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Data availability
All data analyzed in this study have been previously published2 and have been deposited on the Figshare digital repository, accessible from https://figshare.com/s/77b90d060901fa8b4cb3.
Code availability
The SLIC code for removal of high-intensity clusters of pixels, implemented in MATLAB, has been deposited on the Figshare digital repository and is accessible from https://doi.org/10.6084/m9.figshare.11500089.
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Acknowledgements
This work was partly funded by National Science Foundation grant PHY-1626450 awarded to V.R.
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M.R.S. performed data analysis. G.B. implemented the spot removal algorithms and performed data analysis. V.R. designed and supervised the study. The manuscript was written jointly by M.R.S. and V.R., with contributions from G.B.
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Supplementary Figure 1 Illustration of the effect of removal of high intensity clusters from fluorescence images.
a, Typical fluorescence image of Flp-In™ T-REx™ 293 cells expressing a plasma membrane-targeted mEGFP. Regions of interest (ROI), indicated by red overlaid polygons, were segmented, and each segment analyzed to obtain a single εeff and concentration value. b, Same image as in (a) after applying a spot removal procedure to remove clusters of pixels containing high intensities (relative to the intensities of neighboring regions within a cell). The spot removal procedure first used the SLIC method described in Stoneman et al2, to generate segments based not only on pixel location, but also intensity level. A distribution of the average intensity of all the segments in a single ROI was then created and fit with a Gaussian function. Pixels within segments with average intensity greater than three standard deviations from the mean of the fitted Gaussian were set to 0. The filtered images were then segmented again, and each segment analyzed to obtain a single εeff and concentration value. The pixels which were set to 0 as a result of the filtering procedure were not included in the calculation of εeff and concentration. c, The normalized frequency distribution assembled from the brightness values obtained from unfiltered images. The normalized distribution was fit with a sum (solid black curve) of Gaussians (dashed lines with various colors), to find the brightness of single mEGFP protomers, \(\varepsilon _{eff}^{proto}\)=11.9. The various Gaussian peak positions were set to \(n\varepsilon _{eff}^{proto}\), where n is the number of protomers in an oligomer, and their standard deviations, σ, were set equal to one another and determined from data fitting (σ=11.9). d, Same analysis as performed in (c) only to images filtered as described in (b). The fitting resulted in a value of \(\varepsilon _{eff}^{proto}\)=10.2 and σ=10.2.
Supplementary Figure 2 Investigating the effect of high intensity clusters on meta-analysis of brightness distributions.
a, Typical fluorescence image of CHO cells expressing wild-type secretin receptor (untreated). Regions of interest (ROI), indicated by red overlaid polygons, were segmented, and each segment analyzed to obtain a single εeff and concentration value. b, Same image as in (a) after applying a spot removal procedure to remove clusters of pixels containing high intensities, as described in the Supplementary Methods. c,d Brightness spectrograms constructed for a single concentration range (300-420 proto/μm2) for the unfiltered (c) as well as filtered (d) set of images. Of the 59,017 total brightness values, 1308 (that is, 2.2%) were greater than the cutoff value and therefore were filtered out of our analysis for the unfiltered data (see main text and caption to Fig. 1 for details). The εeff distributions for each concentration range was fitted with a sum of five Gaussians; the peak of each Gaussian was set to \(n\varepsilon _{eff}^{proto}\), where n is the number of protomers in a given oligomer (for example, 1, 2, 4, etc.). Only the Gaussian amplitudes (An) were adjusted in the process of data fitting which gave the fraction of protomers for each oligomeric species, that is, \(n_iA_i/\mathop {\sum }\limits_n nA_n\). e,f, Relative concentration of protomers within individual oligomeric species vs. total protomer concentration, as derived by decomposing the spectrograms, like those shown in (c) and (d), for each concentration range. Statistical errors, indicated by the error bars in (e) and (f) have been estimated as described in Stoneman et al2. Each data point was obtained by taking the mean of 1,500 relative fraction values and the error bar for each data point represents ±1 standard deviation of the same set of values. The three \(\varepsilon _{eff}^{proto}\) values selected for analysis of cell images before applying the SLIC spot removal algorithm were 11.9, 15.4, and 13.2, while the widths of the Gaussians were 11.9, 13.7, and 13.2, respectively. For analysis of cell images subjected to the SLIC spot removal algorithm, the \(\varepsilon _{eff}^{proto}\) values used were 10.2, 12.1, and 11.3, while the widths were 10.2, 13.1, and 12.0. Fluorescence image acquisition of cells expressing the secretin receptor was repeated on a second measuring system with similar results as those reported here.
Supplementary Figure 3 Average εeff of an entire distribution vs. segment size (that is, analysis length scale).
The average value of εeff was computed for brightness distributions prepared from three different datasets: CHO cells expressing wild-type secretin receptor (SecR) both (i) treated with ligand (green triangles) and (ii) untreated (yellow squares) as well as (iii) Flp-In™ T-REx™ 293 cells expressing PM1-mEGFP (red circles). Each dataset was analyzed multiple times while varying the size of the segment used to obtain a single brightness/concentration pair as follows: 1.1 μm2 (289 pixels2), 1.9 μm2 (484 pixels2), 7.7 μm2 (1936 pixels2) 18.9 μm2 (4761 pixels2). The average εeff is computed for each of the distributions over all concentrations ranging from 0-1400 protomers/μm2 and εeff values ranging from 0-100, so as to avoid segments which produced extremely high εeff, that is on the order of 10-100 times that of the monomeric brightness value. In our typical analysis, consisting of fitting the brightness spectrograms with a sum of Gaussians, these extremely high εeff values were automatically disregarded because they are significantly higher than the means of the Gaussians corresponding to the monomer, dimer, tetramer, etc. population. However, they do have a noticeable effect when using a simple averaging approach as we are doing in this figure. b, The average εeff values displayed in (a) were divided by the corresponding εeff value of the PM1-mEGFP sample obtained for that particular segment size. This shows that the receptor as well as calibration data from the monomeric sample are affected the same way and to the same extent by the length scale, leaving unchanged the average size of the oligomers formed. The main body of the paper describes a more detailed analysis using the true 2D FIF method.
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Supplementary Figs. 1–3, Supplementary Methods, Supplementary Table 1 and Supplementary Notes 1 and 2
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Stoneman, M.R., Biener, G. & Raicu, V. Reply to: Spatial heterogeneity in molecular brightness. Nat Methods 17, 276–278 (2020). https://doi.org/10.1038/s41592-020-0735-x
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DOI: https://doi.org/10.1038/s41592-020-0735-x
