Review
PALM and STORM: Unlocking Live-Cell Super-Resolution
Ricardo Henriques,1,2 Caron Griffiths,3,4 E. Hesper Rego,5,6 Musa M. Mhlanga1,3
1 Unidade de Biofisica e Expressa˜o Genetica, Instituto de Medicina Molecular,
Faculdade de Medicina Universidade de Lisboa, Lisboa, Portugal
2 Institut Pasteur, Groupe Imagerie et Modelisation, Centre National de la Recherche Scientifique,
Unite de Recherche Associe 2582, Paris, France
3 Gene Expression and Biophysics Group, Synthetic Biology Emerging Research Area,
Council for Scientific and Industrial Research, Pretoria, South Africa
4 Department of Biochemistry, Faculty of Natural and Agricultural Sciences,
University of Pretoria, Pretoria, 0002, South Africa
5 Graduate Group in Biophysics, University of California, San Francisco, CA 94158
6 Howard Hughes Medical Institute, Janelia Farm Research Center, Ashburn, VA 20147
Received 16 November 2010; revised 6 January 2011; accepted 6 January 2011
Published online 19 January 2011 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/bip.21586
This article was originally published online as an accepted
preprint. The ‘‘Published Online’’ date corresponds to the
preprint version. You can request a copy of the preprint by
emailing the Biopolymers editorial office at biopolymers@wiley.
com
INTRODUCTION
M
odern cell biology depends extensively on fluo-
rescence light microscopy to provide key insights
into cellular structure and molecular behavior.
Inherent advantages, such as its non-invasive na-
ture and the ability to use highly specific labeling
tools, have made fluorescence light microscopy the preferred
strategy for imaging fixed and living cells. The maximum
optical resolution of this method is typically restricted to
200-nm
laterally
and
500-nm
axially.
This
limitation
constrains its ability to provide high-resolution structural
information on molecules that are central to the dogma of
biology, namely DNA, RNA, and protein, which exist as sin-
gle molecules at scales of few nanometers. The physics-based
Ricardo Henriques and Caron Griffiths contributed equally to this work.
Review
PALM and STORM: Unlocking Live-Cell Super-Resolution
Correspondence to: Ricardo Henriques; e-mail: rhenriques@fm.ul.pt and Musa M.
Mhlanga; e-mail: musa@mhlangalab.org
ABSTRACT:
Live-cell fluorescence light microscopy has emerged as an
important tool in the study of cellular biology. The
development of fluorescent markers in parallel with
super-resolution imaging systems has pushed light
microscopy into the realm of molecular visualization at
the nanometer scale. Resolutions previously only attained
with electron microscopes are now within the grasp of
light microscopes. However, until recently, live-cell
imaging approaches have eluded super-resolution
microscopy, hampering it from reaching its full potential
for revealing the dynamic interactions in biology
occurring at the single molecule level. Here we examine
recent advances in the super-resolution imaging of living
cells by reviewing recent breakthroughs in single molecule
localization microscopy methods such as PALM and
STORM to achieve this important goal. # 2011 Wiley
Periodicals, Inc. Biopolymers 95: 322–331, 2011.
Keywords: super-resolution; microscopy; single molecule
V
V
C 2011 Wiley Periodicals, Inc.
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resolution limit of light microscopes imposed by their optical
architecture and the wave nature of light was mathematically
described in the 19th century by Abbe.1
Electron microscopy (EM) has been able to surpass the
resolution limit of optical microscopy and for several years
was the routine approach to resolve cellular architecture at
the ultra-structural or atomic level. However, EM lacks the
basic advantages of fluorescence microscopy such as highly
specific multi-color labeling, and live-cell imaging, both of
which remain altogether impossible with EM.
In response to this dilemma, recent developments in mi-
croscopy have aimed to create techniques able to retain the
advantages of fluorescence microscopy while approaching
the resolving power of EM. Indeed recent advances in single-
molecule localization microscopy (SMLM) have shown reso-
lution below the nanometer.2 Variants such as photo-acti-
vated localization microscopy (PALM),3 fluorescence PALM
(FPALM),4 stochastic optical reconstruction microscopy
(STORM),5 direct STORM (dSTORM),6 and PALM with in-
dependent running acquisition (PALMIRA)7–9 have emerged
at the forefront of the new ‘‘super-resolution’’ methods
retaining the labeling advantages of fluorescence imaging.
However, these approaches are also hampered by their inabil-
ity to be robustly used for live-cell imaging.
Ideally to achieve ‘‘EM-like’’ resolution while preserving
the inherent advantages of live-cell fluorescence microscopy,
the imaging needs to be carried out with minimal perturba-
tion of the sample while acquiring multi-wavelength 2D or
3D data rapidly enough to correctly reconstruct a time-lapse
movie of the cell behavior, with nanoscopic resolution.10
Achieving this remains elusive in live-cell SMLM and is
currently a major focus in the field necessitating innovations
in imaging, microscopy, and sample preparation (termed
‘‘the hardware’’) and in computational techniques (termed
‘‘the software’’) that permit its routine implementation. For
the remainder of this review we will discuss a number of
novel strategies to address these goals.
Achieving Single-Molecule Localization
In optical microscopy, any point source of light smaller than
the diffraction limit appears with a fixed size and shape rep-
resented by an airy disk pattern or otherwise known as the
point-spread function (PSF). This spatial broadening effect is
dependent on the emission wavelength of the fluorophore
and optical characteristics of the imaging apparatus such as
the numerical aperture of the objective used. Classically the
resolution limit is then calculated by applying Rayleigh’s
criterion—where the resolution is equal to the minimum
distance between observed points that can still be resolved as
discrete objects. Since individual elements of molecular
assemblies such as DNA, RNA, and proteins exist at scales
beyond this limit they cannot be easily distinguished or pre-
cisely localized as individual molecules. Thus an important
element in achieving single molecule localization is the
‘‘hardware’’ challenge.
SMLM represents a suite of approaches able to achieve
single molecule detection and localization in fluorescence
microscopy. Extremely high nanoscopy resolutions can be
achieved either at the sub-nanometer level for few molecules2
or, more commonly, in the range of tens of nanometers for
structural reconstructions involving thousands to millions of
fluorescently-labeled objects. Such resolutions are typical
of the techniques PALM, FPALM, STORM, dSTORM, and
PALMIRA.
Vital to these approaches is the knowledge that the center
of the detected emission light from the fluorophores can be
localized analytically and computationally with sub-pixel
accuracies beyond the classical resolution limit of optical
microscopes.11,12 To make this possible, three important cri-
teria must be satisfied: (a) the number of photons detected
for each fluorophore needs to be high enough so as to clearly
distinguish individual PSFs from the surrounding back-
ground; (b) fluorophore mobility needs to be slow enough,
as compared to the image acquisition time, so as to present
well-defined PSFs without considerable blur effects from
motion; (c) particle PSFs cannot overlap extensively or they
will lead to an increase in the complexity of analytical
segmentation and localization of neighboring fluorophores.
Meeting these criteria in live-cell imaging has proven to
be a difficult challenge. Several factors can negatively influ-
ence the ability to satisfy the above criteria. These include
motion of cellular and sub-cellular components and light-
induced damage caused by laser illumination. The toxicity of
aqueous solutions needed to manage the photo-dynamic
behavior of fluorophores further hinders live-cell imaging.
Finally variable levels of density and detection of the objects
due to biological stochasticity complicate the localization of
individual molecules. Thus the ‘‘hardware’’ challenges for
live-cell SMLM span from the optical setup and architecture
of the microscope to the sample preparation and buffer con-
ditions under which image acquisition is achieved.
Super-Resolving Large Populations of Fluorophores
PALM, FPALM, STORM, and dSTORM present a solution
for some of these dilemmas by combining the sequential ac-
quisition of images with the stochastic switching-on and -off
of fluorophores. By regulating the number of active fluoro-
phores in each time-instance through a light-induced envi-
PALM and STORM
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ronment it is possible to minimize the probability that in any
given image two or more particles spatially overlap, thus
satisfying condition (c). A super-resolution dataset can then
be reconstructed by plotting the accumulation of the local-
ized particles from a sequence of images. The final resolution
of the reconstruction depends only on the localization
precision for each fluorophore, which in turn depends upon
the particle’s observable signal-to-noise ratio. Effectively, sev-
eral hundred to thousands of images can be collected until
enough detected molecules are accumulated to accurately
generate a super-resolution dataset where cellular ultra-struc-
ture can be highly resolved. The speed of raw-data acquisi-
tion is thus dependent on the rate at which sufficient
particles can be detected with enough photons to be precisely
localized (see Figure 1).
The concept of image and time-point becomes complex
when dealing with these methods. Generally a single acquired
image does not fully illustrate a time-point as it contains too
few detected molecules to resolve a biological structure, such
as a cytoskeletal framework, for example. As a solution,
multiple images may need to be gathered (typically hun-
dreds) in a sufficiently short time, to represent the state of a
structure for the duration of the time-point acquisition while
preventing unwanted cell motion artifacts. In these methods,
fluorophore mobility has to be slow enough in order to
retain the ability to detect and localize moving objects so as
not to violate aforementioned condition (b). As a result,
published live-cell SMLM experiments to date have only
been able to study complexes that are slow in nature. Hess
et al. used FPALM to study slow moving membrane proteins
FIGURE 1
Acquisition steps for PALM, FPALM, STORM, dSTORM, and sptPALM. The typical
acquisition in SMLM for PALM, FPALM, STORM, and dSTORM is divided in three independent
steps: pre-activation bleach—where cells are illuminated by a strong excitation laser, forcing any
unwanted active fluorophore to go into an off-state; activation—the light-induced activation of an
extremely reduced number of fluorophores preventing excessive spatial-overlapping of the visual-
ized particles; readout—excitation and imaging of the activated fluorophores followed by bleaching
to minimize the presence of already visualized particles in posterior acquired images. These final
two-steps can be cycled until a sufficient number of molecules have been identified. In parallel to
the readout phase particles can be detected and assembled into a reconstruction. On sptPALM, the
readout phase is divided into multiple images where particle movement is captioned and tracked in
parallel until bleaching of most active fluorophores has occurred leading to a new imaging cycle
started by the activation phase.
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in fibroblasts at 40-nm resolution,13 while Shroff et al. used
PALM to study adhesion-complex dynamics using 25–60 s
per frame, at 60-nm resolution.14
Switching on the Lights
A cornerstone of SMLM is its use and control of the photo-
activatable, photo-convertible, or photo-switchable (termed
photo-modulatable in this article) properties of certain
organic fluorophores/dyes and fluorescent proteins. This fea-
ture is so crucial to the functioning of the approach that it
has become the principal reason behind the large suite of
techniques surrounding SMLM such as PALM, FPALM,
STORM, dSTORM, and PALMIRA. These techniques all
share the same principle of stochastically switching-on fluo-
rescent molecules to minimize their visual overlap in a
sequence of images thus permitting the precise localization
of individual molecules. The imaging hardware and analysis
algorithms vary only slightly for each approach and are fairly
simple to establish15 (see Figure 2). Where the methods
diverge is in the various classes of fluorophores used and the
underlying protocol to either induce or control their photo-
modulatable properties.
While PALM and FPALM have chosen genetically encoded
fluorophores as their label of choice, STORM on the other
hand, exploits the photo-switching properties of fluorescent
dyes (such as Cy5 or Alexa647) when in close proximity to a
secondary fluorescent dye label (such as Cy3) that functions
as a fluorescence re-activator after bleaching. dSTORM16 has
further simplified the sample labeling (making it similar to
that of classical immuno-fluorescence) by showing that
similar photo-switching properties can be induced in several
FIGURE 2
Setting your own SMLM. Aside from the complexities involving fluorophore selection
discussed extensively in this review, assembling a SMLM compatible microscope is both cost-effec-
tive and straightforward. TIRF microscopes can easily be adapted for SMLM or a custom system
can be set up by combining a commercial microscope body with a high-sensitivity camera and a
custom laser excitation system. A cylindrical lens inserted into a slider below the objective revolver
can provide astigmatism characteristics enabling 3D imaging with the system—an extensive list of
tutorials on the subject can be found in http://code.google.com/p/quickpalm. lManager constitutes
a free, open-source software for hardware control and acquisition compatible with SMLM experi-
ments and can be used in tandem with QuickPALM—a dedicated ImageJ plugin to process SMLM
data.
PALM and STORM
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fluorescent dyes without the necessity of a secondary reacti-
vator fluorescent dye label. This is accomplished by using a
compatible high-intensity laser that sufficiently stimulates
bleached fluorophores to return to a fluorescent state.
Both PALM, FPALM, STORM, and dSTORM experiments
are typically divided into three distinct steps (see Figure 1).
PALMIRA demonstrated that both the activation and read-
out steps can be taken simultaneously while independently
acquiring a sequence of images.8 Given the correct illumina-
tion conditions of both the activation and excitation light-
sources, it is possible to obtain a time-lapse dataset, in which
only a few fluorophores in a fluorescent on-state are captured
per raw frame before they are immediately pushed into a
non-detectable off-state. This procedure permits the accelera-
tion of the acquisition by merging the two previously distinct
activation and readout steps, and removes the need for the
pulsing of the activation laser, which is incompatible with
most acquisition software available.
Live cell SMLM is thus far almost only compatible with
PALM or its sister techniques, which use genetically-encoded
fluorophores. STORM and dSTORM use synthetic fluores-
cent dyes and special buffers able to maintain photo-switch-
ing. These buffers are, in general, highly toxic to cells. Later
in this review we will examine emerging approaches that seek
to overcome these limitations.
Switching on the Lights: Genetically
Encoded Fluorophores
Perhaps the greatest advantage of genetically-encoded fluo-
rescent proteins is the capacity to specifically label molecules
in a non-invasive and live-cell compatible manner when
compared to other methods such as immuno-fluorescence
staining. Furthermore, cell-friendly mediums can be applied
as opposed to the photo-switching buffers commonly used
in STORM and dSTORM.
Interestingly, it has been known for many years that GFP
itself switches between a fluorescent state and a dark state in
response to light.17 However, it was the engineering of pro-
teins to change their spectral properties upon illumination
with light of specific wavelengths that allowed for the possi-
bility of SMLM to become a widely used tool in cell biology.
There are now numerous examples of these proteins, each
with slightly different photo-physical characteristics. Photo-
activatable proteins, such as PA-GFP, undergo a single transi-
tion from a non-fluorescent to a fluorescent state upon light-
induced activation; reversible photo-switchable fluorophores
are capable of multiple cycles of activation from a dark to a
fluorescent state and return to a dark state, as in the case of
Dronpa; photo-shiftable proteins, exemplified by mEos2, can
be stimulated to convert between two spectrally distinct fluo-
rescent forms (colors) by activating irradiation. These
switching processes are manipulated by careful control of the
imaging environment in tandem to the activation and excita-
tion light intensities. This procedure permits for small sub-
sets of fluorophores to be activated and rapidly bleach while
captured in a sequence of images.
Most of the published literature on live-cell SMLM has
utilized genetically-encoded fluorophores. Yet care needs to
be taken when approaching these methods. Most photo-
modulatable fluorophores require activation by near-UV
light, which is toxic to the majority of cells. The Dendra2
fluorophore is a minor exception, since it can be activated
at wavelengths close to a 488 nm wavelength (reviewed in
Ref. 18). In most experiments it is also desirable that fluoro-
phores immediately bleach following activation in order to
eliminate their presence from multiple acquired images
where they would augment the probability of particle spatial-
overlap. A strong excitation light is then applied to bleach
the fluorophores but the penalty is increased photo-toxicity.
For live-cell imaging in SMLM the ‘‘hardware’’ challenge
can be partially overcome by using lower excitation inten-
sities. This can be used to analyze the motility of the acti-
vated portion of fluorophores over a small sequence of
images until the population is bleached, a process that can be
repeated several times. If the fluorophores are confined to a
specific cellular structure or location and motility is suffi-
ciently slow so as not to cause blur artifacts (which degrade
particle localization), then it becomes possible to reconstruct
the domains where the fluorophores have been captured.
This process uses each fluorophore multiple times to land-
mark their enclosing territory and causes less cell damage
due to the reduction in the illumination intensity. Similarly,
this strategy can also be used to study and map single-mole-
cule motion as demonstrated by the single particle tracking
PALM (sptPALM) technique that combines single-particle
tracking with PALM microscopy19 (see Figure 1).
The emergence of proteins with different emission spec-
tra, such as rsCherry, a monomeric red photo-swichable flu-
orescent protein,20 has made multi-color time-lapse SMLM
imaging possible. Further, the development of new fluores-
cent proteins coupling photo-activatable and photo-shiftable
properties, such as mIrisFP, introduces the possibility
of using a pulse-chase approach in conjunction with super-
resolution imaging for single particle tracking in dynamic
processes, such as monomer turnover in macromolecules.21
Switching on the Lights: Synthetic Fluorophores
The two most important photophysical factors determining
the spatial resolution are the brightness of the molecules in
the fluorescent state used for localization, and the ratio
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between this state and the brightness of the molecules in the
inactivated state. The former determines the number of
photons that can be detected, which in turn determines the
localization precision. The latter factor—the contrast ratio—
contribute to the background, which again directly affects
the localization precision. It should also be noted that the
contrast ratio affects the resolution in a slightly more subtle
way: low contrast ratios limit the ability of the system to
localize molecules at high molecular densities, which is cru-
cial for achieving high Nyquist-limited resolution.14 Conse-
quently, it is important to choose fluorescent labels that have
both high brightness and high contrast ratios. Many of the
most commonly used photo-modulatable fluorescent pro-
teins have high contrast ratios but with a smaller photon
output than many small-molecule fluorescent dyes (6000
photons per Cy5 molecule have been detected versus 490
photons per mEos molecule).18 Therefore, small-molecule
dyes may be attractive candidates as probes for live-cell
SMLM. Yet, the impossibility of genetically encoding such
labels leaves researchers with the difficult task of devising
appropriate strategies for specific and sensitive targeting of
fluorophores to biological molecules of interest, in a living
cell. Cell membrane impermeability to many dyes and dye-
conjugates, not least of all conventionally-labeled antibodies,
stands as the greatest barrier to labeling intracellular targets
under live-cell conditions, where the membrane should stay
intact.
Currently available strategies fit into two broad catego-
ries—those that target fluorophores to peptide sequences or
proteins fused to the target protein, and those that use
enzymes to label the target sequence with the fluorescent
tag (see Table I). The small labeling systems used by peptide-
targeting labeling strategies, such as TetraCys,22 HexaHis,24
and PolyAsp,23 cause minimal protein or cell perturbation.
Thus far, however, only the TetraCys system has been success-
fully used in live-cell or intra-cellular labeling.24 Protein-
directed labeling, such as SNAP/CLIP tags,26 Halo Tags,27
and Dihydrofolate reductase (DHFR) targeting with trime-
thoprim (TMP)-conjugates,28,39 allows improved targeting
specificity, but at the cost of an increase in the size of the
recruiting system, increasing the risk of perturbing protein
function. Despite this, the tag-dye conjugates in a number of
these approaches are sufficiently cell permeable to allow in-
tracellular labeling. Covalent labeling with the DHFR-based
system has been successfully used in live-cell STORM imag-
ing of Histone H2B dynamics.39 Enzyme mediated protein
labeling makes use of a small peptide sequence fused to the
target protein and an enzyme, natural or engineered, which
ligates the fluorescent probe to the recognition sequence.
Some of these methods, such as those based on the use of
Table I
Targeting Strategies for Synthetic Dyes
Targeting Peptide
Ligand
Enzyme
Bond
Cellular Targets
Comments
Reference
Peptide/protein-based targeting
TetraCys
Biarsenical fluorophores
—
Covalent
Membrane/intracellular proteins
Live imaging with FlAsH tags
22,23
HexaHis
NTA-conjugates
—
Noncovalent
Membrane proteins
N/A
24
PolyAsp
Dpa tyr conjugates
—
Noncovalent
Membrane proteins
N/A
25
SNAP/CLIP tags
Benzylguanine conjugates
AGT
Covalent
Membrane/intracellular proteins
Greater specificity, larger label
26
Halo tags
Chloroalkane conjugates
Dehalogenase
Covalent
Membrane/intracellular proteins
Greater specificity, larger label
27
DHFR
TMP
DHFR
Noncovalent or
covalent
Membrane/intracellular proteins
Greater specificity, larger label
25,28
Enzyme-based targeting
Sortag
Oligo-glycine dye conjugate
Sortase
Covalent
Membrane proteins, C- and N-terminals
N/A
29,30
Q-tag
Amine-modified probes
Transglutaminase
Covalent
Membrane proteins, N-terminal
Low substrate specificity
31,32
ACP/PCP
Co-enzyme A-conjugates
Phosphopantetheine
transferases (Sfp and AcpS)
Covalent
Membrane proteins
N/A
33,34
AP
Biotin conjugates
Biotin ligase, BirA
Covalent
Membrane proteins
N/A
35
LAP
cyclo-octyne conjugates/
hydroxycoumarin
Lipoic acid ligase
Covalent
Membrane/intracellular proteins
One or two-step process
36–38
DHFR: dihydrofolate reductase; ACP: acyl-carrier protein; PCP: peptidyl-carrier protein; AP: biotin ligase acceptor peptide; LAP: lipoic acid ligase acceptor peptide; NTA: nitrilotriacetic acid; TMP:
trimethoprim; AGT: O6-alkylguanine-DNA alkyltransferase; N/A: not applicable
PALM and STORM
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sortase,29,30 phosphopantetheine transferases,33 and biotin
ligase,35 have been used to ligate fluorophore-conjugates to
recognition sequences on target proteins in living cells or at
the single molecule level. These approaches combine the
benefits of a small directing peptide sequence and those of
specific and rapid covalent labeling, and thus provide an
ideal system for SMLM. However, the primary disadvantage
currently encountered with the above systems is a lack
of cell-permeability of the tags themselves, such that only
membrane-protein labeling is possible.
In contrast, a Lipoic acid ligase-based system has been
developed which makes labeling at both the cell membrane
and intracellular targets possible.36,37 Two engineered forms
of the microbial lipoic acid ligase have been developed by the
Ting lab. The first is able to ligate cyclo-octyne conjugated
probes to a Lipioic Acid Ligase Peptide (LAP) sequence
fused to both cell surface and intracellular targets using a
two-step process.36–39 Practical challenges with the two-
step process when applied to intracellular labeling led to
the development of a second engineered ligase, a highly
specific ‘‘fluorophoreligase,’’ capable of specifically ligating
hydroxycoumarin to intracellular LAP fusion proteins.37
This newly engineered enzyme may be most suitable for
the direct and specific labeling of intracellular targets,
however, the strict restriction to only one dye limits the
applicability of the system for SMLM at this stage. Further
engineered forms, able to make use of multiple dye-conju-
gates, would provide a valuable system for multi-color
labeling in live-cells in the future.
Besides strategies for the specific labeling of intracellular
proteins with a wide variety of fluorescent dyes for live cell
imaging, the suitability of specific fluorescent dyes for
SMLM, particularly their photoswitching abilities, as well as
the necessary conditions for such blinking, are also an impor-
tant consideration, especially for live-cell imaging. Develop-
ments in imaging buffers have allowed photo-switching
properties to be attributed to the majority of synthetic fluo-
rescent dyes.
Switching on the Lights: Blinking-Inducing Buffers
Fluorescence excitation occurs by the absorption of a photon,
which promotes a singlet, ground state molecule (S0), to the
excited singlet state (S1). The subsequent return to S0 pro-
duces a fluorescence photon emission. Alternatively the
competing process of inter-system conversion can occur
maintaining the fluorophore in a long-lived triplet state (T1)
formation (see Figure 3).
While in T1 the fluorophore is unable to undergo fluores-
cence emission until relaxation to S0 is re-achieved. During
this period the fluorophore is sensitive to an irreversible pho-
tobleaching event if it reacts with molecular oxygen. This in
turn results in the production of reactive oxygen species
(ROS)—one of the major sources of photo-toxicity in cells.
Notwithstanding, light-induced damage is not only created
by this process but the overall absorption of light by the cell
can produce toxicity.38,40
If reactions with oxygen can be avoided, then fluorophore
photobleaching can be reversed. This process can be used to
induce switching behavior in fluorophores16,41 as the T1 tran-
sition is stochastic and can be employed as the transient off-
state of a fluorophore. Under these conditions, fluorophore
blinking compatible with single molecule localization of a
large population of fluorophores can be attained by imaging
the cycling of short fluorophore photon bursts caused by S0–
S1 transitions (the on-state) followed by the temporary arrest
of fluorescence in the S1–T1 shift (the off-state). Initially, a
very limited selection of dyes known to be able to undergo
such
photoswitching
processes
was
available.
Cyanine
dyes42,43 have been most commonly used for SMLM as they
can be induced to switch by the presence of a second, activat-
ing fluorophore. Such a photoswitching mechanism requires
oxygen removal and the use of millimolar concentrations of
a reducing agent, such as b-mercaptoethanol, in the imaging
medium.42,44
The demonstration of light-induced reversible photo-
switching of single standard fluorophores for use in SMLM,
FIGURE 3
Scheme of the photoswitching process and redox tri-
plet state depletion mechanism. A ground state fluorophore is
excited to the S1 singlet state by the absorbtion of a photon. The
fluorophore may then return to the ground state or transverse into
a dark triplet state by intersystem crossing, followed by stochastic
relaxation back to ground state. Boxed region: The addition of
reducing and oxidizing agents depletes the triplet state by electron
transfer reactions, producing either form of fluorophore radical and
retrieving the reactive intermediates by the reciprocal reduction or
oxidation. This returns the fluorophore to ground state, avoiding
photobleaching and allowing cycling or switching of the fluoro-
phore between excited, triplet, and ground states. S0: ground state;
S1: excited singlet state; T: triplet state; ex: excitation; fl: fluores-
cence; isc: inter-systen crossing; rel: relaxation; red: reduction; ox:
oxidation; F: fluorophore.
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termed dSTORM,6 initiated significant advances in establish-
ing SMLM imaging systems in which a much larger range of
standard fluorophores can now be used. Central to these
developments is an understanding of this ‘‘blinking’’ mecha-
nism in fluorescent molecules, and concomitantly, the
formulation of a system which modulates the switching rates
(see Figure 3).
Photobleaching can be limited by the depletion of oxygen
in the sample, either by embedding with poly-(vinyl-alcohol)
(PVA) or using enzymatic oxygen scavenging buffers. This
removes singlet oxygen and thus lengthens the T1 lifetime,
while addition of a reducing agent is often used to recover
ionized fluorophores. The versatility of these approaches
remains limited by their dependence on the specific fluoro-
phore’s inherent single-state return rate for establishment of
an appropriate rate of blinking, while oxygen depletion and
toxic reducing agents make this setup incompatible with
most live-cell experiments.
By approaching the photobleaching and triplet state
recovery processes as a redox system, the Sauer and Tinnefeld
groups have determined a simple, live-cell adaptable imaging
setup to allow the fine-tuning of the rate of singlet-state
return relative to triplet state formation. In this system, the
reactive triplet state is rapidly depleted, either by oxidation to
a radical cation, or by reduction to a radical anion. These
ions can be recovered by the addition of a reducing or oxidiz-
ing agent, respectively, returning the fluorophore to the sin-
glet state. Thus a buffering system with both reducing and
oxidizing agents (termed ROXS) recovers reactive triplet state
intermediates, repopulating the ground state and avoiding
photobleaching.45 By adjusting the relative ROXS buffer con-
centrations as required, the rate of photoswitching can be
directly controlled to ensure sufficient fluorophores are in a
dark state at each time point and that fluorescent lifetimes
are sufficient to yield photons for accurate localization.16,45
The toxicity of ROXS reagents has had to be addressed in
order to adapt the system for live-cell imaging (see Table II).
Typically, thiol-reagents such as b-mercaptoethanol or b-
mercaptoethylamine have been used as reducing agents in
SMLM buffers.16,41,44 Recently, glutathione and ascorbic acid
have proven to be appropriate live-cell compatible alterna-
tives to these reducing agents.16,44,45 Despite its toxicity
methylvialogen
remains
the
primary
oxidizing
agent
used.45,48 By taking advantage of the oxidizing potential of
oxygen itself, and using the molecular oxygen present in the
cellular environment to fulfill the role of the oxidant in
ROXS16,48 the challenge of oxidant toxicity, as well as the
need for oxygen-depletion in SMLM, has been neatly side-
stepped. Although the presence of oxygen slightly restricts
the experimentor’s capacity to modulate the dyes’ photophy-
sics, this nevertheless greatly simplifies the application of
ROXS for use in live-cell SMLM.
Essentially any desired fluorophore labeled with suitable
photo-physical properties can be used in a biologically com-
patible ROXS imaging buffer, without the need for oxygen
depletion. The ATTO dyes, such as ATTO520, ATTO565,
ATTO655, ATTO680, and ATTO700, have proven particu-
larly well suited for use in ‘‘blink microscopy’’ with
ROXS.16,48 Investigations are extending into the suitability
of more water-soluble dyes, such as perylene dicarboximide
fluorphores,49 specifically for use in live-cell imaging.
ROXS provides a dye and buffer system that gives us
prime choice of multi-color dyes to use in live-cell SMLM
with minimal perturbation to the cell.
Breaking Through the Technological Limits
SMLM of a large population of fluorophores typically
demands that hundreds to thousands of diffraction-limited
images be acquired and processed in order to reconstruct a
super-resolution dataset, the central ‘‘software’’ challenge.
What is the relationship between localization precision and re-
solution? It is clear that the resolution of an SMLM image can-
not be higher than the precision to which the molecules are
localized. However, the Nyquist theorem, as applied here,
requires that a structural-dynamics be sampled at twice the
finest spatio-temporal resolution one wants to detect. This is
Table II
Redox Buffer Agents to Modulate Photo-Switching
Redox Buffer
Action
Endogenous
Toxicity
Reference
Trolox
Reductant
No
Low
46
b-mercaptoethanol
Reductant
No
High
44,46,47
Propyl galate
Reductant
No
Low
46
Ascorbic acid
Reductant
Yes
Low
46,48
Glutathione
Reductant
Yes
Low
44,46,48
Dithiotriotol
Reductant
No
High
47
Tris(2-carboxyethyl)phosphine hydrochloride
Reductant
No
High
47
Methylviologen
Oxidant
No
High
16,45,48
Ambient oxygen
Oxidant
Yes
Low
16,48
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especially relevant in live-cell SMLM. In this case, a series of
raw data frames are taken and subsequently parsed into
SMLM time-points. For instance, if 1000 raw data frames are
taken, one might parse these into 10 time-points of 100 raw
data-frames or 100 time-points of 10 raw data frames. While
the precision at which the molecules are localized does not
change in either of these examples, the sparseness of detected
particles will be far greater in the latter case than in the former
case, and the underlying sample structure may be unrecogniz-
able. Consequently, there is a fundamental trade-off between
spatial and temporal resolution.
Additionally in order to obtain a reliable super-resolution
reconstruction, algorithms have to analytically detect and localize
each individual sub-diffraction particle present in each acquired
frame. This is generally a major setback because visualization of
the sample in parallel to the acquisition is crucial for making
decisions on how to best adjust imaging conditions. Raw unpro-
cessed images can be partially used to observe the sample but
these are corrupted by the technique itself—each raw-image is
generally composed of few emitting molecules not permitting a
complete understanding of underlying cellular structures.
Recently several algorithms have been published allowing
for
processing
speeds
concurrent
with
the
acquisition
itself.15,50–53 QuickPALM,15 an ImageJ-based algorithm, in
conjunction with lManager,54 an open-source software for
hardware control is able to both acquire and process 3D and
4D SMLM providing the super-resolution reconstruction
in real-time as images are streamed from the camera (see
Figure 2). This feature allows for data-driven algorithmic deci-
sions on how to optimally adapt the acquisition and provides
the user a reconstructed view of the sample being acquired.
A dominant challenge in SMLM is minimizing light-
induced cell damage55,56 as super-resolution techniques tend
to dramatically increase the photo-damage caused to the cell
by either increasing or prolonging the amount of light
needed for imaging when compared to classical fluorescence
microscopy. Conventionally in fluorescence imaging the
entire field of view is illuminated uniformly, both light-exci-
tation and acquisition time are adjusted so as to obtain a
high enough signal-to-noise ratio (SNR) to resolve cellular
structures of interest. Yet, fluorophore concentrations within
cells vary, leaving researchers with the decision of how to
best set the illumination characteristics at the cost of either
under-exposing or over-exposing sub-regions of the image.
Controlled light-exposure microscopy (CLEM) introduces
the concept of applying a non-uniform illumination to the
imaging area in laser scanning systems where on a pixel-by-
pixel basis the light-exposure is interrupted if a sufficient SNR
has been achieved.55 As a combination of ‘‘hardware’’ and
‘‘software’’ approaches, this method improves image-quality
and severely reduces photo-toxicity.55 Problematically, SMLM
uses cameras that only permit the parallel acquisition of all the
pixels composing an image theoretically preventing the imple-
mentation of CLEM. Non-uniform illumination in time has
been previously applied to SMLM in the work of Betzig et al.3
where the sample activation is incrementally increased over
time to compensate for fluorophore depletion. This concept
can be further adapted by modulating the illumination both in
the spatial and temporal domain with the help of a spatial-
light-modulator (SLM). In SMLM two light beams are used: a
low-intensity activation beam to induce fluorophores into an
on-state and a high-intensity readout beam to excite and
bleach the fluorophore. By definition, the images acquired in
SMLM have a sparse concentration of fluorophores. This
means that most of the area subjected to illumination is not
occupied by active fluorophores. By concentrating the readout
illumination to the areas where only actively emitting fluoro-
phores are present, a drastic reduction in the amount of light
used for imaging is achieved therefore minimizing cell damage.
A major focus of the QuickPALM16 development team is to
bring this feature forward by combining the power of real-
time processing with the capacity for both SLM and acquisi-
tion hardware control brought by lManager.54
The authors thank members of the Mhlanga and Zimmer Lab for
comments. They especially thank C. von Middendorff, C. Zimmer,
and T. Duong for valuable comments and advice.
REFERENCES
1. Abbe, E. Arch Mikroskop Anat 1873, 9, 413–420.
2. Pertsinidis, A.; Zhang, Y.; Chu, S. Nature 2010, 466, 647–651.
3. Betzig, E.; Patterson, G. H.; Sougrat, R.; Lindwasser, O. W.;
Olenych, S.; Bonifacino, J. S.; Davidson, M. W.; Lippincott-
Schwartz, J.; Hess, H. F. Science 2006, 313, 1642–1645.
4. Hess, S. T.; Girirajan, T. P.; Mason, M. D. Biophys J 2006, 91,
4258–4272.
5. Rust, M.; Bates, M.; Zhuang, X. Nat Methods 2006, 3, 793–796.
6. Heilemann, M.; van de Linde, S.; Schuttpelz, M.; Kasper, R.;
Seefeldt, B.; Mukherjee, A.; Tinnefeld, P.; Sauer, M. Angew
Chem Int Ed Engl 2008, 47, 6172–6176.
7. Fo¨lling, J.; Belov, V.; Kunetsky, R.; Medda, R.; Scho¨nle, A.;
Egner, A.; Eggeling, C.; Bossi, M.; Hell, S. Angew Chem Int Ed
2007, 46, 6266–6270.
8. Egner, A.; Geisler, C.; von Middendorff, C.; Bock, H.; Wenzel,
D.; Medda, R.; Andresen, M.; Stiel, A.; Jakobs, S.; Eggeling, C.
Biophys J 2007, 93, 3285.
9. Bock, H.; Geisler, C.; Wurm, C.; von Middendorff, C.; Jakobs,
S.; Scho¨nle, A.; Egner, A.; Hell, S.; Eggeling, C. Appl Phys B
Lasers Opt 2007, 88, 161–165.
10. Carlton, P. M.; Boulanger, J.; Kervrann, C.; Sibarita, J. B.; Sala-
mero, J.; Gordon-Messer, S.; Bressan, D.; Haber, J. E.; Haase, S.;
Shao, L.; Winoto, L.; Matsuda, A.; Kner, P.; Uzawa, S.; Gustafs-
son, M.; Kam, Z.; Agard, D. A.; Sedat, J. W. Proc Natl Acad Sci
USA 2010, 107, 16016–16022.
330
Henriques et al.
Biopolymers

----!@#$NewPage!@#$----
11. Burns, D. H.; Callis, J. B.; Christian, G. D.; Davidson, E. R. Appl
Opt 1985, 24, 154–161.
12. Bobroff, N. Rev Sci Instrum 1986, 57, 1152–1157.
13. Hess, S.; Gould, T.; Gudheti, M.; Maas, S.; Mills, K.; Zimmer-
berg, J. Proc Natl Acad Sci USA 2006, 104, 17370–17375.
14. Shroff, H.; Galbraith, C. G.; Galbraith, J. A. Betzig, E. Nat Meth-
ods 2008, 5, 417–423.
15. Henriques, R.; Lelek, M.; Fornasiero, E. F.; Valtorta, F.; Zimmer,
C.; Mhlanga, M. M. Nat Methods 2010, 7, 339–340.
16. van de Linde, S.; Endesfelder, U.; Mukherjee, A.; Schuttpelz, M.;
Wiebusch, G.; Wolter, S.; Heilemann, M.; Sauer, M. Photochem
Photobiol Sci 2009, 8, 465–469.
17. Dickson, R.; Cubitt, A.; Tsien, R. Moerner, W. Nature 1997, 388,
355–358.
18. Henriques, R.; Mhlanga, M. M. Biotechnol J 2009, 4, 846–
857.
19. Manley, S.; Gillette, J. M.; Patterson, G. H.; Shroff, H.; Hess, H.
F.; Betzig, E.; Lippincott-Schwartz, J. Nat Methods 2008, 5,
155–157.
20. Subach, F. V.; Patterson, G. H.; Manley, S.; Gillette, J. M.; Lippin-
cott-Schwartz, J.; Verkhusha, V. V. Nat Methods 2009, 6, 153–159.
21. Fuchs, J.; Bohme, S.; Oswald, F.; Hedde, P. N.; Krause, M.; Wie-
denmann, J.; Nienhaus, G. U. Nat Methods 2010, 7, 627–630.
22. Adams, S. R.; Campbell, R. E.; Gross, L. A.; Martin, B. R.;
Walkup, G. K.; Yao, Y.; Llopis, J.; Tsien, R. Y. J Am Chem Soc
2002, 124, 6063–6076.
23. Martin, B. R.; Giepmans, B. N.; Adams, S. R.; Tsien, R. Y. Nat
Biotechnol 2005, 23, 1308–1314.
24. Guignet, E. G.; Segura, J. M.; Hovius, R.; Vogel, H. ChemPhys
Chem 2007, 8, 1221–1227.
25. Ojida, A.; Honda, K.; Shinmi, D.; Kiyonaka, S.; Mori, Y.; Hama-
chi, I. J Am Chem Soc 2006, 128, 10452–10459.
26. Gautier, A.; Juillerat, A.; Heinis, C.; Correˆa I. R., Jr.; Kindermann,
M.; Beaufils, F.; Johnsson, K. Chem Biol 2008, 15, 128–136.
27. Los, G. V.; Encell, L. P.; McDougall, M. G.; Hartzell, D. D.; Kar-
assina, N.; Zimprich, C.; Wood, M. G.; Learish, R.; Ohana, R. F.;
Urh, M.; Simpson, D.; Mendez, J.; Zimmerman, K.; Otto, P.;
Vidugiris, G.; Zhu, J.; Darzins, A.; Klaubert, D. H.; Bulleit, R. F.;
Wood, K. V. ACS Chem Biol 2008, 3, 373–382.
28. Miller, L. W.; Cai, Y.; Sheetz, M. P.; Cornish, V. W. Nat Methods
2005, 2, 255–257.
29. Popp, M. W.; Antos, J. M.; Grotenbreg, G. M.; Spooner, E.;
Ploegh, H. L. Nat Chem Biol 2007, 3, 707–708.
30. Yamamoto, T.; Nagamune, T. Chem Commun (Camb) 2009,
1022–1024.
31. Lin, C. W.; Ting, A. Y. J Am Chem Soc 2006, 128, 4542–4543.
32. Sunbul, M.; Yin, J. Org Biomol Chem 2009, 7, 3361.
33. Zhou, Z.; Cironi, P.; Lin, A. J.; Xu, Y.; Hrvatin, S.; Golan, D. E.; Sil-
ver, P. A.; Walsh, C. T.; Yin, J. ACS Chem Biol 2007, 2, 337–346.
34. Jacquier, V.; Prummer, M.; Segura, J. M.; Pick, H.; Vogel, H.
Proc Natl Acad Sci USA 2006, 103, 14325–14330.
35. Howarth, M.; Chinnapen, D. J.; Gerrow, K.; Dorrestein, P. C.;
Grandy, M. R.; Kelleher, N. L.; El-Husseini, A.; Ting, A. Y. Nat
Methods 2006, 3, 267–273.
36. Fernandez-Suarez, M.; Baruah, H.; Martinez-Hernandez, L.;
Xie, K. T.; Baskin, J. M.; Bertozzi, C. R.; Ting, A. Y. Nat Biotech-
nol 2007, 25, 1483–1487.
37. Uttamapinant, C.; White, K. A.; Baruah, H.; Thompson, S.; Fer-
nandez-Suarez, M.; Puthenveetil, S.; Ting, A. Y. Proc Natl Acad
Sci USA 2010, 107, 10914–10919.
38. Baruah, H.; Puthenveetil, S.; Choi, Y. A.; Shah, S.; Ting, A. Y.
Angew Chem Int Ed Engl 2008, 47, 7018–7021.
39. Wombacher, R.; Heidbreder, M.; van de Linde, S.; Sheetz, M. P.;
Heilemann, M.; Cornish, V. W.; Sauer, M. Nat Methods 2010, 7,
717–719.
40. Stephens, D. J.; Allan, V. J. Science 2003, 300, 82–86.
41. Fo¨lling, J.; Bossi, M.; Bock, H.; Medda, R.; Wurm, C. A.; Hein,
B.; Jakobs, S.; Eggeling, C.; Hell, S. W. Nat Methods 2008, 5,
943–945.
42. Bates, M.; Huang, B.; Dempsey, G. T.; Zhuang, X. Science 2007,
317, 1749–1753.
43. Bates, M.; Blosser, T. R.; Zhuang, X. Phys Rev Lett 2005, 94,
108101.
44. van de Linde, S.; Kasper, R.; Heilemann, M.; Sauer, M. Appl
Phys B Lasers Opt 2008, 93, 725–731.
45. Vogelsang, J.; Kasper, R.; Steinhauer, C.; Person, B.; Heilemann, M.;
Sauer, M.; Tinnefeld, P. Angew Chem Int Ed 2008, 47, 5465–5469.
46. Rasnik, I.; McKinney, S. A.; Ha, T. Nat Methods 2006, 3, 891–893.
47. Aitken, C. E.; Marshall, R. A.; Puglisi, J. D. Biophys J 2008, 94,
1826–1835.
48. Vogelsang, J.; Cordes, T.; Forthmann, C.; Steinhauer, C.; Tinne-
feld, P. Proc Natl Acad Sci USA 2009, 106, 8107–8112.
49. Cordes, T.; Vogelsang, J.; Anaya, M.; Spagnuolo, C.; Gietl, A.;
Summerer, W.; Herrmann, A.; Mullen, K.; Tinnefeld, P. J Am
Chem Soc 2010, 132, 2404–2409.
50. Wolter, S.; Schuttpelz, M.; Tscherepanow, M.; van de Linde, S.;
Heilemann, M.; Sauer, M. J Microsc 2010, 237, 12–22.
51. Hedde, P. N.; Fuchs, J.; Oswald, F.; Wiedenmann, J.; Nienhaus,
G. U. Nat Methods 2009, 6, 689–690.
52. Smith, C. S.; Joseph, N.; Rieger, B.; Lidke, K. A. Nat Methods
2010, 7, 373–375.
53. Quan, T.; Li, P.; Long, F.; Zeng, S.; Luo, Q.; Hedde, P. N.; Nien-
haus, G. U.; Huang, Z. L. Opt Express 2010, 18, 11867–11876.
54. Stuurman, N.; Amodaj, N.; Vale, R. D. Microsc Today 2007, 15, 42–43.
55. Hoebe, R. A.; Van Oven, C. H.; Gadella, T. W., Jr.; Dhonukshe,
P. B.; Van Noorden, C. J.; Manders, E. M. Nat Biotechnol 2007,
25, 249–253.
56. Hoebe, R. A.; Van der Voort, H. T.; Stap, J.; Van Noorden, C. J.;
Manders, E. M. J Microsc 2008, 231, 9–20.
Reviewing Editor: Julie Biteen
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