Biology
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Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy
Chapters
Summary August 2nd, 2018
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.
Transcript
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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