ARTICLE
Received 16 Oct 2015
| Accepted 8 Feb 2016 | Published 21 Mar 2016
Open-source image reconstruction of
super-resolution structured illumination
microscopy data in ImageJ
Marcel Mu¨ller
1
, Viola Mo
¨
nkemo
¨
ller
1
, Simon Hennig
1
, Wolfgang Hu¨bner
1
& Thomas Huser
1,2
Super-resolved structured illumination microscopy (SR-SIM) is an important tool for
fluorescence microscopy. SR-SIM microscopes perform multiple image acquisitions with
varying illumination patterns, and reconstruct them to a super-resolved image. In its most
frequent, linear implementation, SR-SIM doubles the spatial resolution. The reconstruction is
performed numerically on the acquired wide-field image data, and thus relies on a software
implementation of specific SR-SIM image reconstruction algorithms. We present fairSIM, an
easy-to-use plugin that provides SR-SIM reconstructions for a wide range of SR-SIM
platforms directly within ImageJ. For research groups developing their own implementations
of super-resolution structured illumination microscopy, fairSIM takes away the hurdle of
generating yet another implementation of the reconstruction algorithm. For users of
commercial microscopes, it offers an additional, in-depth analysis option for their data
independent of specific operating systems. As a modular, open-source solution, fairSIM can
easily be adapted, automated and extended as the field of SR-SIM progresses.
DOI: 10.1038/ncomms10980
OPEN
1
Biomolecular Photonics, Department of Physics, University of Bielefeld, 33615 Bielefeld, Germany.
2
Department of Internal Medicine and NSF Center for
Biophotonics, University of California, Davis, Sacramento, California 95817, USA. Correspondence and requests for materials should be addressed to M.M.
(email: mmueller@physik.uni-bielefeld.de) or to T.H. (email: thomas.huser@physik.uni-bielefeld.de).
NATURE COMMUNICATIONS | 7:10980 | DOI: 10.1038/ncomms10980 | www.nature.com/naturecommunications 1
T
he improvement in spatial resolution achieved in super-
resolved structured illumination fluorescence microscopy
(SR-SIM) is accomplished by illuminating a sample with a
well-defined set of sinusoidal illumination intensity patterns,
that is, typically a set of interference patterns
1
. The light
modulation leads to frequency mixing between the harmonic
pattern frequency and the sample frequencies, which is then
demodulated by a digital image reconstruction step
2
. This enables
access to previously unobservable high-frequency components,
and thus improves spatial resolution. For linear SR-SIM,
the illumination pattern adheres to (approximately) the same
resolution limit as the imaging path, hence SR-SIM doubles the
spatial resolution in comparison with a wide-field image
3
.
The principle and design of the instrumentation for SR-SIM is
well documented in the literature
1,2
, and the technique is now in
wide use
4–10
. It has also been successfully combined with other
optical techniques
11–16
, where non-linear approaches
17–20
allow
to surpass the factor of 2 in resolution improvement.
SIM data sets are usually acquired by a modified wide-field
microscope, where a light-modulating component is introduced
into the excitation path. Nowadays, commercial SR-SIM plat-
forms are available by different manufacturers. Also, spatial light
modulators (SLMs) offer a simple, robust and cost-efficient
way to custom-build such systems. Recent publications
provide detailed blueprints for home-built SR-SIM microscope
set-ups
21–23
, focusing on the design of customizable, cost-
effective and fast systems.
The algorithm required for SR-SIM reconstructions can
readily be found in the literature, for example, in the publication
by Gustafsson et al.
2
. However, as of now, there is no imple-
mentation available for ImageJ. Existing solutions are either
provided as proprietary components of commercial SIM
microscopes, often bound to a dedicated workstation computer,
or exist as purpose-written tools by different research groups
24
.In
the related field of super-resolved localization microscopy, the
situation is rather different. Various well-known open-source
solutions, for example, QuickPALM
25
and rapidSTORM
26
, with
different feature sets (a summary of which was published
recently
27
), and often direct integration with ImageJ, are
available for data analysis.
This, together with our own need for a SIM reconstruction
software, motivated the development of fairSIM (free analysis and
interactive reconstruction for structured illumination micro-
scopy). FairSIM is aimed at providing a ready to use, easy to
operate, free and open-source solution for SR-SIM. It features a
plugin that integrates directly into ImageJ
28
/Fiji
29
, allowing it to
use all image formats supported by ImageJ, and easy integration
with its other pre- and post-processing steps.
Results
Concept and motivation for developing fairSIM. FairSIM was
primarily designed to provide single-slice reconstructions of
SR-SIM systems working with both two-beam interference
(utilized by many home-built and total internal reflection-excited
fluorescence (TIRF)-based systems) and three-beam interference
(utilized in all commercially available systems) for pattern gen-
eration. This development was motivated by the need for both a
rapid and flexible single-slice reconstruction mode for our own
commercial SR-SIM system
30
, and a stand-alone reconstruction
tool for our home-built, two-beam illumination SIM set-ups.
FairSIM is implemented as Java plugin, so it allows to carry out
SR-SIM reconstructions directly from within ImageJ/Fiji. It is
based on the well-established SIM illumination technique intro-
duced by Gustaffson
1,2
and Heintzmann
31
, and the corresponding
reconstruction algorithms. By combining multiple raw images,
each acquired under structured illumination by a defined pattern,
these algorithms allow to enhance the resolution twofold in
comparison with the corresponding wide-field image.
The advantage of single-slice reconstruction of SR-SIM images
is that only raw images from one focal plane are needed. Thus,
because it is not necessary to acquire multiple image sequences
obtained at different vertical focus positions (z-stacks), image
acquisition is much quicker. This is especially useful for live-cell
applications, where short exposure times reduce photodamage
and motion blur. As a drawback, however, the axial resolution is
not improved. Lateral resolution enhancement is not impaired,
and fairSIM retains optical sectioning through optical transfer
function (OTF) attenuation
32–34
. Employable especially for three-
beam interference data, the attenuation greatly reduces
background contributions in thicker samples and thus mitigates
reconstruction artefacts (Supplementary Fig. 1). Importantly, for
advanced users and in-depth analysis, fairSIM also provides
access to various intermediate results of the parameter estimation
and reconstruction process in both frequency and spatial domains
(Fig. 1). This greatly helps expert users to quickly judge progress
and quality of the image reconstruction process, as well as in the
analysis of more critical data sets, obtained for example when
tuning home-built SR-SIM set-ups.
Testing fairSIM with different samples and microscopes.We
extensively tested the capabilities of fairSIM with SR-SIM data
sets acquired on different microscope platforms. A commercial
DeltaVision|OMX (GE Healthcare, Issaquah, WA, USA) was used
to provide the high-quality three-beam interference illumination
data, and a much simpler, home-built, SLM-based two-beam
interference illumination system was used to test the compat-
ibility with less refined systems. Furthermore, data sets from
systems available by other commercial manufacturers were also
tested as described in detail below.
To characterize SR-SIM set-ups and software, fluorescent bead
surfaces have become a standard test sample, as they provide
easily quantifiable results. Using a defined bead size slightly below
the optical resolution limit, beads in a dense surface cannot be
distinguished in a wide-field fluorescence image. Only after
applying SR-SIM, and thus improving the resolution twofold,
individual beads become distinguishable. We imaged fluorescent
ba
Figure 1 | Examples for intermediate SR-SIM results displayed as power
spectra in frequency space. (a) Visualization of the cross-correlation used
for parameter estimation, with circles marking the low-frequency region
excluded from the fit and the detected modulation frequency. The three
insets on the upper left visualize the iterative sub-pixel fits and provide a
quick feedback if the fit was successful. (b) Power spectrum of the
complete, reassembled SR-SIM reconstruction of fluorescent beads (Fig. 3).
The circular structure visible in the spectrum is located at B4.5 mm
1
,
which coincides with the beads’ size of 200 nm, and is thus expected. Scale
bar, 2.5 mm
1
.
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms10980
2 NATURE COMMUNICATIONS | 7:10980 | DOI: 10.1038/ncomms10980 | www.nature.com/naturecommunications
Tetraspeck bead surfaces on both our home-built system (Fig. 2)
and on the OMX (Fig. 3). In both cases, the beads are only
separable in the SR-SIM reconstruction, but not in the wide-field
image assembled from the SR-SIM image stack. A comparison
with a full three-dimensional (3D) reconstruction is provided for
images acquired on the OMX, where the cross-section plots show
that both single-slice and full 3D reconstruction achieve a
comparable improvement in lateral resolution.
As a typical sample encountered in biological applications, liver
sinusoidal endothelial cells (LSECs) stained with a fluorophore
that selectively stains the plasma membrane of cells (Fig. 4) were
imaged on the DeltaVision|OMX and reconstructed both as
single slice (fairSIM) images and in full 3D (SoftWORX by
DeltaVision). The main structural feature of these rather flat cells,
that is, the cellular fenestrations (nano-sized pores that cross the
cytoplasm), vary in size between 50 and 200 nm (typically
120 nm)
16,36–38
, and are thus ideal candidates for SR-SIM
imaging. As with the fluorescent beads, they are too blurred in
standard wide-field images, but become clearly visible in both
single-slice and full 3D SIM reconstructions. The dynamics of
fenestrations in living LSECs is a subject of current research.
Therefore, being able to successfully image these submicroscopic
structures in single-slice mode (and thus very fast) is of high
interest.
As additional tests, other typical cellular structures often
imaged by super-resolution optical microscopy methods, namely,
cytoskeletal protein fibrils (actin and tubulin) and mitochondria
were imaged (Supplementary Figs 2, 3 and 4). Again, comparison
between single-slice and full 3D reconstructions demonstrates the
validity and usefulness of our plugin.
Data sets of actin filaments and mitochondria, acquired on
another commercial SR-SIM system (Elyra S1, Zeiss, Jena,
Germany—see Supplementary Figs 5 and 6), were also recon-
structed by fairSIM as single-slice images and by the manufac-
turer software in full 3D and further demonstrate the
compatibility with other commercial implementations. A TIRF
SR-SIM data set of tubulin fibres (Supplementary Fig. 7), acquired
on the set-up built and used by Kner et al.
5
, shows the
compatibility with other, advanced custom-built SR-SIM systems.
Compatibility with different SR-SIM microscopes. FairSIM has
been developed with a focus on compatibility with a wide range of
0
0.5
1
nm
Int.(norm)
c
Wide field
Filtered wide field
SIM reconstruction
0
0.5
1
–400 –200 0 200 400 0 200 400 600 800 1,000 1,200
nm
Int.(norm)
d
Wide field
Filtered wide field
SIM reconstruction
a
c
d
b
c
d
Filters
Objective,
sample
Camera
Spatial
light
modulator
Light source
e
Figure 2 | FairSIM reconstruction of data sets obtained on a simple SLM-
based SR-SIM setup. A glass surface with 200 nm Tetraspeck beads was
used as test sample, excitated at 642 nm wavelength. In contrast to the
(Wiener filtered) wide-field image (a), the SR-SIM reconstruction by
fairSIM (b) yields clearly distinct beads. This can be found quantitatively
from the cross-section plots, given for a single bead in c and two close-by
beads (indistinguishable in wide-field mode) in d. A simplified sketch of the
set-up used is given in e. Scale bar, 5 mm, inset 1.2 mm.
0
0.5
1
–400 –200 0 200 400
nm
Int.(norm)
e
Wide field
Wide field (filtered)
FairSIM reconstruction
OMX reconstruction
0
0.5
1
0 200 400 600 800
nm
Int.(norm)
f
Wide field
Wide field (filtered)
FairSIM reconstruction
OMX reconstruction
c
e
f
d
e
f
a
e
f
b
e
f
Figure 3 | FairSIM reconstruction of data sets obtained on the GE
Healthcare DeltaVision|OMX. A glass surface with 200 nm Tetraspeck
beads was used as a test sample, excitated at 642 nm wavelength. In
contrast to the wide-field image (a) and its Wiener-filtered version (b), the
2D reconstruction by fairSIM (c) yields clearly distinct beads. The 3D SR-
SIM reconstruction by SoftWORX (manufacturer’s software) is provided in
d for comparison. Please note that the 3D reconstruction is based on a
larger amount of input data (complete z-stack), thus resulting in an
improved signal-to-noise ratio. A quantitative comparison between all four
images can be found as cross-section plots for a single bead in e and for
two adjacent beads (indistinguishable in wide-field mode) in f. Scale bar,
5 mm, inset 1.2 mm.
NATURE COMMUNICATIONS | DOI: 10.1038/ncomms10980 ARTICLE
NATURE COMMUNICATIONS | 7:10980 | DOI: 10.1038/ncomms10980 | www.nature.com/naturecommunications 3
experimental SR-SIM implementations. Besides providing sup-
port for both two-beam and three-beam interference-based illu-
mination, SR-SIM datasets can be acquired with any reasonable
number of pattern orientations and phases. Thus, fairSIM is able
to handle data sets from all SR-SIM microscope platforms using
standard sinusoidal illumination patterns as introduced by Gus-
tafsson
1,2
, including the currently available commercial platforms
(GE Healthcare, Zeiss, Nikon) and the now popular custom-built
setups.
Precise knowledge of the SR-SIM acquisition parameters
(pattern orientation, frequency and phases) is required for a
successful image reconstruction. Indeed, most often reliable
parameter estimation is much more involved than the image
reconstruction process itself. State-of-the-art parameter estima-
tion employs weighted cross-correlation of frequency compo-
nents to obtain these parameters
2,32,39
. By default, fairSIM uses
the Gustafsson approach
2
(briefly described in Supplementary
Note 1 and visualized in Supplementary Fig. 8) to obtain
reconstruction parameters, and also features an implementation
of a more current phase-optimization algorithm
32
.
Automated reconstruction parameter estimation. For data sets
of adequate quality, fairSIM offers a largely automated mode of
operation. Here the user selects the raw images for reconstruction
and provides basic parameters of the microscope optics and
acquisition mode, that is, the number of bands (two- or three-
beam interference illumination), the number of SR-SIM pattern
orientations, the number of phases used in the illumination, the
effective pixel size and order of the image sequence. For the
commercial GE Healthcare DeltaVision|OMX and the Zeiss Elyra
systems, presets are available. Also, an OTF, matching the
microscope and the emission wavelength, has to be set. Ideally,
the OTF has been measured experimentally, and can be read in
from file. Alternatively, a basic estimation based on the numerical
aperture and main emission wavelength of the fluorophores used
in the sample is available.
After this initial set-up and raw data import step, the
parameter estimation extracts pattern orientation, angle and
phase from the data automatically via cross-correlation
(Supplementary Note 1), optionally providing visual feedback to
check for plausibility. Obtaining a precise estimate of the SIM
reconstruction parameters is a crucial step for successful image
reconstruction, because even a small error in these parameters
will degrade the quality of the reconstructed image
39–41
.
Providing an algorithm that automatically extracts these
parameters with little knowledge of the microscope platform in
use, without much user interaction and with an easily
interpretable feedback was a major point in the development of
fairSIM.
In the second step, the actual image reconstruction process is
run. As this reconstruction step is much faster than the parameter
estimation, it can easily be run multiple times, either to
reconstruct multiple images where no changes to the illumination
parameters have been made or to tweak image reconstruction
filter settings (OTF attenuation, Wiener filter and apodization).
For both the parameter estimation and the image reconstruc-
tion steps, different levels of intermediate output (results per
band, per pattern orientation, in frequency and spatial domain)
can be selected.
Technical realization of fairSIM. FairSIM features a plugin that
readily integrates into Fiji
29
/ImageJ
28
, allowing it to use all image
formats supported by ImageJ and seamless integration with other
pre- and post-processing steps available in ImageJ. Written
entirely in Java, fairSIM supports all computer platforms and
operating systems that run ImageJ without much installation
effort. FairSIM takes advantage of current multi-core central
processing units, running computationally intensive functions in
parallel. On a typical desktop computer, that is, using a current-
generation quadcore processor, 15 raw images of 512 512 pixels
reconstruct to a high-resolution image of 1,024 1,024 pixels in
o3 s. The initial parameter estimation is performed in o20 s,
providing all intermediate output takes at most 60 s.
A modular layout, providing defined interfaces for, for
example, basic linear algebra, the SR-SIM algorithm, the graphical
user interface and the integration with ImageJ, allows one to
easily reuse and extend fairSIM’s components. Most importantly,
low- and high-level functionality of the SR-SIM algorithm
module can easily be used through Fiji’s scripting facilities,
allowing advanced users to run reconstructions without resorting
to the graphical user interface, and thus to automate their
workflow. Also, data structures and functionality in our linear
algebra package can be employed to implement new reconstruc-
tion methods, providing much more convenience than working
with pure Java data types.
Our source code is freely available under GNU general public
license (GPL) and managed on Github. Together with the
modular layout, this facilitates the ease of modifications and
extensions, for example, towards the reconstruction of non-linear
SR-SIM data sets
17–20
or to implement novel reconstruction
techniques
42–45
.
Discussion
We expect that fairSIM will become a highly useful tool for super-
resolution structured illumination microscopy. With the current
c d
a b
Figure 4 | SR-SIM measurement of an LSEC membrane stain obtained on
a GE Healthcare DeltaVision|OMX. Wide-field image (a), Wiener-filtered
wide-field (b), single-slice/2D SR-SIM reconstruction by fairSIM (c) and full
3D SR-SIM reconstruction by SoftWORX (manufacturer’s software)
(d) are shown for comparison. Both SR-SIM reconstructions allow to clearly
identify the cell’s fenestrations (tiny membrane pores), which is not
possible in the wide-field images. The single-slice reconstructions by
fairSIM can be performed with a much lower number of input images, as no
z-stack has to be acquired. Scale bar, 5 mm, inset 1.6 mm, cells stained with
CellMask Deep Red.
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms10980
4 NATURE COMMUNICATIONS | 7:10980 | DOI: 10.1038/ncomms10980 | www.nature.com/naturecommunications
advent of blue prints for cost-effective, fast and customizable
SR-SIM microscopes, research groups building these set-ups
benefit from a ready-to-use reconstruction software, so they do
not have to create their own customized implementation. Our
solution is also modular and flexible enough to be adapted to new
needs, for example, the automated processing of large time series
or the extension to non-linear SR-SIM methods. Users of
commercial SR-SIM microscope platforms obtain a tool to
perform reconstructions independent of the manufacturer
software, which allows in-depth data analysis and is not bound
to a specific microscope workstation. FairSIM is freely available as
a system-independent, ready-to-use ImageJ plugin and as
open-source code. We provide a collection of raw test image
sequences, including all raw data sets needed to reproduce the
figures presented here, as an additional download for fairSIM.
We also provide a short user manual, which includes all
parameters needed for these reconstructions.
Methods
Access to the plugin and source code. A ready-to-use version of the plugin, the
source code, example data sets and a short user manual can be found online. All
resources are hosted publicly on github, accessible via http://fairsim.org or
https://github.com/fairsim, and can also be reached through our institute website,
http://www.bio-photonics.de.
SR-SIM microscopy systems
. Images from two commercially available SIM
microscopes were analysed, obtained on a Delta-Vision|OMX v4 by GE Healthcare
(Issaquah, WA, USA) and on an Elyra S1 by Zeiss (Jena, Germany). Also, raw
images were acquired on a home-built, SLM-based two-beam interference illumi-
nation SR-SIM microscope. This system consists of a 60 , 1.2 numerical apertur e
water immersion objective (Olympus, Hamburg, Germany), a 642 nm, 85 mW
fiber-coupled diode laser for excitation, a charge-coupled device camera (Coolsnap
HQ, Photometrics, Tuscon, AZ, USA) and a liquid crystal display-based SLM
for light modulation (LC-R 1920, Holoeye Photonics, Berlin, Germany). A sketch
of the set-up can be found in Fig. 2e. The TIRF SR-SIM set-up is documented
by Kner et al.
5
.
Preparation of TetraSpeck bead surfaces. TetraSpeck microspheres (0.2 mm,
T-7280) were purchased from Thermo Fisher (Waltham, MA, USA). Commercially
available coverslips (B150 mm) with 24 60 mm in size were carefully cleaned
with HelmanexIII (Hellma GmbH Go
¨
ttingen, Germany) for 20 min in a supersonic
bath at 45 °C. Then, the coverslides were rinsed for two times with pure H
2
O,
followed by another 20 min in the supersonic bath in pure H
2
O. Then, the
coverslides were dried in an air flow prior use. A silicone sheet (self-adhesive;
Sigma-Aldrich (GBL666182-5EA)) with a hole of 4 mm in diameter was disposed
to the coverslip. A measure of 5 ml stock solution of TetraSpeck Microspheres was
mixed with 5 mlH
2
O and vortexed for 2 min. The solution was dispensed onto the
coverslip and dried headfirst over night at 4 °C.
Cells. Rat LSECs were isolated from Sprague Dawley male rats (Scanbur BK,
Sollentuna, Sweden) kept and fed under standard conditions. The treatment of the
animals was performed in accordance with the Norwegian Animal Experimental
and Scientific Purposes Act of 1986. The experimental protocols were approved by
the Norwegian National Animal Research Authority (NARA). Young rats with a
body weight between 150 and 300 g were killed with a mixture of medetomidin
(Domitor vet, Orion, Turku, Finland) and ketamine (Ketalar, Pfizer, New York,
NY). The liver was perfused with collagenase and the isolated liver cells were
centrifuged on a Percoll density cushion. The fraction containing the LSECS was
plated for selective adherence on fibronectin coated #1.5H coverslips (Paul
Marienfeld GmbH & Co KG, Germany) for 3 h in RPMI-1640. At 3 h after plating,
they were fixed with 4% paraformaldehyde for 15 min. For fluorescent staining of
membranes, the LSECs were incubated with CellMask Deep Red plasma membrane
stain (Thermo Fisher, #C10046; 1:2,000) for 10 min at room temperature (Fig. 4).
LSECs were additionally permeabilized with 0.5% Triton-X for 30 s prior
staining with 165 nM phalloidin Atto488 (Sigma Aldrich, #49409) for 20 min at
room temperature (Supplementary Fig. 2). Human osteosarcoma cells U2OS
(ACC785—DSMZ, Braunschweig, Germany) plated on uncoated #1.5H cover
glass were fixed with 4% paraformaldehyde for 10 min. The U2OS cells shown
were permeabilized with 0.5% Triton-X100 for 30 s prior staining with 165 nM
phalloidin Atto488 (Sigma Aldrich, 49409) for 20 min at room temperature
(Supplementary Fig. 1). For additional immunofluorescent staining, U2OS cells
were permeabilized for 60 s with 0.5% Triton-X100, then washed in PBS, blocked
with a 5% bovine serum albumin in PBS solution for 60 min at room temperature
and incubated with 5 mgml
1
of mouse anti-a-tubulin-Alexa488 (Thermo Fisher,
#322588) in PBS containing 5% bovine serum albumin for 90 min at room
temperature. Prior mounting, the samples were washed three times for 5 min with
0.1% Tween-20 in PBS and once with PBS (Supplementary Fig. 3). All fixed
samples were mounted in Vectashield (Vector Laboratories, H-1200) on a glass
slide and sealed with nailpolish. In Supplementary Fig. 4, the live U2OS were
stained for 30 min with 500 nM MitoTracker Red CM-H2Xros (Thermo Fisher,
#M-7513) in DMEM for 30 min at 37 °C and then imaged at room temperature on
the Deltavision|OMX.
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