The CNIC is supported by the Ministry of Ciencia, Innovacion y Universidades and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505).&#160;We are also supported by European Regional Development Fund (FEDER) &#34;Una manera de hacer Europa&#34;. UC acknowledges the support from the Associazione Italiana Ricerca sul Cancro, the Association for International Cancer Research (now known as Worldwide Cancer Research), and the Italian Ministry of Health. A.T. acknowledge the &#34;Fondazione Banca del Monte di Lombardia&#34; for partly supporting his work with the PV Fellowship &#34;Progetto Professionalit&#224; Ivano Becchi&#34; 2011-2012.

1. Sample Preparation
<ol>
	<li>Day 1. Seed HeLa cells in complete medium at a concentration of 100,000-200,000 cell/mL in glass-bottom dishes. Seed 6-8 replicate dishes. 
	NOTE: In this example, the medium is supplemented with 10% heat inactivated Fetal Bovine Serum (FBS), 1 mM sodium pyruvate, 100 U/100 &#181;g penicillin/streptomycin. Several replicate dishes are prepared.</li>
	<li>Day 2-3. When cells are at sub-confluency, transfect half of the dishes with the protein plasmid and the second h...

<ol>
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A., Dalal, R., Horwitz, A. F., Gratton, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mapping+the+number+of+molecules+and+brightness+in+the+laser+scanning+microscope.">Mapping the number of molecules and brightness in the laser scanning microscope.</a> Biophysical Journal. 94 (6), 2320-2332 (2008).</li><li>Ossato, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+two-step+path+to+inclusion+formation+of+huntingtin+peptides+revealed+by+number+and+brightness+analysis.">A two-step path to inclusion formation of huntingtin peptides revealed by number and brightness analysis.</a> Biophysical Journal. 98 (12), 3078-3085 (2010).</li><li>Nagy, P., Claus, J., Jovin, T. M., Arndt-Jovin, D. 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The authors have nothing to disclose.

N&#38;B requires several precautions in the choice of the cell model and labelling strategy. It can be applied only to live cells that remain stably adhered during the image capture time. Extra fluctuations due to the whole cell rigid displacement might be handled with appropriate image restoration approaches38. However, generally when a cell moves, the cell membrane also deforms, and structure deformation, producing large extra variance, introduces serious limitation to the analysis of membrane p...

We describe a Total Internal Reflection Fluorescence-Number and Brightness (TIRF-N&#38;B) imaging approach for determining the average oligomeric state of receptor molecules at the plasma membrane of live cells, aiming at linking the receptor assembly dynamics to the biological function of the proteins (Figure 1).
Upon extracellular ligand binding, receptors initiate the intracellular signal transduction depending on their conformation, oligomerization, potential co-receptors and membrane composition. Despite the importance and ubiquity of receptor oligomerization, recognized as a key event in cellular signaling1,2,3,4,5,6,7, few methods can detect clustering events and measure the degree of clustering experimentally8,9. The confocal volume (x,y &#8776; 300 nm, z &#8776; 900 nm) is insufficiently resolved for proving molecular interaction and stoichiometry, even after optimization by image restoration algorithms10. The sub-unit composition of protein oligomers cannot be resolved on a purely spatial basis even by super-resolution methods at x,y resolution of 20-70 nm such as PALM11, STORM12, and STED13. Moreover, their temporal resolution (in the order of minutes per image) cannot follow kinetics in the range of seconds. Single molecule step-bleaching resolves the stoichiometry of protein oligomers only if they are immobile14.
One of the most versatile methods to measure density and oligomerization of fluorescently tagged proteins within single images is the spatial intensity distribution analysis (SpIDA), which relies on spatial sampling. It is applicable to both chemically fixed and live cells, and allows the analysis of several regions of interest of the cell simultaneously using standard fluorescence microscopy15. Alternatively, moment methods, such as fluorescence-correlation spectroscopy (FCS)16, photon counting histogram (PCH)17, and Number and Brightness (N&#38;B)18,19, are suitable for quantitative oligomer measurements. These methods analyze the fluorescence intensity fluctuations that can be observed in time when the fluorophores diffuse in and out of an illumination volume. The amplitudes of the intensity fluctuations can be uniquely described by the molecular brightness of the fluorophore (&#949;) and the average number of fluorophores (n) within the illumination volume17 (Figure 2). Typically, the diffusion coefficient of the fluorophores and the average number of molecules (inversely related to the G(0) value) within the illumination volume can be obtained by FCS20. However, since the diffusion time only scales with the cubic root of the mass, FCS is not sufficiently sensitive to detect changes in molecular mass21. In practice, single color FCS cannot detect dimerization of membrane receptors. PCH resolves mixtures of different oligomers accurately. Using more than two moments of the amplitude distribution, it detects molecules of different brightness that occupy the same illumination volume. Scanning FCS22 and developments, such as the interesting pair-correlation of molecular brightness (pCOMB) approach23, introduced to extend the range of applicability of fluorescence correlation methods in biological systems24, remain single point methods lacking the capability of fast measurements in a large area of a cell, requiring many consecutive observations at each pixel and data acquisition in the order of seconds.
N&#38;B is a simplified version of PCH that considers only the first and second moments of the amplitude of the fluorescence distribution, namely the mean intensity, &#60;I&#62;, and the variance, &#963;2 (Figure 2)18,19 and, because of that, it cannot determine the molar fraction of unknown oligomers in a mixture, but only estimates the average oligomerization state of the mixture. Nevertheless, N&#38;B has the advantage of working with relatively smaller time series of images of live cells than PCH on a pixel-by-pixel basis, simply by monitoring the fluctuations on time of the fluorescence intensity. Because N&#38;B reduces the time-per-pixel to a few microseconds, it can follow fast oligomerization kinetics over large cell areas, allowing image acquisition on a time scale of seconds in raster scanning microscopy (e.g., confocal, 2-photon) and milliseconds in camera-based microscopy (e.g., TIRFM).
Several reports have demonstrated the capability of N&#38;B to quantify the number of subunits in protein clusters by imaging extended cell regions. Paxillin-EGFP clusters were detected at the adhesion sites in CHO-K1 cells25, and the intracellular aggregation of the pathogenic Httex1p peptide was described in COS-7 cells26. N&#38;B was applied for following the ligand-driven oligomerization of the ErbB receptor27, and the effect of the ligand FGF21 on Klothob (KLB) and FGFR1c in HeLa cells28. The combination of TIRF imaging and N&#38;B analysis was used to show that dynamin-2 is primarily tetrameric throughout the entire cell membrane29. We applied N&#38;B to both raster scanning and TIRF images to prove ligand-driven dimerization of uPAR and FGFR1 cell membrane receptors30,31.
Fluorescence correlation methods, such as N&#38;B, FCS and PCH, are based on the notion that in an open volume the occupation number of particles follows a Poisson distribution. Because only the photons that the fluorophores emit can be detected, the mean value for a measured fluorescence intensity versus time in a pixel of the image, <img src="/files/ftp_upload/60398/60398eq1.jpg" />, is the product of the average number of fluorophores in the illumination volume, n, and their molecular brightness, &#949;17:
<img src="/files/ftp_upload/60398/60398eq2.jpg" />
where &#949; is expressed as the number of photons emitted per unit of time (conventionally per second) per molecule when the molecule is at the center of the illumination volume.
Brightness is a property of each fluorophore in a given acquisition set up, while intensity is the sum of all contributions from all fluorophores. In biological contests, brightness will increase with the increase of the number of fluorophores that fluctuate together, giving information on the oligomerization state of the fluorescently-tagged protein. The fluctuation amplitudes at a given pixel is measured from the variance of the fluorescence signal, &#963;2:
<img src="/files/ftp_upload/60398/60398eq3.jpg" />
Where the mean of the square of intensity, <img src="/files/ftp_upload/60398/60398eq4.jpg" />, and the square of the mean of intensity, <img src="/files/ftp_upload/60398/60398eq5.jpg" />, are computed from the individual intensity values in each pixel of each frame:
<img src="/files/ftp_upload/60398/60398eq6.jpg" />
where K is the number of total frames in the time series. Experimentally, it is necessary to compute for the entire image series the variance that describes the scatter of the individual intensity values at each pixel of a single image around the mean intensity value. The variance includes all fluctuations of different origins. In a first approximation, the variance due the diffusing particles in the illumination volume, &#963;20, can be separated from the variance due to the detector shot noise, &#963;2d. The two variances are independent; thus, the total variance is given by their sum:
<img src="/files/ftp_upload/60398/60398eq7.jpg" />
The variance, due to molecular fluctuations in and out of the detection volume, is linearly dependent on the molecular brightness and intensity:
<img src="/files/ftp_upload/60398/60398eq8.jpg" />
Rearranging eq. 6 according to eq. 1:
<img src="/files/ftp_upload/60398/60398eq9.jpg" />
According to the typical concept in fluorescence correlation spectroscopy, equation 7 states that the variance due to the number of fluctuations depends on the square of the particle brightness.
Then, the variance due to detector fluctuations is a linear function of the detected intensity, under the assumption that the detector is operated below its saturation limit19:
<img src="/files/ftp_upload/60398/60398eq10.jpg" />
In the case of photon counting detectors a=1 and c=0, thus the detector variance is equal to the average intensity:
<img src="/files/ftp_upload/60398/60398eq11.jpg" />
To apply these concepts to real measurements in live cells, Gratton and colleagues18 define the apparent brightness, B, for each pixel as the ratio of the variance over the average intensity:
<img src="/files/ftp_upload/60398/60398eq12a.jpg" />
B is the parameter that is measured experimentally. In this work, time series images of FGFR1 receptors at the plasma membrane of HeLa cells are captured by TIRF microscopy and the average apparent brightness, B, is determined by the N&#38;B analysis. Then, after addition of FGF2, consecutive time series are captured to follow the changes in the self-assembly of the receptor molecules in the membrane surface after stimulation of the receptor with the canonical ligand.
However, since the detector of the TIRF microscope is a EMCCD camera, the expression for the apparent brightness needs to be modified as19:
<img src="/files/ftp_upload/60398/60398eq13a.jpg" />
where offset is the intensity offset of the detection electronics that is a characteristic of the detector settings. The variance and average intensity for an analog detector are respectively given by:
<img src="/files/ftp_upload/60398/60398eq14a.jpg" />
where G is the analog gain in digital levels (DL/photons), S, the digital levels per photon19, is given by the slope of an intensity versus variance plot for a light source with constant intensity (no temporal fluctuations). The &#947; factor is related to the shape of the pixel detection volume. According to Hassler et al.32, the &#947; factor is equal to 0.3 for TIRF imaging working at the maximum gain of the detection camera19. The offset, S and G parameters are characteristics of the camera and the microscope. The apparent brightness, B, is obtained by rearranging eq. 11 according to eq. 12 and 13:
<img src="/files/ftp_upload/60398/60398eq15.jpg" />
Experimentally, &#949; is a complex function of laser intensity and the detection efficiency of the system. Nevertheless, since B/S is linearly dependent on &#949;, it is only important to determine the relative value of &#949; for a given detection mode:
<img src="/files/ftp_upload/60398/60398eq16.jpg" />
where &#949;&#39; is proportional to &#949;. Still, a calibration is performed using an internal reference.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>3-Colour Fast TIRF Leica AM TIRF MC inverted microscope, with smi-automatic TIRF alignment. The microscope is equipped with a diode 488 nm laser, a 100x1.46 oil TIRF objective, Ex/Em Bandpass filters at 490/20 and 525/50, temperature/CO2 incubator and Andor DU 8285 VP EMCCD camera. The microscope is operated by Leica LIF software.</td>
			<td>Leica Microsystems, Wetzlar, Germany</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Albumin from Bovine Serum 98% minimun</td>
			<td>Sigma-Aldrich, St. Louis, MI, USA</td>
			<td>A7906-100G</td>
			<td></td>
		</tr>
		<tr>
			<td>DMEM without Phenol Red with 25 mM HEPES</td>
			<td>GIBCO Thermo Fisher Scientific,Waltham, MA, USA</td>
			<td>21063029</td>
			<td>Used serum free for microscopy</td>
		</tr>
		<tr>
			<td>DMEM high-glucose GlutaMAX I</td>
			<td>GIBCO Thermo Fisher Scientific,Waltham, MA, USA</td>
			<td>10566-016</td>
			<td>Used for complete medium</td>
		</tr>
		<tr>
			<td>Dulbecco&#39;s Phosphate Buffered Saline 10x (PBS)</td>
			<td>Biowest, Nuaill&#233;, France</td>
			<td>X0515-500</td>
			<td></td>
		</tr>
		<tr>
			<td>Emission splitting system Photometrics DV2</td>
			<td>TeledynePhotometrics, Tucson, AZ, USA</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Fetal Bovine Serum, qualified, Brazil</td>
			<td>GIBCO Thermo Fisher Scientific,Waltham, MA, USA</td>
			<td>10270106</td>
			<td>10% inactivated supplement for complete medium</td>
		</tr>
		<tr>
			<td>Glass bottom 35-mm sterile 1.5 dishes</td>
			<td>MatTek, Ashland, MA, USA</td>
			<td>P35G-0.170-14-C</td>
			<td>uncoated, glass thickness 0.17 microns</td>
		</tr>
		<tr>
			<td>GraphPad Prism</td>
			<td>GraphPad Software Inc., San Diego, CA, USA</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Human cervical carcinoma (HeLa), serum-free animal component (AC) cells</td>
			<td>Millipore-Sigma ECACC, Darmstadt, Germany</td>
			<td>CB_08011102</td>
			<td></td>
		</tr>
		<tr>
			<td>iXonEM+ 897 EMCCD (back-illuminated) ANDOR camera controlled by ANDOR Solis software</td>
			<td>Oxford Instruments, Andor TM Technology, Abingdon-on-Thames, UK</td>
			<td></td>
			<td>This camera, installed in an additional port of the microscope, is used for acquiring the N&#38;B time series</td>
		</tr>
		<tr>
			<td>Matlab Executable N&#38;B routine</td>
			<td>Unit of Microscopy and Dynamic Imaging, CNIC, Madrid, Spain</td>
			<td></td>
			<td>download at https://www.cnic.es/en/investigacion/2/1187/tecnologia</td>
		</tr>
		<tr>
			<td>MatLab v.2018b</td>
			<td>The MathWorks, Inc. Natick, MA, USA</td>
			<td></td>
			<td><a href="https://www.mathworks.com/products/matlab.html">download at https://www.mathworks.com/products/matlab.html</a></td>
		</tr>
		<tr>
			<td>Penicillin:Streptomycin for tissue culture 100x</td>
			<td>Biowhittaker Inc. Walkersville, MD, USA</td>
			<td>LONZA 17-602E</td>
			<td>supplement for medium at Penicillin/Streptomycin 100U/100&#181;g.</td>
		</tr>
		<tr>
			<td>pN1-mEGFP-FGFR1 expression vector</td>
			<td>Unit of Gynecological Oncology Research, European Institute of Oncology IRCCS, Milan, Italy</td>
			<td></td>
			<td>Zamai et al., 2019</td>
		</tr>
		<tr>
			<td>pN1-N-Gly-mEGFP-GPI expression vector</td>
			<td>Unit of Microscopy and Dynamic Imaging, CNIC, Madrid, Spain</td>
			<td></td>
			<td>Hellriegel et al., 2011</td>
		</tr>
		<tr>
			<td>pN1-N-Gly-mEGFP-mEGFP-GPI expression vector</td>
			<td>Unit of Microscopy and Dynamic Imaging, CNIC, Madrid, Spain</td>
			<td></td>
			<td>Hellriegel et al., 2011</td>
		</tr>
		<tr>
			<td>Recombinant FGF2</td>
			<td>PeproTech EC, Ltd., London, UK</td>
			<td></td>
			<td>Ligand solution: 20ng/mL of FGF2 in PBS supplemented with 0.01%BSA.</td>
		</tr>
		<tr>
			<td>Sodium pyruvate GIBCO</td>
			<td>ThermoFisher Scientific</td>
			<td>11360070</td>
			<td>1mM supplement for medium</td>
		</tr>
		<tr>
			<td>TransIt-LT1 Transfection Reagent</td>
			<td>MirusBio LLC, Madison, WI, USA</td>
			<td>MIR 2300</td>
			<td></td>
		</tr>
		<tr>
			<td>Trypsin-EDTA (0.25%), phenol red</td>
			<td>GIBCO Thermo Fisher Scientific,Waltham, MA, USA</td>
			<td>25200056</td>
			<td></td>
		</tr>
		<tr>
			<td>Type F Immersion liquid 10 mL</td>
			<td>Leica Microsystems, Wetzlar, Germany</td>
			<td>11513 859</td>
			<td></td>
		</tr>
		<tr>
			<td>UltraPure BSA (50 mg/mL)</td>
			<td>ThermoFisher Scientific</td>
			<td>AM2618</td>
			<td>0.1% supplement for medium without phenol red used for transfections</td>
		</tr>
	</tbody>
</table>

oligomerization dynamics of cell surface receptors in living cells by total internal reflection fluorescence microscopy combined with number and brightness analysis

Despite the importance and ubiquity of receptor oligomerization, few methods are applicable for detecting clustering events and measuring the degree of clustering. Here, we describe an imaging approach to determine the average oligomeric state of mEGFP-tagged-receptor homocomplexes in the membrane of living cells. The protocol is based on Total Internal Reflection Fluorescence (TIRF) microscopy combined with Number and Brightness (N&#38;B) analysis. N&#38;B is a method similar to fluorescence-correlation spectroscopy (FCS) and photon counting histogram (PCH), which are based on the statistical analysis of the fluctuations of the fluorescence intensity of fluorophores diffusing in and out of an illumination volume during an observation time. In particular, N&#38;B is a simplification of PCH to obtain information on the average number of proteins in oligomeric mixtures. The intensity fluctuation amplitudes are described by the molecular brightness of the fluorophore and the average number of fluorophores within the illumination volume. Thus, N&#38;B considers only the first and second moments of the amplitude distribution, namely, the mean intensity and the variance. This is, at the same time, the strength and the weakness of the method. Because only two moments are considered, N&#38;B cannot determine the molar fraction of unknown oligomers in a mixture, but it only estimates the average oligomerization state of the mixture. Nevertheless, it can be applied to relatively small time series (compared to other moment methods) of images of live cells on a pixel-by-pixel basis, simply by monitoring the time fluctuations of the fluorescence intensity. It reduces the effective time-per-pixel to a few microseconds, allowing acquisition in the time range of seconds to milliseconds, which is necessary for fast oligomerization kinetics. Finally, large cell areas as well as sub-cellular compartments can be explored.

The results for two representative HeLa-mEGFP-FGFR1 cells seeded in the same culture dish are shown in Figure 5 and Supplemental Table 1. The two cells were captured at time 0 min (Figure 5A, top) and 7 min (Figure 5A, bottom) after addition of the FGF2 ligand.
Figure 5...

Watch this Scientific Journal Video about Oligomerization Dynamics of Cell Surface Receptors in Living Cells by Total Internal Reflection Fluorescence Microscopy Combined with Number and Brightness An

Oligomerization Dynamics of Cell Surface Receptors in Living Cells by Total Internal Reflection Fluorescence Microscopy Combined with Number and Brightness Analysis

We describe an imaging approach for the determination of the average oligomeric state of mEGFP-tagged-receptor oligomers induced by ligand binding in the plasma membrane of living cells. The protocol is based on Total Internal Reflection Fluorescence (TIRF) microscopy combined with Number and Brightness (N&#38;B) analysis.

Despite the importance and ubiquity of receptor oligomerization, few methods are applicable for detecting clustering events and measuring the degree of ...

Receptor clustering is ubiquitous and often necessary for the signaling a cascade activation. However, few methods are available to measure the degree of clustering. We use total internal reflection fluorescence, TIRF microscopy, to follow the diffusion of FGFR1-mEGFP molecules in the plasma membrane of live cells.Then we apply number brightness, the N&B analysis, to describe the dynamics of stimulated receptor clustering. TIRF is ideal for fast temporal imaging of molecular events that occur near the cell surface, while N&B determines the average oligomeric state of fluorescent molecules based on where diffusion in and out the illumination volume of the microscope. Indeed, this is a tool to connect the spatial temporal organization of the receptors at the cell surface where they are signaling.N&B only needs the access to a microscope with a fast acquisition module. You can analyze large area of live cells having just the basic knowledge of fluorescence fluctuation methods. This method can be applied to proteins that form dimers or clusters and can be stoichiometrically labeled with the fluorescent dye.If the proteins are in intracellular compartments, other types of microscopes are used for acquiring the images. It is important to prepare a construct that expresses the monomeric form of the fluorophore for measuring under experimental conditions the pure monomeric brightness as a control. On the first day, seed HeLa cells in 1.5 milliliters of complete medium at a concentration of 100, 000 to 200, 000 cells per milliliter in glass-bottom dishes.Seed six to eight replicate dishes, and incubate in an incubator at 37 degrees Celsius. On the second or third day, cells have reached subconfluency. Add serum-free medium with protein plasmid to half of the dishes, and add reference plasmids with the monomer and dimer to the second half of dishes to transfect the cells.After one day, check that the transfected cells are adherent to the bottom of the dishes and the cell membrane is fluorescent. Discard dishes with overgrown cells or with very low fluorescence. Four hours before the experiment, activate the temperature incubator of the microscope at 37 degrees Celsius.Turn on the microscope, computers, and cameras, and wait for the cameras to reach the proper working temperature of minus 75 degrees Celsius. Place a small drop of oil on the objective lens. Put a sample dish into the incubator of the microscope, and close the doors of the incubator to let the temperature of the dish equilibrate for 10 minutes.Turn on the epifluorescence lamp and the 488-nanometer laser. Select the epifluorescence contrast mode to explore the sample, searching a cell to focus from the ocular lens. Select the proper filter for collecting the green emission through the microscope camera with bandpass Ex 490/20 500 and bandpass Em 525/50 or similar.Switch from the ocular to the cam report in epifluorescence mode. Refine the focus, and change to TIRF mode. Then activate the auto alignment following the instructions of the TIRF microscope.Choose a suitable illumination depth, and optimize the direction of the evanescent field. Switch to a second camera. Define a region of interest of at least 256 by 256 pixels.Set the exposure to one millisecond and the EM gain to 1, 000. Adjust the laser power to 0.5 milliwatts. It is necessary to have some information about the diffusion coefficient of the protein because the exposure time must be shorter than the average time spent by the fluorescent molecules in the luminated volume.Run a first trial sequence under initial conditions, and roughly estimate the signal-to-noise value. The conditions are acceptable at signal-to-noise equal to two to three or higher as measured in the first time series. Then use the slider of the emission splitting system connecting camera number two to the microscope for masking a side of the image.Select the camera file auto save option. Start the acquisition of the image series for a minimum of 700 frames at a minimum signal-to-noise ratio of two. Without taking the dish out of the microscope, add the ligand.Select a cell with a bright fluorescence membrane, and quickly start the first time series of the kinetic run. Search a second cell, and acquire the second time point of the kinetics. Capture a new cell for each time point of the kinetic run.For each new dish, repeat the alignment of the laser line and optimization of TIRF illumination. Now convert and save the image files acquired with the camera software as tif files. Import tif files in the analysis software routine by activating the N&B graphical user interphase MATLAB.Discard series for which the average intensity profile shows more than 10%photobleaching and series in which there has been an evident cell membrane distortion or translation during acquisition. Crop frames that are evidently out of focus. Keep for the analysis only series with at least 500 timeframes.First determine the camera parameters. Activate the routine calibrate camera. Select an area of at least 20 by 50 pixels in the detector noise region.In the log frequency versus digital level plot, move the linear red cursor to delimit the Gaussian in the linear part of the curve. Activate the B key. Apply a minimum binning of 2.2 to reduce the dispersion of the data and to generate the B-I histogram.Use the iterative square cursor to inspect the B-I histogram. Select a square ROI for the analysis, and generate the B-map of the selected ROI. Then save the ASCII file of the B-values associated to the selection.Import the ASCII in a graphics software to compute the frequency distribution of the data and obtain the average B-value plus or minus S.E.Apply the epsilon equation to derive the average brightness for each cell at each time point of the kinetic run. Normalize the data according to the normalization equation, where B't is the average B-value measured at time t after ligand addition and B'0 is the average B-value measured at the time t equals zero, which is 10 to 20 seconds after ligand additino. Plot the normalized average brightness versus acquisition time to build the kinetic run.In this study, representative results from two HeLa cells in the same dish expressing mEGFP-FGR1 captured at time zero and seven, after addition of 20 nanograms per milliliter of FGF2, are shown here. Compared with the reference constructs, GPI-mEGFP and GPI-mEGFP-mEGFP dimer, the entire analysis sequence shows the average intensity of the time series. Plot of all B-values.B-I histogram. B-map of the chosen ROI. And the associated B-distribution histogram.After stimulation with 20 nanograms per milliliter of FGF2, representative kinetic runs showing the average brightness as function of time describes the slow process of dimerization that persists for several minutes at the cell surface. When stimulated with 50 micrograms per milliliter of NCAM-Fc, the kinetic profile reveals fast and cyclic transitions of the receptor in oligomeric mixtures, which also reaches brightness values above that of the dimer. A normalized average value of three is repeatedly observed.It is important to minimize the extra variants due to no molecular fluctuations and bleaching. And cells must be well-adhered to the glass-bottom dish, not overgrown, and not piled up. If we are interested in a protein that diffuses faster inside intracellular compartments, we can apply faster zero times and use scanning on focal or multiphoton microscopes to image inside the cell.With our approach, we minimize the detrimental effects of photobleaching when we want to explore the dynamics of events such as dimerization. These events control a variety of cellular responses and are very difficult to prove in live cells with other techniques.

oligomerization-dynamics-cell-surface-receptors-living-cells-total

Research

JoVE Journal

Biology

6.7K vistas.  Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain. La agrupación en clústeres de receptores es omnipresente y a menudo necesaria para la señalización de una activación en cascada. Sin embargo, hay pocos métodos disponibles para medir el grado de agrupación en clústeres. Utilizamos fluorescencia de reflexión interna total, microscopía TIRF, para seguir la difusión de moléculas FGFR1-mEGFP en la membrana plasmática de células vivas.Luego aplicamos el brillo numérico, el análisis N&B, para describir la dinámica de la agru...

Video: Oligomerization Dynamics of Cell Surface Receptors in Living Cells by Total Internal Reflection Fluorescence Microscopy Combined with Number and Brightness Analysis