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 half with reference plasmids (monomer and dimer), in serum-free medium. 
	NOTE: Transfection is made in serum free medium supplemented with antibiotics, 0.1% Bovine Serum Albumin and 25 mM HEPES buffer, without Phenol Red.</li>
	<li>Day 3-4. 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. 
	NOTE: Do not let cells overgrow. Cells must be well distributed and be adhered to the glass area of the dish (Figure 1A). Precoated glass bottom dishes can be used for favoring cell adhesion. The cell culture is tested for mycoplasma contamination before any experiment. In this example, cells are transfected with a (A207K)mEGFP-FGFR1 plasmid and the reference cells are transfected with a GPI-(A207K)mEGFP and a GPI-(A207K)mEGFP-(A207K)mEGFP plasmids using standard protocols. For live cell microscopy, an indicator-free medium is recommended; 25 mM HEPES buffer is added to prevent pH changes during imaging.</li>
</ol>
2. TIRF Imaging &#8212; Alignment of the Laser Line and Optimization of TIRF Illumination
<ol>
	<li>Four hours before experiment, activate the temperature incubator of the microscope at 37 &#176;C.</li>
	<li>Turn on the microscope, computers and cameras and wait for the cameras to reach the proper working temperature. 
	NOTE: The working temperature of the camera used in this study is -75 &#176;C.</li>
	<li>Place a little drop of oil on the objective. Put a sample dish in place. Close the doors of the incubator and let the temperature of the dish equilibrate (~10 min).</li>
	<li>Turn on the epifluorescence lamp and the 488 nm laser.</li>
	<li>Select the epifluorescence contrast mode to explore the sample, searching a cell to focus from the ocular. 
	NOTE: The use of a fluorescent lamp for searching cells trough the ocular is not mandatory. A suitable laser line can be used instead.</li>
	<li>Select the proper filter for collecting the green emission through the microscope camera (Band Pass Ex 490/20 (500) Band Pass Em 525/50, or similar.</li>
	<li>Switch from the ocular to the camera port (camera #1 in Figure 1) in epifluorescence mode, refine the focus and change to TIRF mode. Epifluorescence and TIRF modes might be named with a different nomenclature depending on the brand of the microscope. 
	NOTE: There may be issues focusing or aligning the laser if there are no fluorescent markers at the coverslip interface. To align the laser properly (essential for good TIRF), focus on the coverslip. It is often very difficult to determine whether the coverslip is in focus. As a suggestion, focus on the edges of the cells.</li>
	<li>Activate the auto-alignment following the instructions of the TIRF microscope. 
	NOTE: Briefly, for steps from 2.4 to 2.8, first find the cells through the ocular and focus on them, then send the emission to the camera port of the TIRF microscope, re-focus the cells on the microscope computer screen and activate the procedure for laser alignment. The alignment consists in finding the critical angle at which illumination becomes evanescent (Figure 3). Commercial microscopes might have slightly different alignment protocols and also be fully automated; others might have a small camera for facilitating the visualization of the critical angle conditions.</li>
	<li>Choose a suitable illumination depth and optimize the direction of the evanescent field (Figure 3). 
	NOTE: The penetration depth is kept constant for all controls and samples.</li>
</ol>
3. TIRF Imaging: Capture of the Time Series
<ol>
	<li>Define a region of interest (ROI) of at least 256 x 256 pixels. 
	NOTE: In this set up, the capture is done with camera #2 under software that directly controls only the camera (See Figure 1 legend).</li>
	<li>Set the exposure to 1 ms and the EM gain to 1,000 (this is the G factor in eq. 12 and 13). At such a speed, it might be necessary to adjust or increase the laser power. Here laser power is 0.5 mW. 
	NOTE: Depending on the type of the camera and the limits imposed by the diffusion coefficient of the protein, fluorescence intensity and background, the general criteria for setting the laser power are not to saturate the detector, minimize photobleaching, and capture as fast as possible at a reasonable S/N. The EM gain is always set at the maximum of the camera (see Introduction).</li>
	<li>Run a first trial sequence under initial conditions and roughly estimate the S/N value. The conditions are acceptable at S/N = 2-3 or higher, as measured in the first frame of the first time series.</li>
	<li>Use the slider of the emission splitting system connecting camera #2 to the microscope for masking a side of the image (Figure 1B, Figure 4A-B) 
	NOTE: In this set up a multichannel imaging connector is installed on camera #2 to enable the acquisition of two spatially identical images simultaneously. The system is equipped with slides for mounting different emission filters. One of the sliders mounts a black mask to cover a side of the image. The masked area is used for the internal calibration of each time series, to determine the camera parameters (eq. 12 and eq. 13). In this way there is no need for an independent calibration step and, importantly, calibration is performed in parallel to the capture of each time series. In the absence of this system, the camera can be calibrated applying published protocols33.</li>
	<li>Select the camera file autosave option.</li>
	<li>Start the acquisition of the image series. Acquire a minimum of 700 frames at a minimum S/N ratio of 2. 
	NOTE: The number of frames that are necessary for analysis depends on the sample stability to photobleaching and on the dispersion of the data. Therefore, the quality of each time series is assessed during N&#38;B analysis.</li>
	<li>Without taking the dish out of the microscope, add the ligand.</li>
	<li>Select a cell with a bright fluorescence membrane and quickly start the first time series of the kinetic run. 
	NOTE: If the addition of the ligand is done quickly, this first capture sets the point = 0 time of the ligand kinetics. The software registers the exact time of the capturing.</li>
	<li>Search a second cell and acquire the second time point of the kinetics. 
	NOTE: Point-visiting routines are available in some microscopes equipped with x,y,z motorized stages. These allow the memorization of multiple positions on the cell dish, and can help in keeping a more constant interval of time between image-series on different cells.</li>
	<li>Capture a new cell for each time point of the kinetic run. 
	NOTE: After capturing, a cell is partially photobleached and it cannot be re-imaged. Because of that, the kinetics is obtained by combining time series of many cells, each captured at a different time point.</li>
	<li>For each new dish, repeat the protocol from step 2.3 to 3.9. 
	NOTE: For reference dishes, add a volume of the vehicle (PBS supplemented with 0.01% bovine serum albumin) equivalent to that used for the ligand.</li>
</ol>
4. Number &#38; Brightness (N&#38;B): Quality Check of the Time Series
<ol>
	<li>Convert and save as .TIFF the files acquired with the camera software (.sif files in this example).</li>
	<li>Import .TIFF files in the analysis software routine by activating the N&#38;B graphical user interface (GUI) MATLAB. 
	NOTE: A customized Matlab executable N&#38;B routine is used here (N&#38;B analysis at https://www.cnic.es/en/investigacion/2/1187/tecnologia). By opening an imported .TIFF file, the routine generates the average intensity image, the average intensity profile and it allows inspecting the series frame-by-frame (Supplemental Figure 1). Other software are available for N&#38;B analysis (e.g., SimFCS software).</li>
	<li>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.</li>
	<li>Crop frames that are evidently out-of-focus. 
	NOTE: A cropping tool is implemented in the routine to discards single or multiple frames within the image series. This operation is allowed because frame-to-frame time is not critical whereas the pixel dwell time (exposure time) is (see Discussion).</li>
	<li>Keep for the analysis only series with at least 500 time frames.</li>
</ol>
5. Number &#38; Brightness (N&#38;B): Determination of the Camera Parameters (Offset, &#963; and S)
<ol>
	<li>Activate the routine Calibrate Camera.</li>
	<li>Select an area of at least 20 x 50 pixels in the detector noise region (Figure 4). 
	NOTE: The routine originates a histogram of the values (also defined Digital Level, DL) and it returns a logarithm plot of the Frequency versus Digital Levels.</li>
	<li>In the log Frequency versus Digital level plot, move the linear red cursor to delimit the Gaussian and the linear part of the curve. 
	NOTE: The red cursor divides the two sections of the curve, and activates the routine returning the offset, which is the center of the Gaussian function of the camera response, the &#963; of the Gaussian fit, and the S factor, which is the slope of the linear part of the camera response (Figure 4C-D).</li>
</ol>
6. Number &#38; Brightness (N&#38;B): Computation of the B-values in Selected Region-of-interest (ROI)
<ol>
	<li>Activate the B key. 
	NOTE: This action generates the average intensity image (Figure 5, first column) and the B-image in which each individual B-value is associated to the related pixel in the image (Supplemental Figure 1).</li>
	<li>Apply a minimum binning (2 2) to reduce the dispersion of the data and to generate the B-I histogram (Figure 5, second column). 
	NOTE: The B-I histogram represents the distribution of the B-values of all pixels of the image versus the pixel intensity. Y = B/S; X = (<img src="/files/ftp_upload/60398/60398eq1.jpg" /> - offset)/S (Supplemental Figure 1 and eq. 11 and 15).</li>
	<li>Inspect the B-I histogram using the interactive square cursor.</li>
	<li>Select a square ROI for the analysis (Figure 5, third column). 
	NOTE: The cursor synchronizes a mobile mask on the average intensity image, highlighting the pixels that are selected inside the square cursor area (Supplemental Figure 1). By this inspection, it is possible to exclude from the analysis the background and areas with very low intensity.</li>
	<li>Generate the B-map of the selected ROI (Figure 5, fourth column).</li>
	<li>Save the ASCII file of the B-values associated to the selection.</li>
	<li>Import the ASCII file in a graphic software to compute the frequency distribution of the data and obtain the average B-value &#177; S.E (Figure 5, fifth column). 
	NOTE: If data are homogeneous, the frequency distribution of the B-values approximates a Gaussian distribution.</li>
	<li>Apply eq. 15 to derive the average brightness <img src="/files/ftp_upload/60398/60398eq18.jpg" /> = <img src="/files/ftp_upload/60398/60398eq19.jpg" /> - 1 [(counts/molecule) per dwell time] for each cell at each time point of the kinetic run. Normalize the data according to: 
	<img src="/files/ftp_upload/60398/60398eq17.jpg" /> 
	where <img src="/files/ftp_upload/60398/60398eq20.jpg" /> is the average B-value measured at time &#34;t&#34; after ligand addition, and <img src="/files/ftp_upload/60398/60398eq21.jpg" /> is the average B-value measured at the time t=0 (10-20 s after ligand addition). 
	NOTE: The normalization of the results allows the direct comparison of experiments that are carried out in different days. It compensates for differences in the measured brightness due to laser power and technical fluctuations.</li>
	<li>Plot the Normalized Average Brightness versus acquisition time to build the kinetic run (Figure 6).</li>
</ol>

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(92), e51994 (2014).</li><li>Beenken, A., Mohammadi, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+FGF+family:+biology,+pathophysiology+and+therapy.">The FGF family: biology, pathophysiology and therapy.</a> Nature Reviews Drug Discovery. 8 (3), 235-253 (2009).</li><li>Joubert, J., Sharma, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Light+microscopy+digital+imaging.">Light microscopy digital imaging.</a> Current Protocols in Cytometry. , (2011).</li><li>Gell, C., Berndt, M., Enderlein, J., Diez, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TIRF+microscopy+evanescent+field+calibration+using+tilted+fluorescent+microtubules.">TIRF microscopy evanescent field calibration using tilted fluorescent microtubules.</a> Journal of Microscopy. 234 (1), 38-46 (2009).</li><li>Burghardt, T. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measuring+incidence+angle+for+through-the-objective+total+internal+reflection+fluorescence+microscopy.">Measuring incidence angle for through-the-objective total internal reflection fluorescence microscopy.</a> Journal of Biomedical Optics. 17 (12), 126007 (2012).</li><li>Trullo, A., Corti, V., Arza, E., Caiolfa, V. R., Zamai, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Application+limits+and+data+correction+in+number+of+molecules+and+brightness+analysis.">Application limits and data correction in number of molecules and brightness analysis.</a> Microscopy Research and Technique. 76 (11), 1135-1146 (2013).</li><li>Caiolfa, V. R., 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=Monomer-dimer+dynamics+and+distribution+of+GPI-anchored+uPAR+are+determined+by+cell+surface+protein+assemblies.">Monomer-dimer dynamics and distribution of GPI-anchored uPAR are determined by cell surface protein assemblies.</a> Journal of Cell Biology. 179 (5), 1067-1082 (2007).</li><li>Campbell, R. E., 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+monomeric+red+fluorescent+protein.">A monomeric red fluorescent protein.</a> Proceedings of the National Academy of Sciences of the United States of America. 99 (12), 7877-7882 (2002).</li><li>Cutrale, F., 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=Using+enhanced+number+and+brightness+to+measure+protein+oligomerization+dynamics+in+live+cells.">Using enhanced number and brightness to measure protein oligomerization dynamics in live cells.</a> Nature Protocols. 14 (2), 616-638 (2019).</li><li>Dunsing, V., Chiantia, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Fluorescence+Fluctuation+Spectroscopy+Assay+of+Protein-Protein+Interactions+at+Cell-Cell+Contacts.">A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts.</a> Journal of Visualized Experiments. (142), (2018).</li></ol>

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 ...

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

Research

JoVE Journal

Biology

6.7K Views.  Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain. 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&B) analysis.

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

Time-lapse Imaging of Mitosis After siRNA Transfection

Changes in cellular organization and chromosome dynamics that occur during mitosis are tightly coordinated to ensure accurate inheritance of genomic and cellular content. Hallmark events of mitosis, such as chromosome movement, can be readily tracked on an individual cell basis using time-lapse fluorescence microscopy of mammalian cell lines expressing specific GFP-tagged proteins. In combination with RNAi-based depletion, this can be a powerful method for pinpointing the stage(s) of mitosis where defects occur after levels of a particular protein have been lowered. In this protocol, we present a basic method for assessing the effect of depleting a potential mitotic regulatory protein on the timing of mitosis. Cells are transfected with siRNA, placed in a stage-top incubation chamber, and imaged using an automated fluorescence microscope. We describe how to use software to set up a time-lapse experiment, how to process the image sequences to make either still-image montages or movies, and how to quantify and analyze the timing of mitotic stages using a cell-line expressing mCherry-tagged histone H2B. Finally, we discuss important considerations for designing a time-lapse experiment. This strategy is complementary to other approaches and offers the advantages of 1) sensitivity to changes in kinetics that might not be observed when looking at cells as a population and 2) analysis of mitosis without the need to synchronize the cell cycle using drug treatments. The visual information from such imaging experiments not only allows the sub-stages of mitosis to be assessed, but can also provide unexpected insight that would not be apparent from cell cycle analysis by FACS.

Here we describe a basic protocol to image and quantify the mitotic timing of live mammalian tissue culture cells after siRNA transfection.

Changes in cellular organization and chromosome dynamics that occur during mitosis are tightly coordinated to ensure accurate inheritance of genomic and ...

Cell Electrofusion Visualized with Fluorescence Microscopy

Cell electrofusion is a safe, non-viral and non-chemical method that can be used for preparing hybrid cells for human therapy. Electrofusion involves application of short high-voltage electric pulses to cells that are in close contact. Application of short, high-voltage electric pulses causes destabilization of cell plasma membranes. Destabilized membranes are more permeable for different molecules and also prone to fusion with any neighboring destabilized membranes. Electrofusion is thus a convenient method to achieve a non-specific fusion of very different cells in vitro. In order to obtain fusion, cell membranes, destabilized by electric field, must be in a close contact to allow merging of their lipid bilayers and consequently their cytoplasm. In this video, we demonstrate efficient electrofusion of cells in vitro by means of modified adherence method. In this method, cells are allowed to attach only slightly to the surface of the well, so that medium can be exchanged and cells still preserve their spherical shape. Fusion visualization is assessed by pre-labeling of the cytoplasm of cells with different fluorescent cell tracker dyes; half of the cells are labeled with orange CMRA and the other half with green CMFDA. Fusion yield is determined as the number of dually fluorescent cells divided with the number of all cells multiplied by two.

In this video we demonstrate efficient electrofusion of cells in vitro by means of modified adherence method using electroporation and the subsequent detection of fused cells visualization with fluorescence microscopy.

Cell electrofusion is a safe, non-viral and non-chemical method that can be used for preparing hybrid cells for human therapy. Electrofusion involves ...

In Vivo 4-Dimensional Tracking of Hematopoietic Stem and Progenitor Cells in Adult Mouse Calvarial Bone Marrow

Through a delicate balance between quiescence and proliferation, self renewal and production of differentiated progeny, hematopoietic stem cells (HSCs) maintain the turnover of all mature blood cell lineages. The coordination of the complex signals leading to specific HSC fates relies upon the interaction between HSCs and the intricate bone marrow microenvironment, which is still poorly understood[1-2].
We describe how by combining a newly developed specimen holder for stable animal positioning with multi-step confocal and two-photon in vivo imaging techniques, it is possible to obtain high-resolution 3D stacks containing HSPCs and their surrounding niches and to monitor them over time through multi-point time-lapse imaging. High definition imaging allows detecting ex vivo labeled hematopoietic stem and progenitor cells (HSPCs) residing within the bone marrow. Moreover, multi-point time-lapse 3D imaging, obtained with faster acquisition settings, provides accurate information about HSPC movement and the reciprocal interactions between HSPCs and stroma cells.
Tracking of HSPCs in relation to GFP positive osteoblastic cells is shown as an exemplary application of this method. This technique can be utilized to track any appropriately labeled hematopoietic or stromal cell of interest within the mouse calvarium bone marrow space.

In Vivo 4-Dimensional Tracking of Hematopoietic Stem and Progenitor Cells in Adult Mouse Calvarial Bone Marrow

The nature of the interactions between hematopoietic stem and progenitor cells (HSPCs) and bone marrow niches is poorly understood. Custom hardware modifications and a multi-step acquisition protocol allow the use of two-photon and confocal microscopy to image ex vivo labeled HSPCs homed within bone marrow areas, tracking interactions and movement.

Through a delicate balance between quiescence and proliferation, self renewal and production of differentiated progeny, hematopoietic stem cells (HSCs) ...

Fluorescence Imaging with One-nanometer Accuracy (FIONA)

Fluorescence imaging with one-nanometer accuracy (FIONA) is a simple but useful technique for localizing single fluorophores with nanometer precision in the x-y plane. Here a summary of the FIONA technique is reported and examples of research that have been performed using FIONA are briefly described. First, how to set up the required equipment for FIONA experiments, i.e., a total internal reflection fluorescence microscopy (TIRFM), with details on aligning the optics, is described. Then how to carry out a simple FIONA experiment on localizing immobilized Cy3-DNA single molecules using appropriate protocols, followed by the use of FIONA to measure the 36 nm step size of a single truncated myosin Va motor labeled with a quantum dot, is illustrated. Lastly, recent effort to extend the application of FIONA to thick samples is reported. It is shown that, using a water immersion objective and quantum dots soaked deep in sol-gels and rabbit eye corneas (&#62;200 &#181;m), localization precision of 2-3 nm can be achieved.

Fluorescence Imaging with One-nanometer Accuracy FIONA

Single fluorophores can be localized with nanometer precision using FIONA. Here a summary of the FIONA technique is reported, and how to carry out FIONA experiments is described.

Fluorescence imaging with one-nanometer accuracy (FIONA) is a simple but useful technique for localizing single fluorophores with nanometer precision in ...

Analysis of Autophagy in Penicillium chrysogenum by Using Starvation Pads in Combination With Fluorescence Microscopy

The study of cellular quality control systems has emerged as a highly dynamic and relevant field of contemporary research. It has become clear that cells possess several lines of defense against damage to biologically relevant molecules like nucleic acids, lipids and proteins. In addition to organelle dynamics (fusion/fission/motility/inheritance) and tightly controlled protease activity, the degradation of surplus, damaged or compromised organelles by autophagy (cellular &#8216;self-eating&#8217;) has received much attention from the scientific community. The regulation of autophagy is quite complex and depends on genetic and environmental factors, many of which have so far not been elucidated. Here a novel method is presented that allows the convenient study of autophagy in the filamentous fungus Penicillium chrysogenum. It is based on growth of the fungus on so-called &#8216;starvation pads&#8217; for stimulation of autophagy in a reproducible manner. Samples are directly assayed by microscopy and evaluated for autophagy induction / progress. The protocol presented here is not limited for use with P. chrysogenum and can be easily adapted for use in other filamentous fungi.

Analysis of Autophagy in Penicillium chrysogenum by Using Starvation Pads in Combination With Fluorescence Microscopy

A convenient and powerful method for studying autophagy in Penicillium chrysogenum by using starvation pads (mixtures of agarose and tap water in a microscope slide containing a central cavity) is presented here.

The study of cellular quality control systems has emerged as a highly dynamic and relevant field of contemporary research. It has become clear that cells ...

Real-time Imaging of Plant Cell Surface Dynamics with Variable-angle Epifluorescence Microscopy

A plant&#8217;s cell surface is its interface for perceiving environmental cues; it responds with cell biological changes such as membrane trafficking and cytoskeletal rearrangement. Real-time and high-resolution image analysis of such intracellular events will increase the understanding of plant cell biology at the molecular level. Variable angle epifluorescence microscopy (VAEM) is an emerging technique that provides high-quality, time-lapse images of fluorescently-labeled proteins on the plant cell surface. In this article, practical procedures are described for VAEM specimen preparation, adjustment of the VAEM optical system, movie capturing and image analysis. As an example of VAEM observation, representative results are presented on the dynamics of PATROL1. This is a protein essential for stomatal movement, thought to be involved in proton pump delivery to plasma membranes in the stomatal complex of Arabidopsis thaliana. VAEM real-time observation of guard cells and subsidiary cells in A. thaliana cotyledons showed that fluorescently-tagged PATROL1 appeared as dot-like structures on plasma membranes for several seconds and then disappeared. Kymograph analysis of VAEM movie data determined the time distribution of the presence (termed &#8216;residence time&#8217;) of the dot-like structures. The use of VAEM is discussed in the context of this example.

The goal of this protocol is to demonstrate how to monitor fluorescently-tagged protein dynamics on plant cell surfaces with variable-angle epifluorescence microscopy, showing blinking dots of GFP-tagged PATROL1, a membrane trafficking protein, in the cell cortex of the stomatal complex in Arabidopsis thaliana.

A plant&#8217;s cell surface is its interface for perceiving environmental cues; it responds with cell biological changes such as membrane trafficking and ...

High-resolution Time-lapse Imaging and Automated Analysis of Microtubule Dynamics in Living Human Umbilical Vein Endothelial Cells

The physiological process by which new vasculature forms from existing vasculature requires specific signaling events that trigger morphological changes within individual endothelial cells (ECs). These processes are critical for homeostatic maintenance such as wound healing, and are also crucial in promoting tumor growth and metastasis. EC morphology is defined by the organization of the cytoskeleton, a tightly regulated system of actin and microtubule (MT) dynamics that is known to control EC branching, polarity and directional migration, essential components of angiogenesis. To study MT dynamics, we used high-resolution fluorescence microscopy coupled with computational image analysis of fluorescently-labeled MT plus-ends to investigate MT growth dynamics and the regulation of EC branching morphology and directional migration. Time-lapse imaging of living Human Umbilical Vein Endothelial Cells (HUVECs) was performed following transfection with fluorescently-labeled MT End Binding protein 3 (EB3) and Mitotic Centromere Associated Kinesin (MCAK)-specific cDNA constructs to evaluate effects on MT dynamics. PlusTipTracker software was used to track EB3-labeled MT plus ends in order to measure MT growth speeds and MT growth lifetimes in time-lapse images. This methodology allows for the study of MT dynamics and the identification of how localized regulation of MT dynamics within sub-cellular regions contributes to the angiogenic processes of EC branching and migration.

Protocols for Human Umbilical Vein Endothelial Cell (HUVEC) culture, transient transfection of fluorescently-labeled markers of microtubule growth, live-cell imaging and automated analysis of interphase microtubule growth dynamics are detailed.

The physiological process by which new vasculature forms from existing vasculature requires specific signaling events that trigger morphological changes ...

Light Sheet Fluorescence Microscopy of Plant Roots Growing on the Surface of a Gel

One of the key questions in understanding plant development is how single cells behave in a larger context of the tissue. Therefore, it requires the observation of the whole organ with a high spatial- as well as temporal resolution over prolonged periods of time, which may cause photo-toxic effects. This protocol shows a plant sample preparation method for light-sheet microscopy, which is characterized by mounting the plant vertically on the surface of a gel. The plant is mounted in such a way that the roots are submerged in a liquid medium while the leaves remain in the air. In order to ensure photosynthetic activity of the plant, a custom-made lighting system illuminates the leaves. To keep the roots in darkness the water surface is covered with sheets of black plastic foil. This method allows long-term imaging of plant organ development in standardized conditions.

This protocol shows a plant sample preparation method for light-sheet microscopy. The setup is characterized by mounting the plant vertically on the surface of a gel and letting it grow in controlled bright conditions. This allows long-term observation of plant organ development in standardized conditions.

One of the key questions in understanding plant development is how single cells behave in a larger context of the tissue. Therefore, it requires the ...

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Super-resolution fluorescence microscopy is currently revolutionizing cell biology research. Its capacity to break the resolution limit of around 300 nm allows for the routine imaging of nanoscale biological complexes and processes. This increase in resolution also means that methods popular in electron microscopy, such as single-particle analysis, can readily be applied to super-resolution fluorescence microscopy. By combining this analytical approach with super-resolution optical imaging, it becomes possible to take advantage of the molecule-specific labeling capacity of fluorescence microscopy to generate structural maps of molecular elements within a metastable structure. To this end, we have developed a novel algorithm &#8212; VirusMapper &#8212; packaged as an easy-to-use, high-performance, and high-throughput ImageJ plugin. This article presents an in-depth guide to this software, showcasing its ability to uncover novel structural features in biological molecular complexes. Here, we present how to assemble compatible data and provide a step-by-step protocol on how to use this algorithm to apply single-particle analysis to super-resolution images.

This manuscript uses the Fiji-based open-source software package VirusMapper to apply single-particle analysis to super-resolution microscopy images in order to generate precise models of nanoscale structure.

Super-resolution fluorescence microscopy is currently revolutionizing cell biology research. Its capacity to break the resolution limit of around 300 nm ...

Examination of Mitotic and Meiotic Fission Yeast Nuclear Dynamics by Fluorescence Live-cell Microscopy

Live-cell imaging is a microscopy technique used to examine cell and protein dynamics in living cells. This imaging method is not toxic, generally does not interfere with cell physiology, and requires minimal experimental handling. The low levels of technical interference enable researchers to study cells across multiple cycles of mitosis and to observe meiosis from beginning to end. Using fluorescent tags such as Green Fluorescent Protein (GFP) and Red Fluorescent Protein (RFP), researchers can analyze different factors whose functions are important for processes like transcription, DNA replication, cohesion, and segregation. Coupled with data analysis using Fiji (a free, optimized ImageJ version), live-cell imaging offers various ways of assessing protein movement, localization, stability, and timing, as well as nuclear dynamics and chromosome segregation. However, as is the case with other microscopy methods, live-cell imaging is limited by the intrinsic properties of light, which put a limit to the resolution power at high magnifications, and is also sensitive to photobleaching or phototoxicity at high wavelength frequencies. However, with some care, investigators can bypass these physical limitations by carefully choosing the right conditions, strains, and fluorescent markers to allow for the appropriate visualization of mitotic and meiotic events.

Here, we present live-cell imaging which is a non-toxic microscopy method that allows researchers to study protein behavior and nuclear dynamics in living fission yeast cells during mitosis and meiosis.

Live-cell imaging is a microscopy technique used to examine cell and protein dynamics in living cells. This imaging method is not toxic, generally does ...