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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The data collection for this study was done using our custom-made algorithms available at http://bioimaging.usc.edu/software.html. The data analysis for this study was done using our custom-made algorithms available at http://bioimaging.usc.edu/software.html. An example dataset is available at the same link.
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Key references using this protocol
Ojosnegros, S. et al. Proc. Natl Acad. Sci. USA 114, 13188–13193 (2017): http://www.pnas.org/content/114/50/13188
Hortigüela, V. et al. Nano Lett. 18, 629–637 (2018): https://pubs.acs.org/doi/abs/10.1021/acs.nanolett.7b04904
Hartman, N. C. & Groves, J. T. Signaling clusters in the cell membrane. Curr. Opin. Cell Biol. 23, 370–376 (2011).
Ali, M. H. & Imperiali, B. Protein oligomerization: how and why. Bioorg. Med. Chem. 13, 5013–5020 (2005).
Marianayagam, N. J., Sunde, M. & Matthews, J. M. The power of two: protein dimerization in biology. Trends Biochem. Sci. 29, 618–625 (2004).
Janes, P. W., Nievergall, E. & Lackmann, M. Concepts and consequences of Eph receptor clustering. Semin. Cell Dev. Biol. 23, 43–50 (2012).
Lemmon, M. A. & Schlessinger, J. Cell signaling by receptor tyrosine kinases. Cell 141, 1117–1134 (2010).
Bray, D., Levin, M. D. & Morton-Firth, C. J. Receptor clustering as a cellular mechanism to control sensitivity. Nature 393, 85–88 (1998).
Nashmi, R. et al. Assembly of α4β2 nicotinic acetylcholine receptors assessed with functional fluorescently labeled subunits: effects of localization, trafficking, and nicotine-induced upregulation in clonal mammalian cells and in cultured midbrain neurons. J. Neurosci. 23, 11554–11567 (2003).
Chiu, C. L. et al. Nanoimaging of focal adhesion dynamics in 3D. PLoS ONE 9, e99896 (2014).
Digman, M. A., Wiseman, P. W., Choi, C., Horwitz, A. R. & Gratton, E. Stoichiometry of molecular complexes at adhesions in living cells. Proc. Natl Acad. Sci. USA 106, 2170–2175 (2009).
Adu-Gyamfi, E. et al. A loop region in the N-terminal domain of Ebola virus VP40 is important in viral assembly, budding, and egress. Viruses 6, 3837–3854 (2014).
Chiu, C. L., Digman, M. A. & Gratton, E. Measuring actin flow in 3D cell protrusions. Biophys. J. 105, 1746–1755 (2013).
Vishwasrao, H. D., Trifilieff, P. & Kandel, E. R. In vivo imaging of the actin polymerization state with two-photon fluorescence anisotropy. Biophys. J. 102, 1204–1214 (2012).
Lampe, M., Vassilopoulos, S. & Merrifield, C. Clathrin coated pits, plaques and adhesion. J. Struct. Biol. 196, 48–56 (2016).
Bhambhani, C., Chang, J. L., Akey, D. L. & Cadigan, K. M. The oligomeric state of CtBP determines its role as a transcriptional co-activator and co-repressor of Wingless targets. EMBO J. 30, 2031–2043 (2011).
Khan, M. R. et al. Amyloidogenic oligomerization transforms Drosophila Orb2 from a translation repressor to an activator. Cell 163, 1468–1483 (2015).
Marston, N. J., Jenkins, J. R. & Vousden, K. H. Oligomerisation of full length p53 contributes to the interaction with mdm2 but not HPV E6. Oncogene 10, 1709–1715 (1995).
Hass, M. R. et al. SpDamID: marking DNA bound by protein complexes identifies Notch-dimer responsive enhancers. Mol. Cell 64, 213 (2016).
Schlierf, B., Ludwig, A., Klenovsek, K. & Wegner, M. Cooperative binding of Sox10 to DNA: requirements and consequences. Nucleic Acids Res. 30, 5509–5516 (2002).
Stein, E. et al. Eph receptors discriminate specific ligand oligomers to determine alternative signaling complexes, attachment, and assembly responses. Genes Dev. 12, 667–678 (1998).
Hinde, E. et al. Quantifying the dynamics of the oligomeric transcription factor STAT3 by pair correlation of molecular brightness. Nat. Commun. 7, 11047 (2016).
Conway, A. et al. Multivalent ligands control stem cell behaviour in vitro and in vivo. Nat. Nanotechnol. 8, 831–838 (2013).
Salaita, K. et al. Restriction of receptor movement alters cellular response: physical force sensing by EphA2. Science 327, 1380–1385 (2010).
Dunsing, V., Mayer, M., Liebsch, F., Multhaup, G. & Chiantia, S. Direct evidence of APLP1 trans interactions in cell-cell adhesion platforms investigated via fluorescence fluctuation spectroscopy. Mol. Biol. Cell 28, 3609-3620 (2017).
Plotegher, N., Gratton, E. & Bubacco, L. Number and brightness analysis of alpha-synuclein oligomerization and the associated mitochondrial morphology alterations in live cells. Biochim. Biophys. Acta 1840, 2014–2024 (2014).
Luna, E. & Luk, K. C. Bent out of shape: alpha-synuclein misfolding and the convergence of pathogenic pathways in Parkinson’s disease. FEBS Lett. 589, 3749–3759 (2015).
Cardenas-Aguayo Mdel, C., Gomez-Virgilio, L., DeRosa, S. & Meraz-Rios, M. A. The role of tau oligomers in the onset of Alzheimer’s disease neuropathology. ACS Chem. Neurosci. 5, 1178–1191 (2014).
Goedert, M. Alzheimer’s and Parkinson’s diseases: the prion concept in relation to assembled Aβ, tau, and α-synuclein. Science 349, 1255555 (2015).
Ojosnegros, S. et al. Eph-ephrin signaling modulated by polymerization and condensation of receptors. Proc. Natl Acad. Sci. USA 114, 13188–13193 (2017).
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).
Qian, H. & Elson, E. L. On the analysis of high order moments of fluorescence fluctuations. Biophys. J. 57, 375–380 (1990).
Qian, H. & Elson, E. L. Distribution of molecular aggregation by analysis of fluctuation moments. Proc. Natl Acad. Sci. USA 87, 5479–5483 (1990).
Moens, P. D., Gratton, E. & Salvemini, I. L. Fluorescence correlation spectroscopy, raster image correlation spectroscopy, and number and brightness on a commercial confocal laser scanning microscope with analog detectors (Nikon C1). Microsc. Res. Tech. 74, 377–388 (2011).
Unruh, J. R. & Gratton, E. Analysis of molecular concentration and brightness from fluorescence fluctuation data with an electron multiplied CCD camera. Biophys. J. 95, 5385–5398 (2008).
Hellriegel, C., Caiolfa, V. R., Corti, V., Sidenius, N. & Zamai, M. Number and brightness image analysis reveals ATF-induced dimerization kinetics of uPAR in the cell membrane. FASEB J. 25, 2883–2897 (2011).
Trullo, A., Corti, V., Arza, E., Caiolfa, V. R. & Zamai, M. Application limits and data correction in number of molecules and brightness analysis. Microsc. Res. Tech. 76, 1135–1146 (2013).
Ossato, G. et al. A two-step path to inclusion formation of huntingtin peptides revealed by number and brightness analysis. Biophys. J. 98, 3078–3085 (2010).
Hur, K. H. et al. Quantitative measurement of brightness from living cells in the presence of photodepletion. PLoS ONE 9, e97440 (2014).
Nolan, R. et al. Calibration-free in vitro quantification of protein homo-oligomerization using commercial instrumentation and free, open source brightness analysis software. J. Vis. Exp. 2018, e58157 (2018).
Nolan, R., Iliopoulou, M., Alvarez, L. & Padilla-Parra, S. Detecting protein aggregation and interaction in live cells: a guide to number and brightness. Methods 140–141, 172–177 (2018).
Adu-Gyamfi, E., Digman, M. A., Gratton, E. & Stahelin, R. V. Investigation of Ebola VP40 assembly and oligomerization in live cells using number and brightness analysis. Biophys. J. 102, 2517–2525 (2012).
Hilsch, M. et al. Influenza A matrix protein M1 multimerizes upon binding to lipid membranes. Biophys. J. 107, 912–923 (2014).
Crosby, K. C. et al. Quantitative analysis of self-association and mobility of annexin A4 at the plasma membrane. Biophys. J. 104, 1875–1885 (2013).
James, N. G. et al. Biophys. J. 102, L41–L43 (2012).
Perumal, V., Krishnan, K., Gratton, E., Dharmarajan, A. M. & Fox, S. A. Int. J. Biochem. Cell Biol. 64, 91–96 (2015).
Youker, R. T. et al. Multiple motifs regulate apical sorting of p75 via a mechanism that involves dimerization and higher-order oligomerization. Mol. Biol. Cell 24, 1996–2007 (2013).
Nagy, P., Claus, J., Jovin, T. M. & Arndt-Jovin, D. J. Distribution of resting and ligand-bound ErbB1 and ErbB2 receptor tyrosine kinases in living cells using number and brightness analysis. Proc. Natl Acad. Sci. USA 107, 16524–16529 (2010).
James, N. G. et al. A mutation associated with centronuclear myopathy enhances the size and stability of dynamin 2 complexes in cells. Biochim. Biophys. Acta 1840, 315–321 (2014).
Labilloy, A. et al. Altered dynamics of a lipid raft associated protein in a kidney model of Fabry disease. Mol. Genet. Metab. 111, 184–192 (2014).
Olivera-Couto, A. et al. Eisosomes are dynamic plasma membrane domains showing Pil1-Lsp1 heteroligomer binding equilibrium. Biophys. J. 108, 1633–1644 (2015).
Ross, J. A. et al. Biophys. J. 100, L15–L17 (2011).
Salvemini, I. L. et al. Low PIP2 molar fractions induce nanometer size clustering in giant unilamellar vesicles. Chem. Phys. Lipids 177, 51–63 (2014).
Presman, D. M. et al. DNA binding triggers tetramerization of the glucocorticoid receptor in live cells. Proc. Natl Acad. Sci. USA 113, 8236–8241 (2016).
Presman, D. M. et al. Live cell imaging unveils multiple domain requirements for in vivo dimerization of the glucocorticoid receptor. PLoS Biol. 12, e1001813 (2014).
Abdisalaam, S., Davis, A. J., Chen, D. J. & Alexandrakis, G. Scanning fluorescence correlation spectroscopy techniques to quantify the kinetics of DNA double strand break repair proteins after γ-irradiation and bleomycin treatment. Nucleic Acids Res. 42, e5 (2014).
Vetri, V. et al. Fluctuation methods to study protein aggregation in live cells: concanavalin A oligomers formation. Biophys. J. 100, 774–783 (2011).
Mieruszynski, S., Briggs, C., Digman, M. A., Gratton, E. & Jones, M. R. Live cell characterization of DNA aggregation delivered through lipofection. Sci. Rep. 5, 10528 (2015).
Kania, A. & Klein, R. Mechanisms of ephrin-Eph signalling in development, physiology and disease. Nat. Rev. Mol. Cell Biol. 17, 240–256 (2016).
Schaupp, A. et al. The composition of EphB2 clusters determines the strength in the cellular repulsion response. J. Cell Biol. 204, 409–422 (2014).
Klein, R. Eph/ephrin signalling during development. Development 139, 4105–4109 (2012).
Hortiguela, V. et al. Nanopatterns of surface-bound EphrinB1 produce multivalent ligand-receptor interactions that tune EphB2 receptor clustering. Nano Lett. 18, 629–637 (2018).
Gambin, Y. et al. Confocal spectroscopy to study dimerization, oligomerization and aggregation of proteins: a practical guide. Int J. Mol. Sci. 17, E655 (2016).
Sahoo, B., Drombosky, K. W. & Wetzel, R. Fluorescence correlation spectroscopy: a tool to study protein oligomerization and aggregation in vitro and in vivo. Methods Mol. Biol. 1345, 67–87 (2016).
Herrick-Davis, K., Grinde, E., Lindsley, T., Cowan, A. & Mazurkiewicz, J. E. Oligomer size of the serotonin 5-hydroxytryptamine 2C (5-HT2C) receptor revealed by fluorescence correlation spectroscopy with photon counting histogram analysis: evidence for homodimers without monomers or tetramers. J. Biol. Chem. 287, 23604–23614 (2012).
Krieger, J. W. et al. Imaging fluorescence (cross-) correlation spectroscopy in live cells and organisms. Nat. Protoc. 10, 1948–1974 (2015).
Chen, Y., Muller, J. D., So, P. T. & Gratton, E. The photon counting histogram in fluorescence fluctuation spectroscopy. Biophys. J. 77, 553–567 (1999).
Muller, J. D., Chen, Y. & Gratton, E. Resolving heterogeneity on the single molecular level with the photon-counting histogram. Biophys. J. 78, 474–486 (2000).
Caiolfa, V. R. et al. Monomer dimer dynamics and distribution of GPI-anchored uPAR are determined by cell surface protein assemblies. J. Cell Biol. 179, 1067–1082 (2007).
Srinivasan, R. et al. Forster resonance energy transfer (FRET) correlates of altered subunit stoichiometry in cys-loop receptors, exemplified by nicotinic α4β2. Int. J. Mol. Sci. 13, 10022–10040 (2012).
Tosatto, L. et al. Single-molecule FRET studies on alpha-synuclein oligomerization of Parkinson’s disease genetically related mutants. Sci. Rep. 5, 16696 (2015).
Cremades, N. et al. Direct observation of the interconversion of normal and toxic forms of alpha-synuclein. Cell 149, 1048–1059 (2012).
Paredes, J. M. et al. Early amyloidogenic oligomerization studied through fluorescence lifetime correlation spectroscopy. Int. J. Mol. Sci. 13, 9400–9418 (2012).
Zanacchi, F. C. et al. A DNA origami platform for quantifying protein copy number in super-resolution. Nat. Methods 14, 789–792 (2017).
Hines, K. E. Inferring subunit stoichiometry from single molecule photobleaching. J. Gen. Phys. 141, 737–746 (2013).
Youker, R. T. & Teng, H. Measuring protein dynamics in live cells: protocols and practical considerations for fluorescence fluctuation microscopy. J. Biomed. Opt. 19, 90801 (2014).
Milo, R. & Phillips, R. Cell Biology by the Numbers (Garland Science, New York, 2015).
Elowitz, M. B., Surette, M. G., Wolf, P. E., Stock, J. & Leibler, S. Photoactivation turns green fluorescent protein red. Curr. Biol. 7, 809–812 (1997).
Gambin, Y. et al. Lateral mobility of proteins in liquid membranes revisited. Proc. Natl Acad. Sci. USA 103, 2098–2102 (2006).
Saffman, P. G. & Delbruck, M. Brownian motion in biological membranes. Proc. Natl Acad. Sci. USA 72, 3111–3113 (1975).
Rossing, T. Springer Handbook of Acoustics (Springer, New York, 2007).
Press, W. H., Teukolsky, S. A., Vetterling, W. T. & Flannery, B. P. Numerical Recipes 3rd Edition: The Art of Scientific Computing (Cambridge University Press, New York, 2007).
Dalal, R. B., Digman, M. A., Horwitz, A. F., Vetri, V. & Gratton, E. Determination of particle number and brightness using a laser scanning confocal microscope operating in the analog mode. Microsc. Res. Tech. 71, 69–81 (2008).
Wohland, T., Shi, X., Sankaran, J. & Stelzer, E. H. Single plane illumination fluorescence correlation spectroscopy (SPIM-FCS) probes inhomogeneous three-dimensional environments. Opt. Exp. 18, 10627–10641 (2010).
Sisan, D. R., Arevalo, R., Graves, C., McAllister, R. & Urbach, J. S. Spatially resolved fluorescence correlation spectroscopy using a spinning disk confocal microscope. Biophys. J. 91, 4241–4252 (2006).
Dempsey, G. T., Vaughan, J. C., Chen, K. H., Bates, M. & Zhuang, X. Evaluation of fluorophores for optimal performance in localization-based super-resolution imaging. Nat. Methods 8, 1027–1036 (2011).
Ricci, M. A., Manzo, C., Garcia-Parajo, M. F., Lakadamyali, M. & Cosma, M. P. Chromatin fibers are formed by heterogeneous groups of nucleosomes in vivo. Cell 160, 1145–1158 (2015).
Shaner, N. C., Steinbach, P. A. & Tsien, R. Y. A guide to choosing fluorescent proteins. Nat. Methods 2, 905–909 (2005).
Kredel, S. et al. mRuby, a bright monomeric red fluorescent protein for labeling of subcellular structures. PLoS ONE 4, e4391 (2009).
Shaner, N. C. et al. A bright monomeric green fluorescent protein derived from Branchiostoma lanceolatum. Nat. Methods 10, 407–409 (2013).
Elson, E. L. & Magde, D. Fluorescence correlation spectroscopy. I. Conceptual basis and theory. Biopolymers 13, 1–27 (1974).
Digman, M. A. & Gratton, E. Lessons in fluctuation correlation spectroscopy. Annu. Rev. Phys. Chem. 62, 645–668 (2011).
Lakowicz, J. R. Principles of Fluorescence Spectroscopy (Springer, Berlin, 2006).
Zhao, Z. W. et al. Quantifying transcription factor-DNA binding in single cells in vivo with photoactivatable fluorescence correlation spectroscopy. Nat. Protoc. 12, 1458–1471 (2017).
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
Rossow, M. J., Sasaki, J. M., Digman, M. A. & Gratton, E. Raster image correlation spectroscopy in live cells. Nat. Protoc. 5, 1761–1774 (2010).
Jaqaman, K. et al. Robust single-particle tracking in live-cell time-lapse sequences. Nat. Methods 5, 695–702 (2008).
Ovesny, M., Krizek, P., Borkovec, J., Svindrych, Z. & Hagen, G. M. ThunderSTORM: a comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution imaging. Bioinformatics 30, 2389–2390 (2014).
Kaur, G. et al. Probing transcription factor diffusion dynamics in the living mammalian embryo with photoactivatable fluorescence correlation spectroscopy. Nat. Commun. 4, 1637 (2013).
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.2010.2.1.2.2)); 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.
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.