, E., Gö hler, A., Lagerholm, B. C., Wainman, A., Uphoff, S., Kaufmann, R.
and Dobbie, I. M. (2016). Imaging cellular structures in super-resolution with SIM,
STED and localisation microscopy: a practical comparison. Sci. Rep. 6, 27290.
doi:10.1038/srep27290
Whelan, D. R. and Bell, T. D. M. (2015). Image artifacts in single molecule
localization microscopy: why optimization of sample preparation protocols
matters. Sci. Rep. 5, 7924. doi:10.1038/srep07924
Willig, K. I., Rizzoli, S. O., Westphal, V., Jahn, R. and Hell, S. W. (2006). STED
microscopy reveals that synaptotagmin remains clustered after synaptic vesicle
exocytosis. Nature 440, 935-939. doi:10.1038/nature04592
Wu, Y. and Shroff, H. (2018). Faster, sharper, and deeper: structured illumination
microscopy for biological imaging. Nat. Methods 15, 1011-1019. doi:10.1038/
s41592-018-0211-z
York, A. G., Parekh, S. H., Nogare, D. D., Fischer, R. S., Temprine, K., Mione, M.,
Chitnis, A. B., Combs, C. A. and Shroff, H. (2012). Resolution doubling in live,
multicellular organisms via multifocal structured illumination microscopy. Nat.
Methods 9, 749-754. doi:10.1038/nmeth.2025
York, A. G., Chandris, P., Nogare, D. D., Head, J., Wawrzusin, P., Fischer, R. S.,
Chitnis, A. and Shroff, H. (2013). Instant super-resolution imaging in live cells
and embryos via analog image processing. Nat. Methods 10, 1122-1126. doi:10.
1038/nmeth.2687
Zhang, X., Zhang, M., Li, D., He, W., Peng, J., Betzig, E. and Xu, P. (2016). Highly
photostable, reversibly photoswitchable fluorescent protein with high contrast ratio
for live-cell superresolution microscopy. Proc. Natl. Acad. Sci. USA 113,
10364-10369. doi:10.1073/pnas.1611038113
Zhao, Y., Bucur, O., Irshad, H., Chen, F., Weins, A., Stancu, A. L., Oh, E.-Y.,
DiStasio, M., Torous, V., Glass, B. et al. (2017). Nanoscale imaging of clinical
specimens using pathology-optimized expansion microscopy. Nat. Biotechnol.
35, 757-764. doi:10.1038/nbt.3892
Zhao, W., Liu, J., Kong, C., Zhao, Y., Guo, C., Liu, C., Ding, X., Ding, X., Tan, J.
and Li, H. (2018). Faster super-resolution imaging with auto-correlation two-step
deconvolution. arXiv 1809.07410. https://arxiv.org/abs/1809.07410v4
9
CELL SCIENCE AT A GLANCE
Journal of Cell Science (2020) 133, jcs240713. doi:10.1242/jcs.240713
Journal of Cell Science