Spatial heterogeneity in molecular brightness

The Original Article was published on 20 May 2019

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Fig. 1: Molecular brightness analysis of GPCR oligomerization in the presence of spatial heterogeneity.

Data availability

Microscopy data used in this article include those reported in the original manuscript by Stoneman et al. ( The data that support the findings of this study are available from the corresponding author upon request.

Code availability

Data analysis code used in this article is available from the corresponding author upon request. The SpIDA code used in the article is available at


  1. 1.

    Stoneman, M. R. et al. A general method to quantify ligand-driven oligomerization from fluorescence-based images. Nat. Methods 16, 493–496 (2019).

    CAS  Article  Google Scholar 

  2. 2.

    Godin, A. G. et al. Revealing protein oligomerization and densities in situ using spatial intensity distribution analysis. Proc. Natl Acad. Sci. USA 108, 7010–7015 (2011).

    CAS  Article  Google Scholar 

  3. 3.

    Qian, H. & Elson, E. L. Distribution of molecular aggregation by analysis of fluctuation moments. Proc. Natl Acad. Sci. USA 87, 5479–5483 (1990).

    CAS  Article  Google Scholar 

  4. 4.

    Chen, Y., Muller, J. D., So, P. T. C. & Gratton, E. The photon counting histogram in fluorescence fluctuation spectroscopy. Biophys. J. 77, 553–567 (1999).

    CAS  Article  Google Scholar 

  5. 5.

    Digman, M. A., Dalal, R., Horwitz, A. F. & Gratton, E. Mapping the number of molecules and brightness in the laser scanning microscope. Biophys. J. 94, 2320–2332 (2008).

    CAS  Article  Google Scholar 

  6. 6.

    Calebiro, D. et al. Single-molecule analysis of fluorescently labeled G-protein-coupled receptors reveals complexes with distinct dynamics and organization. Proc. Natl Acad. Sci. USA 110, 743–748 (2013).

    CAS  Article  Google Scholar 

  7. 7.

    Scarselli, M. et al. Revealing G-protein-coupled receptor oligomerization at the single-molecule level through a nanoscopic lens: methods, dynamics and biological function. FEBS J. 283, 1197–1217 (2016).

    CAS  Article  Google Scholar 

  8. 8.

    Singer, S. J. & Nicolson, G. L. The fluid mosaic model of the structure of cell membranes. Science 175, 720–72 (1972).

    CAS  Article  Google Scholar 

  9. 9.

    Serfling, R. et al. Quantitative single-residue bioorthogonal labeling of G protein-coupled receptors in live cells. ACS Chem. Biol. 14, 1141–1149 (2019).

    CAS  Article  Google Scholar 

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P.A. and M.J.L. acknowledge funding by the Deutsche Forschungsgemeinschaft (German Research Foundation) through CRC 1423, project number 421152132, subproject C03, Cluster of Excellence EXC 2046 MATH + and NIH DA038882. We are grateful to J. Unruh and L. Lanzanó for useful discussion.

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P.A. and M.J.L. conceived the study and wrote the manuscript. P.A. performed the experiment and analyzed the data.

Corresponding author

Correspondence to Paolo Annibale.

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The authors declare no competing interests.

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Peer review information Rita Strack was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Integrated supplementary information

Supplementary Fig. 1 From intensity histograms to MB.

Extracting the MB from individual segments/ROIs. a) The four ROIs from Fig. 1e are displayed and numbered. b) For each ROI the intensity histogram is calculated and c) Fit to a model in order to extract a MB for the whole segment/ROI. If a Gaussian model is chosen to fit the Intensity Histogram, then the MB directly relates to the variance of the fitting Gaussian curve. d) The MB of each ROI is calculated and shown according to the color-code. The number of molecules N in each ROI is calculated by dividing the average intensity of the ROI by the MB. e) Theoretical dependence of the MB upon the fraction of monomers [m]/c in the ROI. Here εM=εeffproto in the notation used by Stoneman et al. f) MB Histogram for mEGFP imaged in 2-photon excitation (top panel adapted from Supplementary Fig. 1 of Stoneman et al.1), together with a schematic rendering of the individual molecules oligomeric arrangement in segments/ROIs yielding the bins at the center of the MB Histogram monomer (blue dashed) and dimer peak (green dashed) peaks.

Supplementary Fig. 2 Hotspots in Temporal Brightness.

Zoom-ins of one frame of the movie of the basolateral membrane of a HEK293 cell expressing the β1-AR C-terminally tagged with EYFP. a) 10 μm square region of the basolateral membrane, b) 5 μm zoom-in and b) 2.5 μm zoom-in with outlined ROIs comparable in size to those used in Stoneman et al. d) Pixel by pixel MB extracted, with superposed number of molecules in the ROI. Experiment is representative of 10 images, 3 independent experiments.

Supplementary Fig. 3 A selection of plasma membrane hotspots.

Examples of plasma membrane heterogeneities in the distribution of a prototypical GPCR, the β2-adrenergic receptor. a) Confocal image of the basolateral membrane of a HEK293 cell expressing β2-AR-EYFP (representative of 50 images collected in 10 independent experiments. Zoom-ins showing respectively a b) homogeneous region of the plasma membrane c) a tubular structure, presumably subplasmalemmal endoplasmic reticulum, c) a gap in the ER network d) a small endosome or clathrin coated pit e) the tip of a filopodium twisted under the plasma membrane f) a large endosome. Panels (bg) are adjusted to the same contrast level. h) Further examples of confocal micrograph of β2-AR subplasmalemmal morphology, with i) corresponding counterstains for specific organelles (each panel size 6.5 μm). Acquisitions were collected in sequential mode, in order to avoid cross-talk between spectral channels. Imaging pairs were: β2-AR-EYFP: clc-mRuby2 (representative of 12 images in 3 independent experiments); β2-AR-EYFP : Rab5A-mCerulean (representative of 8 images collected in 3 independent experiments); β2-AR-EYFP : calreticulin-mTurquoise2 (representative of 5 images collected in 2 independent experiments); β2-AR-mTq2 : lifeact-tdEos (red photoconverted form) (representative of 10 images collected in 2 independent experiments).

Supplementary Fig. 4 Effect of hotspots on MB histogram.

a) Representative 10x10 mosaic of 1,000 simulated images (32 pixels=1.5 µm size) containing a homogeneous mixture of monomers (1,000 molecules, ε=1E6 photons/s) and dimers, with sizable ‘hot spots’ of varying diameter and intensity b) Representative 10x10 mosaic of 1,000 simulated images (32 pixels=1.5 µm size) containing a homogeneous mixture of monomers (1,000 molecules, ε=1E6 photons/s) and dimers without any additional feature. c) Recovered histograms of the molecular brightness for the two cases (red= homogeneous mixture, blue=with added ‘hot spots’).

Supplementary Fig. 5 Manual selection of ROIs to avoid hotspots does not affect recovery of proper MB values.

Manual selection of homogeneous areas does not bias towards lower oligmeric values. a) Simulated Image representative of a set of 10, displaying a membrane containing a mixture of monomers (25/µm2) and dimers (25/µm2). b) Simulated Image representative as in a, with addition of a number of randomly generated (size, position and amplitude) hot-spots. c) Result of performing Brightness analysis on the series as in a and c, without and with area selection by two users blinded to the type of sample. d) same as in a, where the dimers are replaced by trimers. e) as in b, where dimers are replaced by trimers. f) Result of performing Brightness analysis on the series as in d and e, without and with area selection by two users blinded to the type of sample.

Supplementary Fig. 6 MB histogram of a monomeric sample and of a monomer/dimer mixture have the exact same shape.

MB extracted from simulated monomeric and monomeric/dimeric datasets. a) Montage (30x30) of 900 out of 1,000 ROIs/segments containing 160 protomers/µm2, all monomeric, with dashed lines highlighting the size/position of four representative individual ROIs and b) 160 protomers/µm2 equally divided between monomers and dimers. c) Overlap of the histogram of the variances of pixel intensities measured on each ROI/segment for both monomer (red solid) and monomer/dimer (blue dashed) sets. d) Overlap of the histogram of the Molecular Brightness (recovered from SpIDA) of each ROI/segment for both monomer (red solid) and monomer/dimer (blue dashed) sets.

Supplementary Fig. 7 Pre-scoring ROIs in order to screen for intensity hotspots.

Representative mosaics of simulated sets of 1,000 ROIs, a) expressing a homogeneous mixture of monomers and dimers b) expressing an homogeneous mixture of monomers and dimers with additional ‘hot-spots’ c) comprising an equal combination of ROIs with ‘hot spots’ and without. d) Empirical cumulative distribution function of the pixel intensity values from a representative ROI, compared to that of a Gaussian distribution having the mean and variance intensity values from the ROI. e) Histogram of the ‘Gaussianity’ scores for each ROIs set (see Statistics in the Supplementary Information). f) MB Histograms for the ROI set in a (blue), for the ROI set in c (gold) and for the ROI set in c after filtering out the ROIs containing the ‘hot-spots’ using our scoring approach.

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Supplementary Information

Supplementary Figs. 1–7, Supplementary Methods, Supplementary Table 1 and Supplementary Notes 1–5

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Annibale, P., Lohse, M.J. Spatial heterogeneity in molecular brightness. Nat Methods 17, 273–275 (2020).

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