Using enhanced number and brightness to measure protein oligomerization dynamics in live cells


Protein dimerization and oligomerization are essential to most cellular functions, yet measurement of the size of these oligomers in live cells, especially when their size changes over time and space, remains a challenge. A commonly used approach for studying protein aggregates in cells is number and brightness (N&B), a fluorescence microscopy method that is capable of measuring the apparent average number of molecules and their oligomerization (brightness) in each pixel from a series of fluorescence microscopy images. We have recently expanded this approach in order to allow resampling of the raw data to resolve the statistical weighting of coexisting species within each pixel. This feature makes enhanced N&B (eN&B) optimal for capturing the temporal aspects of protein oligomerization when a distribution of oligomers shifts toward a larger central size over time. In this protocol, we demonstrate the application of eN&B by quantifying receptor clustering dynamics using electron-multiplying charge-coupled device (EMCCD)-based total internal reflection microscopy (TIRF) imaging. TIRF provides a superior signal-to-noise ratio, but we also provide guidelines for implementing eN&B in confocal microscopes. For each time point, eN&B requires the acquisition of 200 frames, and it takes a few seconds up to 2 min to complete a single time point. We provide an eN&B (and standard N&B) MATLAB software package amenable to any standard confocal or TIRF microscope. The software requires a high-RAM computer (64 Gb) to run and includes a photobleaching detrending algorithm, which allows extension of the live imaging for more than an hour.

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Fig. 1: Enhanced number and brightness exploits fluorescence fluctuation analysis to extract the oligomerization state of proteins.
Fig. 2: Comparison of eN&B and N&B analysis of data from simulations of oligomers freely diffusing in a liquid solution.
Fig. 3: Analog calibration for eN&B analysis.
Fig. 4: Typical ACF curve obtained from FCS analysis.
Fig. 5: eN&B software interface and data loading.
Fig. 6: eN&B software produces a comprehensive output of oligomerization data.

Data/code availability

The data collection for this study was done using our custom-made algorithms available at The data analysis for this study was done using our custom-made algorithms available at An example dataset is available at the same link.


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S.O. was supported by a Marie Curie International Outgoing Fellowship (276282) within the EU Seventh Framework Programme for Research and Technological Development (2007–2013), a postdoctoral fellowship from the Human Frontier Science Program Organization (LT000109/2011), and a postdoctoral fellowship (EX2009-1136) from the Ministerio de Educación through the Programa Nacional de Movilidad de Recursos Humanos del Plan Nacional de I-D+i 2008-2011. F.C. was supported by grants from the Moore Foundation and the NIH (R01 HD075605 and R01 OD019037). J.O. acknowledges financial support from ICFONEST+, funded by the Marie Curie COFUND (FP7-PEOPLE-2010-COFUND) action of the European Commission and by the MINECO Severo Ochoa action at ICFO. Additional funding for this project came from the Generalitat de Catalunya (2017-SGR-1079 and 2017-SGR-899); the Spanish Ministry of Economy and Competitiveness (MINECO; SAF2015-69706-R, MINAHE5, TEC2014-51940-C2-2-R, TEC2017-83716-C2-1-R; SEV-2015-0522); ISCIII/FEDER (RD16/0011/0024) EU (GLAM Project, GA-634928; Systems Microscopy Network of Excellence Consortium (FP-7-HEALTH.2010.; and the ERC (337191-MOTORS and 647863-COMIET); the Fundació Privada Cellex; and the CERCA Programme/Generalitat de Catalunya. The results presented here reflect only the views of the authors; the European Commission is not responsible for any use that may be made of the information contained in this article. We acknowledge assistance with imaging and a fee waiver from the Nikon Center of Excellence at ICFO.

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S.O., J.O. and A.S. performed the experiments. S.O., D.R., C.-L.C. and F.C. analyzed the results and designed the algorithms. V.H., E.L. and E.M. designed the micro-printing protocol. S.M. performed the FCS analysis. M.L., E.M., A.R. and S.E.F. contributed to the experimental design. S.O., F.C., J.O., C.-L.C., D.R. and S.E.F. wrote the manuscript.

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Correspondence to Scott E. Fraser or Samuel Ojosnegros.

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Key references using this protocol

Ojosnegros, S. et al. Proc. Natl Acad. Sci. USA 114, 13188–13193 (2017):

Hortigüela, V. et al. Nano Lett. 18, 629–637 (2018):

Integrated supplementary information

Supplementary Figure 1 Example of photobleaching detrending in cells that were not stimulated with the ligand shows consistent brightness correction.

A) Comparison of two single-cell experiments: first, normalized detrended brightness (color) in a time-lapse experiment, second, an experiment showing initial and final time point (dashed and dotted lines). B) Averaged relative center of mass for multiple cells (n=3= detrended time points and (n=3) initial-final. For each time-lapse recording, the weighted center of mass of the brightness plot (counts, brightness) is calculated after detrending (blue line). The center of mass value is normalized with respect to the initial time point, providing a percentage change. Similarly, the initial-final time points acquired on a separate sample are represented for reference (red line). The center of mass shift for detrended cells is within 10% of the non-detrended non-time-lapse value. The average bleaching rate for the time-lapse recordings was 14.5 ± 7.4% after 6 sequential time points. Each time-lapse time point consisted in 200 frames each acquired at 500 ms exposure. Reference initial and final used same camera acquisition settings on a different sample plate imaged at 10 and 55 minutes.

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Supplementary Figure 1

Reporting Summary

Supplementary Video 1

Screen recording of the analysis of a single cell with eN&B software.

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Cutrale, F., Rodriguez, D., Hortigüela, V. et al. Using enhanced number and brightness to measure protein oligomerization dynamics in live cells. Nat Protoc 14, 616–638 (2019).

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