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Determination of G-protein–coupled receptor oligomerization by molecular brightness analyses in single cells

Nature Protocols volume 16, pages 1419–1451 (2021)Cite this article

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Abstract

Oligomerization of membrane proteins has received intense research interest because of their importance in cellular signaling and the large pharmacological and clinical potential this offers. Fluorescence imaging methods are emerging as a valid tool to quantify membrane protein oligomerization at high spatial and temporal resolution. Here, we provide a detailed protocol for an image-based method to determine the number and oligomerization state of fluorescently labeled prototypical G-protein–coupled receptors (GPCRs) on the basis of small out-of-equilibrium fluctuations in fluorescence (i.e., molecular brightness) in single cells. The protocol provides a step-by-step procedure that includes instructions for (i) a flexible labeling strategy for the protein of interest (using fluorescent proteins, small self-labeling tags or bio-orthogonal labeling) and the appropriate controls, (ii) performing temporal and spatial brightness image acquisition on a confocal microscope and (iii) analyzing and interpreting the data, excluding clusters and intensity hot-spots commonly observed in receptor distributions. Although specifically tailored for GPCRs, this protocol can be applied to diverse classes of membrane proteins of interest. The complete protocol can be implemented in 1 month.

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Fig. 1: Overview of the procedure.
Fig. 2: Labeling strategies.
Fig. 3: Partial photobleaching experiments in reference constructs.
Fig. 4: Example of a temporal brightness experiment.
Fig. 5: Example of a spatial brightness experiment.
Fig. 6: Rational ROI selection.
Fig. 7: Labeling efficiency and 2D map of GPCR oligomerization behavior.
Fig. 8: Application of our approach to screen the efficiency of bio-orthogonal labeling of β2-AR.

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Data availability

The original data for the experiments shown in Figs. 3–8 are available as Source Data with this protocol. Source data are provided with this paper.

Code availability

The data analysis for this study was done by using our custom-made IgorPro routine available on GitHub (https://github.com/PaoloAnnibale/MolecularBrightness). Image analysis and conversion were performed by using ImageJ, freely available at https://imagej.nih.gov/ij/. The ImageJ Stowers brightness plugins are freely available at https://research.stowers.org/imagejplugins/.

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Acknowledgements

We acknowledge funding by the Deutsche Forschungsgemeinschaft (German Research Foundation, DFG) CRC 1423, project number 421152132, subproject C03 (to P.A. and M.J.L.); DFG Cluster of Excellence EXC2046 Math+ (to P.A. and M.J.L.); DFG CRC 1423, project number 421152132, subproject C05 (to I.C. and A.B.); the National Institutes of Health (R01-DA038882; to M.J.L.); the Elite Network Bavaria graduate program ‘Receptor Dynamics’ (to M.J.L.); and DFG Grant CO822/2-1 (to I.C.).

Author information

Authors and Affiliations

  1. Max Delbrück Center for Molecular Medicine, Berlin, Germany

    Ali Işbilir, Jan Möller, Romy Thomas, Andreas Bock, Martin J. Lohse & Paolo Annibale

  2. Institute of Pharmacology and Toxicology, University of Würzburg, Würzburg, Germany

    Ali Işbilir, Jan Möller, Ulrike Zabel & Martin J. Lohse

  3. Institute of Biochemistry, Faculty of Life Sciences, University of Leipzig, Leipzig, Germany

    Robert Serfling, Chiara De Faveri, Annette G. Beck-Sickinger & Irene Coin

  4. Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy

    Marco Scarselli

  5. ISAR Bioscience Institute, Munich, Germany

    Martin J. Lohse

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  1. Ali Işbilir
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Contributions

A.I., R.S., J.M. and P.A. performed data collection and data analysis and developed the data acquisition and analysis procedure. A.I., A.B. and U.Z. developed the cloning procedure. P.A., A.G.B.-S., R.S. and U.Z. contributed analysis tools and materials used in the research. P.A. wrote the manuscript and initiated the research. A.B. and A.I. contributed to writing the manuscript. R.T., C.D.F., M.S. and M.J.L. contributed edits to the manuscript. P.A., I.C. and M.J.L. supervised the primary research. All authors reviewed the manuscript and approved the final article.

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Correspondence to Martin J. Lohse or Paolo Annibale.

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The authors declare no competing interests.

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Peer review information Nature Protocols thanks Andrew Clayton, Catherine Ann Royer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Key references using this protocol

Serfling, R. et al. ACS Chem. Biol. 14, 1141–1149 (2019): https://doi.org/10.1021/acschembio.8b01115

Annibale, P. & Lohse, M. J. Nat. Methods 17, 273–275 (2020): https://doi.org/10.1038/s41592-020-0732-0

Möller, J. et al. Nat. Chem. Biol. 16, 946–954 (2020): https://doi.org/10.1038/s41589-020-0566-1

Işbilir, A. et al. Proc. Natl Acad. Sci. USA 117, 29144–29154: https://doi.org/10.1073/pnas.2013319117

Extended data

Extended Data Fig. 1 Use of fluorescent ligands to label receptor of interest.

Confocal image of the basolateral membrane of a HEK293-AD cell expressing the Y2-receptor C-terminally labeled with EYFP (left) and labeled with 1 μM TAMRA-Ahx(5-24)NPY (center) followed by washout. Right, Upon displacement with 10 μM unlabeled Ahx(5-24)NPY, the fluorescent ligand is almost entirely displaced within tens of seconds. Scale bar, 10 μm.

Extended Data Fig. 2 Microscope calibration.

a, Theoretical autocorrelation function (ACF) for a sample of 10 particles diffusing in 2D through a point spread function (PSF) with a waist of 0.3 μm and D = 0.1 μm2/s. The pixel dwell time should allow for an accurate recording of the fluctuations. A general guideline is for the dwell time to be ~10 times smaller than the decay time of the diffusing species. In this case, the decay time is of the order of 300 ms, so any pixel dwell time smaller than 1 ms would be a very safe choice. The characteristic dwell time used in our temporal brightness acquisitions (2.4 μs) is way below this value. However, because the associated frame time is of the order of 640 ms on the Leica microscope, we felt that this was the best compromise between an acceptable photon collection and a not-too-slow acquisition time (about >1 min for 100 frames). b, Apparent brightness B versus intensity scatter plot originating from a movie (256 × 256 pixels, 100 frames) of a homogeneous mixture of Alexa488 imaged in a 90% (wt/wt) glycerol/water solution, for increasing values of the laser power. c, Change of brightness (fold change) as a function of the increase in intensity (fold change). As the mean pixel intensity increases (almost linearly with the laser power), the mean brightness scales proportionally. The linear fit (constrained to 0), has a slope of 0.9, indicating that the increase in intensity is matched by a proportional increase in brightness, as expected. d, Example of a dark count histogram for the Leica SP8 HyD photon-counting detector and for the analog PMT, the latter superposed to Gaussian + exponential fits (black dashed lines) to determine calibration parameters (see Box 2).

Source data

Extended Data Fig. 3 Example of labeling efficiency quantification (Box 2).

a, Export dual-color movies from the Leica LAS X software as tiff multipage files. b, Run the N&B processing macro. It relies on a set of plugins developed by Jay Unruh at the Stowers Institute for Biomedical Research. The macro guides the user in a step-by-step fashion to convert the tiff images to 16 bits, apply a moving average detrend to remove effects from photobleaching (optional) and calculate the average intensity and brightness value for each pixel of the movie. c, As a result, the sum intensity image is displayed in parallel to the brightness versus intensity plot (highlighted here as B/S vs I/S plot). Note that this plugin also allows for processing images that were collected with an analog detector (in this case, Slope, Zero Variance and Offset need to be adjusted to the properties of the detector of interest. For a photon-counting acquisition, leave as indicated here). A cursor selection allows for selecting those pixels belonging to cluster in as a B/S vs I/S plot. These pixels are highlighted in red in the intensity image above. The x avg value provides the average intensity, while the y avg value provides the average brightness value extracted from the selected pixels. d, For each cell analyzed, these values can be noted and then exported in the software of choice (in this case, Excel), where the number of emitters for each channel (Cy3 versus EGFP) can be plotted as a scatter and fit to a line (constrained through the origin, because in the absence of detectable aspecific binding for EGFP = 0, we assume Cy3 = 0) to determine the labeling efficiency.

Supplementary information

Source data

Source Data Fig. 3

Tabulated brightness values for Fig. 3b,c

Source Data Fig. 4

Raw acquisition move used in Fig. 4c–h

Source Data Fig. 5

Spatial brightness acquisition for Fig. 5b–d

Source Data Fig. 6

Spatial brightness for Fig. 6a–c

Source Data Fig. 7

Tabulated brightness values for Fig. 7a,b

Source Data Fig. 8

Tabulated number values for Fig. 8a,b

Source Data Extended Data Fig. 2

Combined tiff file containing acquisition of dye in solution at 5% laser power (100 frames), 10% laser power (100 frames) and 20% laser power (100 frames). Dark acquisition with HyD (100 frames) and with PMT (100 frames)

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Işbilir, A., Serfling, R., Möller, J. et al. Determination of G-protein–coupled receptor oligomerization by molecular brightness analyses in single cells. Nat Protoc 16, 1419–1451 (2021). https://doi.org/10.1038/s41596-020-00458-1

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