Nonlinear structured-illumination microscopy
with a photoswitchable protein reveals
cellular structures at 50-nm resolution
E. Hesper Regoa,b,1,2, Lin Shaob, John J. Macklinb, Lukman Winotoc, Göran A. Johanssond, Nicholas Kamps-Hughesd,
Michael W. Davidsone, and Mats G. L. Gustafssonb,3
aGraduate Group in Biophysics, University of California, San Francisco, CA 94158; bHoward Hughes Medical Institute, Janelia Farm Research Campus,
Ashburn, VA 20147; cThe Keck Center for Advanced Microscopy and the Department of Biochemistry and Biophysics, University of California, San
Francisco, CA 94158; dDepartment of Physiology, University of California, San Francisco, CA 94158; eNational High Magnetic Field Laboratory,
Florida State University, Tallahassee, FL 32310
Edited by* Jennifer Lippincott-Schwartz, National Institutes of Health, Bethesda, MD, and approved October 18, 2011 (received for review May 16, 2011)
Using ultralow light intensities that are well suited for investigat-
ing biological samples, we demonstrate whole-cell superresolution
imaging by nonlinear structured-illumination microscopy. Struc-
tured-illumination microscopy can increase the spatial resolution
of a wide-field light microscope by a factor of two, with greater
resolution extension possible if the emission rate of the sample
responds nonlinearly to the illumination intensity. Saturating the
fluorophore excited state is one such nonlinear response, and a
realization of this idea, saturated structured-illumination micro-
scopy, has achieved approximately 50-nm resolution on dye-filled
polystyrene beads. Unfortunately, because saturation requires ex-
tremely high light intensities that are likely to accelerate photo-
bleaching and damage even fixed tissue, this implementation is of
limited use for studying biological samples. Here, reversible photo-
switching of a fluorescent protein provides the required nonlinear-
ity at light intensities six orders of magnitude lower than those
needed for saturation. We experimentally demonstrate approxi-
mately 40-nm resolution on purified microtubules labeled with
the fluorescent photoswitchable protein Dronpa, and we visualize
cellular structures by imaging the mammalian nuclear pore and
actin cytoskeleton. As a result, nonlinear structured-illumination
microscopy is now a biologically compatible superresolution ima-
ging method.
patterned excitation ∣ moiré ∣ subdiffraction
Recent years have seen many advances in fluorescence lightmicroscopy, and it has become clear that diffraction—once
thought of as an insurmountable physical limit—is no longer
the barrier to obtaining high-resolution information on a biolo-
gical specimen. Several methods are now capable of resolving
structures well below the classical Abbe diffraction limit, which
states that the resolution of a microscope is limited to about half
the wavelength of light. Each of these subdiffraction methods can
be traced to one of two ideas: localizing individual fluorophores
in the sample to subdiffraction precision, or structuring the illu-
mination light to yield small, subdiffraction regions of fluoro-
phores in the emitting or nonemitting state. Localization-based
techniques like PALM, STORM, and others (1–6) need thou-
sands to tens of thousands of raw images to achieve the best
resolution, whereas light-structuring techniques like stimulated-
emission depletion (STED) (7) and saturated structured-illumi-
nation microscopy (SSIM) (8) typically use extremely high light
intensities from either a focused laser beam or an illumination
pattern in wide field, respectively, to generate a high-resolution
image. These and other requirements impose unfortunate prac-
tical limitations that prevent subdiffraction imaging from taking
full advantage of the benefits that light microscopy has to offer.
Namely, fluorescence microscopy excels at studying biological
samples because it can be minimally invasive, acquire data
rapidly, and target molecules of interest with specific labeling
strategies. Consequently, there is a need to continue to advance
subdiffraction—or superresolution—microscopy such that it rea-
lizes ultrahigh resolutions while retaining those qualities of a light
microscope that make it an invaluable resource for cell biology.
In this article, we present a biologically compatible superresolu-
tion method based on structured illumination with a reversibly
photoswitchable protein; our technique uses 10- to 1,000-fold
fewer images than localization-based techniques, and many
orders of magnitude lower light intensities than other light-struc-
turing techniques.
The first set of techniques introduced above treats the sample
as a collection of individual fluorophores. Various implementa-
tions of this localization-based idea—PALM (1), STORM (2),
F-PALM (3), and others (4–6)—have achieved resolution down
to 10–20 nm by relying on the precision to which a light distribu-
tion from a single molecule can be mathematically determined.
An image is reconstructed by combining hundreds to tens of thou-
sands of raw frames, each containing only a few single molecules.
There is a fundamental trade-off between resolution and quantity
of images, and, although there have been published reports on
slowly moving, living samples (9, 10), ultimately, such high num-
bers of raw images limit the speed at which data can be taken and,
by consequence, the temporal resolution that can be achieved.
The method presented in this article is an example of the
second set of techniques introduced above. In general, these
methods treat the sample as a continuous fluorescent object and
gather high-resolution data about that object by structuring the
incoming illumination light (11). This can be done in either a
scanning configuration as in confocal microscopy or in a wide-
field configuration as in linear structured-illumination micro-
scopy (SIM). By choosing an appropriately small pinhole in con-
focal microscopy (12), or an appropriately fine line spacing in
linear SIM (13), structures at roughly
p
2 or 2 times the conven-
tional diffraction limit can be resolved, respectively. However, in
their most basic form (i.e., under conditions of linear fluores-
Author contributions: M.G.L.G. led the project and conceived of the idea; E.H.R. and
M.G.L.G. designed research; E.H.R. performed research; E.H.R., L.S., N.K.-H., and M.W.D.
contributed reagents/analytic tools; L.S. wrote the reconstruction software and edited
the paper; J.J.M. performed a crucial photobleaching experiment; L.W., G.A.J., and
E.H.R. built optical hardware; E.H.R. and M.G.L.G. analyzed data; and E.H.R. wrote
the paper.
The authors declare no conflict of interest.
*This Direct Submission article had a prearranged editor.
Freely available online through the PNAS open access option.
1Present address: Department of Immunology and Infectious Diseases, Harvard School of
Public Health, Boston, MA 02115.
2To whom correspondence should be addressed. E-mail: hesper.rego@gmail.com.
3Deceased April 17, 2011.
This article contains supporting information online at www.pnas.org/lookup/suppl/
doi:10.1073/pnas.1107547108/-/DCSupplemental.
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cence), these methods are still fundamentally diffraction-limited
because the same physical laws that govern the resolution of the
detected structure also determine the resolution of the illumina-
tion pattern.
If appropriate nonlinear fluorescence phenomena are intro-
duced, then an effective illumination pattern can be generated
that is of theoretically unlimited resolution (8, 14). This idea ap-
plies to a point-scanning configuration called STED microscopy
and has shown resolution down to 5.8 nm on nonbiological struc-
tures by using a donut-shaped beam tuned to stimulate the emis-
sion from excited fluorophores and decrease the effective focal
spot of a laser beam (15). Because of the low cross-section for
stimulated emission, such high resolution requires extreme peak
intensities from the depletion laser—on the order of GW∕cm2.
High peak light intensities are known to accelerate the photo-
bleaching of fluorescent molecules (16), and fluorescent proteins
may be especially sensitive to this phenomenon (17). Despite this,
STED has reached 50- to 70-nm resolution using fluorescent pro-
teins depleted with more modest, yet still high, peak intensities of
approximately 400–800 MW∕cm2 (18, 19). Similarly, SSIM, an
implementation of nonlinear SIM (NL-SIM), has achieved a
resolution of approximately 50 nm in a wide-field configuration
on dye-filled polystyrene beads by exploiting saturation of the
excited state of a molecule (8, 14). Although the peak intensities
of approximately 10 MW∕cm2 used for saturation were a few or-
ders of magnitude lower than those required for stimulated emis-
sion, to our knowledge there have been no published results of
improved resolution by SSIM of biological structures due to the
poor photostability of most fluorescent molecules under such ex-
citation intensities.
Photoswitching is an inherently nonlinear process and has
been proposed as an alternative to saturation or stimulated emis-
sion for resolution enhancement purposes because of the geneti-
cally encoded photoswitchable molecules (20, 21) available, and
the low light intensity needed to switch the molecules. In general,
photoswitchable fluorescent molecules can be reversibly switched
between two spectrally distinct states using light; saturating either
of these population states results in a nonlinear relationship be-
tween the fluorescence emission and the illumination intensity.
This method has been implemented on purified protein adsorbed
to nonbiological structures and protein-filled bacteria in one di-
mension with a wide-field configuration (22, 23) or in two dimen-
sions with a point-scanning, donut-mode configuration (24), but,
to our knowledge, has never demonstrated improved resolution
of cellular structures.
Here, we use the photoswitchable fluorescent protein Dronpa
(21) to generate a nonlinear response from the sample using light
intensities on the order of 1–10 W∕cm2. Taking 63 raw images
with a wide-field total internal reflection fluorescence (TIRF)
structured-illumination setup, we resolve information in two
dimensions at four times the conventional diffraction limit. To
verify the enhanced resolution we look at purified microtubules
labeled with Dronpa, the nuclear pore complex in purified mam-
malian nuclei, and the actin cytoskeleton in fixed mamma-
lian cells.
Method Concept
Dronpa can be reversibly switched between a fluorescent on state
with an excitation peak at 503 nm and a nonfluorescent off state
with an absorption peak at 390 nm using light (21). In principle,
either of these states can be saturated, producing a nonlinear
relationship between the illumination intensity and the fluores-
cence emission from the sample. Saturating the off state should
result in cleaner minima of the illumination pattern leading to
higher signal-to-noise ratio of the higher-order harmonics (22);
for this reason we have chosen to saturate the off state. Dronpa
decays to its dark state under blue-light (488-nm) illumination
with a characteristic timescale, τoff , dependent on the illumina-
tion intensity of the light (Fig. S1). We define the “saturation
level” η, as the ratio of the exposure time to τoff . If the sample
is illuminated with a sinusoidal pattern of light that drives the
molecules to their off state, only molecules at the minima of the
pattern will remain on (Fig. 1A; SI Discussion). As the exposure
time and, by consequence, η, increases, the region of molecules
left on will become more confined, its size far smaller than the
conventional diffraction limit (Fig. 1B). Importantly, the final
NL-SIM resolution is not only a function of this saturation factor
but also of this pattern line spacing (Fig. 1C).
This process alone does not affect the resolution of the micro-
scope because the collected fluorescence will be blurred by dif-
fraction; additional processing must be done to reconstruct a
high-resolution image. As in our earlier studies (8, 13, 25–27), we
have chosen to reconstruct the data in frequency space. In fre-
quency space, the resolution of a microscope is represented by
the support, or the nonzero region, of the optical transfer func-
tion (OTF) (Fig. 2A). The radius of the OTF support (i.e., the
highest spatial frequency at which the OTF reaches zero) is a
function of the wavelength of detected light, and the numerical
aperture (N.A.) of the objective. In general, the illumination OTF
support can be slightly larger than the detection OTF support
because of the shorter excitation wavelength used in fluorescence
microscopy (due to the Stokes shift), but this is especially true
in objective-style TIRF microscopy: The detection OTF can fall
to zero within the N.A. required to produce TIRF (Fig. 2B and
Fig. S2). In two-dimensional SIM, the sample is illuminated by a
sinusoidally varying pattern of light with a spatial frequency at the
edges of the illumination OTF support (13) (Fig. 2C). This spatial
frequency mixes with the underlying spatial frequencies in the
sample, some of which fall outside the normal OTF support,
and moves this information into the conventional detection OTF
Ill
um
in
at
io
n
in
te
ns
ity
o
f t
he
o
ff
lig
ht
Fraction of m
olecules rem
aining o
n
x
Saturation level, η = 5
Saturation level, η = 30
0 200 300 400 5000
20
40
60
80
100
100
pattern linespacing, p, (nm)
FW
H
M
(n
m)
0 2 4 6 8 100
20
40
60
80
100
saturation level, η
FW
H
M
(n
m)
p
FWHMA
B C
Fig. 1. Real-space representation. (A) Spatially patterned off light (blue) is
used to drive the molecules to the off state. In a small region surrounding the
zeros of this pattern, a fraction of the molecules will remain in the on state
(green). At high saturation level (dashed green) only those molecules that
fall directly in the zero of the pattern will remain fluorescent. This region
can be approximated as a Gaussian with a FWHM, which depends on both
the saturation level (B) and the line spacing of the pattern (C). At high satura-
tion levels and fine line spacings of the pattern, the FWHM of these peaks,
and consequently the resolution of the microscope, can be much higher than
conventional diffraction-limited resolution.
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support in the form of moiré fringes. Linear SIM requires that at
least three different images are collected at different phases of
the illumination pattern so that the information can be properly
unmixed. In NL-SIM, the effective illumination pattern, as repre-
sented by the green curve in Fig. 1A, contains more than one
spatial frequency. In fact, if the nonlinear phenomenon used is
nonpolynomial, like photoswitching, then the effective illumina-
tion pattern theoretically contains an infinite number of higher-
order harmonics with spatial frequencies that are integer multi-
ples of the fundamental illumination spatial frequency (8). Con-
sequently, the final NL-SIM resolution depends linearly on the
spatial frequency of the illumination pattern as described in
Fig. 1C; for this reason, we chose an illumination pattern with
a spatial frequency close to the resolution limit itself. Likewise,
although the number of higher-order harmonics, and by conse-
quence the number of contributing information components, is
in principle infinite, there will only be a finite number that are
detectable above the noise. The saturation level affects the num-
ber of these detectable higher-order harmonics and this in turn
determines the final NL-SIM resolution (Fig. 2D and Fig. S3A).
To unmix the high-resolution sample information that these
higher-order harmonics shift into the conventional OTF support,
more images must be taken at finer phase steps as compared to
linear SIM. The number of contributing higher-order information
components that need to be separated establishes the number of
images needed in each pattern orientation (Fig. S3A), and the
one-dimensional pattern can be rotated in a sufficient number
of orientations to achieve nearly isotropic resolution enhance-
ment in two dimensions (Fig. 2E and Fig. S3 B and C). Moreover,
when using photoswitching, each image corresponds to a photo-
switching cycle, and thus a photoswitchable molecule able to
withstand multiple rounds of cycling before photobleaching is
needed.
Results
Dronpa. The photophysical properties of Dronpa were evaluated
to determine its suitability as a photoswitchable molecule for our
purposes. Two parameters were tested: the emission intensity of
Dronpa in its on state as compared to the intensity in its off state,
and the overall photobleaching rate of the molecules. The first
parameter affects the signal-to-noise ratio of the higher-order
harmonics: Higher fluorescence background in the off state in-
creases the contribution of the conventional component to the
NL-SIM image, which in turn lowers the relative contribution
of the higher-order harmonics (Fig. S4). The second parameter
determines how many images can be taken without a significant
loss in fluorescence signal.
Dronpa was illuminated with 488-nm laser light, which both
excites the molecule to fluoresce and drives the transition from
the on to the off state (21). The off to on state transition was
driven with 405-nm laser light. The emission fluorescence inten-
sity of the on state was found to be roughly 50 times greater than
that of the off state at η ¼ 5 (Fig. S1C). Although Dronpa mo-
lecules are efficiently driven to their reversible dark state with
488-nm light, there is a nonzero probability of the molecules
being driven to an irreversible dark state (28). After each round
of cycling, fewer molecules can be converted to their fluorescent
state, contributing to an overall photobleaching rate of the sam-
ple. This rate was determined for Dronpa molecules in buffer
at pH 6.9, and it was observed that after roughly 15 cycles the
fluorescence dropped to 1∕e of the initial value (Fig. S1 B and C).
We were able to increase the number of switching cycles before
photobleaching in two ways. First, a known antifade reagent—p-
phenylenediamine (PPD)—was added to the sample (Fig. S1 B
and C). Second, the intensity of the laser was decreased while
simultaneously increasing the exposure time, so that the dose of
photons (defined as the illumination intensity multiplied by ex-
posure time) remained constant (Fig. S1B). This was seen to have
a dramatic effect on the photobleaching rate of the molecules.
With the addition of 5 mM PPD under an illumination light in-
tensity of 5 W∕cm2, we observed Dronpa switch 60–70 times.
Although this number of switching cycles would not allow us to
extend our technique to three dimensions as has been done for
linear SIM (25, 26), and could be potentially improved with a
more exhaustive search for an optimal mounting condition, it was
sufficient for resolution enhancement in two dimensions. Subse-
quently, we decided to implement our technique in TIRF. With
TIRF microscopy we were able to use our inherently wide-field
technique to enhance the resolution in two dimensions over large
fields of view.
Resolution Test. Microtubules conjugated to Dronpa by a biotin-
streptavidin interaction were used as a resolution test (Movie S1).
Unlabeled microtubules are 25 nm in diameter; biotin-PEG4 has
a length of 2.9 nm; streptavidin tetramers are reported to have
dimensions of 5.4 × 5.8 × 4.8 nm (29); Dronpa itself has a similar
fold to GFP, which has dimensions 2.4 × 4.2 nm (30, 31). There-
fore, we estimate that coated tubules will have diameters no lar-
ger than 50 nm, which is equal to or smaller than the resolution
we expect given the number of photoswitching cycles we were
able to obtain from Dronpa. The linear microtubules display a
strong feature in frequency space, which provided us a metric
by which to determine if we were indeed able to detect superre-
solution information centered around the spatial frequencies of
the higher-order harmonics.
We first wanted to verify the dependence of resolution on
saturation level. To this end, we took four structured-illumination
datasets on the same region of Dronpa-coated microtubules, each
with seven phases in one dimension. We increased the saturation
Fig. 2. Frequency-space representation. The reconstruction of the data was
done in frequency space. (A) The support, or nonzero region, of the OTF is a
frequency-space-based representation of the resolution limit of a micro-
scope. In a conventional microscope, the radius of the OTF support is the in-
verse of the Abbe diffraction limit. (B) Using TIRF illumination, the spatial
frequency of the excitation pattern can be slightly outside the detection
OTF support. (C) In linear SIM, the illumination pattern has a single spatial
frequency, in addition to the DC component at the origin (red dots), which
mixes with frequencies in the sample itself, some of which are too fine to
resolve normally. The sample information centered at the nonzero spatial
frequency of the illumination pattern is shifted into the OTF support where
it can be resolved by the microscope. When reconstructed, this high-resolu-
tion sample information is shifted back to its proper place in frequency space
and the resolution becomes approximately twice that of a conventional mi-
croscope. (D) In NL-SIM, the effective illumination pattern contains spatial
frequencies that are integer multiples of the illumination pattern. Similar
to linear SIM, these mix with the underlying sample information and when
reconstructed can extend the resolution even further. (E) By rotating the one-
dimensional pattern, the entire two-dimensional space can be sampled.
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broader, giving them a “streak”-like appearance (Fig. S5). This
effect can also be seen on the microtubules themselves at the
highest resolution because there are fewer Dronpa molecules
per pixel as the resolution improves and the pixel area decreases.
To understand how this stochasticity affected the reproducibility
of our images, we took NL-SIM datasets in which we repeated the
phase series for each orientation of the pattern (η ¼ 5; five
phases; seven orientations). After acquisition we separated the
interleaved datasets and processed each set individually. To simu-
late low and high labeling densities, we did this experiment on
microtubules coated with 0.2 mg∕mL streptavidin (Fig. S6 A–F)
and 1 mg∕mL streptavidin, respectively (Fig. S6 G–L). As ex-
pected, at low labeling densities the effect of the stochastic single
molecules was more apparent than at high densities. However, at
low labeling density this phenomenon was slightly mitigated by
processing the data using the images resulting from the off expo-
sure in addition to the on exposure images as described in the
Materials and Methods (Fig. S6 D–F); this processing step seemed
to have little to no effect on the final processed image at high
labeling densities (Fig. S6 J–L). Although this phenomenon does
not affect the overall resolution of the microtubules, which are
well labeled along their width and thus the direction of the
b
POM121Nup98
-200 -100 0 100 200
position (nm) -200 -100 0 100 200position (nm)
Conventional
Linear SIM
NL-SIM 1HOH
NL-SIM 2HOH
flu
or
es
ce
nc
e
(ar
b.
u.) Nup98 POM121
BA
DC
E 1 2 3 4
F1
2
3
4
G
2 µm
H
Fig. 5. Nuclear pores in a mammalian nucleus. Human HEK293 cells were transiently transfected with either (A and C) Dronpa-Nup98 or with (B and D)
POM121-Dronpa, both of which are associated with the nuclear pore complex. We extracted the nuclei to ensure that the nuclear membranes resided in
the TIRF zone. Entire nuclei were imaged by (A and B) conventional TIRF microscopy and (C and D) by NL-SIM-TIRF microscopy using two additional high-
er-order harmonics (HOH). (E and F) The two proteins showed different localization patterns, which was especially apparent at the highest resolution in en-
larged subsets of the data: (1) conventional, (2) linear-SIM, (3) NL-SIM with 1 HOH, and (4) NL-SIM with 2 HOH. (F) Dronpa-Nup98 localized to small uniform
punctae, whereas (E) POM121-Dronpa, an integral membrane protein, showed a variety of different structures, many of which resemble rings with an inner
diameter between 40–70 nm. We repeated these observations in another nucleus on the same coverslip (gray box and Fig. S7). (G) The average FWHM of 90
nuclear pores in the Dronpa-Nup98 expressing cells decreased from 223
12 nm using conventional microscopy (blue) to 55
8 nm using NL-SIM-TIRF with
2HOH (green). (H) Simply taking a line profile through one nuclear pore in the POM121-Dronpa expressing cells confirms that a ring structure is only resolved
using NL-SIM. Scale bars, 2 μm (A–D) and 200 nm (E–F).
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resolution enhancement we measured, it does contribute to the
qualitative nature of the images and should be taken into account
when interpreting nonlinear structured-illumination data.
Biological Structures in Cells. We next looked at the nuclear pore
complex in human HEK293 cells transiently transfected with a
nuclear pore protein—either Nup98 (32) or POM121 (33)—ge-
netically fused to Dronpa. The nuclei were purified to ensure that
the nuclear membrane resided in the TIRF zone. They were fixed
in 2% paraformaldehyde (PFA), adsorbed to a poly-lysine coated
coverslip, mounted in PPD, and imaged with 5 W∕cm2 of 488-nm
light and saturated by a factor of η ¼ 8–10. The localization
patterns of the two proteins were strikingly different especially as
the resolution increased (Fig. 5 E and F). Nup98, which is known
to associate with both the nucleoplasmic and cytoplasmic side of
the pore (34), was found in tightly localized spots with an average
FWHM of 55 nm (Fig. 5 A, C, F, andG). In contrast, POM121, an
integral membrane protein, displayed a wide range of structures,
many of them resembling rings with a diameter of 40–70 nm
(Fig. 5 B, D, E, and H and Fig. S7). These observations are con-
sistent with the locations of the yeast homologues of these pro-
teins in the yeast nuclear pore complex (35).
In order to verify that our technique works on more compli-
cated structures, we next imaged the actin cytoskeleton in mam-
malian cells expressing the Lifeact peptide (36) genetically fused
to Dronpa (Fig. 6). The cells were fixed in 1% PFA, mounted in
PPD, illuminated by 5 W∕cm2 of 488-nm light and saturated by a
factor of 10. We detected and reconstructed one higher-order
harmonic corresponding to a resolution of approximately 60 nm
over a field of view that is roughly the size of an entire mamma-
lian cell (Fig 6A). Enlarging a subset of the data revealed a closely
packed network of f-actin bundles, the structure of which is
most clearly resolved with nonlinear structured illumination
(Fig. 6 B–D and Inset).
Discussion
We have used a photoswitchable protein to enhance the resolu-
tion of biological structures at extremely low light intensities
using NL-SIM. In the future, NL-SIM should be a capable tech-
nique for addressing a wide range of fundamental biological ques-
tions. For example, the nuclear pore is a highly conserved and
complex macromolecular structure consisting of at least 400 in-
dividual protein molecules (37). The most detailed knowledge of
the yeast nuclear pore complex has been gained through a set of
groundbreaking, yet technically challenging, studies using both
experimental and computational techniques (35, 38). NL-SIM,
on the other hand, may be able to provide knowledge of this
protein structure simply using light microscopy and all the ben-
efits it affords—genetically targetable probes, multiple colors,
etc. Likewise, there is much we do not understand about the actin
cytoskeleton and all its molecular components. A superresolution
light microscope capable of rapid live-cell imaging—like NL-SIM
could be with further technical developments—may prove critical
for visualizing the actin network’s various structures and dy-
namics with the spatial and temporal resolution such a biological
system demands (39).
We consider the processing method used in this paper to be
advantageous over similar methods (22, 23). The processing al-
gorithm described in ref. 23 requires that the illumination pattern
be easily resolvable—i.e., the spatial frequency of the pattern
B
C
D
5 µm
1 µmA
-200 -100 0 100 200
position (nm)
flu
or
es
ce
nc
e
(ar
b.
u
.)
Fig. 6. Dronpa–Lifeact in a mammalian CHO cell imaged with NL-SIM. (A) Because SIM is a wide-field technique, high-resolution data over a large field of view
is possible. On the left, a portion of the entire image is shown with conventional microscopy. The image is displayed using a nonlinear intensity scale
(gamma ¼ 0.65) to highlight the small filaments in the background over the thick and bright stress fibers. A subset of the data is enlarged and shown with
(B) conventional TIRF microscopy, (C) linear SIM-TIRF, and (D) nonlinear SIM-TIRF. Lifeact marks the actin network, the structure of which is most clearly resolved
with nonlinear SIM-TIRF as demonstrated by the normalized intensity profiles (Inset) taken between the white triangles in B–D). Two filaments are clearly
resolved with NL-SIM (solid), but are not resolved with either conventional microscopy (dot dashed) or linear SIM (dashed). Scale bars, 5 μm (A) and 1 μm (B–D).
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must fall well within the support of the conventional OTF—a
requirement that our algorithm does not impose. Such a require-
ment means that the spatial frequency of the pattern used in
ref. 23 was ∼0.4 kmax, where kmax is the highest detectable spatial
frequency. In this article, we used a spatial frequency k
0
that lies
on the very edges of the illumination OTF support and falls out-
side the support of the detection OTF (i.e., k
0
> kmax) rendering
it irresolvable in the fluorescence image. Even at this high spatial
frequency, however, our coherent and s-polarized illumination
source produced a pattern with almost perfect modulation con-
trast (95–96.5%). Such an illumination pattern does not sacrifice
the signal strength of the high-resolution information present in
the raw data. Our algorithm was designed to (I) unmix this raw
data into information components representing different regions
of frequency space, (ii) estimate the contribution of each compo-
nent, and (iii) reassemble them in their proper frequency-space
locations. This processing method allowed us to take advantage
of an illumination pattern with the highest possible diffraction-
limited line spacing. As described in Fig. 1C, the resolution of
the microscope is linearly proportional to the line spacing of the
pattern (1∕k). In this way, given the same saturation level, struc-
tured-illumination methods used in this article allowed for an im-
provement in resolution by a factor of approximately 1∕0.4, or
approximately 2.5 over methods used in ref 23; or, equivalently,
for the same resolution, the methods we describe require approxi-
mately 2.5 fewer images and less saturation.
Although we have demonstrated NL-SIM is possible in fixed
mammalian cells, in general, SIM is well suited for live-cell super-
resolution imaging (27) because significantly fewer images are
needed to reconstruct a high-resolution image than are needed
for localization-based methods. Furthermore, SIM’s wide-field
nature permits data on large fields of view, without a significant
cost in acquisition time, in contrast to scanning methods, for
which acquisition time is proportional to the scanned area. What
obstacles must be overcome for live-cell NL-SIM? The most ob-
vious is motion blur. This concern is the same for all microscopy:
To prevent motion blurring, the acquisition time for a single time
point must be short enough such that the sample moves less than
an amount close to the final resolution. For superresolution tech-
niques, the acquisition time requirements become more stringent
because the resolution length scale is much smaller, underscoring
the need for development of both faster hardware and more
photostable photoswitchable probes. To address the former
concern, in recent years we and others (27, 40) have developed
methods for electronic pattern generation based on spatial light
modulators. As yet, this technology has been used only for live-
cell linear SIM, but similar technology could be applied toward
live-cell NL-SIM. Therefore, for live-cell NL-SIM to be feasible
the most needed development is that of a better photoswitch-
able probe.
In principle, any nonlinear phenomenon can be used for
resolution enhancement; however, in practice, we believe photo-
switching to be the most applicable for biological imaging because
photoswitchable molecules typically need low light intensities—
a crucial requirement for live-cell microscopy. We were initially
encouraged by published reports of high numbers of switching
cycles on single Dronpa proteins (41), but ultimately we were un-
able to obtain such high numbers of cycles in ensemble without
the addition of toxic antibleaching chemicals and long exposure
times that drastically increased the total acquisition time. For
live-cell NL-SIM to be possible, another, more photostable,
photoswitchable protein must be developed. We note that this
has been possible for other proteins—notably, EosFP was engi-
neered to be a better probe for PALM (42). Our requirements are
different than those of the localization microscopies, however.
First, localization microscopies favor photoactivatable proteins
that emit all their photons in a single activation step; our tech-
nique needs photoswitchable molecules that are able to emit their
photon budgets over 50 to a few hundred cycles (depending
on the desired resolution and dimension), without a significant
decrease in photon output. Second, localization microscopies
typically need probes with very high contrast ratios of thousands
or tens of thousands to detect signals from the activated single
molecules, whereas NL-SIM may be able to tolerate contrast
ratios of 50–100, depending on the resolution desired. Another
option is to employ small-molecule organic dyes or dye pairs that
STORM and similar techniques use and that have been shown to
switch hundreds of times as single molecules or pairs of molecules
(2). But, like the photoswitchable proteins, how they behave in
ensemble remains to be fully explored. Furthermore, to photo-
switch, many of these dyes or dye pairs require oxygen scavengers
and thiol-containing compounds and higher light intensities that
may be incompatible with live-cell imaging. Nevertheless, small-
molecule organic dyes may be useful now for NL-SIM imaging
on fixed tissue, and with further development could prove very
useful for detecting proteins in living samples.
With technological advances in fast pattern generation and a
photoswitchable probe that is able to tolerate short exposure
times, a 60-nm resolution full-frame NL-SIM image consisting
of 25 raw-data exposures could be acquired on a timescale faster
than other superresolution techniques. For example, mEos2, a
photoactivatable protein, has been able to withstand multiple
PALM time points—each comprised of 1,500 40-ms raw-data ex-
posures—resulting in a Nyquist-limited resolution of 60 nm (9).
With a similarly photostable, photoswitchable protein, we see no
technological reasons that NL-SIM could not take data at similar
frame rates. This would allow for an improvement in time resolu-
tion by a factor proportional to the number of raw-data expo-
sures. Moreover, NL-SIM’s wide-field nature may permit larger
area or volume rates than a point-scanning method like live-cell
STED, which has demonstrated a 62 nm resolution at 28 frames
per second on a small region of 2.5 × 1.8 μm2 (43).
Perhaps even more than live-cell imaging, NL-SIM could excel
at studying three-dimensional structures. Linear SIM has been
implemented in three dimensions, providing optical sectioning
and doubling of the resolution in all three dimensions with one
objective (25) or isotropic resolution in all three dimensions with
two objectives (26). NL-SIM could be extended in similar ways,
but the photostability requirements of the dyes dramatically in-
crease. With such a probe, though, 3D NL-SIM represents a way
for sub-100-nm resolution imaging of three-dimensional struc-
tures at volumes of cubic microns. Extending other superresolu-
tion techniques to three-dimensions is possible but is typically
limited to thicknesses of less than a micron near the coverslip (44)
or near the focal spot of a laser (45).
Although there is much room for improvement, we believe
NL-SIM to be a powerful approach, among others, in the exciting
new field of superresolution light microscopy. All superresolution
techniques excel in certain aspects and fail in others—the best
technique will be determined by the demands of the application.
We believe NL-SIM to be best for those applications that require
low light intensity and few exposures, at a resolution of approxi-
mately 40 nm in two dimensions with currently available photo-
switchable fluorescent probes.
Materials and Methods
Microscope. A custom SIM-TIRF microscope (Fig. S8) was built similar to those
previously described (13, 25, 27). Both 405-nm and 488-nm continuous-
wave laser light were directly coupled into one single mode, polarization-
maintaining PC/APC fiber (Thorlabs P5-488PM-FC-2), using an achromatic
lens (f ¼ 10 mm). The output light was collimated and circularly polarized
(Meadowlark AQ-050-0545) and then passed through a custom phase grating
with a period of 33.5 μm that was translated and rotated to achieve the
necessary pattern phases and orientations (8). The polarization of the light
was linearized and corotated with the grating to maintain s-polarized light
and maximum pattern contrast at each orientation as described in ref. 13. All
diffraction orders except the þ1 and −1 were blocked at a secondary pupil
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plane; these orders were focused at the edges of the back focal plane of a
100×, 1.46 N.A. Zeiss TIRF objective lens. Intermediate lens focal lengths were
chosen to produce a maximum illumination N.A. of the two side orders of
1.46, resulting in a projected pattern period on the sample of 166 nm. To
produce a consistent light intensity across the field of view—and thus a con-
sistent saturation factor—we chose a collimation lens in front of the fiber
such that the excitation light distribution dropped by approximately 20%
at the edges of the field of view corresponding to the full CCD chip. The fluor-
escence was collected by the same objective, passed through a Zeiss 1× tube
lens, separated by a custom dichroic setup described elsewhere (27), filtered
using stacked emission filters (Semrock 525∕40), and finally imaged onto a
cooled back-illuminated 1;024 × 1;024 Andor iXon EMCCD running in con-
ventional 16-bit mode. The 13-μm pixels on the camera mapped to 46-nm
pixels in sample space. A metric for determining the optimal focus was de-
signed by calculating the ratio ðjSHFj2 − jSvarj2Þ∕ðjSLFj2 − jSvarj2Þ, where SHF is
the high-frequency signal within the OTF support, SLF is the low-frequency
signal within the OTF support, and Svar is the variance of each as determined
by measuring the noise outside of the OTF support. We created immersion oil
from Cargille laser liquids with an index of refraction different than the one
supplied with the Zeiss objective to optimize the 2D OTF. With the optimized
immersion oil of 1.505, we observed that the detection OTF went to zero at
an N.A. of 1.2–1.3 (Fig. S2).
To maintain optimal focus, we designed a custom sample stage. A single-
ended capacitive sensor (Physik Instrumente part no. D-510) was mounted
directly to the sample holder and in closed-loop with a piezoelectric stage
(Physik Instrumente part no. P-611) that controlled the sample stage’s height
above the objective. The axial position of the sensor was stable relative to
a polished steel plate that directly mounted to a floating optical table.
The microscope and sample stage were enclosed in a custom box to maximize
thermal stability. Using this setup, minimal sample drift (<50 nm) in the axial
direction was observed for up to 45 min as determined by the focus metric
described above.
Image Acquisition and Reconstruction. Custom image acquisition software was
written using LabVIEW. Each subimage was a result of three exposures: (i)
First, the molecules were uniformly and completely turned on with 405-
nm light; (ii) second, using a sinusoidal pattern of 488-nm light, the molecules
were turned off to a given saturation level as described in Fig. 1A; (iii) third,
the phase of the pattern was shifted by π to collect the fluorescence from the
molecules remaining on. This process was repeated for a predetermined
number of phases and orientations of the pattern. To account for phase
errors resulting from lateral sample drift, online phase correction was done.
Successive conventional images—generated from the sum of the images re-
sulting from (ii) and (iii) above—within a phase series were cross-correlated
to determine the amount of sample drift between them in the direction of
the pattern vector. This calculated number was fed back to the diffraction
grating, and the diffraction grating was translated to compensate the next
image. Ideally, such an algorithm would generate equally spaced phases of
the illumination pattern after drift correcting the raw images. However, in
practice, because the estimated phase correction for an imagewas generated
by comparing the two previous images, there may still have been residual
phase errors if this correction did not accurately reflect the true sample drift
for the image.
The data were processed and reconstructed similarly to previously de-
scribed methods (8, 13). In brief, the images within a phase series (i.e., a pat-
tern orientation) were drift corrected and registered. These images were
Fourier transformed and separated into the 2N þ 1 information components,
where N is the number of orders, including the conventional component.
Instead of assuming equal phase steps of 2π∕ð2N þ 1Þ, and thus accounting
for any residual phase errors, the online drift estimation was compared with
the postacquisition drift estimation; the differences between the two were
used to modify the ideal matrix for separating the raw data. The separated
conventional orders for each pattern orientation were cross-correlated, and
the result was used to correct for any drift between orientations of the pat-
tern. The starting phase, pattern vector, and modulation amplitudes of each
information component were determined by comparing successive informa-
tion components within each pattern orientation in the region of frequency
space where they overlap. As described in ref. 8, we typically found it desir-
able to manually enter the modulation amplitudes empirically because the
signal-to-noise ratio of the highest information components was often too
low for an accurate computational determination; this was especially true at
the orientations acquired last and thus of the lowest signal due to photo-
bleaching. The different components were then combined through a gener-
alized Weiner filter (25). Rather than being rotationally averaged as in our
earlier studies, instead the measured OTF represented the true two-dimen-
sional transfer function. To maintain high signal-to-noise ratio of the nonro-
tationally averaged 2D OTF, we averaged many isolated point sources
(Invitrogen Yellow-Green 100-nm beads, catalog no. F8803) to obtain the
final OTF used during processing. Finally, the enlarged, effective OTF support
of the processed image was apodized at the edges to prevent ringing and
Fourier transformed back to real space.
To detect two higher-order harmonics, we observed that 63 images—
seven phases, nine orientations—were sufficient for nearly isotropic resolu-
tion in two dimensions isotropically. The final NL-SIM images on the purified
microtubules took advantage of all 63 images. Under similar imaging condi-
tions, we were unable to detect the second higher-order harmonic on the
mammalian cells transfected with Dronpa–Lifeact. In this case, we simply dis-
carded the second higher-order harmonic. In theory, only 25 images—five
phases, five orientations—would be enough to detect one higher order in
two dimensions. Moreover, the nuclear pore and Lifeact data taken on mam-
malian cells were processed in a slightly different manner. As mentioned
above, each image processed by the reconstruction software is a result of
three laser exposures. If imaging speed is of concern, only the last needs
to be captured by the camera. However, to increase the signal-to-noise ratio
of the higher-order harmonics, we chose to acquire the image resulting from
the off exposure, (ii) above. Although most of the information contained
in this exposure is the same information contained in the conventional and
linear structured-illumination components, there is also a slight nonlinear
component. We added this component in a noise optimal way to the non-
linear components that were separated from the data collected in (iii) above;
that is, we combined the separated components through a weighted
sum in which the weights were the inverse variance of the noise for each
component.
The conventional images we have displayed in this article were generated
by summing the images resulting from (ii) and (iii) above.
Sample Preparation and Light Intensity Calibration. All other methods can be
found in the SI Materials and Methods accompanying this manuscript.
ACKNOWLEDGMENTS. We thank M. Coleman for acquisition software;
H. White for cell culture assistance; C. Galbraith for useful discussions and
suggestions; L. Henry for the nuclear extraction protocol and useful sug-
gestions; E. Ingerman for useful discussion; and N. Ji, R. Fiolka, E. Betzig,
L. Looger, and R. Heintzmann for providing valuable comments on the
manuscript. We dedicate this work to Mats Gustafsson, whose science in-
spired so many in the field of microscopy, and whose life inspired all who
knew him.
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