Name: 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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Description: 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Research

JoVE Journal

Biology

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 ...

Quantitative Live Cell Fluorescence-microscopy Analysis of Fission Yeast

Several microscopy techniques are available today that can detect a specific protein within the cell. During the last decade live cell imaging using fluorochromes like Green Fluorescent Protein (GFP) directly attached to the protein of interest has become increasingly popular 1. Using GFP and similar fluorochromes the subcellular localisations and movements of proteins can be detected in a fluorescent microscope. Moreover, also the subnuclear localisation of a certain region of a chromosome can be studied using this technique. GFP is fused to the Lac Repressor protein (LacR) and ectopically expressed in the cell where tandem repeats of the lacO sequence has been inserted into the region of interest on the chromosome2. The LacR-GFP will bind to the lacO repeats and that area of the genome will be visible as a green dot in the fluorescence microscope. Yeast is especially suited for this type of manipulation since homologous recombination is very efficient and thereby enables targeted integration of the lacO repeats and engineered fusion proteins with GFP 3. Here we describe a quantitative method for live cell analysis of fission yeast. Additional protocols for live cell analysis of fission yeast can be found, for example on how to make a movie of the meiotic chromosomal behaviour 4. In this particular experiment we focus on subnuclear organisation and how it is affected during gene induction. We have labelled a gene cluster, named Chr1, by the introduction of lacO binding sites in the vicinity of the genes. The gene cluster is enriched for genes that are induced early during nitrogen starvation of fission yeast 5. In the strain the nuclear membrane (NM) is labelled by the attachment of mCherry to the NM protein Cut11 giving rise to a red fluorescent signal. The Spindle Pole body (SPB) compound Sid4 is fused to Red Fluorescent Protein (Sid4-mRFP) 6. In vegetatively growing yeast cells the centromeres are always attached to the SPB that is embedded in the NM 7. The SPB is identified as a large round structure in the NM. By imaging before and 20 minutes after depletion of the nitrogen source we can determine the distance between the gene cluster (GFP) and the NM/SPB. The mean or median distances before and after nitrogen depletion are compared and we can thus quantify whether or not there is a shift in subcellular localisation of the gene cluster after nitrogen depletion.

The fission yeast, Schizosaccharomyces pombe, is a good model system to study basic cellular processes. Here we describe a method to perform quantitative live cell analysis of fission yeast. In this particular experiment we focus on organisation of the genome within the cell nucleus, but the method can also be used to study cytosolic factors.

Several microscopy techniques are available today that can detect a specific protein within the cell. During the last decade live cell imaging using ...

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 ...