Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy

Summary

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.

Abstract

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.

Introduction

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):

     

where F represents the Fourier transform; F⁻¹ the inverse Fourier transform; F* its complex conjugate; and the spatial lag variables ε and η. In spatial ICS, as described in this protocol, the autocorrelation function can be calculated using a 2D fast Fourier transform algorithm^7,9. 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):

     

As the pixels captured within an image are contained within a set area and these measurements do not extend to infinity, the term g∞ is used as an offset to account for long-range spatial correlations contained within the image. For molecular-sized aggregates, ω 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 program^11,12. 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):

     

where I_b is the mean intensity from a background region excluding the cell. The cluster density, or density of fluorescent objects, is defined by (Eq 4):

    

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):

     

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.

Protocol

1. Seeding of Adherent Cell Lines for Imaging Experiment

1. Seed 10,000 A431 epidermoid carcinoma cells per well overnight (O/N) in 300 µL of Dulbecco'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).
2. Stimulate receptor aggregation on A431 cells with addition of 100 ng/mL EGF ligand in media for 10 min at room temperature (RT).

2. Primary Antibody Incubation

1. Remove media and wash cells with 300 µL of room temperature (RT) phosphate buffered saline (1x PBS) twice.
2. Fix the cells with 200 µL 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.
3. Remove PFA and wash cells with 300 µL of room temperature PBS twice.
4. Incubate cells with 200 µL of PBS containing primary antibody at a concentration of 10 µg/mL for at least 2 h at 4 °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.
5. Following completion of desired incubation time, wash cells with 300 µL of RT PBS twice.
6. Block the sample with 5% bovine serum albumin (BSA) in PBS for at least 2 h at 4 °C.
   NOTE: Fixed samples are routinely blocked overnight (O/N) at 4 °C.

3. Secondary Antibody Incubation

1. Prepare a 10 µg/mL stock of fluorescently labeled secondary antibody (e.g. Alexa Fluor 488 IgG) in PBS. 2 µg/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.
2. Remove 5% BSA/PBS from the cells and incubate with the secondary antibody solution for 2 h at 4 °C, while protecting the samples from ambient light.
3. Upon completion of secondary antibody incubation time, wash the cells with 300 µL of PBS (twice) and store at 4 °C, protecting from ambient light.
4. To prevent evaporation, wrap the edges of the chamber slide with parafilm.

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.

1. 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 "range indicator" setting or analogous option in the acquisition software for this.
2. 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.
3. 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.
4. Increase the capture resolution and slow the scan speed when ready to collect image data.
5. 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.
6. Set the pinhole for each laser line used to 1 Airy unit (AU).
7. 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.
8. 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).
9. 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 µm² 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).
10. 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.
11. Capture a cell free background region using the same instrument settings to be used for sample normalization.

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.

1. Install the Fiji program (http://fiji.sc/Downloads).
2. Load the acquired datasets.
3. 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). "Menu: Image > Duplicate…"
4. Calculate the average intensity of the cropped area of interest "Menu: Analyse > Measurements."
5. Identify a background region and duplicate to the same pixel size of the capture cell surface membrane (2ⁿ: 256x256, 128x128, 64x64). "Menu: Image > Duplicate…"
6. Calculate the average intensity of the background cropped area of interest. "Menu: Analyse > Measurements."
7. Subtract the average intensity of the background from the image containing the region of interest. "Process > Image Calculator > Subtract."
8. Perform Autocorrelation calculation. This calculation is contained within the Menu Structure: "Process > FTT > FD Math."
   1. In the FD Math dialog box select: Image 1: Cropped Image name, Operation as Correlation, Image 2: Cropped Image name, Inverse transformation ON.
9. Normalize the resulting image by dividing by the total number of pixels. "Menu: Process > Math > Divide…" (i.e.: 64x64 pixels = 4096)
10. Normalize again by dividing by the average intensity of the normalized cropped area squared. "Menu: Process > Math > Divide…"
    NOTE: This can be achieved by dividing the image by the average intensity value twice. 
11. Draw a line through the point spread function.
12. Plot the profile of this line to calculate the peak value. "Menu: Analyse > Plot Profile."
13. 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):
    

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.

1. Establish a macro ICS workflow by recording each menu command in the ICS protocol
   "Menu: Plugins > Macros > Record…".
2. Select "Create" to generate Macro.
3. Select "Language > IJ1 Macro."
4. Save Macro.
5. 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)
   1. Dialog Box Macro Code
      title = "Dialog Box Title";
      msg = "Dialog Box/Protocol Step Message";
      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 "a collection of Broadly Applicable Routines" ¹⁴. One such routine is Find Peaks. Menu structure: "BAR > Data Analysis > Find Peaks." This script provides a quick means of calculating the peak value in the profile of the point spread function.

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:

1. Mount sub-resolution (i.e. <100 nm diameter) fluorescent beads onto an 8 chamber imaging slide.
2. Image beads as per described protocol using confocal microscope utilized for ICS experiments.
3. Calculate the ICS function from the resulting image as per protocol steps 5.1-5.13.
4. From the plotted profile, calculate the beam radius (r) as the full width at half maximum value of the ICS function.
5. Calculate the beam area (BA) as (Eq 7):

8. Protocol Extensions: Calculation of Clusters Per Unit of Area

1. Compute the cluster density (CD) per unit of area as (Eq 8):
   
   
2. Divide the clusters per beam area (result of 5.13) by the area of the beam (result of 7.5).

Results

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...

Discussion

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 ...

Materials

List of materials used in this article

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