Name: confocal microscopy reveals cell surface receptor aggregation through image correlation spectroscopy
Uploaded: 2018-08-02T12:30:00
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 additio...

<ol>
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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 ...

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 border="0" cellpadding="0" cellspacing="0" width="1408">
	<tbody>
		<tr height="40">
			<td height="40" style="height:40px;width:561px;">Nunc Lab-Tek II Chambered Coverglass - 8 well</td>
			<td style="width:183px;">ThermoFisher Scientific</td>
			<td style="width:103px;">155409</td>
			<td style="width:561px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">DPBS, no calcium, no magnesium</td>
			<td>ThermoFisher Scientific</td>
			<td>14190144</td>
			<td style="width:561px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Fetal Bovine Serum</td>
			<td>ThermoFisher Scientific</td>
			<td>10099141</td>
			<td style="width:561px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:561px;">TrypLE Express Enzyme (1x), no phenol red</td>
			<td>ThermoFisher Scientific</td>
			<td>12604021</td>
			<td style="width:561px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:561px;">Penicillin-Streptomycin (10,000 U/mL)</td>
			<td>ThermoFisher Scientific</td>
			<td>15140122</td>
			<td style="width:561px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">GlutaMAX Supplement</td>
			<td>ThermoFisher Scientific</td>
			<td>35050061</td>
			<td style="width:561px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Goat anti-Human IgG (H+L) Cross-Adsorbed Secondary Antibody, Alexa Fluor 488</td>
			<td>ThermoFisher Scientific</td>
			<td>A11013</td>
			<td style="width:561px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:561px;">TetraSpeck Fluorescent Microsphere Standards 0.1&#181;m</td>
			<td>ThermoFisher Scientific</td>
			<td>T7279</td>
			<td style="width:561px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Cetuximab</td>
			<td>Merck Serono</td>
			<td>3023715501</td>
			<td style="width:561px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Parafilm M 38mx100mm</td>
			<td>Merck Millipore</td>
			<td>BRND701605</td>
			<td style="width:561px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">16% Paraformaldehyde (formaldehyde) aqueous solution</td>
			<td>ProSciTech</td>
			<td>C004</td>
			<td style="width:561px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Recombinant Human EGF</td>
			<td>R&#38;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 identific...

Watch this Scientific Journal Video about Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy at JoVE.com

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 ...

The overall goal of this image correlation spectroscopy protocol is to provide an accessible methodology for the quantification of clustering events occurring at the cell surface. Antibodies that bind to receptors at the cell surface can confer confirmation and clustering alterations. Analyses of these dynamic processes are important for the characterization of drug targets.The main advantage of this technique is that it provides an accessible methodology for the quantification of clustering events at the cell surface using easily accessible imaging apparatus. To begin, seed 10, 000 A431 epidermoid carcinoma cells into each well of an eight-chambered imaging slide. The next day, add 100 nanograms per milliliter of EGF ligand immediate to the cells in order to stimulate EGF receptor aggregation.Incubate the cells with the ligand at room temperature for 10 minutes before fixing them. Next, follow the immunocytochemistry and confocal imagery protocol in the accompanying text protocol, paying close attention to the settings used during collection, and load the acquired data sets into Fiji. Over saturation must be avoided.The final images must contain pixel sizes of less than 1 micron squared. Additionally, capture a flat surface on the apical or basal surface as well as a cell-free region for later sample normalization. Start by identifying the apical or basal cell surface membrane region of interest in the first image.Select a pixel size of 2 to the n in the range of 256 by 256, 128 by 128, or 64 by 64. Then, go to the image menu, and select duplicate. Next, go to analyze, and down to measurements.Calculate the average intensity of the cropped area of interest. Now, identify a background region of the same size from the original image. Duplicate it as before.And calculate the average intensity of the background cropped area of interest. With both the area of interest and background measured, go to process, and select image calculator. Place the area of interest as Image 1 and the background image as Image 2, and select subtract as the operation.Next, go to the process menu. Go down to FFT and select FD Math. In the FFT math dialogue box, select enter the images as shown here, and set the operation to correlate.Ensure that the inverse transformation is on. Normalize the resulting image by selecting process, scrolling down to math, selecting divide, and entering the total number of pixels into the dialogue box. Then, normalize again by dividing by the average intensity of the normalized cropped area squared.Next, draw a line through the point spread function. Plot the profile of this line to calculate the peak value by going to analyze, and selecting plot profile. Calculate the clusters per beam area by transferring the peak value to a spread sheet.Then set the clusters per beam area equal to one over the peak value minus one. In order to batch process the images, it is recommended that a Fiji macro be established. This allows for consistent and rapid analysis of multiple confocal images for image correlation spectroscopy analysis.To establish a macro for the image correlation spectroscopy work flow, set up the program to record each menu command in the protocol. For this, go to plug ins, down to marcos, and select record. Then, go back to the main window and run through the protocol described in the previous section of this video.When finished, return to the macro window, and select create to generate a macro. Next, in the macro editing window, be sure to select the language as LJ1 macro. If not selected, the program will be unable to run.Finally, save the macro. The optical transfer function of a microscope ensures that even molecular sized objects appear as images between 200 and 300 nanometers in the XY plane. By imaging sub-resolution fresin beads at the same way as cells as described in this protocol, the area of the point spread function of the microscope can be calculated.These images represent EGF stimulated and unsimulated adherent A431 epidermoid carcinoma cells labeled with a cetuximab primary antibody targeting cell surface EGF receptor. The yellow boxes represent cell membrane containing crop regions with pixel dimensions of 64 by 64. These images were used to perform image correlation spectroscopy analysis.Following normalization of the autocorrelation function, the point spread function can be measured using the line tool. Profile of this line function is plotted to detect the peak value. Using these methods, the EGF stimulation was observed to induce a 2.56 fold decrease in detected EGFR clusters per beam area compared to unstimulated cells.Once mastered, the analysis of your confocal images will only take a few minutes per image. While attempting this procedure, it is important to collect multiple replicates of every experimental condition using the settings outlined in this protocol. Following this procedure, image correlation spectroscopy can be expanded to explore the temporal changes of clustering events in living cells using time lapse microscopy.After its development, this technique paved the way for researchers to explore higher order aggregation states using photo bleaching ICS while the co-localization of two membrane-based proteins using image cross correlation spectroscopy methodologies. After watching this video, you should have a good understanding of how to calculate the clustering state of your cell surface molecule of interest.

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