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><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 100x 1.46 oil TIRF objective, Ex/Em Bandpass filters at 490/20 and 525/50, temperature/CO&lt;sub&gt;2&lt;/sub&gt; 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&#039;s Phosphate Buffered Saline 10x (PBS)</td><td>Biowest, Nuaill&amp;eacute;, 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&amp;amp;B time series</td></tr><tr><td>Matlab Executable N&amp;amp;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>&lt;a href=&quot;https://www.mathworks.com/products/matlab.html&quot;&gt;https://www.mathworks.com/products/matlab.html&lt;/a&gt;</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 100 U/100&amp;micro;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: 20 ng/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>1 mM 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

6.8K Views.  Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain. Receptor clustering is alomtegenwoordig en vaak noodzakelijk voor het signaleren van een cascade activering. Er zijn echter weinig methoden beschikbaar om de mate van clustering te meten. We gebruiken totale interne reflectiefluorescentie, TIRF-microscopie, om de verspreiding van FGFR1-mEGFP-moleculen in het plasmamembraan van levende cellen te volgen.Vervolgens passen we getalhelderheid, de N&B-analyse, toe om de dynamiek van gestimuleerde receptorclustering te beschrijven. TIRF is id...

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

Visualization of Cortex Organization and Dynamics in Microorganisms, using Total Internal Reflection Fluorescence Microscopy

TIRF microscopy has emerged as a powerful imaging technology to study spatio-temporal dynamics of fluorescent molecules in vitro and in living cells1. The optical phenomenon of total internal reflection occurs when light passes from a medium with high refractive index into a medium with low refractive index at an angle larger than a characteristic critical angle (i.e. closer to being parallel with the boundary). Although all light is reflected back under such conditions, an evanescent wave is created that propagates across and along the boundary, which decays exponentially with distance, and only penetrates sample areas that are 100-200 nm near the interface2. In addition to providing superior axial resolution, the reduced excitation of out of focus fluorophores creates a very high signal to noise ratios and minimizes damaging effects of photobleaching2,3. Being a widefield technique, TIRF also allows faster image acquisition than most scanning based confocal setups.
At first glance, the low penetration depth of TIRF seems to be incompatible with imaging of bacterial and fungal cells, which are often surrounded by thick cell walls. On the contrary, we have found that the cell walls of yeast and bacterial cells actually improve the usability of TIRF and increase the range of observable structures4-8. Many cellular processes can therefore be directly accessed by TIRF in small, single-cell microorganisms, which often offer powerful genetic manipulation techniques. This allows us to perform in vivo biochemistry experiments, where kinetics of protein interactions and activities can be directly assessed in living cells.
We describe here the individual steps required to obtain high quality TIRF images for Saccharomyces cerevisiae or Bacillus subtilis cells. We point out various problems that can affect TIRF visualization of fluorescent probes in cells and illustrate the procedure with several application examples. Finally, we demonstrate how TIRF images can be further improved using established image restoration techniques.

Total Internal Reflection Fluorescence (TIRF) microscopy is a powerful approach to observe structures close to the cell surface at high contrast and temporal resolution. We demonstrate how TIRF can be employed to study protein dynamics at the cortex of cell wall-enclosed bacterial and fungal cells.

TIRF microscopy has emerged as  a powerful imaging technology to study spatio-temporal dynamics of fluorescent molecules  in vitro and in living cells1. ...

Bioengineering

A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts

A variety of biological processes involves cell-cell interactions, typically mediated by proteins that interact at the interface between neighboring cells. Of interest, only few assays are capable of specifically probing such interactions directly in living cells. Here, we present an assay to measure the binding of proteins expressed at the surfaces of neighboring cells, at cell-cell contacts. This assay consists of two steps: mixing of cells expressing the proteins of interest fused to different fluorescent proteins, followed by fluorescence fluctuation spectroscopy measurements at cell-cell contacts using a confocal laser scanning microscope. We demonstrate the feasibility of this assay in a biologically relevant context by measuring the interactions of the amyloid precursor-like protein 1 (APLP1) across cell-cell junctions. We provide detailed protocols on the data acquisition using fluorescence-based techniques (scanning fluorescence cross-correlation spectroscopy, cross-correlation number and brightness analysis) and the required instrument calibrations. Further, we discuss critical steps in the data analysis and how to identify and correct external, spurious signal variations, such as those due to photobleaching or cell movement.
In general, the presented assay is applicable to any homo- or heterotypic protein-protein interaction at cell-cell contacts, between cells of the same or different types and can be implemented on a commercial confocal laser scanning microscope. An important requirement is the stability of the system, which needs to be sufficient to probe diffusive dynamics of the proteins of interest over several minutes.

This protocol describes a fluorescence fluctuation spectroscopy-based approach to investigate interactions among proteins mediating cell-cell interactions, i.e. proteins localized in cell junctions, directly in living cells. We provide detailed guidelines on instrument calibration, data acquisition and analysis, including corrections to possible artefact sources.

A variety of biological processes involves cell-cell interactions, typically mediated by proteins that interact at the interface between neighboring ...

Biochemistry

High-resolution Spatiotemporal Analysis of Receptor Dynamics by Single-molecule Fluorescence Microscopy

Single-molecule microscopy is emerging as a powerful approach to analyze the behavior of signaling molecules, in particular concerning those aspect (e.g., kinetics, coexistence of different states and populations, transient interactions), which are typically hidden in ensemble measurements, such as those obtained with standard biochemical or microscopy methods. Thus, dynamic events, such as receptor-receptor interactions, can be followed in real time in a living cell with high spatiotemporal resolution. This protocol describes a method based on labeling with small and bright organic fluorophores and total internal reflection fluorescence (TIRF) microscopy to directly visualize single receptors on the surface of living cells. This approach allows one to precisely localize receptors, measure the size of receptor complexes, and capture dynamic events such as transient receptor-receptor interactions. The protocol provides a detailed description of how to perform a single-molecule experiment, including sample preparation, image acquisition and image analysis. As an example, the application of this method to analyze two G-protein-coupled receptors, i.e., &#946;2-adrenergic and &#947;-aminobutyric acid type B (GABAB) receptor, is reported. The protocol can be adapted to other membrane proteins and different cell models, transfection methods and labeling strategies.

This protocol describes how to use total internal reflection fluorescence microscopy to visualize and track single receptors on the surface of living cells and thereby analyze receptor lateral mobility, size of receptor complexes as well as to visualize transient receptor-receptor interactions. This protocol can be extended to other membrane proteins.

Single-molecule microscopy is emerging as a powerful approach to analyze the behavior of signaling molecules, in particular concerning those aspect (e.g., ...

From Fast Fluorescence Imaging to Molecular Diffusion Law on Live Cell Membranes in a Commercial Microscope

It has become increasingly evident that the spatial distribution and the motion of membrane components like lipids and proteins are key factors in the regulation of many cellular functions. However, due to the fast dynamics and the tiny structures involved, a very high spatio-temporal resolution is required to catch the real behavior of molecules. Here we present the experimental protocol for studying the dynamics of fluorescently-labeled plasma-membrane proteins and lipids in live cells with high spatiotemporal resolution. Notably, this approach doesn&#8217;t need to track each molecule, but it calculates population behavior using all molecules in a given region of the membrane. The starting point is a fast imaging of a given region on the membrane. Afterwards, a complete spatio-temporal autocorrelation function is calculated correlating acquired images at increasing time delays, for example each 2, 3, n repetitions. It is possible to demonstrate that the width of the peak of the spatial autocorrelation function increases at increasing time delay as a function of particle movement due to diffusion. Therefore, fitting of the series of autocorrelation functions enables to extract the actual protein mean square displacement from imaging (iMSD), here presented in the form of apparent diffusivity vs average displacement. This yields a quantitative view of the average dynamics of single molecules with nanometer accuracy. By using a GFP-tagged variant of the Transferrin Receptor (TfR) and an ATTO488 labeled 1-palmitoyl-2-hydroxy-sn-glycero-3-phosphoethanolamine (PPE) it is possible to observe the spatiotemporal regulation of protein and lipid diffusion on &#181;m-sized membrane regions in the micro-to-milli-second time range.

Spatial distribution and temporal dynamics of plasma membrane proteins and lipids is a hot topic in biology. Here this issue is addressed by a spatio-temporal image fluctuation analysis that provides conceptually the same physical quantities of single particle tracking, but it uses small molecular labels and standard microscopy setups.

It has become increasingly evident that the spatial distribution and the motion of membrane components like lipids and proteins are key factors in the ...

Visualizing Single Molecular Complexes In Vivo Using Advanced Fluorescence Microscopy

Full insight into the mechanisms of living cells can be achieved only by investigating the key processes that elicit and direct events at a cellular level. To date the shear complexity of biological systems has caused precise single-molecule experimentation to be far too demanding, instead focusing on studies of single systems using relatively crude bulk ensemble-average measurements. However, many important processes occur in the living cell at the level of just one or a few molecules; ensemble measurements generally mask the stochastic and heterogeneous nature of these events. Here, using advanced optical microscopy and analytical image analysis tools we demonstrate how to monitor proteins within a single living bacterial cell to a precision of single molecules and how we can observe dynamics within molecular complexes in functioning biological machines. The techniques are directly relevant physiologically. They are minimally-perturbative and non-invasive to the biological sample under study and are fully attuned for investigations in living material, features not readily available to other single-molecule approaches of biophysics. In addition, the biological specimens studied all produce fluorescently-tagged protein at levels which are almost identical to the unmodified cell strains ("genomic encoding"), as opposed to the more common but less ideal approach for generating significantly more protein than would occur naturally ('plasmid expression'). Thus, the actual biological samples which will be investigated are significantly closer to the natural organisms, and therefore the observations more relevant to real physiological processes.

Visualizing Single Molecular Complexes In Vivo Using Advanced Fluorescence Microscopy

Here we demonstrate the protocols for performing single-molecule fluorescence microscopy on living bacterial cells to enable functional molecular complexes to be detected, tracked and quantified.

Full insight into the mechanisms of living cells can be achieved only by investigating the key processes that elicit and direct events at a cellular ...

Single Molecule Fluorescence Microscopy on Planar Supported Bilayers

In the course of a single decade single molecule microscopy has changed from being a secluded domain shared merely by physicists with a strong background in optics and laser physics to a discipline that is now enjoying vivid attention by life-scientists of all venues 1. This is because single molecule imaging has the unique potential to reveal protein behavior in situ in living cells and uncover cellular organization with unprecedented resolution below the diffraction limit of visible light 2. Glass-supported planar lipid bilayers (SLBs) are a powerful tool to bring cells otherwise growing in suspension in close enough proximity to the glass slide so that they can be readily imaged in noise-reduced Total Internal Reflection illumination mode 3,4. They are very useful to study the protein dynamics in plasma membrane-associated events as diverse as cell-cell contact formation, endocytosis, exocytosis and immune recognition. Simple procedures are presented how to generate highly mobile protein-functionalized SLBs in a reproducible manner, how to determine protein mobility within and how to measure protein densities with the use of single molecule detection. It is shown how to construct a cost-efficient single molecule microscopy system with TIRF illumination capabilities and how to operate it in the experiment.

Preparation of protein-functionalized planar glass-supported lipid bilayers, determination of protein mobility within and measurement of protein densities is shown here. A roadmap to building a noise-reduced Total Internal Reflection microscope is outlined, which allows visualizing single bilayer-resident fluorochromes with high spatiotemporal resolution.

In the course of a single decade single molecule microscopy has changed from being a secluded domain shared merely by physicists with a strong background ...

Automated System for Single Molecule Fluorescence Measurements of Surface-immobilized Biomolecules

Fluorescence Resonance Energy Transfer (FRET) microscopy has been widely used to study the structure and dynamics of molecules of biological interest, such as nucleic acids and proteins. Single molecule FRET (sm-FRET) measurements on immobilized molecules permit long observations of the system -effectively until both dyes photobleach- resulting in time-traces that report on biomolecular dynamics with a broad range of timescales from milliseconds to minutes. To facilitate the acquisition of large number of traces for statistical analyses, the process must be automated and the sample environment should be tightly controlled over the entire measurement time (~12 hours). This is accomplished using an automated scanning confocal microscope that allows the interrogation of thousands of single molecules overnight, and a microfluidic cell that permits the controlled exchange of buffer, with restricted oxygen content and maintains a constant temperature throughout the entire measuring period. Here we show how to assemble the microfluidic device and how to activate its surface for DNA immobilization. Then we explain how to prepare a buffer to maximize the photostability and lifetime of the fluorophores. Finally, we show the steps involved in preparing the setup for the automated acquisition of time-resolved single molecule FRET traces of DNA molecules.

In this article we describe how we obtain FRET traces from individual DNA molecules immobilized to a surface using an automated scanning confocal microscope.

Fluorescence Resonance Energy Transfer (FRET) microscopy has been widely used to study the structure and dynamics of molecules of biological interest, ...

Visualizing the Conformational Dynamics of Membrane Receptors Using Single-Molecule FRET

The ability of cells to respond to external signals is essential for cellular development, growth, and survival. To respond to a signal from the environment, a cell must be able to recognize and process it. This task mainly relies on the function of membrane receptors, whose role is to convert signals into the biochemical language of the cell. G protein-coupled receptors (GPCRs) constitute the largest family of membrane receptor proteins in humans. Among GPCRs, metabotropic glutamate receptors (mGluRs) are a unique subclass that function as obligate dimers and possess a large extracellular domain that contains the ligand-binding site. Recent advances in structural studies of mGluRs have improved the understanding of their activation process. However, the propagation of large-scale conformational changes through mGluRs during activation and modulation is poorly understood. Single-molecule fluorescence resonance energy transfer (smFRET) is a powerful technique to visualize and quantify the structural dynamics of biomolecules at the single-protein level. To visualize the dynamic process of mGluR2 activation, fluorescent conformational sensors based on unnatural amino acid (UAA) incorporation were developed that allowed site-specific protein labeling without perturbation of the native structure of receptors. The protocol described here explains how to perform these experiments, including the novel UAA labeling approach, sample preparation, and smFRET data acquisition and analysis. These strategies are generalizable and can be extended to investigate the conformational dynamics of a variety of membrane proteins.

This study presents a detailed procedure to perform single-molecule fluorescence resonance energy transfer (smFRET) experiments on G protein-coupled receptors (GPCRs) using site-specific labeling via unnatural amino acid (UAA) incorporation. The protocol provides a step-by-step guide for smFRET sample preparation, experiments, and data analysis.

The ability of cells to respond to external signals is essential for cellular development, growth, and survival. To respond to a signal from the ...

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

Particle tracking on a video sequence and the posterior analysis of their trajectories is nowadays a common operation in many biological studies. Using the analysis of cell membrane receptor clusters as a model, we present a detailed protocol for this image analysis task using Fiji (ImageJ) and Matlab routines to: 1) define regions of interest and design masks adapted to these regions; 2) track the particles in fluorescence microscopy videos; 3) analyze the diffusion and intensity characteristics of selected tracks. The quantitative analysis of the diffusion coefficients, types of motion, and cluster size obtained by fluorescence microscopy and image processing provides a valuable tool to objectively determine particle dynamics and the consequences of modifying environmental conditions. In this article we present detailed protocols for the analysis of these features. The method described here not only allows single-molecule tracking detection, but also automates the estimation of lateral diffusion parameters at the cell membrane, classifies the type of trajectory and allows complete analysis thus overcoming the difficulties in quantifying spot size over its entire trajectory at the cell membrane.

Here, we present a protocol for single particle tracking image analysis that allows quantitative evaluation of diffusion coefficients, types of motion and cluster sizes of single particles detected by fluorescence microscopy.

Particle tracking on a video sequence and the posterior analysis of their trajectories is nowadays a common operation in many biological studies. Using ...

Immunology and Infection

Internalization and Observation of Fluorescent Biomolecules in Living Microorganisms via Electroporation

The ability to study biomolecules in vivo is crucial for understanding their function in a biological context. One powerful approach involves fusing molecules of interest to fluorescent proteins such as GFP to study their expression, localization and function. However, GFP and its derivatives are significantly larger and less photostable than organic fluorophores generally used for in vitro experiments, and this can limit the scope of investigation. 
We recently introduced a straightforward, versatile and high-throughput method based on electroporation, allowing the internalization of biomolecules labeled with organic fluorophores into living microorganisms. Here we describe how to use electroporation to internalize labeled DNA fragments or proteins into Escherichia coli and Saccharomyces cerevisi&#230;, how to quantify the number of internalized molecules using fluorescence microscopy, and how to quantify the viability of electroporated cells. Data can be acquired at the single-cell or single-molecule level using fluorescence or FRET. The possibility of internalizing non-labeled molecules that trigger a physiological observable response in vivo is also presented. Finally, strategies of optimization of the protocol for specific biological systems are discussed.

Studies of biomolecules in vivo are crucial for understanding molecular function in a biological context. Here we describe a novel method allowing the internalization of fluorescent biomolecules, such as DNA or proteins, into living microorganisms. Analysis of in vivo data recorded by fluorescence microscopy is also presented and discussed.

The ability to study biomolecules in vivo is crucial for understanding their function in a biological context. One powerful approach involves fusing ...

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

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