Fluctuation-Based Super-Resolution Traction Force Microscopy
Aki Stubb, Romain F. Laine, Mitro Miihkinen, Hellyeh Hamidi, Camilo Guzmán, Ricardo Henriques,
Guillaume Jacquemet,* and Johanna Ivaska*
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ABSTRACT: Cellular mechanics play a crucial role in tissue
homeostasis and are often misregulated in disease. Traction force
microscopy is one of the key methods that has enabled researchers to
study fundamental aspects of mechanobiology; however, traction force
microscopy is limited by poor resolution. Here, we propose a simplified
protocol and imaging strategy that enhances the output of traction
force microscopy by increasing i) achievable bead density and ii) the
accuracy of bead tracking. Our approach relies on super-resolution
microscopy, enabled by fluorescence fluctuation analysis. Our pipeline
can be used on spinning-disk confocal or widefield microscopes and is
compatible with available analysis software. In addition, we
demonstrate that our workflow can be used to gain biologically
relevant information and is suitable for fast long-term live measurement of traction forces even in light-sensitive cells. Finally, using
fluctuation-based traction force microscopy, we observe that filopodia align to the force field generated by focal adhesions.
KEYWORDS: Fluctuation-based super-resolution microscopy, SACD, SRRF, traction force microscopy, live imaging, mechanobiology
C
ell adhesion to the extracellular matrix (ECM) is a
fundamental feature of multicellular life, and it is finely
tuned during almost every cellular process including cell
migration, cell proliferation, and cell fate. Cells are not
passively attached to the ECM but instead constantly apply
forces on ECM molecules and actively remodel their
microenvironment.1 The major cellular structures responsible
for transmitting these forces to the ECM are focal adhesions.1
These multiprotein signaling platforms also translate physical
forces into intracellular biochemical signaling cascades.2 The
ability of cells to apply mechanical forces on their environment
is emerging as one of the key regulators of tissue patterning
and morphogenesis,3,4 while dysregulation of this process is
associated with diseases including aging, fibrosis, and
cancer.5−7 As our understanding of mechanobiology is rapidly
unveiling promising novel therapeutic opportunities,8 the
development of methods that can facilitate these investigations
is of paramount importance.
While several strategies can be used to map and quantify the
forces exerted by cells on their microenvironments, traction
force microscopy (TFM) is one of the most convenient and
widely used methods.9 To perform TFM, cells are allowed to
adhere to a deformable material of defined stiffness, classically
a polyacrylamide (PAA) gel, containing fluorescent beads. The
forces exerted by cells on their substrate can then be
monitored as a function of bead movement within the gel.
As the stiffness and the elastic modulus of the gel are pre-
established, bead displacement can then, using mathematical
equations, be converted into a read-out of local forces.10
The sensitivity and accuracy of TFM are directly linked to
the ability to detect and track moving beads within a thick gel
and are generally limited to the detection of forces at the
micron-scale.9,10 As focal adhesions can be small,11 there is a
need to develop methods that can map cellular forces at high
spatial resolution. For TFM, this can be achieved by (1)
increasing the number of trackable beads12−14 and by (2)
improving the computational algorithms used to map cellular
forces.15 Multiple imaging and sample preparation strategies
have been developed to increase the number of trackable beads
in TFM experiments, each with unique strengths and
shortcomings (Table 1).12−14 Improvements in TFM algo-
rithms often require further biological assumptions16 as well as
heavy computational processing power.15 Here we propose a
simplified protocol that can resolve densely packed beads using
software enabling super-resolution microscopy through fluo-
rophore intensity fluctuation analysis. For simplicity, these
algorithms are hereafter termed fluctuation-based super-
resolution (FBSR) imaging. In this study, we demonstrate
that FBSR combined with TFM considerably enhances
traction force outputs.
Received:
October 3, 2019
Revised:
March 2, 2020
Published: March 6, 2020
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FBSR harnesses the intrinsic property of fluorophores that,
when excited with continuous light, display random variation
in intensity over time due to transitions between fluorescent
and nonfluorescent states.17,18 After capturing these intensity
oscillations (typically tens to hundreds of images), algorithms
such as super-resolution optical fluctuation imaging (SOFI,19),
super-resolution radial fluctuations (SRRF20), or autocorrela-
tion two-step deconvolution (SACD)21 can be used to predict
the location of fluorophores at improved resolution.
Increasing the Bead Density of TFM Gels Using
Fluctuation-Based Microscopy. To increase the number of
trackable beads in our TFM experiments, we decided to
employ FBSR (Figure 1a) as it has several advantages over
other SR modalities (Table 1). Of note, FBSR is easy to
implement, is compatible with most pre-existing microscopes
including spinning-disk confocal and widefield systems,20 and
is only mildly phototoxic with improved resolution being
achieved using illumination intensities typical for conventional
fluorescence imaging.23 Classically, when performing TFM
experiments, 200 nm fluorescent beads are embedded
throughout the PAA gel.10 This can result in substantial out-
of-focus light, which limits the use of widefield microscopes for
TFM. To increase the number of trackable beads in our TFM
experiments and enable better quantification of cellular forces,
we used gels containing densely packed 40 nm fluorescent
beads.
To validate that FBSR can improve the detection of 40 nm
beads, we performed simulations with known and increasing
bead densities (see Materials and Methods for details;
Supplementary Figure 1a−d). These simulations show that,
at low bead densities, accurate bead numbers can be recovered
from both widefield and FBSR images with FBSR processing
clearly improving the quality and resolution of the final images
(Supplementary Figure 1a,b). However, at higher bead
densities (over 1 bead per square micrometer), FBSR
processing allowed a higher recovery of bead numbers
compared to the widefield images (Supplementary Figure
1a,b). To assess the improvement in bead trackability enabled
by the detection of higher bead density using FBSR processing,
a realistic displacement field was applied to our simulated data
(see Materials and Methods for details; Supplementary Figure
1c). The bead displacement maps generated using FBSR
imaging demonstrated that while the overall displacement field
was apparent at low bead densities, fine details could only be
retrieved at high bead densities (Supplementary Figure 1c,d).
Altogether, our simulations demonstrate that FBSR processing
allows for the detection of higher bead densities, which leads to
increased trackability of the beads after image reconstruction
and in turn to improved recovery of spatial details in the force
map.
To optimize TFM gels for FBSR, and inspired by previous
work,13,15,24−26 we optimized a simplified gel casting protocol
where the 40 nm beads are embedded only on the topmost
layer of the gel (Supplementary Figure 2a,b). This was
achieved by precoating the top coverslip, used to flatten the gel
solution prior to casting, with the beads instead of mixing the
beads within the gel solution itself (Supplementary Figure 2a).
Importantly, using the FBSR algorithms LiveSRRF and SACD
and our optimized protocol, we were able to improve the
detection of 40 nm beads located on top of the TFM gel using
both spinning-disk confocal and widefield microscopes (Figure
1b). To ensure that as few artefacts as possible were
introduced during the FBSR reconstruction process, the
Table 1. High-Resolution TFM of Cells Plated in 2D
STED TFM13
SIM TFM14
high-resolution TFM12,22
FBSR TFM (this study)
required microscope
STED equipped with long working distance
objective
SIM
Confocal
spinning-disk confocal or widefield microscope
number of other fluo-
rescent channels typi-
cally available
1
3
2
3
expected bead density
2.2 μm2
1.0 μm2
information not available.
1.2 μm2
temporal resolution
(cells + beads)
Depends on the field of view (FOV) size. Frame
rate of 0.05 s−1 was demonstrated over 120 s
(FOV: 10 μm2).
Depends on the Z stack size. Frame rate of 0.1
s−1 was demonstrated over 200 s (FOV: 30
μm2 × 2 μm thick).
Depends on the field of view (FOV) size. Frame
rate of 0.2 s−1 was demonstrated over 150 s
(FOV: around 10 μm2).
Depends on the number of frames used for FBSR. Frame
rate of 0.05 s−1 was demonstrated over 16 min (100
frames, FOV: 127 μm2).
TFM analysis
Classical analysis pipeline can be applied. Open
access software readily available.
Normal pipeline for 2D, specific for 3D.
Specific analysis pipeline required. No specific
software readily available.
Classical analysis pipeline can be applied. Open access
software readily available.
control for imaging ar-
tifacts
no
no
not required
yes
bead size used
40 nm
100 nm
40 nm
40 nm
bead tracking
2D tracking
3D tracking using dedicated software
2D tracking
2D tracking
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Figure 1. FBSR processing enhances bead recognition in TFM specific PAA gels. (a) Cartoon illustrating the key steps used to produce TFM gels
for FBSR imaging. (1) TFM gel where 40 nm fluorescent beads are embedded only on the topmost layer of the gel is generated. (2) TFM gels are
then imaged using a spinning-disk confocal or widefield microscope. To allow for FBSR processing, each field of view is imaged 100 times. (3) SR
images are then generated using available FBSR algorithms such as LiveSRRF or SACD. (b) TFM gel prepared using our improved protocol (40
nm beads embedded only at the top) was imaged using spinning-disk confocal or widefield modes. To allow for FBSR, 50 (SACD) or 100 (SRRF)
frames were recorded. Average projections, LiveSRRF and SACD images are displayed. For each condition, the yellow square highlights a region of
interest (ROI) that is magnified. All images are from the same field of view. Scale bar: (main) 10 μm; (inset) 2 μm. (c) Resolution scaled error
maps of the LiveSRRF and SACD images displayed in b (ROI only) generated with NanoJ-SQUIRREL. Maps are color-coded to visualize areas of
low (purple) and high error (yellow). As a control, the same analysis was performed using a reference frame that was rotated 90°. Scale bars 2 μm.
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image quality was assessed using NanoJ SQUIRREL27 and the
resolution scaled Pearson’s correlation (RSP) and resolution
scaled error (RSE) parameters were calculated by the software
(Figure 1c). In addition to these parameters, when choosing
the reconstruction settings, the amount of beads detected and
the absence of patterning in the final image were also taken
into consideration (Supplementary Figure 2c,d). FBSR
processing led to a 2−3-fold improvement in the resolution
of bead images as measured by Fourier ring correlation and
decorrelation analyses (Figure 1d).
Prior to FBSR, our confocal-based TFM analyses have
yielded between 0.2 to 0.5 trackable beads per square
micrometer28−31 (Figure 1e), in agreement with values
reported by others.13 Here, by taking advantage of the densely
packed 40 nm bead layer gels and by implementing FBSR, and
conservative reconstruction parameters, we were able to
substantially increase the number of trackable beads to 1.2
beads per square micrometer (Figure 1e). This is a modest
improvement over a protocol using structured illumination
microscopy14 (1 bead per square micrometer) but remains
inferior to another protocol based on STED imaging within
small fields of view (2.2 beads per square micrometer) (Table
1).13 Interestingly, FBSR performed especially well when
images were acquired using widefield microscopy as the final
SR images were more homogeneous (Figure 1b). When the
images were acquired using spinning-disk confocal, the corners
of the field of view were often off focus due to uneven/
wrinkled gels resulting in much lower bead density in these
areas. In particular, spinning-disk confocal images recon-
structed by SACD appear to be especially sensitive to out of
focus light, arisen from uneven gels, leading to more variable
bead density than the other imaging modalities (Figure 1b).
However, when imaging cellular structures (e.g., cytoskeleton
or focal adhesions) on TFM gels, spinning-disk confocal
imaging is likely to outperform widefield imaging and may be
favored despite generating less homogeneous bead images.
Implementation of Fluctuation-Based Traction Force
Microscopy. In a typical TFM experiment, beads are imaged
before (pre) and after (post) removing cells, the pre and post
images are then aligned, and the beads are detected and
tracked in both images.10 From the tracking data, bead
displacement maps and force maps can be generated using
available TFM software.15,32 FBSR-TFM follows the same
workflow with the addition of image reconstruction prior to
the pre and post image alignment (Figure 2a).
To assess the improvement generated by FBSR-TFM over
classically used confocal-based TFM, cells were plated on gels
containing both 200 nm beads (distributed throughout the gel,
classic protocol) and 40 nm beads (distributed only at the top
of the gel, new protocol described here) (Figure 2b,c and
Supplementary Figures 2b and 3). This strategy enabled us to
measure and visualize traction forces using both methodologies
within the same field of view (Figure 2). Using spinning-disk
confocal imaging of the 200 nm beads (classic confocal TFM)
or of the 40 nm beads, we were able to track 8253 beads and
11 328 beads, respectively (full field of view, Supplementary
Figure 3). In contrast, FBSR imaging of the 40 nm beads,
yielded 20 799 (LiveSRRF processing using 100 frames) and
22 908 trackable beads (SACD processing using 50 frames)
within the same field of view. The spinning-disk confocal-based
TFM generated displacement and traction force maps that
closely recapitulated the shape of the cell (Figure 2c).
However, at this resolution, areas corresponding to cell-ECM
contacts such as focal adhesions could not be pinpointed, and
results were not substantially improved when using the
spinning-disk confocal images of the 40 nm beads (Figure
2c). Strikingly, applying the same TFM pipeline to the FBSR
images of the 40 nm beads (regardless of the FBSR and the
bead tracking methods used) drastically improved the
resolution of the displacement and force maps (Supplementary
Figures 3 and 4). In particular, defined regions of high forces
were specifically detected at the cell perimeter, which could
correspond to focal adhesions (Figure 2c and Supplementary
Figure 4). In addition, due to enhanced bead tracking, we
could better segregate cellular regions corresponding to weaker
forces. While the final force maps are affected by the
algorithm/mathematical framework used to perform force
reconstruction,15 the bead displacement maps are a direct
reflection of the amount of beads used as well as the quality of
bead tracking.9,12 In particular, errors in displacement
measurements caused by a lack of accuracy in the tracking
routines strongly affect the resolution of TFM. Notably, in the
case of the spinning-disk confocal TFM, large beads are only
tracked over subpixel movements, which is likely to lead to
tracking inaccuracies (Figure 2c Supplementary Figure 3). In
contrast, in the case of FBSR-TFM the tracking accuracy is
likely to be improved as (1) the beads are smaller and (2) they
are now tracked over several pixels (due to the smaller pixel
size of the FBSR images). Overall, we believe that FBSR
improves the TFM outputs by both increasing the bead density
(more data points) and by refining the accuracy of bead
tracking.
Versatility of Fluctuation-Based TFM. One of the
advantages of FBSR is that it can easily accommodate
multicolour imaging. To demonstrate this capability, we set
out to measure forces in cells endogenously tagged for paxillin,
a marker of focal adhesions. In this case, FBSR not only
enhanced bead tracking and identification but also the
resolution in images of paxillin-positive focal adhesions (Figure
3a, Supplementary Figure 5a). Importantly, this easy multi-
colour imaging capability combined with enhanced image
quality enabled us to confirm that the observed regions of
higher force correlate with the localization of cell−ECM
adhesions (Figure 3a,b).
To demonstrate that FBSR is compatible with multiple
existing TFM pipelines, we compared the displacement and
force maps generated by two freely available software
(MATLAB15 or ImageJ-based software;32 see Materials and
Figure 1. continued
(d) Estimation of the resolution of bead images before and after FBSR processing using Fourier ring correlation (FRC) or decorrelation analysis
(see Materials and Methods for details). Results are displayed as dot plots (average projections confocal, n = 26, n = 63; LiveSRRF confocal, n = 53,
n = 84; SACD confocal, n = 53, n = 56; average projections widefield, n = 24, n = 26; LiveSRRF widefield, n = 26, n = 24; SACD widefield, n = 26, n
= 26). (e) Graph showing bead densities (beads per square micrometer) measured from multiple published TFM data sets28−31 and from the TFM
gels (improved protocol described here) imaged using either spinning-disk confocal or widefield followed by FBSR processing using LiveSRRF or
SACD.
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Figure 2. Implementation of FBSR for TFM. (a) Schematic pipeline of a TFM experiment that includes FBSR and image quality control. The
software needed to complete each step are listed. (b, c) To assess the improvement generated by FBSR-TFM over the classically used confocal-
based TFM, U2OS cells expressing endogenously tagged paxillin were plated on 9.6 kPa gels containing both 40 and 200 nm beads and TFM
analyses were performed (as in panel a) on the ROI (yellow square, b). Spinning-disk confocal images of 200 and 40 nm beads and FBSR images of
the 40 nm beads (LiveSRRF; SACD) were used for TFM analysis using a MATLAB-based software.15 For each method, images of beads alone and
beads (black) + displacement vectors (blue arrows, length scaled up by 2) and maps of bead displacement and traction force are displayed (c). The
magnitudes of bead displacement and traction force are color-coded as indicated. Scale bars 10 μm. Analyses of the full field of view from panel b
can be found in Supplementary Figure 3. Bead tracking was performed here using cross-correlation within the search window. The same analysis
performed using PIV can be found in Supplementary Figure 4.
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Figure 3. Applying FBSR-TFM to cell biological experiments. (a−g) U2OS cells expressing endogenously tagged paxillin were plated on 9.6 kPa
gels containing 40 nm beads and were imaged using a spinning-disk confocal. In these data sets, both the beads and paxillin were imaged for FBSR
processing. All TFM analyses displayed here were performed using MATLAB.15 Scale bars 10 μm. (a, b) Representative images of paxillin-positive
focal adhesions before and after FBSR processing using LiveSRRF. Yellow squares highlight a ROI that is magnified. (a) For the ROI, the
resolution scaled error map is also displayed as in Figure 1c. (b) Associated bead displacement and traction force maps are also displayed. In the
ROI, the focal adhesion outlines are drawn in white. (c−g) U2OS cells expressing endogenously tagged paxillin were treated with either (c) DMSO
or (d) 10 μM blebbistatin for 15 min and FBSR-TFM was performed at both time points. (c, d) Representative images of cells and the
corresponding traction maps are displayed. (e, f) Quantification of overall total forces and strain energy (SE) after treatments (cropped to include
only one cell) and the fold change in total force and SE per field of view are displayed as dot plots (DMSO, n = 22; blebbistatin, n = 17; 2 biological
repeats). Statistics: Mann−Whitney U test. ∗∗∗p ≤ 0.004. (g) Correlations between SE and multiple focal adhesion parameters are also shown (n =
78 cells).
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Figure 4. Live cell imaging and large field of view FBSR-TFM. (a) U-251 glioma cells expressing endogenously tagged paxillin were plated on 9.6
kPa TFM gels containing 40 nm beads. Cells were imaged live, every 5 min and FBSR-TFM was performed (spinning-disk confocal imaging, Live-
SRRF processing and TFM analysis using MATLAB). The paxillin channel was denoised using the Noise2VOID algorithm.34 A representative field
of view is displayed for the paxillin channel as well as the matching traction force map. The yellow square highlights a ROI that is magnified and
displayed for several time points. The full movie is provided as Supporting Information (Video 1). White line depicts the leading edge of the cell.
Scale bar: (main) 10 μm; (inset) 5 μm. (b) DCIS.COM lifeact-RFP cells were plated on 9.6 kPa TFM gels containing 40 nm beads. Cells were
imaged live, every 20 s (80 ms exposure time per frame) over 16 min and FBSR-TFM was performed (spinning-disk confocal imaging, Live-SRRF
processing and TFM analysis using MATLAB). A time projection of the lifeact channel and matching traction force maps for several time points are
displayed. The full movie is provided as Supporting Information (Video 2). White line depicts the leading edge of the cell. Scale bar: 25 μm. (c, d)
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Methods for details). Regardless of the software used, the
displacement and force maps matched well to the outline of
the focal adhesions (Supplementary Figure 5b−e). Further-
more, the total amount of forces measured using these two
software showed a remarkable correlation (Supplementary
Figure 5e).
Biologically Relevant Applications of Fluctuation-
Based TFM. Next, we sought to demonstrate that our
improved TFM pipeline could be used to answer biologically
relevant questions. In particular, TFM is very commonly used
to assess how a protein or a drug treatment influences the
ability of cells to exert forces on their environment.28,29,31 For
this purpose, we aimed to cause a mild perturbation to
simulate a plausible biological response and treated cells with
either DMSO or the myosin II inhibitor blebbistatin for 15
min (Figure 3c,d). FBSR imaging of focal adhesions and FBSR
TFM measurements were performed before and after the
treatments to allow the quantification of force changes in each
cell. Importantly, 15 min of treatment with blebbistatin was
not sufficient to trigger the collapse of focal adhesions (Figure
3c,d). While blebbistatin treatment triggered a notable
decrease in the traction forces exerted by cells (Figure 3c,d),
this effect was masked by the high variability in both DMSO
and blebbistatin-treated cell populations when comparing the
15 min time point only (Figure 3e). Therefore, we directly
compared cells before (time point 0) and after treatments
(time point 15 min) and calculated the fold change in traction
force and strain energy (SE) for each condition (DMSO and
blebbistatin). We found that blebbistatin treatment signifi-
cantly decreased both traction forces and SE (Figure 3f), in
line with our traction force maps. These results indicate that
the negative effect of blebbistatin on cellular forces, usually
associated with a dramatic loss in focal adhesions, occurs, and
can be detected by FBSR-TFM, at earlier stages preceding
focal adhesion disassembly. In addition, our FBSR-TFM
analysis pipeline highlights the value of performing TFM
prior to and after a perturbation on the same cell to remove
cell-to-cell variability33 and thus to more accurately detect
changes in cellular forces. Interestingly, in this data set, the SE
exerted by an individual cell did not correlate with individual
focal adhesion properties such as “average focal adhesion size”
or “paxillin intensity” at focal adhesions. Instead, forces
generated by cells correlated well with cell-wide parameters
such as “cell area” and “total area covered by focal adhesions”
(Figure 3g).
Live-Cell FBSR TFM. FBSR is only mildly phototoxic, an
important property for extended live-cell imaging.23 Therefore,
we next sought to assess if FBSR would be suitable for
extended live TFM experiments. Glioma cells with endoge-
nously tagged paxillin were imaged every 5 min, over a 100
min time period and FBSR TFM measurements were
performed (Figure 4a, Video 1 and Supplementary Figure
6a). In this experiment, the signal-to-noise ratio of endogenous
paxillin was improved using a recent denoising approach based
on convolutional neural network.34 Using these images,
modulation of forces could clearly be observed, at high
resolution, as cells protruded (Figure 4a, Video 1, Supple-
mentary Figure 6a). Glioma cells imaged using this strategy
were not visibly disturbed by the imaging. In addition, the
same strategy could be used to perform extended live TFM
imaging of human-induced pluripotent stem cells, which, in
our experience, are very sensitive to phototoxicity (Supple-
mentary Figure 6b).
The temporal resolution of FBSR TFM depends on (1) the
number of frames used for the reconstruction, (2) the exposure
time used for the acquisition of individual images, and (3) the
number of channels to image. We could perform TFM
measurement at maximal resolution (100 frames, 80 ms
exposure time), in breast cancer cells, every 20 s over 16 min
(Figure 4b and Video 2). At this speed, force fluctuations in
defined regions of high force were clearly visible (Video 2),
resembling the tugging behavior of focal adhesions described
by others.22 If faster acquisition speeds are required, the
parameters listed above can be carefully tuned, but decreasing
them too much may result in lowering the final image
resolution (Supplementary Figure 7). Altogether, our data
demonstrate that FBSR-TFM is suitable for fast long-term live
TFM imaging.
Large Field of View FBSR TFM. Next, we tested the
capability of FBSR to improve bead detection over very large
fields of view (399 μm × 399 μm) using low magnification
objectives. In this case, single cells were plated on gels
containing 200 nm fluorescent beads and imaged using a 20×
air objective (Figure 4c,d). TFM analyses were performed on
both the widefield and FBSR images (Figure 4c,d). In the case
of single cells, the force maps generated from widefield images
were of poor quality and relatively noisy with forces being
detected in cell-free areas (Figure 4c,d). In contrast, FBSR
processing of the same field of view drastically improved
traction force maps and areas of high force closely matched the
outline of individual cells (Figure 4c,d). To assess if this variant
of FBSR-TFM is also capable of measuring forces exerted by
cell clusters, organoids generated from breast cancer cells were
plated on fibronectin-coated gels containing 200 nm
fluorescent beads and imaged live every 5 min over 1 h
(Figure 4e,f). In this data set, we were able to detect forces that
match closely the shape of the cell clusters as well as to
pinpoint high forces within small and dynamics protrusions
(Figure 4e,f). We believe that this variant of FBSR-TFM could
prove useful to improve force measurements in migrating cell
monolayers or to perform high throughput TFM screens.35,36
Figure 4. continued
U2OS cells were plated on 2.6 kPa TFM gels containing 200 nm beads (classic protocol), (c) treated with SiR-DNA to label nuclei, and imaged for
FBSR TFM using a widefield microscope (20× air objective). (c) The SiR-DNA images were denoised using the Noise2VOID algorithm34. Both
widefield and FBSR images (LiveSRRF) were used to perform TFM analyses (MATLAB software). (d) Images of the beads and the matching
traction force maps are displayed. The outline of the nucleus is overlaid in magenta. Yellow squares highlight ROI that are magnified. Scale bar:
(main) 100 μm; (inset) 10 μm. (e, f) Spheroids were generated from DCIS.COM lifeact-RFP cells kept in suspension for 7 days. Spheroids were
then seeded on top of 9.6 kPa TFM gels containing 200 nm beads (classic protocol) for 24 h before being imaged using a confocal microscope
(20× air objective). Spheroids were imaged live, every 5 min and FBSR-TFM was performed (Live-SRRF processing and TFM analysis using
MATLAB). (e) Several time points of a representative field of view are displayed for the lifeact channel as well as the matching traction force maps.
(f) A single time point of a larger cell cluster is displayed. White lines depict the outline of the cell clusters. Scale bars: 100 μm.
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Figure 5. Relationship between filopodia adhesions and focal adhesions. (a) U2OS cells transiently expressing Paxillin-GFP and Myosin-X-mScarlet
were plated on fibronectin-coated glass bottom dishes, fixed, stained for F-actin, and imaged using structured illumination microscopy. A
representative field of view is displayed. The yellow square highlights a ROI that is magnified. Scale bar: (main) 20 μm; (inset) 5 μm. The
percentage of filopodia directly connected to a paxillin-positive focal adhesion (FA) was quantified and the results are displayed as a bar chart. (b,
c) U2OS cells transiently expressing Paxillin-mKate2 and Myosin-X-GFP were plated on 9.6 kPa TFM gels containing 40 nm beads. Cells were
imaged using a spinning-disk confocal and FBSR-TFM was performed (Live-SRRF processing and TFM analysis using MATLAB). In this data set,
the beads, myosin-X (MYO10) and paxillin were imaged for FBSR processing. A representative field of view is displayed for the paxillin and
MYO10 channels with and without the displacement vectors (blue arrows, length scaled up by 2) as well as the matching traction force map. The
yellow square highlights a ROI that is magnified. The white lines in the displacement vectors map indicate the filopodia shafts (visible as very low
intensity in the MYO10 channel). White circles in the traction map depict the location of filopodia tips. (b) Scale bar: (main) 20 μm; (inset) 5 μm.
The alignment of filopodia tips to the force field was then measured using ImageJ. (c) The results are displayed as a frequency bar chart (n = 1022
filopodia). (d, e) U2OS cells transiently expressing Myosin-X-GFP were plated on fibronectin-coated glass bottom dishes for 1 h before being
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Investigating the Mechanical Relationship between
Focal Adhesions and Filopodia. Filopodia are small and
dynamic finger-like actin-rich protrusions and are often the
very first point of contact between a cell and its immediate
surroundings.37 We and others have previously described that
integrin-mediated mechanosensitive adhesions form at filopo-
dia tips and that these filopodia adhesions can mature into
focal adhesion.28,38 Interestingly, the formation of filopodia
adhesions has been reported to require cellular contractility.39
In U2OS cells expressing MYO10-GFP (to visualize and
induce filopodia formation) we found that 80% of filopodia are
directly connected to a paxillin-positive focal adhesion (Figure
5a). To investigate the mechanical interplay between filopodia
adhesions and focal adhesions, we took advantage of the
increased resolution and multicolour capability of FBSR-TFM.
U2OS cells expressing MYO10-GFP and paxillin-RFP were
plated on TFM gels and FBSR-TFM measurements were
performed (Figure 5b). Importantly, all detected cellular forces
could be mapped back to focal adhesions and the forces
generated by filopodia adhesions appeared to be negligible.
Further careful analysis of the force maps revealed that
filopodia tend to align to the force field generated at focal
adhesions (Figure 5b,c). To assess the contribution of the
force field to filopodia properties, freshly plated U2OS cells
expressing MYO10-GFP were treated with either DMSO or
the myosin II inhibitor blebbistatin for 1 h (Figure 5d).
Interestingly, cells treated with blebbistatin displayed more,
longer and curvier filopodia compared to DMSO-treated cells
(Figure 5d,e). Altogether these data suggest that cellular
contractility, transmitted to the ECM at focal adhesion, may
contribute to the straightening of filopodia as well as restricting
filopodia extension.
Discussion. We propose a simplified protocol and imaging
strategy, relying on FBSR, which improves TFM measure-
ments. Our strategy only requires off-the-shelf reagents and
access to commonly available widefield or spinning-disk
confocal microscopes and the analysis pipeline is fully
compatible with freely available TFM analysis software.
Importantly, FBSR-improved TFM data in combination with
FBSR-enhanced detection of cellular proteins (e.g., paxillin or
MYO10) can be used to correlate force data with specific
cellular structures such as focal adhesions. In addition, we
demonstrate that our workflow can be used to gain biologically
relevant information and is suitable for fast and long-term live
measurement of traction forces. Our strategy can also be used
over a large field of view using low magnification high
numerical aperture objectives.
One current limitation of the FBSR-based TFM workflow
described here is that it is currently not compatible with 3D
TFM (3D tracking of beads underneath cells plated on a 2D
substrate) as demonstrated recently using SIM.14 However, the
strategy described here has not yet reached full potential and
could be further developed. In particular, as FBSR
reconstruction algorithms are under constant development
and continue to improve,19−21 we expect parallel advances in
the quality of FBSR-TFM. For example, while FBSR
algorithms can be capable of axial resolution improvement,19
this feature is not yet widely implemented and is likely to be
improved in the future. In addition, as FBSR-TFM is fully
compatible with existing TFM software, it can be further
developed by fine-tuning the computational algorithms
responsible for bead recognition, tracking and methods used
to derive cellular forces.15,40,41 Here, we principally used the
Fourier transform traction cytometry (FTTC) method to
reconstruct forces, but it is tempting to speculate that even
further quality enhancement could be gained by employing
more computationally heavy mathematical frameworks.15 In
addition, it is theoretically possible that the resolution of
FBSR-TFM could be further enriched by mixing beads of
different colors as demonstrated for confocal-based micros-
copy.12,22
Materials and Methods. Cells. U2OS and U-251 glioma
cells were grown in DMEM/F-12 (Dulbecco’s modified Eagle’s
medium/Nutrient Mixture F-12; Life Technologies, 10565−
018) supplemented with 10% fetal bovine serum (FCS)
(Biowest, S1860). U2OS cells were purchased from DSMZ
(Leibniz Institute DSMZ-German Collection of Microorgan-
isms and Cell Cultures, Braunschweig DE, ACC 785). U-251
glioma cells were a generous gift from Professor David Odde
(University of Minnesota, US). U2OS and U-251 glioma cells
expressing endogenously tagged paxillin-GFP were generated
using CRISPR/Cas9 as described by.42 The gRNA sequence
targeting paxillin (5′-GCACCTAGCAGAAGAGCTTG-3′)
was cloned into the pSpCas9(BB)-2A-GFP backbone using
the BbsI restriction site.43 Cells were then transfected with the
GFP-Cas9-paxillin_gRNA construct and the template plasmid
AICSDP-1:PXN-EGFP in an equimolar ratio (1:1). After
transfection, cells were grown for 5 days before being sorted
based on green fluorescence using a fluorescence-activated cell
sorter (FACS; FACSAria IIu, BD). U2OS and U-251 glioma
cells were transfected using Lipofectamine 3000 and the
P3000TM Enhancer Reagent (Thermo Fisher Scientific)
according to the manufacturer’s instructions.
MCF10 DCIS.COM (DCIS.COM) lifeact-RFP cells were
cultured in a 1:1 mix of DMEM (Sigma-Aldrich) and F12
(Sigma-Aldrich) supplemented with 5% horse serum (16050−
122; GIBCO BRL), 20 ng/mL human EGF (E9644; Sigma-
Aldrich), 0.5 mg/mL hydrocortisone (H0888−1G; Sigma-
Aldrich), 100 ng/mL cholera toxin (C8052−1MG; Sigma-
Aldrich), 10 μg/mL insulin (I9278−5 ML; Sigma-Aldrich),
and 1% (v/v) penicillin/streptomycin (P0781−100 ML;
Sigma-Aldrich). Parental DCIS.COM cells were provided by
J.F. Marshall (Barts Cancer Institute, Queen Mary University
of London, London, England, UK). DCIS.COM lifeact-RFP
cells were generated using lentiviruses, produced using pCDH-
LifeAct-mRFP, psPAX2, and pMD2.G constructs (see ref 44
for more details). To generate DCIS.COM organoids,
DCIS.COM lifeact-RFP cells were seeded at very low density
(500 cells per well) in low adhesion plates (Corning, 3474) for
Figure 5. continued
treated with either DMSO or 10 μM blebbistatin for 1 h. Cells were fixed and stained for paxillin and actin before being imaged using a spinning-
disk confocal. A representative field of view is displayed. The yellow square highlights a ROI that is magnified. (d) Scale bar: (main) 20 μm; (inset)
10 μm. For each condition, the number of MYO10-positive filopodia per cell (DMSO, n = 70 cells; blebbistatin, n = 77 cells; ∗p value = 0.049),
their length (DMSO, n > 446 filopodia; blebbistatin, n = 945 filopodia; ∗∗∗p value < 0.001), and their curvature (DMSO, n = 640 filopodia;
blebbistatin, n = 945 filopodia; ∗∗∗p value < 0.001) were quantified. (e) P-values were determined using a randomization test.
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1 week before being transferred to TFM gels. The formation of
organoids was monitored using bright field microscopy.
The human induced pluripotent stem cell (hPSC) line
HEL24.3 was a kind gift from Professor Timo Otonkoski
(University of Helsinki). This cell line was created using
Sendai viruses45 and was cultured on Matrigel (Corning,
354277) in Essential 8 Basal medium (Life Technologies,
A15169−01) supplemented with E8 supplements (Life
Technologies, A1517−01). hPSC expressing endogenously
tagged paxillin-GFP was described previously46 and were
generated using CRISPR/Cas9 as described by.42
Antibodies and Plasmids. The mouse monoclonal anti-
paxillin antibody (PXN, Clone 349, 1:100 for IF) was provided
by BD Biosciences (catalogue number: 610051). The
mScarlet-MYO10 construct was described previously.28 The
pmKate2-paxillin vector was purchased from Evrogen (cat.#
FP323). mEmerald-Paxillin-22 was a gift from Michael
Davidson (Addgene plasmid # 54219).47 psPAX2 and
pMD2.G were gifts from D. Trono (École polytechnique
fédérale de Lausanne, Lausanne, Switzerland; Addgene plasmid
#12260 and #12259). pCDH-LifeAct-mRFP was a gift from P.
Caswell (University of Manchester, UK). AICSDP-1:PXN-
EGFP was a gift from The Allen Institute for Cell Science
(Addgene plasmid # 87420).
Sample Preparation for Light Microscopy. For SIM
imaging, U2OS cells transiently expressing mEmerald-
Paxillin-22 and Myosin-X-mScarlet were plated on high
tolerance glass-bottom dishes (MatTek Corporation, coverslip
#1.7) precoated first with Poly-L lysine (10 mg/mL, 1 h at 37
°C) and then with bovine plasma fibronectin (10 mg/mL, 2 h
at 37 °C). After 2 h, samples were fixed and permeabilised
simultaneously using a solution of 4% (wt/vol) PFA and 0.25%
(v/v) Triton X-100 for 10 min. Cells were then washed with
PBS, quenched using a solution of 1 M glycine for 30 min, and
incubated with SiR-actin (100 nM in PBS; Cytoskeleton;
catalogue number: CY-SC001) at 4 °C until imaging
(minimum length of staining, overnight at 4 °C; maximum
length, 1 week). Just before imaging, samples were washed
three times in PBS and mounted in Vectashield (Vectorlabs).
For the filopodia formation assays, cells expressing human
Myosin-X-GFP were plated on fibronectin-coated glass-bottom
dishes (MatTek Corporation) for 1 h before being treated with
either DMSO or 10 μM blebbistatin for 1 h. Samples were
fixed for 10 min using a solution of 4% (wt/vol) PFA, then
permeabilized using a solution of 0.25% (v/v) Triton X-100 for
3 min. Cells were then washed with PBS, quenched using a
solution of 1 M glycine for 30 min, and incubated with the
primary antibody for 1 h (1:100). After three washes, cells
were incubated with a secondary antibody for 1 h (1:100).
Samples were then washed three times and stored in PBS or in
PBS containing SiR-actin (100 nM; Cytoskeleton; catalogue
number: CY-SC001) at 4 °C until imaging. Just before
imaging, samples were washed three times in PBS. Images were
acquired using an spinning disk confocal microscope (100×
objective). The number of filopodia per cell and their length
was manually scored using Fiji. Filopodia curvature was
analyzed, from manually traced filopodia, using the Kappa Fiji
plug-in.48
TFM Gel Preparation. The 35 mm glass-bottom dishes
(Cellvis, D35−14−1N) were cleaned twice using absolute
ethanol and air-dried. Dishes were treated with a Bind-Silane
solution [714 μl Bind-SIlane (GE Healthcare, Silane A-174),
714 μl Acetic acid and 8572 μl of absolute ethanol) for 15 min
ar RT. Dishes were then washed once with 95% EtOH and
twice with mQH2O before being left to dry completely. In
parallel, 13 mm glass coverslips were coated with Poly-D-lysine
(10 μg/mL in mQH2O, Sigma-Aldrich, A-003-E) for 20 min at
+4 °C, washed in mQH2O and then left to dry out.
Fluorescent beads (either dark red, excitation 660 nm/
emission 680 nm; or orange, excitation 540 nm/emission
560 nm; Thermo Fisher Scientific, F10720) were diluted
1:5000 in mQH2O. The bead solution was then subjected to
repeated sonication for 30 s, followed by a 30-s pause, over a
10 min period. Importantly, beads were kept on ice for this
entire duration to prevent bead clustering. Each Poly-D-lysine
coated coverslip was then incubated with a 150 μL drop of the
bead solution at +4 °C for 20 min. Coverslips were then
washed and kept in mQH2O. Before use, the glass coverslips
were left to dry completely.
A premixture composed of 40% acrylamide (Sigma-Aldrich,
A4058), 2% N, N′-Methylenebis(acrylamide) solution (Sigma-
Aldrich, M1533) and PBS was prepared according to the
desired gel stiffness (see Table 2). In this study, most
experiments were performed using ∼9.6 kPa TFM gels with
the exception of hPSC live imaging (∼32 kPa gel;
Supplementary Figure 6b) and the large field of view TFM
(∼2.6 kPa and ∼9.6 kPa gels, Figure 4e and 4f).
From this stage onward, the premixture was kept on ice and
sonicated for 30 s followed by a 30-s pause over 10 min. The
premixture was then vortexed briefly, and 0.2% TEMED (vol/
vol; Sigma-Aldrich, T9281), and 1% ammonium persulfate
(vol/vol; 10% stock solution) was added to start PAA
polymerization. After a brief vortex, 11.8 μL of gel mixture
was added onto the glass of each glass-bottom dish. The bead-
coated 13 mm coverslip was then carefully placed on top of the
drop, ensuring that a thin layer of liquid remained between the
two glass surfaces. The plates were then incubated dry at room
temperature for 60 min after which they were submerged in
PBS to allow the careful removal of the top glass coverslip
using forceps. Gels can be stored submerged in PBS at +4 °C
for up to a week.
In the experiments where spinning-disk confocal TFM was
performed in addition to FBSR TFM, 200 nm green
fluorescent beads (excitation 505 nm/emission 515 nm)
(Life Technologies, F8811) were also added to the premixture
prior to the sonication step (Table 2).
To allow for functionalization, TFM gels were incubated for
30 min at RT with an activation solution [0.2 mg/mL of Sulfo-
SANPAH (Thermo Fisher Scientific, 22589), 2 mg/mL N-(3-
(dimethylamino)propyl)-N′-ethylcarbodiimide hydrochloride
(EDC) (Sigma-Aldrich, 03450) diluted in 50 mM HEPES
(4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid; Sigma-
Aldrich, H0887)] under gentle agitation.49 The glass-bottom
dishes containing the gels were then irradiated, without their
plastic lids, with ultraviolet (UV) light for 10 min using a UV-
chamber (Jelight Company Inc., UVO CLEANER, 342−220).
Gels were then washed three times with PBS and coated with
Table 2. TFM Gel Premixture Recipe
AA.
40%
(μL)
Bis. AA.
2% μL
PBS
(μL)
200 nm beads
(μL), optional
APS.
10%
(μL)
TEMED
(μL)
∼E
(kPa)
94
15
346
3.4
5
1
2.6
94
50
356
3.4
5
1
9.6
225
100
175
3.4
5
1
31.7
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either 10 μg/mL fibronectin (Merck-Millipore, 341631)
(U2OS, DCIS.COM, and U-251 cells) or 5 μg/mL vitronectin
(Thermo Fisher Scientific, A14700) (hPSCs).
Microscopy Setup. The spinning disk confocal microscope
(spinning-disk confocal) used was a Marianas spinning disk
imaging system with a Yokogawa CSU-W1 scanning unit on an
inverted Zeiss Axio Observer Z1 microscope controlled by
SlideBook 6 (Intelligent Imaging Innovations, Inc.). Images
were acquired using a Photometrics Evolve, back-illuminated
EMCCD camera (512 × 512 pixels). The microscope was used
either in confocal or widefield mode as indicated in the figure
legends. The objectives used were a 20× (NA 0.8 air, Plan
Apochromat, DIC) objective (Zeiss), a 63× (NA 1.15 water,
LD C-Apochromat) objective (Zeiss), and a 100× (NA 1.4 oil,
Plan-Apochromat, M27) objective.
The structured illumination microscope (SIM) used was
DeltaVision OMX v4 (GE Healthcare Life Sciences) fitted
with a 60x Plan-Apochromat objective lens, 1.42 NA
(immersion oil RI of 1.516) used in SIM illumination mode
(five phases × three rotations). Emitted light was collected on
a front-illuminated pco.edge sCMOS (pixel size 6.5 mm,
readout speed 95 MHz; PCO AG) controlled by SoftWorx.
Traction Force Microscopy. Cells were plated on TFM gels
(in a 1 mL volume of media) and left to adhere for at least 2 h
prior to imaging. To avoid drifting during imaging, the
spinning disk microscope was prewarmed to 37 °C prior to
image acquisition. To perform TFM measurements, beads
were imaged before (Pre) and after (Post) removing cells, the
Pre and Post images were then aligned (see methods below),
the beads detected in both images and their movements
tracked and local forces measured (see methods below). To
perform FBSR, 100 frames of the Pre and Post bead planes and
of the paxillin staining (when indicated) were acquired. In
between acquiring the Pre and Post images, the cells were
removed by adding 500 μL of a 20% SDS in mQH2O (5 min
incubation).
To perform the blebbistatin treatment experiment (Figure
3c,d), a first set of FBSR images of beads and focal adhesions
were acquired (first Pre image). Then a warm solution
containing 10 μM blebbistatin (final concentration in cell
medium; Stemcell Technologies, 72402) or DMSO (Sigma-
Aldrich, D2650) was added to the cells (1 mL added) for 15
min. A second set of FBSR images of beads and focal adhesions
was then acquired (second Pre image). The cells were then
detached as described above and the final set of FBSR images
was then acquired (Post image). To perform extended live
TFM imaging of the iPSCs and U-251 cells, FBSR image sets
of the beads were acquired every 5 min and the cells were
detached as described above. When estimating the temporal
resolution of FBSR TFM, DCIS.COM cells were seeded on 9.6
kPa fibronectin-coated gel and imaged continuously over 16
min. To perform the experiments using a large field of view
and 20x air objective the cells (U2OS or DCIS.COM) were
seeded on fibronectin-coated PAA hydrogels with 200 nm
beads cast inside as in the classical TFM protocol. When using
the U2OS cells the stiffness of the hydrogel was 2.6 kPa (∼3h)
whereas the DCIS.com organoids were seeded on top of 9.6
kPa gels (∼24 h). One-hundred pre and post images were
taken as described previously, however, using 20× air
objective. U2OS cells were imaged using the widefield mode
of the spinning disk confocal microscope whereas the
DCIS.COM organoids were imaged using confocal mode.
Only the beads were imaged using 100 frame acquisition.
Images of SiR-DNA and Lifeact were acquired only once per
time frame.
Image Alignment. Prior to bead tracking and force
mapping, the pre and post bead images were aligned in the
Fiji distribution of ImageJ50−52 using either the NanoJ-Core53
or the “Linear stack alignment with SIFT” plugins. The “Linear
stack alignment with SIFT” plugin was only used when affine
registration was required. In all cases, the first Pre image was
used as a reference image. For the blebbistatin and for the live
TFM experiments, focal adhesion images were registered with
bead images using the drift table generated by the NanoJ-Core
plugin.53
Generation of Simulated Bead Images. The positions of
the beads were randomly distributed over the chosen field-of-
view size (here 128 × 128 μm2). The number of beads was
chosen to obtain the desired density. Each bead was simulated
as a group of ∼650 dyes homogeneously distributed in a 40 nm
sphere. The field of simulated dye distribution thus obtained
was simulated over a 5 nm resolution grid. Each dye was
allowed to blink independently with on/off rate of 100 s−1 and
50 s−1 respectively over the entire acquisition without
bleaching (200 frames at 10 ms exposure). The simulated
fluorescence image produced by this distribution of beads was
created by convolution with a Gaussian kernel with
σ =
×
λ
0.21
NA,54 where λ is the wavelength of emission
(here 700 nm) and NA is the numerical aperture of the
microscope (here NA = 1.12). A pixel size of 250 nm was
chosen for the final fluorescence image in agreement with our
experimental setup. A realistic Poisson photon noise and a
Gaussian read-out noise were added to the images to simulate
experimental data set (SNR ≈ 7). Subsequently, a displace-
ment field previously obtained from a representative
experimental data set was applied to the original bead
positions and the fluorescence simulation was run again
independently. This way, a pair of stacks can be generated
from the same bead distribution and density. Simulated stacks
were then processed and analyzed as described for the
experimentally generated images.
FBSR Processing. FBSR processing was performed using
NanoJ-LiveSRRF and SACD.21 NanoJ-LiveSRRF is the newest
implementation of NanoJ-SRRF within the ImageJ software.
NanoJ-LiveSRRF is available upon request (will be openly
available for download soon), whereas NanoJ-SRRF is already
an open-source software.20
For FBSR processing using LiveSRRF, 100 frames were used
for the reconstruction. The parameter sweep option as well as
SQUIRREL analyses (resolution-scaled error (RSE) and
resolution-scaled Pearson (RSP) values),27 integrated within
LiveSRRF, were used to define the optimal reconstruction
parameters (32 different conditions as shown in Supplemen-
tary Figure 1b). RSE and RSP are two metrics that indicate
how well the two images agree at the resolution of the wide-
field image. In SQUIRREL, the super-resolution images are
blurred to achieve an equivalent resolution as that of the wide-
field image and the two are then compared. Any deviation
between the wide-field image and the resolution-scaled super-
resolution image will highlight artifacts/agreement. RSE and
RSP respectively represent the sum of all the intensity errors
between the two images (for RSE the smaller the number, the
better the agreement, 0 being a perfect agreement) and a
correlation-based metric (based on Pearson correlation,
therefore for RSP, the closer to 1, the better the agreement).
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For each liveSRRF setting combination, RSP and RSE
values, and bead density were measured and ranked. The
optimal LiveSRRF settings were then determined based on all
criteria (best overall rank) (see Supplementary Figure 2c) and
the final parameters used to process images are listed in Table
3.
For FBSR processing with SACD,21 the first 50 frames were
used for the reconstruction. SACD reconstructions were
performed within MATLAB (Mathworks, version R2019a)
and the following parameters: A, 1.15; pixel size, 247 × 10−9;
lambda, 647 × 10−9; iter, 1; mag, 5; square, 2, order, 3. The
MATLAB script used to process SACD images is available on
GitHub and can be found at https://github.com/
guijacquemet/.
Quantification of Bead Density. Bead density was
quantified by dividing the number of beads detected (in a
field of view) by the size of the field of view. The number of
beads for each field of view was measured in Fiji using the “find
maxima” option. The threshold used was tuned for each data
set so that only beads were counted. Importantly, the number
of beads measured using this strategy was nearly identical to
the number of beads identified by the MATLAB-based TFM
software.
Assessment of Image Resolution. Fourier ring correlation
(FRC) analysis was performed using NanoJ-SQUIRREL
implemented within ImageJ.27 As FRC analyses require two
images to be performed, raw FBSR data sets (composed of 100
frames) were split in half by sorting the odd and even frames.
Even and odd data sets were then processed separately as
indicated (Average Z projection, LiveSRRF or SACD) and the
two output images were used for the FRC analyses.
Image decorrelation analysis was performed in ImageJ using
the Image decorrelation analysis plugin.55 This analysis
requires a single image as input and therefore the full FBSR
data sets were used here.
Bead Tracking and Local Force Measurements. The bead
tracking and local force measurements were performed either
using MATLAB (Mathworks, version R2019a) or using
Fiji.50−52 For the MATLAB-based analyses, the TFM software
developed by the Danuser laboratory was used.15 If not
indicated otherwise, bead trackings were performed by cross-
correlation within the search window. Key parameters used can
be found in Table 4.
To generate the displacement and traction maps in Fiji, the
particle image velocity (PIV) plugin and the Fourier transform
traction cytometry (FTTC) plugin32 were used. The aligned
images of the pre and post TFM images of the beads were first
processed with the PIV plugin using the correlation coefficient
iteration option (interrogation window sizes: 128 pixel first
round, 64 pixel second round and 32 pixel third round). The
resulting PIV text file was then saved and plotted as a
displacement map using the plot function. Images shown in
Supplementary Figure 5d were postprocessed using the
normalized median test option (parameters used: 0.2 for
noise and 5.0 for threshold). The traction force maps were
generated using the ImageJ FTTC plugin (Poisson ratio, 0.5;
Young’s modulus, 10 kPa; regularization parameter, 4.0 ×
10−10). The total forces were calculated by measuring the
integrated density of the 32-bit images produced by the plugin.
Filopodia and Force Field Alignment. U2OS cells
transiently expressing mEmerald-Paxillin-22 and mScarlet-
MYO10 were seeded on 9.6 kPa fibronectin-coated TFM
gels for at least 3 h. The 40 nm beads, MYO10, and paxillin
were all imaged to allow for FBSR processing using Live-SRRF
(100 frames). TFM analyses were then performed using the
MATLAB software as previously described. To measure the
filopodia alignment to the force field, images containing the
Table 3. LiveSRRF Parameters
vibration
correction
radius
sensitivity
magnification
temporal
analysis
intensity
weighting
macro-pixel patterning
correction
40 nm beads images taken with confocal
on
1.5
1
5
average
on
on
40 nm beads images taken with widefield
on
2
2
5
average
on
on
200 nm beads images acquired with 20× air
objective and widefield
on
2
2
5
average
on
on
Paxillin images
on
2
1
5
average
on
on
MYO10 images
on
2
2
5
average
on
on
Lifeact images
on
2
1
5
average
on
on
Table 4. Key Parameters for TFM Analyses Using MATLAB-Based Software
bead detection parameters
“template size” and “maximum displacement for
calculating displacement field”
force field calculation
Figure 2c
high-resolution subsampling of beads and use subpixel
correlation via image interpolation
20 and 21 px
FTTC (Fourier transform
traction cytometry)
Figure 3b
high-resolution subsampling of beads and use subpixel
correlation via image interpolation
40 and 41 px
FTTC
Figure 3c,d
high-resolution subsampling of beads and use subpixel
correlation via image interpolation
80 and 81 px
FTTC
Figure 4a
PIV
80 and 81 px
FTTC
Figure 4b
high-resolution subsampling of beads and use subpixel
correlation via image interpolation
40 and 41 px
FTTC
Figure 4c
high-resolution subsampling of beads and use subpixel
correlation via image interpolation
80 and 81 px
FTTC
Figure 4e and f
high-resolution subsampling of beads and use subpixel
correlation via image interpolation
20 and 21 px
FTTC
Figure 5b
high-resolution subsampling of beads and use subpixel
correlation via image interpolation
60 and 61 px
FTTC
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force field vectors as well as the MYO10 and paxillin staining
were generated in MATLAB. The angle between the filopodia
and the force field was measured in ImageJ using the angle
calculation tool. The closest force field vector to each filopodia
tip was used.
Image Denoising Using Noise2VOID. The signal-to-
noise ratios of endogenously tagged paxillin (Figure 4a) and of
DNA (SiR-DNA) (Figure 4c) were improved using the recent
denoising approach Noise2VOID,34 which is based on
convolutional neural networks. Noise2VOID was executed
through the Google Colaboratory platform, which can run
Jupyter Notebooks in the cloud. The Jupyter Notebooks used
are available on GitHub and can be found at https://github.
com/guijacquemet/.
Statistical Analysis. Dot plots and box plots were
generated using the online tool PlotsOfData (https://
huygens.science.uva.nl/PlotsOfData/).56 Correlation analyses
were performed using Spearman’s Rank-order. Statistical
analyses were performed using the Mann−Whitney U test or
a randomization test as indicated in the figure legends.
Randomization tests were performed using the online tool
PlotsOfDifferences
(https://huygens.science.uva.nl/
PlotsOfDifferences/).57 The error bars presented in figures
depict the standard deviation. N numbers are indicated in the
figure legends.
■ ASSOCIATED CONTENT
*
sı Supporting Information
The Supporting Information is available free of charge at
https://pubs.acs.org/doi/10.1021/acs.nanolett.9b04083.
Video of Live FBSR TFM in a migrating glioma cell
(MP4)
Video of Live FBSR TFM in a DCIS.COM cell (MP4)
Results of simulations that demonstrate that FBSR TFM
improves TFM measurements; bead detection within
TFM gels using FBSR; full field of view of Figure 2; data
presented in Figure 2 reanalyzed using PIV; full field of
view of Figure 3; examples of live cell imaging
fluctuation-based TFM; effect of exposure time and
number of frames in SRRF reconstructions (PDF)
■ AUTHOR INFORMATION
Corresponding Authors
Guillaume Jacquemet − Turku Bioscience Centre, University of
Turku and Åbo Akademi University, FI-20520 Turku, Finland;
Faculty of Science and Engineering, Cell Biology, Åbo Akademi
University, 20520 Turku, Finland;
Email: guillaume.jacquemet@abo.fi
Johanna Ivaska − Turku Bioscience Centre, University of Turku
and Åbo Akademi University, FI-20520 Turku, Finland;
Department of Biochemistry, University of Turku, FIN-20520
Turku, Finland;
orcid.org/0000-0002-6295-6556;
Email: johanna.ivaska@utu.fi
Authors
Aki Stubb − Turku Bioscience Centre, University of Turku and
Åbo Akademi University, FI-20520 Turku, Finland
Romain F. Laine − MRC-Laboratory for Molecular Cell
Biology, University College London, London WC1E 6BT, U.K.;
The Francis Crick Institute, London NW1 1AT, U.K.;
orcid.org/0000-0002-2151-4487
Mitro Miihkinen − Turku Bioscience Centre, University of
Turku and Åbo Akademi University, FI-20520 Turku, Finland
Hellyeh Hamidi − Turku Bioscience Centre, University of Turku
and Åbo Akademi University, FI-20520 Turku, Finland
Camilo Guzmán − Turku Bioscience Centre, University of
Turku and Åbo Akademi University, FI-20520 Turku, Finland
Ricardo Henriques − MRC-Laboratory for Molecular Cell
Biology, University College London, London WC1E 6BT, U.K.;
The Francis Crick Institute, London NW1 1AT, U.K.
Complete contact information is available at:
https://pubs.acs.org/10.1021/acs.nanolett.9b04083
Author Contributions
Conceptualization, A.S., G.J., and J.I.; methodology, A.S., R.L.,
and G.J.; formal analysis, G.J., R.L., M.M., C.G., and A.S.;
investigation, A.S. and M.M.; resources, J.I., R.L., and R.H.;
writing original draft, A.S., G.J., and J.I.; writing review and
editing, A.S., R.L., H.H., R.H., G.J., and J.I.; visualization, A.S.,
H.H., G.J., and J.I.; supervision, G.J. and J.I.; funding
acquisition, G.J. and J.I.
Notes
The authors declare no competing financial interest.
The authors declare that the data supporting the findings of
this study are available within the article and from the authors
upon request.
■ ACKNOWLEDGMENTS
This study has been supported by the Academy of Finland
(G.J., and J.I.), the Academy of Finland CoE for Translational
Cancer Research (J.I.), an ERC CoG grant (#615258), and the
Sigrid Juselius Foundation (J.I and G.J.) and the Finnish
Cancer Organization (J.I.). G.J. was supported by grants
awarded by the Åbo Akademi University Research Foundation
(CoE CellMech) and by the Drug Discovery and Diagnostics
strategic funding of Åbo Akademi University. A.S. has been
supported by the University of Turku Doctoral programme for
Molecular Medicine (TuDMM). R.F.L. and R.H. were funded
by grants from the UK Biotechnology and Biological Sciences
Research Council [BB/S507532/1], the UK Medical Research
Council [MR/K015826/1], the Wellcome Trust [203276/Z/
16/Z], and core funding by the MRC Laboratory for
Molecular Cell Biology, University College London
[MC_UU12018/7]. We thank Aleksi Isomursu for providing
us with the U-251 glioma cell line expressing endogenously
tagged paxillin-GFP. We thank J. Siivonen and P. Laasola for
technical assistance and M. Saari for help with the micro-
scopes. The Cell Imaging and Cytometry Core facility (Turku
Bioscience, University of Turku, Åbo Akademi University and
Biocenter Finland) is acknowledged for services, instrumenta-
tion, and expertise. The Allen Institute for Cell Science, David
Odde (University of Minnesota, US), Patrick Caswell
(University of Manchester), Michael Davidson, and Timo
Otonkoski (University of Helsinki) are acknowledged for
providing reagents.
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