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TCRs are randomly distributed on the plasma membrane of resting antigen-experienced T cells

Nature Immunology volume 19, pages 821–827 (2018)Cite this article

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Abstract

The main function of T cells is to identify harmful antigens as quickly and precisely as possible. Super-resolution microscopy data have indicated that global clustering of T cell antigen receptors (TCRs) occurs before T cell activation. Such pre-activation clustering has been interpreted as representing a potential regulatory mechanism that fine tunes the T cell response. We found here that apparent TCR nanoclustering could be attributed to overcounting artifacts inherent to single-molecule-localization microscopy. Using complementary super-resolution approaches and statistical image analysis, we found no indication of global nanoclustering of TCRs on antigen-experienced CD4+ T cells under non-activating conditions. We also used extensive simulations of super-resolution images to provide quantitative limits for the degree of randomness of the TCR distribution. Together our results suggest that the distribution of TCRs on the plasma membrane is optimized for fast recognition of antigen in the first phase of T cell activation.

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Fig. 1: Blinking and multiple observations lead to over-representation of single molecules in SMLM images.
Fig. 2: Label-density-variation dSTORM of TCRβ.
Fig. 3: Label-density-variation PALM of CD3ζ.
Fig. 4: Sensitivity of label-density-variation SMLM to detect nanoclustering.
Fig. 5: STED microscopy of CD3ε and image autocorrelation analysis.
Fig. 6: Sensitivity of STED image autocorrelation analysis to detect nanoclustering.

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Acknowledgements

We thank S. Hell for providing infrastructure for the STED experiments. This work was supported by the Austrian Science Fund (FWF) projects P 26337-B21, P 25730-B21, P 30214-N36 (G.J.S.), P27941-B28 (F.B.) and I953-B20 (M.B.); and by the Vienna Science and Technology Fund (WWTF) LS13-030 (J.B.H. and G.J.S.). Funding was further provided by a Boehringer Ingelheim PhD Fellowship (R.P.) and a DOC Fellowship (24793) from the Austrian Academy of Sciences (A.M.A.).

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Authors and Affiliations

  1. Institute of Applied Physics, TU Wien, Vienna, Austria

    Benedikt Rossboth, Andreas M. Arnold, Mario Brameshuber, Florian Baumgart & Gerhard J. Schütz

  2. Department of NanoBiophotonics, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany

    Haisen Ta

  3. Institute for Hygiene and Applied Immunology, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria

    René Platzer, Florian Kellner & Johannes B. Huppa

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Contributions

B.R. and A.M.A. performed simulations and analysis algorithms; B.R., A.M.A., F.B. and G.J.S. analyzed data; B.R., H.T. and M.B. performed and analyzed STED experiments; B.R. and F.B. performed and analyzed SMLM experiments; R.P. and F.K. provided key reagents; J.B.H. provided key reagents and mouse T cells and infrastructure, as well as ideas; M.B. provided ideas and performed the initial PALM experiments; F.B. and G.J.S. conceived of the project; and B.R., F.B. and G.J.S. wrote the manuscript.

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Correspondence to Mario Brameshuber, Florian Baumgart or Gerhard J. Schütz.

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Integrated supplementary information

Supplementary Figure 1 T cell activation model systems, quantification of T cell activation by ratiometric Ca2+ imaging and titration steps in label-density-variation dSTORM with H57-AF647.

(a) Schematic of model system: primary murine CD4+ TEFF cells on supported lipid bilayers displaying ICAM-1 (green) for non-activating conditions (top), or ICAM-1, B7-1 (red), and cognate pMHCII (blue) for activating conditions (bottom). (b) Ratiometric Ca2+ imaging via Fura-2 to assess activation state of primary murine CD4+ TEFF cells. T cell activation and concomitant increase in cytosolic Ca2+ under activating conditions (black, 2,128 cells from 7 independent experiments) or non-activating conditions (see a): unlabeled cells (blue, 2,362 cells from 7 independent experiments), or cells labeled with H57-AF647 (magenta, 763 cells from 2 independent experiments), H57-scFv-AS635P (green, 293 cells from 3 independent experiments), or KT3-scFv-AS635P (orange, 1205 cells from 4 independent experiments). Data shown as medians ± SE of the median. (c) Label-density-variation dSTORM using H57-AF647. Data represent one titration experiment shown in Fig. 2b with each data point color-coded according the antibody concentrations used for labeling; Red line with pink shaded region indicates reference line and its uncertainty for a random distribution derived from simulations based on the experimentally determined blinking statistics of H57-AF647 (mean ± SEM; n = 50 independent simulations)

Supplementary Figure 2 Single molecule blinking statistics.

Blinking statistics of labels used for label-density-variation SMLM experiments derived from time traces of recorded localizations. Data were recorded on fixed primary murine CD4+ TEFF cells labeled with antibody concentrations or PS-CFP2 expression levels so low that well-separated single molecule signals could be observed. (a) ton: time a single molecule was detectable in consecutive frames, toff: time a molecule remained in a dark state. (b) Histograms summarizing the blinking data with respect to the total number of observation per molecule, N, ton, toff and the first frame in which a molecule was detected; n = 1,827 for H57-AF647, n = 6,151 for KT3-AF647 and n = 1,409 for PS-CFP2

Supplementary Figure 3 Effect of blinking on SMLM data and Ripley’s K analysis.

A molecular density of 70 molecules/µm² in ROIs of 5 × 5 µm was simulated in 10 independent runs per condition. Blinking was simulated based on experimentally determined blinking statistics (Supplementary Fig. 2). Localization maps are shown for each condition (left panels) with corresponding Ripley’s K analysis (right panels). Random molecular distributions were simulated (a) without blinking, i.e. each molecule is represented by one localization or (b) including blinking statistics determined for anti-TCRβ H57-AF647. Yellow arrows indicate clearly visible blinking-related localization clusters. (c-j) Further scenarios relevant for this study were simulated as follows: (c) Randomly distributed dimers without blinking; (d) randomly distributed dimers with blinking based on anti-CD3ε KT3-AF647; (e) randomly distributed dimers with blinking based on PS-CFP2; (f) randomly distributed dimers that diffuse according to parameters determined for the TCR (mobile fraction of 64% and D = 0.047 µm²/s) with blinking based on PS-CFP2; (g) randomly distributed monomers with blinking based on PS-CFP2; (h) a mix of 70% randomly distributed dimers and 30% randomly distributed monomers without blinking; (i) a randomly distributed mix of 70% dimers and 30% monomers that both diffuse according to parameters determined for the TCR (mobile fraction of 64% and D = 0.047 µm²/s) and that both show blinking based on PS-CFP2; (j) a randomly distributed mix of 70% dimers and 30% monomers that both show blinking based on PS-CFP2. Ripley’s K curves are shown as mean ± SD; Scale bars: 2 µm

Supplementary Figure 4 Label-density-variation SMLM of CD3ε labeled with KT3-AF647 and CD3ζ-PS-CFP2.

(a) Representative diffraction-limited microscopy images (left) and dSTORM localization maps (right) of fixed primary murine CD4+ TEFF cells labeled with KT3-AF647 during interaction with non-activating (top) or activating (bottom) supported lipid bilayers (n = 10 and n = 19 biologically independent samples for activating and non-activating conditions, respectively); Scale bars: 3 µm. (b) Normalized ρ versus η plot derived from label density variation dSTORM of fixed primary murine CD4+ TEFF cells under non-activating (blue) and activating (black) conditions using KT3-AF647 (0.02, 0.2, 2, 10 and 20 µg/ml); data were binned based on η with a bin size of 0.1 and represented as means ± SEM; n = 58 for non-activating and n = 20 for activating conditions. Data for individual cells are shown in gray and light blue; red line with pink shaded region indicates reference line and its uncertainty, respectively, for a random distribution derived from simulations based on the experimentally determined blinking statistics of KT3-AF647 (mean ± SEM; n = 50 independent simulations); red arrows indicate data points corresponding to the cells shown in a; Top left, cartoon to illustrate the labeling strategy. (c) Label-density-variation dSTORM using KT3-AF647. Data are identical to b with each data point color-coded according the antibody concentrations used for labeling; Red line with pink shaded region indicates reference line and its uncertainty for a random distribution derived from simulations based on the experimentally determined blinking statistics of KT3-AF647 (mean ± SEM; n = 50 independent simulations). (d) Live primary murine CD4+ TEFF expressing CD3ζ-PS-CFP2 under non-activating conditions. Representative map of all recorded localizations is shown (n = 29 biologically independent samples. Scale bars: 3 µm. (e) Normalized ρ versus η plot derived from label density variation PALM based on the intrinsic expression level variabilities of primary murine CD4+ TEFF cells expressing CD3ζ-PS-CFP2 under non-activating (blue) conditions; data were binned based on η with a bin size of 0.1 and represented as means ± SEM; n = 29. Data for individual cells are shown in light blue; red line with pink shaded region indicates reference line and its uncertainty, respectively, for a random distribution derived from simulations based on the experimentally determined blinking statistics of PS-CFP2 (mean ± SEM; n = 50 independent simulations); red arrow indicates the data point corresponding to the cell shown in d; Top left, cartoon to illustrate the labeling strategy

Supplementary Figure 5 Classification criteria for cluster detection and sensitivity of label-density-variation SMLM to detect nanoclustering in experiments using H57-AF647.

(a) Schematic normalized ρ versus η plots of random (red) and different cluster scenarios (blue): cluster scenarios were classified as clearly detectable, when confidence intervals did not show overlap at η = 0.3 (left); as borderline, when confidence intervals overlapped at η = 0.3, but mean values were outside the confidence intervals (center); as not detectable, when mean values were lying within the respective confidence intervals at η = 0.3 (right). All curves are plotted as mean ± SEM. The dashed line indicates η = 0.3, where the detectability was evaluated. (b) Normalized ρ versus η plots were calculated for different simulated clustering scenarios and assessed for the difference from simulated random molecular distributions; detectable difference (dark gray), borderline (light gray) and not detectable difference (white) as shown in a. Reference numbers indicate scenarios published in the literature. Simulations of nanoclusters with radii of 20, 40, 60, 80, 100 and 150 nm for 3, 5, 10, 15 and 20 clusters/µm². The fraction of molecules inside clusters was varied between 40% and 100%. Average molecular densities were adjusted to 73 molecules/µm² based on H57-AF647 labeling of TCRβ (Fig. 2a). Blinking statistics were based on experimental data of H57-AF647. Numbers in boxes indicate the average number of molecules per cluster and the relative enrichment of molecules inside versus outside of clusters is indicated. The relative clustered area (right) was calculated from thresholded binary cluster maps. The figure is an extension of Fig. 4a: panels for 20 nm and 60 nm are identical to Fig. 4a

Supplementary Figure 6 Sensitivity of label-density-variation SMLM to detect nanoclustering in experiments using KT3-AF647 (a) and PS-CFP2 (b).

Normalized ρ versus η plots were calculated for different simulated clustering scenarios and assessed for the difference from simulated random molecular distributions; detectable difference (dark gray), borderline (light gray) and not detectable difference (white) (see Supplementary Fig. 5a). Reference numbers indicate scenarios published in the literature. Simulations of nanoclusters with radii of 20, 40, 60, 80, 100 and 150 nm for 3, 5, 10, 15 and 20 clusters/µm². The fraction of molecules inside clusters was varied between 40% and 100%. Average molecular densities were adjusted to (a) 70 molecules/µm² based on KT3-AF647 labeling of CD3ε (Supplementary Fig. 5a) or (b) 76 molecules/µm² based on ζ-PS-CFP2 PALM experiments (Fig. 3a). Blinking statistics were based on experimental data of the respective fluorescent probes. Numbers in boxes indicate the average number of molecules per cluster and the relative enrichment of molecules inside versus outside of clusters is indicated. The relative clustered area (right) was calculated from thresholded binary cluster maps. b is an extension of Fig. 4b: panels for 20 nm and 60 nm are identical to Fig. 4b

Supplementary Figure 7 Characterization of single molecule signals in STED microscopy and simulated STED microscopy data of different clustering scenarios.

Characterization of single molecule signals in STED microscopy: (a) Fitting of single molecule signals with a Gaussian intensity profile yielded the integrated intensity (I) and the width (σ). Values for I could be fitted well with a log-normal distribution (dashed line), yielding a mean of 35.14 and a variance of 508.52. (b) The signal width σ showed a characteristic dependence on the intensity. The data were fitted with Eq. 1 (black line). Simulated STED microscopy images (left) analyzed by image autocorrelation analysis. Red curves represent analysis of simulated random data (n = 5); blue curves represent analysis of simulated cluster scenarios (n = 5). Molecular densities were matched to the experiment. (c) A random distribution of molecules. (d-j) Different cluster scenarios characterized by cluster radius (r), fraction of molecules inside clusters and density of clusters per area (clusters/µm²): (d) r = 20 nm, 80% in clusters, 20 clusters/µm²; (e) r = 20 nm, 100% in clusters, 10 clusters/µm²; (f) r = 60 nm, 100% in clusters, 10 clusters/µm²; (g) r = 20 nm, 100% in clusters, 20 clusters/µm²; (h) r = 60 nm, 100% in clusters, 20 clusters/µm²; (i) r = 100 nm, 15% in clusters, 0.5 clusters/µm²; (j) r = 40 nm, 10% in clusters, 0.5 clusters/µm². Curves are shown as means ± SEM; Scale bars: 500 nm

Supplementary Figure 8 STED microscopy of TCRβ or CD3ε and image autocorrelation analysis.

Representative STED microscopy images of fixed primary murine CD4+ TEFF cells labeled with (a) KT3-scFv-AS635P (n = 2 biologically independent samples for both activating and non-activating conditions) or (b) H57-scFv-AS635P during interaction with non-activating or activating supported lipid bilayers as indicated (n = 5 and n = 1 biologically independent samples for non-activating and for activating conditions, respectively); Recorded images (left) were analyzed by image autocorrelation (right) and compared to simulated random distributions at molecular densities matched to the experimental data. Red curves represent simulated random data (mean ± SEM.; n = 5); blue curves represent analysis of multiple ROIs in the respective images (mean ± SEM.; n = 5). Red boxes in STED images indicate ROI positions chosen for ACF analysis. ROI positions were chosen similarly in other images (not shown). Scale bars: 3 μm; Cartoons illustrate the labeling strategy

Supplementary Figure 9 Classification criteria for cluster detection and sensitivity of STED image autocorrelation analysis to detect nanoclustering.

(a) Schematic of image autocorrelation curves of random (red) and different cluster scenarios (blue): cluster scenarios were classified as clearly detectable, when confidence intervals did not show overlap within the first two data points (left); as borderline, when confidence intervals overlapped within the first two data points, but mean values were outside the confidence intervals (center); as not detectable, when mean values were lying within the respective confidence intervals within the first two data points (right). All curves are plotted as mean ± SEM. The dashed line indicates that detectability was evaluated within the first two data points. (b) Image autocorrelation analysis was performed for different simulated clustering scenarios and assessed for the difference from simulated random molecular distributions; detectable difference (dark gray), borderline (light gray) and not detectable difference (white) as shown in a. Reference numbers indicate scenarios published in the literature. Simulations of nanoclusters with radii of 20, 40, 60, 80, 100 and 150 nm for 3, 5, 10, 15 and 20 clusters/µm². The fraction of molecules inside clusters was varied between 40% and 100%. Average molecular densities were adjusted to 75 molecules/µm² based on KT3-scFv-AS635P labeling of CD3ε (Fig. 5). Parameters of the simulated point spread function were based on experimental data. Numbers in boxes indicate the average number of molecules per cluster and the relative enrichment of molecules inside versus outside of clusters is indicated. The relative clustered area (right) was calculated from thresholded binary cluster maps. The figure is an extension of Fig. 6: panels for 20 nm and 60 nm are identical to Fig. 6

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Rossboth, B., Arnold, A.M., Ta, H. et al. TCRs are randomly distributed on the plasma membrane of resting antigen-experienced T cells. Nat Immunol 19, 821–827 (2018). https://doi.org/10.1038/s41590-018-0162-7

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