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Single-molecule imaging reveals receptor–G protein interactions at cell surface hot spots

Nature volume 550, pages 543–547 (2017)Cite this article

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Abstract

G-protein-coupled receptors mediate the biological effects of many hormones and neurotransmitters and are important pharmacological targets1. They transmit their signals to the cell interior by interacting with G proteins. However, it is unclear how receptors and G proteins meet, interact and couple. Here we analyse the concerted motion of G-protein-coupled receptors and G proteins on the plasma membrane and provide a quantitative model that reveals the key factors that underlie the high spatiotemporal complexity of their interactions. Using two-colour, single-molecule imaging we visualize interactions between individual receptors and G proteins at the surface of living cells. Under basal conditions, receptors and G proteins form activity-dependent complexes that last for around one second. Agonists specifically regulate the kinetics of receptor–G protein interactions, mainly by increasing their association rate. We find hot spots on the plasma membrane, at least partially defined by the cytoskeleton and clathrin-coated pits, in which receptors and G proteins are confined and preferentially couple. Imaging with the nanobody Nb37 suggests that signalling by G-protein-coupled receptors occurs preferentially at these hot spots. These findings shed new light on the dynamic interactions that control G-protein-coupled receptor signalling.

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Figure 1: Single-molecule imaging of receptors and G proteins.
Figure 2: Complex diffusion dynamics generates hot spots for receptor–G protein interactions.
Figure 3: Analysis of receptor–G protein interactions.
Figure 4: Hot spots for receptor–G protein signalling.

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Acknowledgements

We thank S. P. Watson, M. Sauer, C. Manzo and E. Cocucci for discussions. This work was supported by grants from the Deutsche Forschungsgemeinschaft (Sonderforschungsbereich/Transregio 166–Project C1 to D.C., CA 1014/1-1 to D.C. and FZT82 Rudolf Virchow Center to M.J.L.), the IZKF Würzburg (grant B-281 to D.C.), the European Research Council (Advanced Grant 232944–TOPAS to M.J.L.) and the Polish National Science Center (Maestro Grant No. 2012/06/A/ST1/00258 to K.B. and A.W.). T.S. was in part supported by an Alexander-von-Humboldt/Bayer Foundation fellowship.

Author information

Authors and Affiliations

  1. Institute of Pharmacology and Toxicology, University of Würzburg, Versbacher Straße 9, Würzburg, 97078, Germany

    Titiwat Sungkaworn, Marie-Lise Jobin, Martin J. Lohse & Davide Calebiro

  2. Bio-Imaging Center/Rudolf Virchow Center, University of Würzburg, Versbacher Straße 9, Würzburg, 97078, Germany

    Titiwat Sungkaworn, Marie-Lise Jobin, Martin J. Lohse & Davide Calebiro

  3. Faculty of Pure and Applied Mathematics, Hugo Steinhaus Center, Wroclaw University of Science and Technology, Wyspianskiego 27, Wroclaw, 50-370, Poland

    Krzysztof Burnecki & Aleksander Weron

  4. Max Delbrück Center for Molecular Medicine, Robert-Rössle-Straße 10, Berlin, 13125, Germany

    Martin J. Lohse

  5. Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, B15 2TT, UK

    Davide Calebiro

  6. Centre of Membrane Proteins and Receptors (COMPARE), Universities of Birmingham and Nottingham, Birmingham, B15 2TT, UK

    Davide Calebiro

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Contributions

T.S. and M.-L.J. performed the experiments. T.S., M.-L.J., K.B. and D.C. analysed the data. K.B., A.W. and D.C. developed the mathematical analyses. D.C. wrote the software. D.C., T.S. and M.J.L. wrote the manuscript. All authors discussed the results. D.C. and M.J.L. developed the project. D.C. conceived and supervised the study.

Corresponding author

Correspondence to Davide Calebiro.

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Reviewer Information Nature thanks J. Levitz, S. Marullo and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Figure 1 Control experiments.

a–c, Functional characterization of the SNAP-tagged receptor and CLIP-tagged G protein constructs. a, FRET measurements of Gi protein activation to test the SNAP-tagged α2A-AR construct. HEK-293 cells were co-transfected with a FRET sensor for Gi protein activation (Gβ1-2A-cpV-Gγ2-IRES-Gαi2-mTq2) and either wild-type or SNAP-tagged α2A-AR (n = 9 and 11 cells, respectively). Concentration response relationships were obtained from FRET measurements in which the cells were stimulated with increasing concentrations of noradrenaline. The SNAP-tagged β2-AR construct was tested in a previous study4. b, GTPγS binding assay to test the CLIP-tagged Gαi and Gαs constructs. The corresponding wild-type constructs were used as controls. Shown are time courses of GTPγS binding in the presence or absence of agonist (left, clonidine, 10 μM; right, isoproterenol, 10 μM) (n = 3 biological replicates per condition). c, Efficiency of extracellular labelling with the cell-impermeable SNAP substrate S549-BG in cells transfected with CD86 carrying two SNAP tags at its N terminus (n = 28, 13, 17, 14 and 11 cells for concentrations of 0.1, 0.5, 1, 2 and 4 μM, respectively). d, Efficiency of intracellular labelling with the cell-permeable CLIP substrate SiR-BC in cells transfected with CD86 carrying two CLIP tags at its C terminus (n = 18, 26, 31 and 27 cells for concentrations of 0.5, 2, 4 and 8 μM, respectively). Labelling efficiencies in c and d were determined by fitting single-particle intensity data with a mixed Gaussian model4. The following concentrations were chosen for subsequent experiments: 4 μM S549-BG (labelling efficiency 91.1 ± 2.9%) and 8 μM SiR-BC (labelling efficiency 82.6 ± 2.1%). e, Specificity of labelling. Shown are TIRF images of CHO cells transfected with different combinations of SNAP–α2A-AR and Gαi–CLIP, followed by labelling with S549-BG or SiR-BC (except for the positive control that was cotransfected with SNAP–α2A-AR and Gαi–CLIP, which was labelled with both fluorescent substrates). Nonspecific labelling in either mock or cross-transfected cells was responsible for 2–3 spots per cell on average, compared with about 300–400 in the positive controls. This very low number of nonspecific immobile localizations (<1%) does not significantly interfere with the analyses of this study. f, Representative potential energy (V) maps calculated in mock-transfected cells. Results were obtained after adding simulated trajectories with Brownian motion to reproduce a condition of diffusing particles over immobile nonspecific localizations. The presence of immobile nonspecific localizations was not sufficient to generate local low potential energy areas (see Extended Data Fig. 3b for comparison). Data are mean ± s.e.m. Images in e, f are representative of three independent experiments.

Source data

Extended Data Figure 2 MSD analysis of receptor and G protein trajectories.

a, Scatter plot of diffusion coefficient (D) and anomalous diffusion exponent (α) values estimated for simulated trajectories with Brownian motion and characteristics similar to those of α2A-ARs. The results were used to set the cut-offs for classifying the trajectories into four groups according to their motion: immobile (D < 0.01 μm2 s−1), sub-diffusion (α < 0.75), normal diffusion (0.75 ≤ α ≤ 1.25) and super-diffusion (α > 1.25). b, Scatter plot as in a for α2A-AR trajectories. c, Representative α2A-AR and Gαi trajectories classified into the four groups. d, Relative frequency distributions of the trajectories in the four groups (left) and corresponding D values (right) for receptors and G proteins under basal and stimulated conditions. The control CD86 was expressed together with wild-type α2A-AR and stimulated with noradrenaline (NA) to test whether the effects observed upon agonist stimulation were specific for Gαi. Differences in d are statistically significant by two-way ANOVA. #P < 0.05, ##P < 0.01 and ####P < 0.0001 versus the corresponding basal condition and **P < 0.01, ***P < 0.001 and ****P < 0.0001 versus α2A-AR basal (top) or β2-AR basal (bottom) by Tukey’s multiple comparison test. Data are mean ± s.e.m. n = 30 (9,273), 17 (6,623), 37 (8,309), 30 (4,699), 18 (2,182), 36 (5,240), 28 (11,267), 27 (12,697), 29 (10,760), 47 (16,461), 29 (41,079) and 47 (7,585) cells (trajectories) for α2A-AR basal, α2A-AR noradrenaline, α2A-AR UK-14,304, Gαi basal, Gαi noradrenaline, Gαi UK-14,304, CD86 basal, CD86 noradrenaline, β2-AR basal, β2-AR isoproterenol, Gαs basal and Gαs isoproterenol, respectively. Images in c are representative of five independent experiments.

Source data

Extended Data Figure 3 Complex diffusion dynamics of α2A-AR and Gαi.

a, Density maps of α2A-AR and Gαi localizations (selected trajectories overlaid in different colours; arrowheads, areas of high density). b, Potential energy (V) maps for α2A-AR and Gαi, calculated for the same membrane region. c, Merge of potential energy maps in b (top) and line-profile plot along the dashed line (bottom). Arrowheads, hot spots where V is low for both α2A-AR and Gαi. d, Additional examples of α2A-AR potential energy (V) maps over actin PALM images and corresponding expanded views of dashed boxes. Images are representative of three (a–c) and two (d) independent experiments.

Extended Data Figure 4 Complex diffusion dynamics of β2-AR and Gαs.

a, β2A-AR localizations over Gαs potential energy (V) map and vice versa (top) and quantifications of relative potential energy at the localizations (VL,rel; bottom). A negative value indicates relatively lower potential energy at the localizations. b, β2-AR–Gαs interactions over merged β2-AR and Gαs potential energy map (top) and corresponding quantifications (bottom). Arrowheads, localizations concentrated at hot spots. c–e, β2-AR trajectories over images of tubulin (c), actin (d) or CCPs (e) (top) and corresponding colocalization analyses (bottom). Negative colocalization index (I) values indicate preferential avoidance of the imaged structures by the receptors. f, Trajectory of β2-AR stopping at CCP (arrowhead; top) and corresponding quantitative analysis (bottom). The trajectory is colour-coded according to the HMM states. Results in a–e were compared to those obtained with random localizations. See Supplementary Methods for details. Data are mean ± s.d. n, number of cells. #P < 0.05, ##P < 0.01, ###P < 0.001, ####P < 0.0001 versus random localizations by two-sided paired t-test. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 versus β2-AR (basal) by two-sided unpaired t-test.

Extended Data Figure 5 Complex diffusion dynamics of Gαi.

a, Gαi trajectories over actin PALM image. Arrowheads, Gαi subunits crossing over actin fibres. b, Gαi trajectories over image of CCPs. c, Gαi potential energy (V) map over PALM image of actin fibres (left) and expanded view of dashed box (right). Images are representative of two (a, c) and three (b) independent experiments.

Source data

Extended Data Figure 6 Validation of the analysis of receptor–G protein interactions using simulated data.

a–d, Test of the Lucy–Richardson deconvolution algorithm on data obtained with numerical simulations. A numerical simulation of particles interacting and dissociating following an exponential law (n = 20,000 particles; koff = 0.7 s−1) was performed. In addition, we considered particles undergoing random colocalizations (also terminating following an exponential law; n = 20,000 particles; k = 1.75 s−1). a, Underlying distribution of the interaction times for the true interactions. b, Distribution of the colocalization times for the random colocalizations. c, Convolution of the distribution in a with that in b, corresponding to the observed colocalization times. d, Deconvolution of the distribution in c with that in b. Note that the algorithm was capable of correctly retrieving the distribution of the true interaction times. e, f, Simulated two-channel image sequences of particles undergoing transient interactions. A defined fraction of particles in the first channel was simulated to undergo interactions with particles in the second channel. The synthetic image sequences were then analysed using automated particle detection and tracking as for the experimental ones. e, Representative frame of a simulated two-colour image sequence. f, Trajectories obtained by automated single-particle detection and tracking. g, Relaxation curve obtained from simulations of non-interacting particles. h, Relaxation curve obtained from simulations of interacting and non-dissociating particles. i, Result of the Lucy–Richardson deconvolution analysis of the data in h with those in g. Data were fitted with an exponential decay, used to estimate the rate of premature termination (see Supplementary Methods). j, Results of the Lucy–Richardson deconvolution analysis on simulated image sequences comparing the input dissociation rate constants (koff) and the ones estimated by the analysis. The results were corrected for premature termination of the interactions as described in Supplementary Methods. All simulations were repeated three times with similar results.

Extended Data Figure 7 Simulations with deterministic model of GPCR signalling.

a, Schematic representation of the model. r, inactive receptor; R, active receptor; L, ligand (agonist). b, Model reactions and kinetics parameters used in the simulations. c, d, FRET measurements of the ensemble kinetics of α2A-AR activation and deactivation in response to transient agonist stimulation. A sensor consisting of the α2A-AR carrying a FlAsH tag in the third intracellular loop and CFP at the C terminus was used. Shown are a representative FRET measurement (c) and the normalized average of the FRET data (d; n = 4 cells). e, Fitting of model parameters using the measured FRET data for receptor activation and deactivation. f, Concentration–response relationships for ligand binding, receptor activation and G protein activation generated with the model. g, h, Simulations of GPCR signalling in response to transient agonist stimulation, applying the estimated kon and koff for receptor–G protein interactions to the model. Simulations were performed considering both low (g) and high (h) receptor and G protein expression levels. i, j, FRET measurements of the ensemble kinetics of α2A-AR–Gαi association and dissociation in response to transient agonist stimulation. A sensor consisting of the α2A-AR carrying YFP at the C terminus and the Gαi1 subunit carrying CFP in the αA–αB loop within the α-helical domain was used. Shown are a representative FRET measurement (i) and the normalized average (n = 16 cells) of the FRET data (j). Association and dissociation time constants (mean, 95%CI) were 44.4 (38.3–52.9) ms and 1.22 (1.16–1.29) s. k, Comparison between the FRET data in j and the results of simulation with the mathematical model. Data are mean ± s.e.m.

Extended Data Figure 8 Hot spots for receptor–G protein interaction in primary human endothelial cells.

a, α2A-AR–Gαi interactions over merged α2A-AR and Gαi potential energy (V) map (left) and corresponding quantifications (right). Arrowheads, α2A-AR–Gαi interactions concentrated at hot spots. b, Same analysis as in a for β2-AR and Gαs. ####P < 0.0001 versus random localizations by two-sided paired t-test. c, Distribution of diffusion states (based on HMM analysis) of receptor and G protein trajectories during apparent interactions. Differences are statistically significant by χ2 test (****P < 0.0001; n = 2,488,438 and 1,382,193 data points for α2A-AR and Gαi with noradrenaline stimulation; n = 1,992,190 and 874,317 data points for α2A-AR and Gαi with UK-14,304 stimulation; n = 5,073,163 and 3,959,938 data points for β2-AR and Gαs with isoproterenol stimulation, respectively). Data in a, b are mean ± s.d. n, number of cells (a, b).

Source data

Extended Data Figure 9 Schematic summary.

a, The complex organization of the plasma membrane, including barriers provided by actin fibres, microtubules and CCPs, generates hot spots for receptor–G protein interactions and signalling. b, Receptors and G proteins undergo random collisions (preferentially within these hot spots), which, via very short-lived encounter complexes, only seldom lead to the formation of productive receptor–G protein (R−G) complexes (low kon). Most of these complexes dissociate with a lifetime of about 1–2 s; very few are long-lived. Agonists act mainly by increasing the kon for receptor–G protein interactions in a ligand-specific manner. These data suggest that most receptor–G protein complexes are transient and that receptor–G protein interactions are not diffusion limited but rather controlled by the large conformational rearrangements that occur during the formation of productive receptor–G protein complexes. See also Supplementary Discussion.

Source data

Extended Data Table 1 Complete results of the hidden Markov model (HMM) analysis
Full size table

Supplementary information

Supplementary Information

This file contains Supplementary Data, Supplementary Discussion and Supplementary Methods. (PDF 1008 kb)

Reporting Summary (PDF 68 kb)

Single-molecule imaging of individual receptors and G proteins at the surface of living cells

Shown are individual α2A-ARs (green) and Gαi subunits (magenta), imaged by fast two-colour TIRF microscopy. Frames were acquired every 28 ms. (MP4 12358 kb)

Single-particle tracking of receptors and G proteins

Shown are individual α2A-AR (green) and Gαi (magenta) trajectories. Frames were acquired every 28 ms. (MP4 11123 kb)

Receptors stopping at areas of high localization density.

Shown are individual receptor trajectories (α2A-ARs; different colours) overlaid on the corresponding localization density map. The playback is accelerated to capture receptor accumulation at high density areas. (MP4 3735 kb)

Relationship between receptor trajectories and underlying microtubules

Shown are individual α2A-AR trajectories (green) over a TIRF image of microtubules (magenta). Frames were acquired every 28 ms. (MP4 17691 kb)

Relationship between receptor trajectories and underlying actin fibres

Shown are individual α2A-AR trajectories (green) over a TIRF image of actin fibres (magenta). Frames were acquired every 28 ms. (MP4 19709 kb)

Relationship between receptor trajectories and underlying clathrin-coated pits (CCPs)

Shown are individual α2A-AR trajectories (green) over a TIRF image of CCPs. Frames were acquired every 28 ms. (MP4 19410 kb)

Combined single-particle tracking of receptors and superresolution imaging of actin fibres

Shown are individual α2A-AR trajectories (green) over a PALM image of actin fibres (orange). Frames were acquired every 28 ms. (MP4 23168 kb)

Combined single-particle tracking of G proteins and superresolution imaging of actin fibres

Shown are individual Gαi trajectories (green) over a PALM image of actin fibres (orange). Frames were acquired every 28 ms. (MP4 28393 kb)

Receptor and G protein stopping during an apparent interaction

Shown are single-molecule images and corresponding trajectories of an α2A-AR (green) and a Gαi subunit (magenta) diffusing and then stopping during an apparent interaction. The trajectories are coloured in blue during the interaction. Afterwards, the receptor resumes its movement, whereas the G protein remains immobile until it disappears due to fluorophore photobleaching. Frames were acquired every 28 ms. (MP4 14485 kb)

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Sungkaworn, T., Jobin, ML., Burnecki, K. et al. Single-molecule imaging reveals receptor–G protein interactions at cell surface hot spots. Nature 550, 543–547 (2017). https://doi.org/10.1038/nature24264

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