Lukas Velas1, Mario Brameshuber1, Johannes B Huppa2, Elke Kurz3, Michael L Dustin3, Philipp Zelger4, Alexander Jesacher4, Gerhard J Schütz1. 1. Institute of Applied Physics, TU Wien, 1040 Vienna, Austria. 2. Institute for Hygiene and Applied Immunology, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, 1090 Vienna, Austria. 3. Kennedy Institute of Rheumatology, University of Oxford, OX3 7FY Oxford, United Kingdom. 4. Division for Biomedical Physics, Medical University of Innsbruck, 6020 Innsbruck, Austria.
Abstract
T-cells engage with antigen-presenting cells in search for antigenic peptides and form transient interfaces termed immunological synapses. Synapse topography affects receptor binding rates and the mutual segregation of proteins due to size exclusion effects. It is hence important to determine the 3D topography of the immunological synapse at high precision. Current methods provide only rather coarse images of the protein distribution within the synapse. Here, we applied supercritical angle fluorescence microscopy combined with defocused imaging, which allows three-dimensional single molecule localization microscopy (3D-SMLM) at an isotropic localization precision below 15 nm. Experiments were performed on hybrid synapses between primary T-cells and functionalized glass-supported lipid bilayers. We used 3D-SMLM to quantify the cleft size within the synapse by mapping the position of the T-cell receptor (TCR) with respect to the supported lipid bilayer, yielding average distances of 18 nm up to 31 nm for activating and nonactivating bilayers, respectively.
T-cells engage with antigen-presenting cells in search for antigenic peptides and form transient interfaces termed immunological synapses. Synapse topography affects receptor binding rates and the mutual segregation of proteins due to size exclusion effects. It is hence important to determine the 3D topography of the immunological synapse at high precision. Current methods provide only rather coarse images of the protein distribution within the synapse. Here, we applied supercritical angle fluorescence microscopy combined with defocused imaging, which allows three-dimensional single molecule localization microscopy (3D-SMLM) at an isotropic localization precision below 15 nm. Experiments were performed on hybrid synapses between primary T-cells and functionalized glass-supported lipid bilayers. We used 3D-SMLM to quantify the cleft size within the synapse by mapping the position of the T-cell receptor (TCR) with respect to the supported lipid bilayer, yielding average distances of 18 nm up to 31 nm for activating and nonactivating bilayers, respectively.
Entities:
Keywords:
3D Single Molecule Localization Microscopy; Immunological synapse; Interference Reflection Microscopy; T-cell receptor; T-cells
Understanding the topography
of cellular interfaces is central
for addressing many cell biological questions. The distance between
the two juxtaposing cell surfaces not only regulates the affinity
of protein–protein transinteractions,[1,2] but
the extension of the intercellular cleft also affects the spatial
distribution of membrane proteins with differently sized ectodomains.[3] A prominent example is the size-exclusion of
the large phosphatase CD45 upon contact formation between a T-cell
and an antigen-presenting cell (APC), which is suspected to represent
an important regulatory mechanism for the phosphorylation of the T-cell
receptor (TCR);[4] according to this model,
shifting the balance between Lck-mediated phosphorylation and CD45-mediated
dephosphorylation induces downstream signaling.A common way
to study antigen-specific T-cell activation involves
the use of functionalized glass-supported lipid bilayers (SLBs) as
surrogates of APCs.[5−7] This experimental design has several advantages when
it comes to the application of high-resolution microscopy techniques
while still preserving the essential hallmarks of T-cell signaling
including the formation of an immunological synapse, the recruitment
of the kinase ZAP-70 and other downstream signaling effectors, the
increase in intracellular calcium, and the release of cytokines.First, interfacing cells with a glass coverslip further allows
for exploiting the interference of light reflected from the glass–water
interface and light reflected from the cell membrane for imaging purposes.[8,9] This technique, termed interference reflection microscopy (IRM),
yields high precision information on the separation of the cell surface
from the surface of the glass coverslip, eventually limited only by
the signal-to-noise ratio of the data and by the knowledge of the
interference model.[9] IRM has been frequently
applied to qualitatively assess the homogeneity of T-cell adhesion
to activating or inert surfaces,[5,10−12] often yielding patches of close contact next to areas of substantial
elevation of the T-cell surface. Quantitative interpretation of the
data, however, is often hampered by unknowns of the interference model.[9] For example, tilted membranes or the presence
of second or third order interferences are difficult to account for.
In addition, multiple reflections from different layers of varying
refractive index induce phase shifts in the IRM intensity profiles,
thereby impeding absolute distance measurements. In fact, protein
ectodomains and the glycocalyx contribute to the change in refractive
index between the aqueous environment and the cell, rendering the
plane of reflection rather poorly defined. Finally, IRM images are
of diffraction-limited spatial resolution and thereby yield averages
of the interference contrast over a few 100 nm. Fluctuations at smaller
scales would hence be averaged out.As a second advantage of
hybrid synapses, they facilitate the application
of total internal reflection (TIR) excitation to accentuate the signal
of dyes proximal to the glass surface over intracellular background.[13] Using TIR excitation, researchers discovered,
for example, the formation of TCR microclusters upon T-cell activation.[14−16]A third advantage relates to the use of single molecule localization
microscopy (SMLM) for studying the organization of signaling molecules
in the course of T-cell activation.[17−22] Briefly, SMLM achieves superior spatial resolution by precisely
localizing well-separated single molecule signals that can be obtained
from blinking chromophores.[23] Although
a two-dimensional localization precision below 20 nm was frequently
reported, it is difficult to achieve similar precision along the optical
axis.[24] In this context, the presence of
the glass coverslip in the vicinity of the fluorophores of interest
allows for using the supercritical angle fluorescence as a parameter
for determining the distance between the dye molecule and the glass
surface.[25,26] Of note, the dye’s distance from
the glass surface affects the shape of the recorded point spread functions
due to different supercritical angle contributions. We have recently
demonstrated that when combined with defocused imaging supercritical
angle three-dimensional SMLM (3D-SMLM) achieves isotropic localization
precision down to ∼10 nm in all three dimensions.[27]Here, we used 3D-SMLM based on supercritical
angle microscopy to
study the topography of the immunological synapse formed between primary
murine CD4+ T-cells and functionalized SLBs at isotropic
localization precision below 15 nm. The obtained TCR localization
maps were correlated with IRM images of the same synapse, thereby
allowing not only cross-validation of the two approaches but also
the identification of artifacts inherent to IRM images. From the TCR z-coordinates, we quantified the roughness of the T-cell
surface within the synapse, as well as its separation from the SLB:
Both for activating and nonactivating conditions we observed multiple
TCR-proximal spots of close contact, which would qualify for CD45
exclusion. We finally quantitatively compared 3D-SMLM images with
diffraction-limited TIR fluorescence microscopy of T-cell synapses
to disentangle different contributions to the appearance of TCR microclusters.
Results
Correlative
3D-Single Molecule Localization Microscopy and Interference
Reflection Microscopy
In order to evaluate the correlation
between 3D-SMLM and IRM data, we sought a system with known separation
of the detected single dye molecules from the glass surface. We opted
for an AlexaF647-coated glass sphere of 1 mm radius adhered to a glass
coverslip, which yielded z-distances of up to 150
nm within the field of view of 22 × 22 μm2 (Figure a). Figure b shows an IRM image recorded
next to the contact point between the sphere and the glass surface.
Concentric interference fringes are clearly visible. When plotting
the recorded IRM intensity values versus the distance of the according
pixels from the glass surface, z0, we
observed the characteristic cosine-dependence (Figure c). In this case, three branches of the IRM
intensity can be distinguished, corresponding to different orders
of the interference pattern. The first branch covers zSMLM values up to 90 nm, the second values of 90–180
nm, and the third values above 180 nm. A slight decrease in amplitude
and wavelength of the recorded IRM curve is noted for increasing IRM
interference orders, which is a consequence of reflections on the
curved surface.[9] In Figure b, we also included the recorded 3D-SMLM
data, with the color-code indicating the calculated displacement from
the glass surface, zSMLM. When plotted
against the radial distance from the sphere’s contact point, r0, the measured single molecule displacements zSMLM follow closely the surface of the sphere.
(Figure d). We further
correlated zSMLM with the IRM intensity
values recorded on the corresponding pixels (Figure e), yielding very good agreement of the two
data sets. The solid black line shows the calibration curve obtained
in Figure c, and the
dashed lines indicate the expected Cramér-Rao lower bound (CRLB).[27,28] To obtain a quantitative measure of the method’s z-precision, we calculated for each localization the difference
between zSMLM and the theoretical z0 (Figure f). The determined standard deviation of 21 nm agrees
well with the Cramér-Rao lower bound of 17 nm.
Figure 1
Control experiment on
a fluorescently labeled glass sphere. A glass
sphere of 2 mm diameter was labeled with BSA, Biotin–Streptavidin-AF647.
On the basis of the known size of the sphere, the distance of the
AF647 to the coverslip z0 was calculated
from the measured radial distance r0 from
the contact point of the sphere with the coverslip (a) (see SI Materials and Methods eq 1). (b) IRM and SMLM
images were recorded next to the contact point of the sphere with
the coverslip (red cross). (c) We plotted the z0 corresponding to the center of the respective pixel against
the normalized IRM intensity and fitted with SI Materials and Methods eq 2. (d) The measured distances zSMLM of all localizations were plotted against their radial
distance r0. The solid line indicates
the expected behavior for a spherical surface (SI Materials and Methods eq 1), dashed lines indicate the
square root of the Cramér-Rao lower bound. (e) Dependence of zSMLM on the normalized IRM intensity. Solid
line indicates fit results from panel (c), dashed lines indicate the
square root of the Cramér-Rao lower bound. (f) The difference
between the measured values of zSMLM and
the calculated values of z0 showed a standard
deviation of 21 nm. Scale bar 5 μm.
Control experiment on
a fluorescently labeled glass sphere. A glass
sphere of 2 mm diameter was labeled with BSA, Biotin–Streptavidin-AF647.
On the basis of the known size of the sphere, the distance of the
AF647 to the coverslip z0 was calculated
from the measured radial distance r0 from
the contact point of the sphere with the coverslip (a) (see SI Materials and Methods eq 1). (b) IRM and SMLM
images were recorded next to the contact point of the sphere with
the coverslip (red cross). (c) We plotted the z0 corresponding to the center of the respective pixel against
the normalized IRM intensity and fitted with SI Materials and Methods eq 2. (d) The measured distances zSMLM of all localizations were plotted against their radial
distance r0. The solid line indicates
the expected behavior for a spherical surface (SI Materials and Methods eq 1), dashed lines indicate the
square root of the Cramér-Rao lower bound. (e) Dependence of zSMLM on the normalized IRM intensity. Solid
line indicates fit results from panel (c), dashed lines indicate the
square root of the Cramér-Rao lower bound. (f) The difference
between the measured values of zSMLM and
the calculated values of z0 showed a standard
deviation of 21 nm. Scale bar 5 μm.
Correlative IRM, TIR, and 3D-SMLM of the Immunological Synapse
Next, we applied the method to image the three-dimensional topography
of the immunological synapse formed between CD4+ murine
5c.c7 TCR-transgenic T-cells and stimulatory or inert surfaces. To
visualize the position of the TCR, T-cells were labeled with an AlexaF647-conjugated
single chain antibody fragment[6] against
the TCR β subunit. Before analysis, all 3D-SMLM images were
corrected for overcounts, which eventually allowed us to obtain a
valid estimation of the surface roughness (see Supporting Information (SI) Materials and Methods). An isotropic
mean precision of 12.7, 12.0, and 14.6 nm was determined for single
molecule localization along the x-, y-, and z-axis, respectively (SI Figure 1).We first addressed the three-dimensional
organization of the TCR in activated T-cells. To this end, T-cells
were seeded onto fluid SLBs functionalized with MCC-loaded I-Ek at a surface density of 100 ± 30 molecules/μm2, which is known to stimulate intracellular calcium release;[29] in addition, bilayers contained the adhesion
molecule ICAM-1 and the costimulatory molecule B7-1 (Figure a). For all applied conditions,
T-cell activation was controlled via ratiometric calcium imaging (SI Figure 3). Prior to conducting imaging experiments,
T-cells were fixed at specific time points (here 5 min) after their
seeding onto the SLBs and imaged by both via IRM and 3D-SMLM. The
IRM images show substantial contrast fluctuations (Figure b), indicating corresponding
fluctuations in the distance of the T-cell membrane from the SLB.
This is supported by the 3D-SMLM data, where the determined TCR z-positions spread between 0 and 300 nm. Plotting zSMLM versus the IRM intensity revealed a good
correlation between the two data sets for the first branch of the
IRM signal (Figure c, solid black line). This correlation vanished, however, for higher
order IRM branches. We attribute this lack of correlation to additional
parameters affecting the measured IRM contrast such as unknown angles
of the reflecting surfaces or the occurrence of multiple interferences,
which particularly disturbs IRM signals originating from reflections
at larger distances from the glass surface. We therefore did not fit
those regions of the IRM curves. In addition, different resolutions
of the two methods impede direct comparison of the two data sets:
while 3D-SMLM data report on z-distances specific
for 2D coordinates that can be determined with a precision below the
diffraction-limit, IRM images are limited by diffraction and hence
provide average values over areas given by the size of the 2D point
spread function.
Figure 2
Correlative 3D-SMLM and IRM within the immunological synapse.
(a)
Experiments were performed on a T-cell adhering to an SLB functionalized
with I-Ek/MCC, B7-1 and ICAM-1; the TCR was labeled via
AF647-conjugated H57-scFv. (b) IRM (left) and SMLM/IRM overlay images
(right) of the immunological synapse. The areas i, ii, and iii are
shown in magnification below. (c) Correlation plots between zSMLM and the normalized IRM intensity for the
areas indicated in panel b. Data points with zSMLM < 100 nm were fitted with SI Materials and Methods eq 3 (solid line). The dashed lines indicate
the square root of the Cramér-Rao lower bound. Scale bars 2
μm.
Correlative 3D-SMLM and IRM within the immunological synapse.
(a)
Experiments were performed on a T-cell adhering to an SLB functionalized
with I-Ek/MCC, B7-1 and ICAM-1; the TCR was labeled via
AF647-conjugated H57-scFv. (b) IRM (left) and SMLM/IRM overlay images
(right) of the immunological synapse. The areas i, ii, and iii are
shown in magnification below. (c) Correlation plots between zSMLM and the normalized IRM intensity for the
areas indicated in panel b. Data points with zSMLM < 100 nm were fitted with SI Materials and Methods eq 3 (solid line). The dashed lines indicate
the square root of the Cramér-Rao lower bound. Scale bars 2
μm.For detailed assessment, we selected
in region (i) of Figure b the cell center
in which the left half of the region was well adhered to the SLB surface
while the right half featured a separation of ∼60 nm from the
SLB. Here, results obtained with the two imaging modalities are in
very good agreement. In region (ii) showing the cell edge, where the
T-cell formed a narrow lamellipodium, we observed two distinct clusters
in the zSMLM data, one reflecting the
bottom, the other reflecting the top membrane of the lamellipodium.
The two clusters were separated by ∼160 nm (SI Figure 2), which corresponded to the thickness of the lamellipodium.[30] Interestingly, the z-positions
of this particular region hardly correlated with the IRM patterns,
likely due to high inclinations of the reflecting membrane at the
lamellipodium edge possibly causing additional interferences in the
IRM image. In region (iii), another feature of membrane topography
became apparent: the central dark IRM area did not indicate an area
of close contact but instead showed rather distal lamellipodium regions
reflected by zSMLM coordinates more than
100 nm away from the glass surface. Also in this region, the separation
of the two lamellipodia membranes of ∼160 nm became apparent
from two well-separated localization clusters in the zSMLM data.It is also instructive to compare the
obtained 3D SMLM and IRM
images with conventional diffraction-limited TIR fluorescence microscopy
images (Figure ; see
also SI supporting gallery Figures 1 and
2 for additional examples). In this particular example, the cell had
been fixed just before the formation of the central supramolecular
activation cluster (cSMAC), when TCR microclusters were observed in
a ringlike structure around the cell center (Figure i). This image hence presumably reflects
a snapshot of the directional microcluster transport toward the cSMAC.[31] While TCRs in the ring itself showed tight contact
with the glass surface, likely due to binding to the cognate pMHC,
3D-SMLM revealed that the engulfed circular membrane patch contained
TCRs substantially elevated by ∼100 nm (Figure iii and Figure ix), in agreement with the observation that
the cSMAC is a site of TCR endocytosis.[32] In addition, some of the SLB engaged TCR in the cSMAC reside in
∼100 nm extracellular microvesicles that elevate the nonengaged
TCR bearing plasma membrane by ∼100 nm above the SLB,[33] potentially contributing to the two layers of
TCR in the cSMAC.
Figure 3
Correlative 3D-SMLM, IRM, and diffraction-limited TIR
microscopy
of the immunological synapse. T-cells were activated on a SLB functionalized
with I-Ek/MCC, B7-1, and high densities of ICAM-1 and fixed
10 min post seeding. The T-cell was imaged with IRM and fluorescence
microscopy. (i) Diffraction-limited TIR image of the T-cell. (ii)
Reconstruction of the diffraction-limited image by convolving the
3D-SMLM image with the corresponding psf. (iii) Overlay of the diffraction-limited
TIR image with the 3D-SMLM image. The color-code indicates distances
to the coverslip zSMLM. (iv) IRM image.
(v) Overlay of the IRM image with the diffraction-limited image. (vi)
Overlay of the IRM and the 3D-SMLM image. Bottom row images were generated
by calculating the pixel-wise average of the 3D-SMLM images (pixel
size of 146 nm is consistent with diffraction-limited image) according
to pixelated mean single molecule (SM) brightness (vii), pixelated
number of localizations (viii), and pixelated mean zSMLM (ix). Scale bars: 5 μm.
Correlative 3D-SMLM, IRM, and diffraction-limited TIR
microscopy
of the immunological synapse. T-cells were activated on a SLB functionalized
with I-Ek/MCC, B7-1, and high densities of ICAM-1 and fixed
10 min post seeding. The T-cell was imaged with IRM and fluorescence
microscopy. (i) Diffraction-limited TIR image of the T-cell. (ii)
Reconstruction of the diffraction-limited image by convolving the
3D-SMLM image with the corresponding psf. (iii) Overlay of the diffraction-limited
TIR image with the 3D-SMLM image. The color-code indicates distances
to the coverslip zSMLM. (iv) IRM image.
(v) Overlay of the IRM image with the diffraction-limited image. (vi)
Overlay of the IRM and the 3D-SMLM image. Bottom row images were generated
by calculating the pixel-wise average of the 3D-SMLM images (pixel
size of 146 nm is consistent with diffraction-limited image) according
to pixelated mean single molecule (SM) brightness (vii), pixelated
number of localizations (viii), and pixelated mean zSMLM (ix). Scale bars: 5 μm.We next analyzed TCR microclusters in more detail. In principle,
TCR microcluster contrast is not only determined by protein enrichment
but also by the closer proximity of the fluorophores to the glass
surface, yielding both increased excitation intensity in the evanescent
field as well as increased detection efficiency due to the collection
of supercritical angle fluorescence. Because our method allows for
disentangling single molecule fluorescence brightness, z-position,
and local clustering, we addressed the different contributions to
the diffraction-limited TIR images. For this, we compared the diffraction-limited
TIR images (Figure i) with data obtained from 3D-SMLM. The single molecule brightness
indeed showed some correlation with the positions of microclusters
in the diffraction-limited image (Figure vii), which was also reflected in a map of
the single molecule z-positions (Figure ix). Furthermore, single molecule
localizations were strongly clustered at the positions of the microclusters
(Figure viii). We
sought to reconstruct the diffraction-limited image by convolving
the single molecule localization map with the brightness-weighted
point spread function (psf) (Figure ii). The reconstructed image agreed well with the original
diffraction-limited image, down to the level of the individual TCR
microclusters.To quantitatively disentangle the different contributions,
we identified
TCR microclusters by intensity-thresholding the diffraction-limited
images (SI Figure 6) and analyzed the single
molecule properties separately for localizations coinciding with the
TCR microcluster regions versus localizations outside of TCR microclusters. Figure shows the ratios
of single molecule brightness and the number of single molecule localizations
per pixel, which were obtained from multiple cells. While the brightness
levels in microclusters increased only by 20%, we observed about 3-fold
enrichment of localizations. The product of the two ratios quantitatively
matched the average brightness ratios of pixels corresponding to microclusters
versus pixels of outside regions obtained from the diffraction-limited
images. Importantly, as this analysis was based on pixel-wise discrimination
of microclusters in the diffraction-limited images, it was not affected
by residual overcounts in the SMLM images. Taken together, we conclude
that the increased brightness of TCR microclusters in diffraction-limited
images is mainly explained by enrichment of TCR molecules. Of note,
we observed the expected increased brightness of microclusters fixed
at late time-points (red data points) compared to those fixed at early
time-points (orange data points);[16] also,
this effect is explained by an increase in the number of single molecule
localizations.
Figure 4
Disentangling single molecule brightness and molecular
enrichment
in TCR microclusters. We quantified the average single molecule brightness, B, and the number of localizations, N,
in pixels corresponding to TCR microclusters (“in”)
and the complementary regions of the synapse (“out”).
Plotted are the ratios in/out for B, N, the product B × N, and the average diffraction-limited brightness per pixel.
Each data point corresponds to the average ratio per cell. Colors
indicate the time point of fixation post seeding (orange, 5–10
min; green, 10 min; red, 10–15 min). (n =
30 cells).
Disentangling single molecule brightness and molecular
enrichment
in TCR microclusters. We quantified the average single molecule brightness, B, and the number of localizations, N,
in pixels corresponding to TCR microclusters (“in”)
and the complementary regions of the synapse (“out”).
Plotted are the ratios in/out for B, N, the product B × N, and the average diffraction-limited brightness per pixel.
Each data point corresponds to the average ratio per cell. Colors
indicate the time point of fixation post seeding (orange, 5–10
min; green, 10 min; red, 10–15 min). (n =
30 cells).We further studied T-cells contacting
SLBs that were functionalized
with the adhesion molecule ICAM-1 only, so that no calcium signal
was triggered (see SI Figure 3 for ratiometric
calcium analysis and SI supporting gallery
Figures 3 and 4 for exemplary images). As expected, we did not observe
the formation of TCR microclusters. While in general the cells spread
well on such substrates, areas of close contact appeared more fragmented
than in the activated situation. This effect was more pronounced when
we reduced the density of the adhesion molecule ICAM-1 in the SLB
from 125 ± 22 to 4 ± 2 molecules/μm 2.
Quantitative Analysis of T-cell Surface Topography within the
Immunological Synapse
For quantification, we determined the
roughness of the T-cell surface within the immunological synapse.
To prevent the inclusion of data originating from the top membrane
of lamellipodia we only considered localizations with zSMLM < 100 nm. At nonactivating conditions and low
ICAM-1 densities, we observed substantial fluctuations of the recorded z-positions (Figure a). For quantitative determination of the surface roughness,
we compared the obtained variances σ2 with values obtained
for fluorescently labeled SLB-anchored I-Ek/MCC, σ2, according to , yielding a standard deviation
of σ
= 37 nm. Fluctuations decreased to 29 nm when we increased the density
of the adhesion molecule ICAM-1 in the lipid bilayer. Upon activation
via higher densities of I-Ek/MCC, T-cells adhered more
smoothly to the surface, as indicated by reduced overall z-fluctuations of 19 nm. Also, in the case of activation we observed
the T-cell surface flattening out to a considerable extent with ICAM-1
present at higher densities.
Figure 5
Contact analysis for T-cells recorded under
activating and nonactivating
conditions: T-cells were seeded on SLBs either functionalized with
I-Ek/MCC, B7-1, and ICAM-1 (termed “activating”)
or with ICAM-1 only (termed “non-activating”). To vary
adhesive strength, we used 125 ICAM-1 molecules per μm2 (termed “high ICAM-1”) or 4 ICAM-1 molecules per μm2 (termed “low ICAM-1”). T-cells were allowed
to spread on SLBs and were fixed after 5–10 (orange), 10 (green),
or 10–15 (red) minutes post seeding. As a control, unlabeled
T-cells were seeded on SLBs containing I-Ek/MCC labeled
with AF647. The recorded localizations were filtered for zSMLM < 100 nm in order to exclude contributions from
the upper surface of lamellipodia. (a) The variance of zSMLM per cell was plotted for different conditions. Arrows
indicate the difference in variance to the control I-Ek/MCC data, numbers indicate the corresponding square root. (b) Mean zSMLM per cell was plotted for different conditions.
Arrows indicate the difference with the control I-Ek/MCC
data. To calculate the mean distance of TCR microclusters from the
glass surface we considered only localizations within microclusters
(termed “only MCs”) in experiments performed both at
high and low densities of ICAM-1 (n = 15 cells per
condition).
Contact analysis for T-cells recorded under
activating and nonactivating
conditions: T-cells were seeded on SLBs either functionalized with
I-Ek/MCC, B7-1, and ICAM-1 (termed “activating”)
or with ICAM-1 only (termed “non-activating”). To vary
adhesive strength, we used 125 ICAM-1 molecules per μm2 (termed “high ICAM-1”) or 4 ICAM-1 molecules per μm2 (termed “low ICAM-1”). T-cells were allowed
to spread on SLBs and were fixed after 5–10 (orange), 10 (green),
or 10–15 (red) minutes post seeding. As a control, unlabeled
T-cells were seeded on SLBs containing I-Ek/MCC labeled
with AF647. The recorded localizations were filtered for zSMLM < 100 nm in order to exclude contributions from
the upper surface of lamellipodia. (a) The variance of zSMLM per cell was plotted for different conditions. Arrows
indicate the difference in variance to the control I-Ek/MCC data, numbers indicate the corresponding square root. (b) Mean zSMLM per cell was plotted for different conditions.
Arrows indicate the difference with the control I-Ek/MCC
data. To calculate the mean distance of TCR microclusters from the
glass surface we considered only localizations within microclusters
(termed “only MCs”) in experiments performed both at
high and low densities of ICAM-1 (n = 15 cells per
condition).According to the kinetic segregation
model, the axial dimension
of the intercellular cleft determines the accessibility of the large
phosphatase CD45. We hence quantified the absolute distance of the
TCR from the SLB, when compared to fluorescently labeled SLB-anchored
I-Ek/MCC. Generally, we observed similar trends as for
the standard deviations: with increasing densities of I-Ek/MCC and ICAM-1 the TCR was observed to be closer to the SLB surface
(Figure b). The separation
varied between 18 nm at high densities of I-Ek/MCC and
ICAM-1 up to 31 nm for scanning T-cells recorded at low densities
of ICAM-1. When selecting only signals corresponding to TCR microclusters
for our distance analysis, we observed the expected close contact
between TCR and I-Ek/MCC with a calculated separation of
5 nm. The residual separation reflected in all likelihood the distance
separating the dye site-specifically conjugated to the single chain
antibody fragment and the dye coupled to the MCC peptide’s
C-terminus as presented by I-Ek.[6]
Discussion
We applied here a novel 3D-SMLM method to
map and analyze the position
of the TCR within the immunological synapse at isotropic localization
precision below 15 nm and put it in the context of IRM imaging. IRM
contrast arises from interferences due to optical path-length differences
between the beam reflected at the glass–water interface and
beams reflected from surfaces within the sample. Recording 3D-SMLM
localization maps is fundamentally different from IRM. Using supercritical
angle detection combined with defocused imaging, our method essentially
determines the three-dimensional position of all visible dye molecules.
We employed here an AlexaF647-conjugated single chain antibody fragment
that specifically recognizes the TCR β subunit.[6] In previous studies, we have shown that labeling neither
activates T-cells nor impedes specific antigen recognition.[6] Of note, we used TIR excitation in order to confine
imaging to the synapse region. While the evanescent field was narrow
enough to prevent contributions from TCRs at the top of the T-cell,
we could observe both the bottom and the top membranes of lamellipodia.Indeed, there was in general a good qualitative agreement between
the two imaging modalities. Substantial differences, however, arose
when distal membrane regions contributed to the IRM contrast values.
Then, contrast values did not unequivocally correspond to the axial
membrane separation. This was especially observed in lamellipodia,
where both the bottom and the top membrane contributed to the signal.Two aspects of our study contain important immunological implications
for understanding T-cell antigen recognition
What Is
the Physical Reason for the Observation
of TCR Microclusters?
In TIR excitation, fluorescent molecules
close to the glass coverslip naturally contribute with higher brightness
than distal molecules. This effect is further amplified by the collection
of supercritical angle fluorescence when using high NA objectives.[25] Taken together, what appears as a bright spot
in diffraction-limited fluorescence microscopy could be the consequence
of increased density or of increased brightness of the fluorophores.
Given the close contact between TCR microclusters and the surface,[12] researchers became concerned whether microclusters
actually reflect TCR enrichment.Three-dimensional SMLM provides
ground truth information on the origin of apparent TCR microclusters
in diffraction-limited TIR microscopy, as it allows for disentangling
molecular enrichment from brightness changes. We observed only marginal
contributions from single molecule brightness increase, as brightness
was largely attributable to molecular enrichment. This finding is
in accord with our observation of a rather smooth interface between
the SLB and the T-cell membrane under activating conditions, so that
there are globally only minor variations in the single molecule’s z-distance in comparison to the TIR penetration depth.
Does the Observed Cleft Size Support CD45 Segregation?
A prominent model for TCR triggering involves the balance in the
activities of the kinase lck and the phosphatase CD45 for ITAM phosphorylation.
In this kinetic segregation model, proteins with bulky extracellular
domains, such as the large phosphatase CD45, are proposed to be segregated
locally from the comparably short pMHC-TCR complexes.[4] Although there are ample reports that would be consistent
with this model,[34−38] the community has not reached a consensus yet.[39−41]One difficulty
has been the precise measurement of the cleft size between the T-cell
surface and the opposing membrane. Given the dimension of the TCR-pMHC-CD4
ternary complex of 10 nm[42] and our apparent
distance measurements between TCR and I-Ek/MCC within TCR
microclusters of 5 nm, the obtained distance undervalues the cleft
size by 5 nm. Upon correcting for this effect, an average cleft size
within the whole synapse ranging between 23 and 36 nm for the conditions
shown in Figure b
can be estimated. In addition to the average cleft size, however,
distance fluctuations within the synapse were of the same order (Figure a), indicating the
presence of multiple contact sites between the two membranes. Assuming
an axial length of CD45R0, the smallest CD45 isoform of approximately
22 nm,[34] our measurements hence indicate
the existence of numerous membrane contact sites that would be too
narrow to host CD45, both for resting and activating conditions. In
particular, an average cleft size of 30 nm together with an average
surface roughness of 29 nm, as observed for scanning T-cells at high
ICAM-1 densities, render the presence of multiple CD45 exclusion zones
likely. Similar data were recently reported for the tips of microvilli
which showed segregation of the TCR and CD45 prior to T-cell activation.[22] Given that none of these scenarios promoted
T-cell activation, it is possible that the ITAMs of these segregated
TCRs are not accessible to kinases.[43] In
addition, TCR-CD45 segregation may be too transient to trigger stable
phosphorylation of TCR ITAMs. Kinetic data about protein mobility
in conjunction with the superresolution images would be needed in
order to obtain a quantitative understanding of these key aspects
of the TCR triggering process.
Authors: Sophie V Pageon; Thibault Tabarin; Yui Yamamoto; Yuanqing Ma; Philip R Nicovich; John S Bridgeman; André Cohnen; Carola Benzing; Yijun Gao; Michael D Crowther; Katie Tungatt; Garry Dolton; Andrew K Sewell; David A Price; Oreste Acuto; Robert G Parton; J Justin Gooding; Jérémie Rossy; Jamie Rossjohn; Katharina Gaus Journal: Proc Natl Acad Sci U S A Date: 2016-08-29 Impact factor: 11.205
Authors: Sabrina Simoncelli; Juliette Griffié; David J Williamson; Jack Bibby; Cara Bray; Rose Zamoyska; Andrew P Cope; Dylan M Owen Journal: Cell Rep Date: 2020-12-22 Impact factor: 9.423
Authors: Benedikt Rossboth; Andreas M Arnold; Haisen Ta; René Platzer; Florian Kellner; Johannes B Huppa; Mario Brameshuber; Florian Baumgart; Gerhard J Schütz Journal: Nat Immunol Date: 2018-07-16 Impact factor: 25.606
Authors: Jan Christoph Thiele; Marvin Jungblut; Dominic A Helmerich; Roman Tsukanov; Anna Chizhik; Alexey I Chizhik; Martin J Schnermann; Markus Sauer; Oleksii Nevskyi; Jörg Enderlein Journal: Sci Adv Date: 2022-06-08 Impact factor: 14.957