Name: confocal microscopy reveals cell surface receptor aggregation through image correlation spectroscopy
Uploaded: 2018-08-02T12:30:00.000+00:00
Duration: 6 min 51 s
Description: Watch this Scientific Journal Video about Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy at JoVE.com

The authors acknowledge funding support from NHMRC (Fellowship 1084178 and Grants 1087850, 1030469, 1075898 (AMS)), Cancer Australia, Ludwig Cancer Research, John T Reid Trusts, Cure Brain Cancer Foundation, La Trobe University and the Victoria Cancer Agency. Funding from the Operational Infrastructure Support Program provided by the Victorian Government, Australia is also acknowledged.

1. Seeding of Adherent Cell Lines for Imaging Experiment
<ol>
	<li>Seed 10,000 A431 epidermoid carcinoma cells per well overnight (O/N) in 300 &#181;L of Dulbecco&#39;s Modified Eagle Medium/Nutrient Mixture F-12 (DMEM/F-12) media containing 10% Fetal Bovine Serum, 1% Penicillin-Streptomycin and 1% GlutaMAX in an 8 chambered imaging slide suitable for high resolution oil immersion objective lenses (e.g., NuncTM Lab-TekTM chambered coverglass).</li>
	<li>Stimulate receptor aggregation on A431 cells with addition of 100 ng/mL EGF ligand in media for 10 min at room temperature (RT).</li>
</ol>
2. Primary Antibody Incubation
<ol>
	<li>Remove media and wash cells with 300 &#181;L of room temperature (RT) phosphate buffered saline (1x PBS) twice.</li>
	<li>Fix the cells with 200 &#181;L of 4% paraformaldehyde in PBS for 10 min at RT. 
	NOTE: To explore the temporal changes of target aggregation, this fixation step must be omitted.</li>
	<li>Remove PFA and wash cells with 300 &#181;L of room temperature PBS twice.</li>
	<li>Incubate cells with 200 &#181;L of PBS containing primary antibody at a concentration of 10 &#181;g/mL for at least 2 h at 4 &#176;C. 
	NOTE: For ICS calculations to be valid, all receptors present at the cell surface must be labeled with primary antibody. This requires preliminary experiments with a dilution series of each primary antibody (1:10, 1:50, 1:100, 1:200, 1:500 1:1,000, etc.) to be performed. Optimum primary antibody concentration is determined through analysis of fluorescence intensity versus cluster density. The selected primary concentration occurs when the cluster density plateaus with increasing primary antibody concentrations prior to a further increase in cluster density as a result of non-specific antibody binding. The primary antibody can be diluted in serum free media if performing a time-course imaging experiment.</li>
	<li>Following completion of desired incubation time, wash cells with 300 &#181;L of RT PBS twice.</li>
	<li>Block the sample with 5% bovine serum albumin (BSA) in PBS for at least 2 h at 4 &#176;C. 
	NOTE: Fixed samples are routinely blocked overnight (O/N) at 4 &#176;C.</li>
</ol>
3. Secondary Antibody Incubation
<ol>
	<li>Prepare a 10 &#181;g/mL stock of fluorescently labeled secondary antibody (e.g. Alexa Fluor 488 IgG) in PBS. 2 &#181;g/mL of Hoechst 33342 may be included in this dilution to allow for improved sample discovery on the microscope. 
	NOTE: For time-lapse experiments, PBS can be exchanged for serum free media.</li>
	<li>Remove 5% BSA/PBS from the cells and incubate with the secondary antibody solution for 2 h at 4 &#176;C, while protecting the samples from ambient light.</li>
	<li>Upon completion of secondary antibody incubation time, wash the cells with 300 &#181;L of PBS (twice) and store at 4 &#176;C, protecting from ambient light.</li>
	<li>To prevent evaporation, wrap the edges of the chamber slide with parafilm.</li>
</ol>
4. Confocal Microscopy of Samples
NOTE: The settings used for confocal analysis will differ depending on the equipment and fluorophores used in the individual experiment. Modern confocal instruments provide mechanisms for saving all imaging parameters used in the experiment in the native image file format of the instrument. It is important that these settings are recorded to provide for appropriate replication of experimental conditions across different periods of data acquisition.
<ol>
	<li>During imaging of fluorescent signals, avoid over-saturation. Use an image look up table that provides a false color representation of pixel saturation and reduce detector gain and laser power accordingly. Most confocal instruments include a &#34;range indicator&#34; setting or analogous option in the acquisition software for this.</li>
	<li>Minimize the exposure of fluorophore of interest to laser power. The use of a DNA-labeling stain such as Hoechst 33342 provides an easy mechanism to detect and position the sample in the field of view before collection.</li>
	<li>Perform the initial scans of the sample at a low resolution and if software permits a fast scan speed. This will limit the possible photobleaching of the sample.</li>
	<li>Increase the capture resolution and slow the scan speed when ready to collect image data.</li>
	<li>If the confocal instrument allows, collect data using a photon counting mode. 
	NOTE: If photon counting is not available on the instrument: ensure grey levels are linear during image acquisition.</li>
	<li>Set the pinhole for each laser line used to 1 Airy unit (AU).</li>
	<li>Use line or frame averaging during image acquisition (x4) to reduce detector associated noise. 
	NOTE: In this variant of ICS as only one time-point is captured acquisition time is not experimental limiting factor.</li>
	<li>For image collection select a high magnification, high numerical aperture objective, such as a Plan-Apochromat objective, to provide maximum resolution and correction for spherical and chromatic aberrations (Examples include Plan-Apochromat 100X or 63X /1.40 Oil objectives).</li>
	<li>Oversample the size of the captured pixels contained within the image. It is recommended each pixel capture a 50 x 50 nm region. For effective ICS, pixel size must be less than 0.1 x 0.1 &#181;m2 per pixel. This is achieved by a combination of zooming in to scan a relatively small area and selecting a relatively large image format (e.g. 1024 x 1024 or 2048 x 2048 pixels).</li>
	<li>Focus on the apical or basal surface of the cell at a region as flat as possible. Multiple cells can be collected in the one image and regions of interest processed individually during ICS analysis.</li>
	<li>Capture a cell free background region using the same instrument settings to be used for sample normalization.</li>
</ol>
5. Calculation of Image Correlation Spectroscopy
NOTE: The open-source imaging-processing platform, Fiji, a distribution of the ImageJ program, is required to perform the autocorrelation and mathematical functions essential to ICS. This distribution of ImageJ is recommended as it includes numerous pre-installed plug-ins and an easier update architecture that can perform operations across multiple imaging experiments.
<ol>
	<li>Install the Fiji program (http://fiji.sc/Downloads).</li>
	<li>Load the acquired datasets.</li>
	<li>Identify the apical or basal cell surface membrane region of interest and duplicate to pixel size of 2n (256 x 256, 128 x 128, 64 x 64). &#34;Menu: Image &#62; Duplicate&#8230;&#34;</li>
	<li>Calculate the average intensity of the cropped area of interest &#34;Menu: Analyse &#62; Measurements.&#34;</li>
	<li>Identify a background region and duplicate to the same pixel size of the capture cell surface membrane (2n: 256x256, 128x128, 64x64). &#34;Menu: Image &#62; Duplicate&#8230;&#34;</li>
	<li>Calculate the average intensity of the background cropped area of interest. &#34;Menu: Analyse &#62; Measurements.&#34;</li>
	<li>Subtract the average intensity of the background from the image containing the region of interest. &#34;Process &#62; Image Calculator &#62; Subtract.&#34;</li>
	<li>Perform Autocorrelation calculation. This calculation is contained within the Menu Structure: &#34;Process &#62; FTT &#62; FD Math.&#34;
	<ol>
		<li>In the FD Math dialog box select: Image 1: Cropped Image name, Operation as Correlation, Image 2: Cropped Image name, Inverse transformation ON.</li>
	</ol>
	</li>
	<li>Normalize the resulting image by dividing by the total number of pixels. &#34;Menu: Process &#62; Math &#62; Divide&#8230;&#34; (i.e.: 64x64 pixels = 4096)</li>
	<li>Normalize again by dividing by the average intensity of the normalized cropped area squared. &#34;Menu: Process &#62; Math &#62; Divide&#8230;&#34; 
	NOTE: This can be achieved by dividing the image by the average intensity value twice.&#160;</li>
	<li>Draw a line through the point spread function.</li>
	<li>Plot the profile of this line to calculate the peak value. &#34;Menu: Analyse &#62; Plot Profile.&#34;</li>
	<li>Calculate the clusters per beam area by transferring the peak value (result of 5.12) to a spreadsheet and perform the following calculation (Eq 6): 
	<img alt="Equation 7" src="/files/ftp_upload/57164/57164eq7v2.jpg" /></li>
</ol>
6. Batch Processing of ICS datasets
NOTE: The establishment of a FIJI/ImageJ macro is recommended to replicate the procedures described in the above protocol. This allows for the consistent and rapid analysis of multiple image files during image correlation spectroscopy analysis.
<ol>
	<li>Establish a macro ICS workflow by recording each menu command in the ICS protocol 
	&#34;Menu: Plugins &#62; Macros &#62; Record&#8230;&#34;.</li>
	<li>Select &#34;Create&#34; to generate Macro.</li>
	<li>Select &#34;Language &#62; IJ1 Macro.&#34;</li>
	<li>Save Macro.</li>
	<li>Add dialog boxes to various steps of the macro, providing reminders to operators of each function. Dialog boxes also serve as a method to pause the analysis process to allow for appropriate selections to be made prior to further processing (i.e., region to crop)
	<ol>
		<li>Dialog Box Macro Code 
		title = &#34;Dialog Box Title&#34;; 
		msg = &#34;Dialog Box/Protocol Step Message&#34;; 
		waitForUser(title, msg); 
		NOTE: Additional efficiencies can be established by subscribing to the BAR update site within the FIJI Updater (Instructions https://imagej.net/BAR). This provides FIJI with &#34;a collection of Broadly Applicable Routines&#34; 14. One such routine is Find Peaks. Menu structure: &#34;BAR &#62; Data Analysis &#62; Find Peaks.&#34; This script provides a quick means of calculating the peak value in the profile of the point spread function.</li>
	</ol>
	</li>
</ol>
7. Protocol Extensions: Calculation of Beam Area of Microscope
NOTE: The optical transfer function of the microscope ensures that even molecular-sized objects appear as images with a radius of about 200-300 nm in the x-y plane. The ICS protocol allows determination of the point-spread function by performing steps 4-5 on sub-resolution fluorescent beads. Specifically:
<ol>
	<li>Mount sub-resolution (i.e. &#60;100 nm diameter) fluorescent beads onto an 8 chamber imaging slide.</li>
	<li>Image beads as per described protocol using confocal microscope utilized for ICS experiments.</li>
	<li>Calculate the ICS function from the resulting image as per protocol steps 5.1-5.13.</li>
	<li>From the plotted profile, calculate the beam radius (r) as the full width at half maximum value of the ICS function.</li>
	<li>Calculate the beam area (BA) as (Eq 7): <img alt="Equation 8" src="/files/ftp_upload/57164/57164eq8v2.jpg" /></li>
</ol>
8. Protocol Extensions: Calculation of Clusters Per Unit of Area
<ol>
	<li>Compute the cluster density (CD) per unit of area as (Eq 8): 
	<img alt="Equation 9" src="/files/ftp_upload/57164/57164eq9v2.jpg" /> 
	<img alt="Equation 10" src="/files/ftp_upload/57164/57164eq10v2.jpg" /></li>
	<li>Divide the clusters per beam area (result of 5.13) by the area of the beam (result of 7.5).</li>
</ol>

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Andrew M Scott has research funding support from AbbVie Pharmaceutics, EMD Serono and Daiichi-Sankyo Co, and has a consultancy and stock ownership of Life Science Pharmaceuticals. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

The technique of image correlation spectroscopy (ICS) that we describe in this protocol uses standard confocal microscopes without the need for specialized detectors. The ICS technique described utilizes well-established immunocytochemistry methods to provide for rapid sampling of multiple treatment conditions for increased statistical analysis. This methodology does so with a slight reduction in absolute precision as compared to alternative single molecule techniques based on correlation of fluorescence fluctuations of mobile molecules diffusing through a confocal volume, such as fluorescence correlation spectroscopy (FCS)15. The more specialized FCS approach also requires high sensitivity photon counting detectors such as avalanche photodiode (APD) or GaAsP detectors together with dedicated specialized software that is not routinely available on standard confocal instruments. A comprehensive analysis of the precision of ICS determined this technique can accurately measure the number of clusters where &#62;1 particle is present within the beam focal spot of the laser scanning microscope7.
The collection of appropriate fluorescent images is of critical importance for a successful ICS analysis. The region of interest must contain a high signal to background fluorescent ratio with laser and gain intensities adjusted to remove any signal saturation. The cellular region captured must be oversampled with a pixel size of approximately 50 nm. This region must be homogeneous, with careful attention payed to avoid cellular edges or junctions which can increase artifacts in the autocorrelation calculation. One of the major pitfalls of an ICS approach is the sensitivity to heterogeneous cellular surfaces. Thus, limiting the region captured to the maximum flat surface, regardless of cell shape will improve the accuracy of any ICS analysis.
Multiple imaging modalities have been explored to study the aggregation of receptor complexes. Electron microscopy (EM) provides high-resolution spatial assessment of protein clustering but operates under extremely high vacuum and is not applicable to studies requiring temporal resolution. EM is further limited as its harsh sample preparation is prohibitive for living tissue investigations5. To overcome the inherent resolution limitations of standard confocal microscopy, F&#246;rster resonance energy transfer (FRET) can be used to calculate the spatial organization of structures labeled with overlapping donor and acceptor fluorophores pairs16. Despite its sensitivity, FRET does not however provide information regarding the size of the aggregates under investigation6. The development of various super resolution fluorescence microscopy methods provide exciting opportunities to resolve cellular objects not possible with standard resolution confocal methods17,18. The expense and expertise required of such technologies and instruments, limits their current commonplace availability.
With increases in sensitivity and low photo-toxicity in confocal instrumentation, image correlation spectroscopy can be expanded to explore temporal changes of receptor aggregation in living cells in time-lapse experiments. This can be juxtaposed with increasing the laser power used to deliberately induce photo bleaching of individual samples with subsequent ICS assessment (pbICS) to tease apart the higher-order aggregation present in some receptor complexes9. A further adaption, image cross-correlation spectroscopy (ICCS) provides an analysis of the co-localization of two membrane based proteins through the analysis of image pairs for each fluorescently labeled protein19. The strength of this image correlation spectroscopy methodology is that it leverages techniques well established in the field and provides a freely available analysis pipeline to quantitative the highly dynamic events occurring at the cell membrane.

The development of therapeutic antibodies has shown remarkable success in the treatment of multiple tumor types1. The recent advancements of antibody-drug conjugates (ADC) as delivery mechanisms for cytotoxic compounds has expanded the requirements for understanding the dynamics of antibody:receptor interactions at the cell surface2. Following the successful targeting of an antibody to the cell surface receptor, these complexes can induce similar aggregation patterns to those observed in ligand:antibody interactions3. Alterations in receptor aggregation can induce changes to the membrane and result in the internalization of the receptor and its removal from the cell surface. In the context of an antibody-drug conjugate, this process subsequently releases the cytotoxic payload into internalized endosomes and subsequently the cytoplasm, resulting in effective cell killing.
Confocal microscopy has provided an effective means of visualizing these important interactions of antibodies and their target receptors4. To explore the changes of aggregation of target molecule at the cell surface this protocol utilizes post processing of confocal microscopy images via a spatial image correlation spectroscopy (ICS) technique 5,6,7.
The foundation of image correlation spectroscopy is the observation that spatial fluorescence intensity fluctuations share a relationship to the density and aggregation state of the labeled structures. This relationship is established following the calculation of a spatial autocorrelation function of a captured image5.
All variants of image correlation spectroscopy require the calculation of an image autocorrelation. This is followed by fitting this function to a two-dimensional Gaussian curve for the extraction of quantitative aggregation state parameters contained within the image. In simple terms the calculation of an image autocorrelation involves comparing all the possible pixel pairs contained within an image and calculating the likelihood that both equally as bright as each other. This is visualized as a function of the distance and directions of pixel separation8.
The theoretical framework for the image correlation spectroscopy was established and defined by Petersen and Wiseman et al.5,6. In this protocol, the autocorrelation calculations are performed in Fiji/ImageJ as well as a spreadsheet application, the basis for the intensity fluctuation spatial autocorrelation function can be described as (Eq 1):
<img alt="Equation 1" src="/files/ftp_upload/57164/57164eq1v2.jpg" />&#160;&#160;&#160;&#160;&#160;
where F represents the Fourier transform; F&#8722;1 the inverse Fourier transform; F* its complex conjugate; and the spatial lag variables &#949; and &#951;. In spatial ICS, as described in this protocol, the autocorrelation function can be calculated using a 2D fast Fourier transform algorithm7,9. The autocorrelation at zero spatial separation otherwise known as zero-lag, g11(0,0), provides the inverse mean number of particles present per beam area of the microscope. It can be obtained by fitting the spatial autocorrelation function to a two-dimensional Gaussian function (Eq 2):
<img alt="Equation 3" src="/files/ftp_upload/57164/57164eq3v2.jpg" />&#160;&#160;&#160;&#160;&#160;
As the pixels captured within an image are contained within a set area and these measurements do not extend to infinity, the term g&#8734; is used as an offset to account for long-range spatial correlations contained within the image. For molecular-sized aggregates, &#969; is the point-spread function of the microscope and described by the full-width at half-maximum of the spatial autocorrelation function. The area contained within the point spread function of the instrument can be calibrated through the use of sub-resolution fluorescent beads.
For the image correlation spectroscopy protocol described herein, the autocorrelation and mathematical functions required to complete ICS are performed using the open-source imaging-processing platform, Fiji10, a distribution of the ImageJ program11,12. Fiji/ImageJ utilizes the preinstalled fast Fourier transformation in the FFT Math function. This function reduces the compute time required of this calculation by reducing the range of data by a factor of two in each dimension13. As the 2D autocorrelation function is approximately symmetrical in x,y axis, a single line profile plot through the autocorrelation image can be used to measure the raw autocorrelation as a function of the spatial lag. Any zero-lag noise is removed prior to further calculation, with the resulting autocorrelation amplitude (peak value, g(0)T) corrected for background with the expression (Eq 3):
<img alt="Equation 4" src="/files/ftp_upload/57164/57164eq4v2.jpg" />&#160;&#160;&#160;&#160;&#160;
where Ib is the mean intensity from a background region excluding the cell. The cluster density, or density of fluorescent objects, is defined by (Eq 4):
<img alt="Equation 5" src="/files/ftp_upload/57164/57164eq5v2.jpg" />&#160;&#160;&#160;&#160;
In the protocol described herein, we further simply the calculation of cluster density (CD), with the assumption based on the observation that a normalized autocorrelation function will decay to a value approaching 1.0 with increasing spatial lag. With maximum spatial lag, there is no longer any correlation of fluorescence intensity values and thus without a correlation the calculations at this region are computing the value of an intensity multiplied by this intensity which is subsequently divided by the square of that intensity, which by definition is equal to 1.0. Thus, cluster density from a normalized autocorrelation function can be computed by subtracting 1.0 from the normalized autocorrelation function prior to taking its reciprocal (Eq 5):
<img alt="Equation 6" src="/files/ftp_upload/57164/57164eq6v2.jpg" />&#160;&#160;&#160;&#160;&#160;
Further calibration of the beam area can be performed to quantitate the number of clusters contained within the area of the point spread function of the instrument. This calibration must be performed using the same optical conditions used during the image correlation spectroscopy analysis.

<table><tbody><tr><td>Nunc Lab-Tek II Chambered Coverglass - 8 well</td><td>ThermoFisher Scientific</td><td>155409</td><td></td></tr><tr><td>DPBS, no calcium, no magnesium</td><td>ThermoFisher Scientific</td><td>14190144</td><td></td></tr><tr><td>Fetal Bovine Serum</td><td>ThermoFisher Scientific</td><td>10099141</td><td></td></tr><tr><td>TrypLE Express Enzyme (1x), no phenol red</td><td>ThermoFisher Scientific</td><td>12604021</td><td></td></tr><tr><td>Penicillin-Streptomycin (10,000 U/mL)</td><td>ThermoFisher Scientific</td><td>15140122</td><td></td></tr><tr><td>GlutaMAX Supplement</td><td>ThermoFisher Scientific</td><td>35050061</td><td></td></tr><tr><td>Goat anti-Human IgG (H+L) Cross-Adsorbed Secondary Antibody, Alexa Fluor 488</td><td>ThermoFisher Scientific</td><td>A11013</td><td></td></tr><tr><td>TetraSpeck Fluorescent Microsphere Standards 0.1&amp;micro;m</td><td>ThermoFisher Scientific</td><td>T7279</td><td></td></tr><tr><td>Cetuximab</td><td>Merck Serono</td><td>3023715501</td><td></td></tr><tr><td>Parafilm M 38mx100mm</td><td>Merck Millipore</td><td>BRND701605</td><td></td></tr><tr><td>16% Paraformaldehyde (formaldehyde) aqueous solution</td><td>ProSciTech</td><td>C004</td><td></td></tr><tr><td>Recombinant Human EGF</td><td>R&amp;amp;D System</td><td>236-EG</td><td></td></tr></tbody></table>

confocal microscopy reveals cell surface receptor aggregation through image correlation spectroscopy

Confocal microscopy provides an accessible methodology to capture sub-cellular interactions critical for the characterization and further development of pre-clinical agents labeled with fluorescent probes. With recent advancements in antibody based cytotoxic drug delivery systems, understanding the alterations induced by these agents within the realm of receptor aggregation and internalization is of critical importance. This protocol leverages the well-established methodology of fluorescent immunocytochemistry and the open source FIJI distribution of ImageJ, with its inbuilt autocorrelation and image mathematical functions, to perform spatial image correlation spectroscopy (ICS). This protocol quantitates the fluorescent intensity of labeled receptors as a function of the beam area of the confocal microscope. This provides a quantitative measure of the state of target molecule aggregation on the cell surface. This methodology is focused on the characterization of static cells with potential to expand into temporal investigations of receptor aggregation. This protocol presents an accessible methodology to provide quantification of clustering events occurring at the cell surface, utilizing well established techniques and non-specialized imaging apparatus.

A successful image correlation spectroscopy experiment is dependent on the proper application of treatment controls. These treatment groups include the examination of the fluorescence in no primary antibody and no secondary antibody treatment groups. Once the optimum confocal laser settings are established for an experiment, images must be captured of these control groups to confirm the lack of non-specific fluorescence within the sample. For many adherent cancer cell lines, the identification of regions suitable for ICS is limited by the lack of flat surfaces. This is compensated by the ability to capture a large field of view of multiple cancer cells in a single confocal image. This provides the ability to generate the required sampling to perform statistical comparisons between treatment groups.
In this representative experiment, A431 EGFR positive epidermoid carcinoma cells, were stimulated with 100ng/mL of EGF ligand for 10 min at room temperature to induce EGFR clustering. ICS analysis was performed on both EGF stimulated (Figure 1A) and unstimulated (Figure 1D) cells following incubation with the EGFR targeting humanized monoclonal antibody, cetuximab. Subsequently, a 64x64 pixel crop of a region (yellow box) contained within the cell membrane (Figure 1B and 1E) was processed utilizing the autocorrelation function was contained in the open-source image-processing platform, FIJI. This resulting autocorrelation image was normalized by subtracting the background fluorescence prior to dividing by both the square of average intensity of the normalized region of interest as well as the square of the number of pixels in the cropped region (Figure 1C and 1F). The line function tool was used to plot the profile of the point-spread function of this image (Figure 1G and 1H). The peak value of these profiles were transposed to a spreadsheet application for further data processing. A FIJI macro was generated to replicate these FIJI processing steps for rapid processing of a series of cells (n=6) from both treatment groups following cetuximab immunocytochemistry (Figure 1I). In this experiment, the EGFR receptor was identified in 5.84 &#177; 0.85 clusters per beam area on the surface of the A431 stimulated cells versus 14.94 &#177; 1.62 clusters per beam area in the unstimulated cells populations. With the assumption that the experimental conditions used for this analysis limit rapid receptor turnover, the EGFR cluster density in stimulated A431 cells decreased 2.56-fold. When conditions are compared for the aggregation state of the same target molecule in conditions when target molecule turn-over is limited, a lower number of clusters per beam area indicates increased target aggregation. Following EGF ligand stimulation EGFR aggregation increases 2.56-fold as expected in this system. The generation of a FIJI macro using the steps described in this protocol allows for the statistical comparison of varied treatments or environmental differences and their effects on receptor aggregation.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57164/57164fig1v2.jpg" /> 
Figure 1: Image correlation spectroscopy to detect fluorescently labeled clusters on the cell surface of adherent cancer cells. Confocal microscopy image of representative EGF stimulated (A) or unstimulated (D) adherent A431 epidermoid carcinoma cells labeled with cetuximab primary antibody targeting cell surface EGFR receptor with Alex Fluor 488 secondary antibody. Stimulated cells were treated with 100 ng/mL EGF for 10 min at room temperature to induce EGFR clustering prior to fixation and immunocytochemistry. Cell membrane containing crop regions (yellow box) with pixel dimensions (64 x 64 [2n]) were used to perform ICS analysis (B and E). Following normalization of the autocorrelation function in FIJI/ImageJ, the line tool (yellow) was used to measure the point spread function (C and F) The profile of this line function is plotted to detect the peak value (G and H). EGF stimulation was observed to induce a 2.56-fold decrease in detected EGFR clusters per beam area 5.84 &#177;0.85 compared to 14.9 5&#177;1.62 to unstimulated cells (n=6) (I). With the assumption, the experimental conditions used in this analysis maintained a constant number of cell membrane receptors, this represents an increase in EGFR aggregation following EGF stimulation. Scale Bars = 10 &#181;m (A and D); 1 &#181;m (B-C and E-F). Box Plot displayed using Tukey Style with whiskers representing maximum and minimum observed values no more than 1.5 &#215; interquartile range (IQR). Clusters per beam area &#177; Standard Error of the Mean (SEM). <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57164/57164fig1v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy

Antibodies that bind to target receptors on the cell surface can confer conformation and clustering alterations. These dynamic changes have implications for characterizing drug development in target cells. This protocol utilizes confocal microscopy and image correlation spectroscopy through ImageJ/FIJI to quantify the extent of receptor clustering on the cell surface.

Confocal microscopy provides an accessible methodology to capture sub-cellular interactions critical for the characterization and further development of ...

confocal-microscopy-reveals-cell-surface-receptor-aggregation-through

Nanyang Technological University

Research

JoVE Journal

Biology

7.0K Views.  Olivia Newton-John Cancer Research Institute. Antilichamen die zich aan target receptoren van het celoppervlak binden kunnen verlenen bevleesdheid en clustering wijzigingen. Deze dynamische veranderingen hebben gevolgen voor het karakteriseren van Geneesmiddelenontwikkeling in de doelcellen. Dit protocol maakt gebruik van de confocal microscopie en afbeelding correlatie spectroscopie via ImageJ/FIJI te kwantificeren van de omvang van de receptor clustering op het celoppervlak.

Video: Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy

Visualisation and Quantification of Intracellular Interactions of Neisseria meningitidis and Human &alpha;-actinin by Confocal Imaging

The Opc protein of Neisseria meningitidis (Nm, meningococcus) is a surface-expressed integral outer membrane protein, which can act as an adhesin and an effective invasin for human epithelial and endothelial cells. We have identified endothelial surface-located integrins as major receptors for Opc, a process which requires Opc to first bind to integrin ligands such as vitronectin and via these to the cell-expressed receptors1. This process leads to bacterial invasion of endothelial cells2. More recently, we observed an interaction of Opc with a 100kDa protein found in whole cell lysates of human cells3. We initially observed this interaction when host cell proteins separated by electrophoresis and blotted on to nitrocellulose were overlaid with Opc-expressing Nm. The interaction was direct and did not involve intermediate molecules. By mass spectrometry, we established the identity of the protein as &alpha;-actinin. As no surface expressed &alpha;-actinin was found on any of the eight cell lines examined, and as Opc interactions with endothelial cells in the presence of serum lead to bacterial entry into the target cells, we examined the possibility of the two proteins interacting intracellularly. For this, cultured human brain microvascular endothelial cells (HBMECs) were infected with Opc-expressing Nm for extended periods and the locations of internalised bacteria and &alpha;-actinin were examined by confocal microscopy. We observed time-dependent increase in colocalisation of Nm with the cytoskeletal protein, which was considerable after an eight hour period of bacterial internalisation. In addition, the use of quantitative imaging software enabled us to obtain a relative measure of the colocalisation of Nm with &alpha;-actinin and other cytoskeletal proteins. Here we present a protocol for visualisation and quantification of the colocalisation of the bacterium with intracellular proteins after bacterial entry into human endothelial cells, although the procedure is also applicable to human epithelial cells.

Visualisation and Quantification of Intracellular Interactions of Neisseria meningitidis and Human &#945;-actinin by Confocal Imaging

Neisseria meningitidis (Nm), a gram negative human-specific respiratory pathogen, can bind to human &alpha;-actinin. Here we present a protocol for visualisation of colocalisation of the bacterium with intracellular &alpha;-actinin after bacterial entry into human brain microvascular endothelial cells (HBMECs).

The Opc protein of Neisseria meningitidis (Nm, meningococcus) is a surface-expressed integral outer membrane protein, which can act as an adhesin and an ...

Immunology and Infection

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

Real-time Live Imaging of T-cell Signaling Complex Formation

Protection against infectious diseases is mediated by the immune system 1,2. T lymphocytes are the master coordinators of the immune system, regulating the activation and responses of multiple immune cells 3,4. T-cell activation is dependent on the recognition of specific antigens displayed by antigen presenting cells (APCs). The T-cell antigen receptor (TCR) is specific to each T-cell clone and determines antigen specificity 5. The binding of the TCR to the antigen induces the phosphorylation of components of the TCR complex. In order to promote T-cell activation, this signal must be transduced from the membrane to the cytoplasm and the nucleus, initiating various crucial responses such as recruitment of signaling proteins to the TCR;APC site (the immune synapse), their molecular activation, cytoskeletal rearrangement, elevation of intracellular calcium concentration, and changes in gene expression 6,7. The correct initiation and termination of activating signals is crucial for appropriate T-cell responses. The activity of signaling proteins is dependent on the formation and termination of protein-protein interactions, post translational modifications such as protein phosphorylation, formation of protein complexes, protein ubiquitylation and the recruitment of proteins to various cellular sites 8. Understanding the inner workings of the T-cell activation process is crucial for both immunological research and clinical applications.
Various assays have been developed in order to investigate protein-protein interactions; however, biochemical assays, such as the widely used co-immunoprecipitation method, do not allow protein location to be discerned, thus precluding the observation of valuable insights into the dynamics of cellular mechanisms. Additionally, these bulk assays usually combine proteins from many different cells that might be at different stages of the investigated cellular process. This can have a detrimental effect on temporal resolution. The use of real-time imaging of live cells allows both the spatial tracking of proteins and the ability to temporally distinguish between signaling events, thus shedding light on the dynamics of the process 9,10. We present a method of real-time imaging of signaling-complex formation during T-cell activation. Primary T-cells or T-cell lines, such as Jurkat, are transfected with plasmids encoding for proteins of interest fused to monomeric fluorescent proteins, preventing non-physiological oligomerization 11. Live T cells are dropped over a coverslip pre-coated with T-cell activating antibody 8,9, which binds to the CD3/TCR complex, inducing T-cell activation while overcoming the need for specific activating antigens. Activated cells are constantly imaged with the use of confocal microscopy. Imaging data are analyzed to yield quantitative results, such as the colocalization coefficient of the signaling proteins.

We describe a live-cell imaging method that provides insight into protein dynamics during the T-cell activation process. We demonstrate the combined usage of the T-cell spreading assay, confocal microscopy and imaging analysis to yield quantitative results to follow signaling complex formation throughout T-cell activation.

Protection against infectious diseases is mediated by the immune system 1,2. T lymphocytes are the master coordinators of the immune system, regulating ...

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., ...

Bioengineering

Studying the Stoichiometry of Epidermal Growth Factor Receptor in Intact Cells using Correlative Microscopy

This protocol describes the labeling of epidermal growth factor receptor (EGFR) on COS7 fibroblast cells, and subsequent correlative fluorescence microscopy and environmental scanning electron microscopy (ESEM) of whole cells in hydrated state. Fluorescent quantum dots (QDs) were coupled to EGFR via a two-step labeling protocol, providing an efficient and specific protein labeling, while avoiding label-induced clustering of the receptor. Fluorescence microscopy provided overview images of the cellular locations of the EGFR. The scanning transmission electron microscopy (STEM) detector was used to detect the QD labels with nanoscale resolution. The resulting correlative images provide data of the cellular EGFR distribution, and the stoichiometry at the single molecular level in the natural context of the hydrated intact cell. ESEM-STEM images revealed the receptor to be present as monomer, as homodimer, and in small clusters. Labeling with two different QDs, i.e.,&#160;one emitting at 655 nm and at 800 revealed similar characteristic results.

This protocol describes the labeling of epidermal growth factor receptor (EGFR) on COS7 fibroblast cells, and subsequent correlative light- and electron microscopy of whole cells in hydrated state. The label contained fluorescent quantum dots. The protocol can be used to study the stoichiometry of EGFR at the single molecule level.

This protocol describes the labeling of epidermal growth factor receptor (EGFR) on COS7 fibroblast cells, and subsequent correlative fluorescence ...

Determination of Lipid Raft Partitioning of Fluorescently-tagged Probes in Living Cells by Fluorescence Correlation Spectroscopy (FCS)

In the past fifteen years the notion that cell membranes are not homogenous and rely on microdomains to exert their functions has become widely accepted. Lipid rafts are membrane microdomains enriched in cholesterol and sphingolipids. They play a role in cellular physiological processes such as signalling, and trafficking1,2 but are also thought to be key players in several diseases including viral or bacterial infections and neurodegenerative diseases3.
Yet their existence is still a matter of controversy4,5. Indeed, lipid raft size has been estimated to be around 20 nm6, far under the resolution limit of conventional microscopy (around 200 nm), thus precluding their direct imaging. Up to now, the main techniques used to assess the partition of proteins of interest inside lipid rafts were Detergent Resistant Membranes (DRMs) isolation and co-patching with antibodies. Though widely used because of their rather easy implementation, these techniques were prone to artefacts and thus criticized7,8. Technical improvements were therefore necessary to overcome these artefacts and to be able to probe lipid rafts partition in living cells.
Here we present a method for the sensitive analysis of lipid rafts partition of fluorescently-tagged proteins or lipids in the plasma membrane of living cells. This method, termed Fluorescence Correlation Spectroscopy (FCS), relies on the disparity in diffusion times of fluorescent probes located inside or outside of lipid rafts. In fact, as evidenced in both artificial membranes and cell cultures, probes would diffuse much faster outside than inside dense lipid rafts9,10. To determine diffusion times, minute fluorescence fluctuations are measured as a function of time in a focal volume (approximately 1 femtoliter), located at the plasma membrane of cells with a confocal microscope (Fig. 1). The auto-correlation curves can then be drawn from these fluctuations and fitted with appropriate mathematical diffusion models11.
FCS can be used to determine the lipid raft partitioning of various probes, as long as they are fluorescently tagged. Fluorescent tagging can be achieved by expression of fluorescent fusion proteins or by binding of fluorescent ligands. Moreover, FCS can be used not only in artificial membranes and cell lines but also in primary cultures, as described recently12. It can also be used to follow the dynamics of lipid raft partitioning after drug addition or membrane lipid composition change12.

Determination of Lipid Raft Partitioning of Fluorescently-tagged Probes in Living Cells by Fluorescence Correlation Spectroscopy FCS

A technique to probe the lipid raft partitioning of fluorescent proteins at the plasma membrane of living cells is described. It takes advantage of the disparity in diffusion times of proteins located inside or outside of lipid rafts. Acquisition can be performed dynamically in control conditions or after drug addition.

In the past fifteen years the notion that cell membranes are not homogenous and rely on microdomains to exert their functions has become widely accepted. ...

An Analytical Tool that Quantifies Cellular Morphology Changes from Three-dimensional Fluorescence Images

The most common software analysis tools available for measuring fluorescence images are for two-dimensional (2D) data that rely on manual settings for inclusion and exclusion of data points, and computer-aided pattern recognition to support the interpretation and findings of the analysis. It has become increasingly important to be able to measure fluorescence images constructed from three-dimensional (3D) datasets in order to be able to capture the complexity of cellular dynamics and understand the basis of cellular plasticity within biological systems. Sophisticated microscopy instruments have permitted the visualization of 3D fluorescence images through the acquisition of multispectral fluorescence images and powerful analytical software that reconstructs the images from confocal stacks that then provide a 3D representation of the collected 2D images. Advanced design-based stereology methods have progressed from the approximation and assumptions of the original model-based stereology1 even in complex tissue sections2. Despite these scientific advances in microscopy, a need remains for an automated analytic method that fully exploits the intrinsic 3D data to allow for the analysis and quantification of the complex changes in cell morphology, protein localization and receptor trafficking. 
Current techniques available to quantify fluorescence images include Meta-Morph (Molecular Devices, Sunnyvale, CA) and Image J (NIH) which provide manual analysis. Imaris (Andor Technology, Belfast, Northern Ireland) software provides the feature MeasurementPro, which allows the manual creation of measurement points that can be placed in a volume image or drawn on a series of 2D slices to create a 3D object. This method is useful for single-click point measurements to measure a line distance between two objects or to create a polygon that encloses a region of interest, but it is difficult to apply to complex cellular network structures. Filament Tracer (Andor) allows automatic detection of the 3D neuronal filament-like however, this module has been developed to measure defined structures such as neurons, which are comprised of dendrites, axons and spines (tree-like structure). This module has been ingeniously utilized to make morphological measurements to non-neuronal cells3, however, the output data provide information of an extended cellular network by using a software that depends on a defined cell shape rather than being an amorphous-shaped cellular model. To overcome the issue of analyzing amorphous-shaped cells and making the software more suitable to a biological application, Imaris developed Imaris Cell. This was a scientific project with the Eidgen&ouml;ssische Technische Hochschule, which has been developed to calculate the relationship between cells and organelles. While the software enables the detection of biological constraints, by forcing one nucleus per cell and using cell membranes to segment cells, it cannot be utilized to analyze fluorescence data that are not continuous because ideally it builds cell surface without void spaces. To our knowledge, at present no user-modifiable automated approach that provides morphometric information from 3D fluorescence images has been developed that achieves cellular spatial information of an undefined shape (Figure 1). 
We have developed an analytical platform using the Imaris core software module and Imaris XT interfaced to MATLAB (Mat Works, Inc.). These tools allow the 3D measurement of cells without a pre-defined shape and with inconsistent fluorescence network components. Furthermore, this method will allow researchers who have extended expertise in biological systems, but not familiarity to computer applications, to perform quantification of morphological changes in cell dynamics.

We developed a software platform that utilizes Imaris Neuroscience, ImarisXT and MATLAB to measure the changes in morphology of an undefined shape taken from three-dimensional confocal fluorescence of single cells. This novel approach can be used to quantify changes in cell shape following receptor activation and therefore represents a possible additional tool for drug discovery.

The most common software analysis tools available for measuring fluorescence images are for two-dimensional (2D) data that rely on manual settings for ...

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.

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 ...

Detection of Protein Aggregation using Fluorescence Correlation Spectroscopy

Protein aggregation is a hallmark of neurodegenerative disorders such as amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and so on. To detect and analyze soluble or diffuse protein oligomers or aggregates, fluorescence correlation spectroscopy (FCS), which can detect the diffusion speed and brightness of a single particle with a single molecule sensitivity, has been used. However, the proper procedure and know-how for protein aggregation detection have not been widely shared. Here, we show a standard procedure of FCS measurement for diffusion properties of aggregation-prone proteins in cell lysate and live cells: ALS-associated 25 kDa carboxyl-terminal fragment of TAR DNA/RNA-binding protein 43 kDa (TDP25) and superoxide dismutase 1 (SOD1). The representative results show that a part of aggregates of green fluorescent protein (GFP)-tagged TDP25 was slightly included in the soluble fraction of murine neuroblastoma Neuro2a cell lysate. Moreover, GFP-tagged SOD1 carrying ALS-associated mutation shows a slower diffusion in live cells. Accordingly, we here introduce the procedure to detect the protein aggregation via its diffusion property using FCS.

We here introduce a procedure to measure protein oligomers and aggregation in cell lysate and live cells using fluorescence correlation spectroscopy.

Protein aggregation is a hallmark of neurodegenerative disorders such as amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), Parkinson's ...

Cell Aggregation Assay to Detect Trans Interactions between Cell Adhesion Molecules

Source: Restrepo, S., et al., <a href="https://app-jove-com.remotexs.ntu.edu.sg/v/61237">Measuring Transcellular Interactions through Protein Aggregation in a Heterologous Cell System.</a> J. Vis. Exp. (2020)
In this video, we demonstrate the technique to measure transcellular interactions between synaptic cell adhesion molecules through the HEK cell aggregation assay. In this assay, an equal number of HEK cells expressing a desired presynaptic cell adhesion molecule and a fluorescent protein are mixed with HEK cells expressing a post-synaptic cell adhesion molecule and another fluorescent protein. The interaction between the cell adhesion molecules forces the cells to aggregate, which can be measured by fluorescence microscopy.

Source: Restrepo, S., et al., Measuring Transcellular Interactions through Protein Aggregation in a Heterologous Cell System. J. Vis. Exp. (2020)
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Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy

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