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>
	<li>Scott, A. M., Wolchok, J. D., Old, L. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Antibody+therapy+of+cancer.">Antibody therapy of cancer.</a> Nature reviews. Cancer. 12 (4), 278-287 (2012).</li><li>Parslow, A. C., Parakh, S., Lee, F. -. T., Gan, H., Scott, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Antibody-Drug+Conjugates+for+Cancer+Therapy.">Antibody-Drug Conjugates for Cancer Therapy.</a> Biomedicines. 4 (3), 14 (2016).</li><li>Sorkin, A., Waters, C. 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Optimization for Ultrasensitive Detection of Preexisting Platelet-Derived Growth Factor-&#946; Receptor Oligomers on Intact Cells.</a> Biophysical Journal. 76 (2), 963-977 (1999).</li><li>Costantino, S., Comeau, J. W. D., Kolin, D. L., Wiseman, P. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Accuracy+and+Dynamic+Range+of+Spatial+Image+Correlation+and+Cross-Correlation+Spectroscopy.">Accuracy and Dynamic Range of Spatial Image Correlation and Cross-Correlation Spectroscopy.</a> Biophysical Journal. 89 (2), 1251-1260 (2005).</li><li>Claire Robertson, S. C. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Theory+and+practical+recommendations+for+autocorrelation-based+image+correlation+spectroscopy.">Theory and practical recommendations for autocorrelation-based image correlation spectroscopy.</a> Journal of Biomedical Optics. 17 (8), 080801 (2012).</li><li>Ciccotosto, G. D., Kozer, N., Chow, T. T. Y., Chon, J. W. M., Clayton, A. H. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Aggregation+Distributions+on+Cells+Determined+by+Photobleaching+Image+Correlation+Spectroscopy.">Aggregation Distributions on Cells Determined by Photobleaching Image Correlation Spectroscopy.</a> Biophysical Journal. 104 (5), 1056-1064 (2013).</li><li>Schindelin, J., Arganda-Carreras, I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fiji:+an+open-source+platform+for+biological-image+analysis.">Fiji: an open-source platform for biological-image analysis.</a> Nature Methods. 9 (7), 676-682 (2012).</li><li>Abr&#224;moff, M. D., Magalh&#227;es, P. J., Ram, S. 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Zenodo Available from: <a target="_blank" href="https://zenodo.org/record/495245">https://zenodo.org/record/495245</a> (2017)</li><li>Elson, E. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fluorescence+Correlation+Spectroscopy:+Past,+Present,+Future.">Fluorescence Correlation Spectroscopy: Past, Present, Future.</a> Biophysical Journal. 101 (12), 2855-2870 (2011).</li><li>Jares-Erijman, E. A., Jovin, T. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Imaging+molecular+interactions+in+living+cells+by+FRET+microscopy.">Imaging molecular interactions in living cells by FRET microscopy.</a> Current opinion in chemical biology. 10 (5), 409-416 (2006).</li><li>Huang, B., Bates, M., Zhuang, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Super-Resolution+Fluorescence+Microscopy.">Super-Resolution Fluorescence Microscopy.</a> dx.doi.org.ez.library.latrobe.edu.au. 78 (1), 993-1016 (2009).</li><li>Schermelleh, L., Heintzmann, R., Leonhardt, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+guide+to+super-resolution+fluorescence+microscopy.">A guide to super-resolution fluorescence microscopy.</a> The Journal of Cell Biology. 190 (2), 165-175 (2010).</li><li>Nohe, A., Petersen, N. 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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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Research

JoVE Journal

Biology

Fluorescence-based Measurement of Store-operated Calcium Entry in Live Cells: from Cultured Cancer Cell to Skeletal Muscle Fiber

Store operated Ca2+ entry (SOCE), earlier termed capacitative Ca2+ entry, is a tightly regulated mechanism for influx of extracellular Ca2+ into cells to replenish depleted endoplasmic reticulum (ER) or sarcoplasmic reticulum (SR) Ca2+ stores1,2. Since Ca2+ is a ubiquitous second messenger, it is not surprising to see that SOCE plays important roles in a variety of cellular processes, including proliferation, apoptosis, gene transcription and motility. Due to its wide occurrence in nearly all cell types, including epithelial cells and skeletal muscles, this pathway has received great interest3,4. However, the heterogeneity of SOCE characteristics in different cell types and the physiological function are still not clear5-7.
The functional channel properties of SOCE can be revealed by patch-clamp studies, whereas a large body of knowledge about this pathway has been gained by fluorescence-based intracellular Ca2+ measurements because of its convenience and feasibility for high-throughput screening. The objective of this report is to summarize a few fluorescence-based methods to measure the activation of SOCE in monolayer cells, suspended cells and muscle fibers5,8-10. The most commonly used of these fluorescence methods is to directly monitor the dynamics of intracellular Ca2+ using the ratio of F340nm and F380nm (510 nm for emission wavelength) of the ratiometric Ca2+ indicator Fura-2. To isolate the activity of unidirectional SOCE from intracellular Ca2+ release and Ca2+ extrusion, a Mn2+ quenching assay is frequently used. Mn2+ is known to be able to permeate into cells via SOCE while it is impervious to the surface membrane extrusion processes or to ER uptake by Ca2+ pumps due to its very high affinity with Fura-2. As a result, the quenching of Fura-2 fluorescence induced by the entry of extracellular Mn2+ into the cells represents a measurement of activity of SOCE9. Ratiometric measurement and the Mn+2 quenching assays can be performed on a cuvette-based spectrofluorometer in a cell population mode or in a microscope-based system to visualize single cells. The advantage of single cell measurements is that individual cells subjected to gene manipulations can be selected using GFP or RFP reporters, allowing studies in genetically modified or mutated cells. The spatiotemporal characteristics of SOCE in structurally specialized skeletal muscle can be achieved in skinned muscle fibers by simultaneously monitoring the fluorescence of two low affinity Ca2+ indicators targeted to specific compartments of the muscle fiber, such as Fluo-5N in the SR and Rhod-5N in the transverse tubules9,11,12.

The extent of store-operated Ca2+ entry (SOCE) can be monitored using fluorescent Ca2+ indicators. Mn2+ quenching of such indicators assays SOCE in cultured cells and skeletal muscle fibers. A technique allowing spatial and temporal resolution of SOCE by confocal imaging of mechanically skinned muscle fibers is also described.

Store operated Ca2+ entry (SOCE), earlier termed capacitative Ca2+ entry, is a tightly regulated mechanism for influx of extracellular Ca2+ into cells to ...

Avidity-based Extracellular Interaction Screening (AVEXIS) for the Scalable Detection of Low-affinity Extracellular Receptor-Ligand Interactions

Extracellular protein:protein interactions between secreted or membrane-tethered proteins are critical for both initiating intercellular communication and ensuring cohesion within multicellular organisms. Proteins predicted to form extracellular interactions are encoded by approximately a quarter of human genes1, but despite their importance and abundance, the majority of these proteins have no documented binding partner. Primarily, this is due to their biochemical intractability: membrane-embedded proteins are difficult to solubilise in their native conformation and contain structurally-important posttranslational modifications. Also, the interaction affinities between receptor proteins are often characterised by extremely low interaction strengths (half-lives &lt; 1 second) precluding their detection with many commonly-used high throughput methods2. 
Here, we describe an assay, AVEXIS (AVidity-based EXtracellular Interaction Screen) that overcomes these technical challenges enabling the detection of very weak protein interactions (t1/2 &le; 0.1 sec) with a low false positive rate3. The assay is usually implemented in a high throughput format to enable the systematic screening of many thousands of interactions in a convenient microtitre plate format (Fig. 1). It relies on the production of soluble recombinant protein libraries that contain the ectodomain fragments of cell surface receptors or secreted proteins within which to screen for interactions; therefore, this approach is suitable for type I, type II, GPI-linked cell surface receptors and secreted proteins but not for multipass membrane proteins such as ion channels or transporters.
The recombinant protein libraries are produced using a convenient and high-level mammalian expression system4, to ensure that important posttranslational modifications such as glycosylation and disulphide bonds are added. Expressed recombinant proteins are secreted into the medium and produced in two forms: a biotinylated bait which can be captured on a streptavidin-coated solid phase suitable for screening, and a pentamerised enzyme-tagged (&beta;-lactamase) prey. The bait and prey proteins are presented to each other in a binary fashion to detect direct interactions between them, similar to a conventional ELISA (Fig. 1). The pentamerisation of the proteins in the prey is achieved through a peptide sequence from the cartilage oligomeric matrix protein (COMP) and increases the local concentration of the ectodomains thereby providing significant avidity gains to enable even very transient interactions to be detected. By normalising the activities of both the bait and prey to predetermined levels prior to screening, we have shown that interactions having monomeric half-lives of 0.1 sec can be detected with low false positive rates3.

Avidity-based Extracellular Interaction Screening AVEXIS for the Scalable Detection of Low-affinity Extracellular Receptor-Ligand Interactions

AVEXIS is a high throughput protein interaction assay developed to systematically screen for novel extracellular receptor-ligand pairs involved in cellular recognition processes. It is specifically designed to detect transient protein interactions that are difficult to identify using other high throughput approaches.

Extracellular protein:protein interactions between secreted or membrane-tethered proteins are critical for both initiating intercellular communication and ...

Easy Measurement of Diffusion Coefficients of EGFP-tagged Plasma Membrane Proteins Using k-Space Image Correlation Spectroscopy

Lateral diffusion and compartmentalization of plasma membrane proteins are tightly regulated in cells and thus, studying these processes will reveal new insights to plasma membrane protein function and regulation. Recently, k-Space Image Correlation Spectroscopy (kICS)1&#160;was developed to enable routine measurements of diffusion coefficients directly from images of fluorescently tagged plasma membrane proteins, that avoided systematic biases introduced by probe photophysics. Although the theoretical basis for the analysis is complex, the method can be implemented by nonexperts using a freely available code to measure diffusion coefficients of proteins. kICS calculates a time correlation function from a fluorescence microscopy image stack after Fourier transformation of each image to reciprocal (k-) space. Subsequently, circular averaging, natural logarithm transform and linear fits to the correlation function yields the diffusion coefficient. This paper provides a step-by-step guide to the image analysis and measurement of diffusion coefficients via kICS.
First, a high frame rate image sequence of a fluorescently labeled plasma membrane protein is acquired using a fluorescence microscope. Then, a region of interest (ROI) avoiding intracellular organelles, moving vesicles or protruding membrane regions is selected. The ROI stack is imported into a freely available code and several defined parameters (see Method section) are set for kICS analysis. The program then generates a &#34;slope of slopes&#34; plot from the k-space time correlation functions, and the diffusion coefficient is calculated from the slope of the plot. Below is a step-by-step kICS procedure to measure the diffusion coefficient of a membrane protein using the renal water channel aquaporin-3 tagged with EGFP as a canonical example.

This paper provides a step by step guide to the fluctuation analysis technique k-Space Image Correlation Spectroscopy (kICS) for measuring diffusion coefficients of fluorescently labeled plasma membrane proteins in live mammalian cells.

Lateral diffusion and compartmentalization of plasma membrane proteins are tightly regulated in cells and thus, studying these processes will reveal new ...

Analyzing Craniofacial Morphogenesis in Zebrafish Using 4D Confocal Microscopy

Time-lapse imaging is a technique that allows for the direct observation of the process of morphogenesis, or the generation of shape. Due to their optical clarity and amenability to genetic manipulation, the zebrafish embryo has become a popular model organism with which to perform time-lapse analysis of morphogenesis in living embryos. Confocal imaging of a live zebrafish embryo requires that a tissue of interest is persistently labeled with a fluorescent marker, such as a transgene or injected dye. The process demands that the embryo is anesthetized and held in place in such a way that healthy development proceeds normally. Parameters for imaging must be set to account for three-dimensional growth and to balance the demands of resolving individual cells while getting quick snapshots of development. Our results demonstrate the ability to perform long-term in vivo imaging of fluorescence-labeled zebrafish embryos and to detect varied tissue behaviors in the cranial neural crest that cause craniofacial abnormalities. Developmental delays caused by anesthesia and mounting are minimal, and embryos are unharmed by the process. Time-lapse imaged embryos can be returned to liquid medium and subsequently imaged or fixed at later points in development. With an increasing abundance of transgenic zebrafish lines and well-characterized fate mapping and transplantation techniques, imaging any desired tissue is possible. As such, time-lapse in vivo imaging combines powerfully with zebrafish genetic methods, including analyses of mutant and microinjected embryos.

Time-lapse confocal imaging is a powerful technique useful for characterizing embryonic development. Here, we describe the methodology and characterize craniofacial morphogenesis in wild-type, as well as pdgfra, smad5, and smo mutant embryos.

Time-lapse imaging is a technique that allows for the direct observation of the process of morphogenesis, or the generation of shape. Due to their optical ...

Correlative Confocal and 3D Electron Microscopy of a Specific Sensory Cell

Delineation of a cell&#8217;s ultrastructure is important for understanding its function. This can be a daunting project for rare cell types diffused throughout tissues made of diverse cell types, such as enteroendocrine cells of the intestinal epithelium. These gastrointestinal sensors of food and bacteria have been difficult to study because they are dispersed among other epithelial cells at a ratio of 1:1,000. Recently, transgenic reporter mice have been generated to identify enteroendocrine cells by means of fluorescence. One of those is the peptide YY-GFP mouse. Using this mouse, we developed a method to correlate confocal and serial block-face scanning electron microscopy. We named the method cocem3D and applied it to identify a specific enteroendocrine cell in tissue and unveil the cell&#8217;s ultrastructure in 3D. The resolution of cocem3D is sufficient to identify organelles as small as secretory vesicles and to distinguish cell membranes for volume rendering. Cocem3D can be easily adapted to study the 3D ultrastructure of other specific cell types in their native tissue.

Here, we introduce a method, cocem3D, to unveil the ultrastructure of a specific cell in its native tissue by bridging confocal and serial block-face scanning electron microscopy.

Delineation of a cell&#8217;s ultrastructure is important for understanding its function. This can be a daunting project for rare cell types diffused ...

Live Cell Imaging and 3D Analysis of Angiotensin Receptor Type 1a Trafficking in Transfected Human Embryonic Kidney Cells Using Confocal Microscopy

Live-cell imaging is used to simultaneously capture time-lapse images of angiotensin type 1a receptors (AT1aR) and intracellular compartments in transfected human embryonic kidney-293 (HEK) cells following stimulation with angiotensin II (Ang II). HEK cells are transiently transfected with plasmid DNA containing AT1aR tagged with enhanced green fluorescent protein (EGFP). Lysosomes are identified with a red fluorescent dye. Live-cell images are captured on a laser scanning confocal microscope after Ang II stimulation and analyzed by software in three dimensions (3D, voxels) over time. Live-cell imaging enables investigations into receptor trafficking and avoids confounds associated with fixation, and in particular, the loss or artefactual displacement of EGFP-tagged membrane receptors. Thus, as individual cells are tracked through time, the subcellular localization of receptors can be imaged and measured. Images must be acquired sufficiently rapidly to capture rapid vesicle movement. Yet, at faster imaging speeds, the number of photons collected is reduced. Compromises must also be made in the selection of imaging parameters like voxel size in order to gain imaging speed. Significant applications of live-cell imaging are to study protein trafficking, migration, proliferation, cell cycle, apoptosis, autophagy and protein-protein interaction and dynamics, to name but a few.

Here we present a protocol to image cells expressing green fluorescent protein-tagged angiotensin type 1a receptors during endocytosis initiated by angiotensin II treatment. This technique includes labeling lysosomes with a second fluorescent marker, and then utilizing software to analyze the co-localization of receptor and lysosome in three dimensions over time.

Live-cell imaging is used to simultaneously capture time-lapse images of angiotensin type 1a receptors (AT1aR) and intracellular compartments in ...

Detection of Ligand-activated G Protein-coupled Receptor Internalization by Confocal Microscopy

Confocal laser scanning microscopy (CLSM) is an optical imaging technique for high-contrast imaging. It is a powerful approach to visualize fluorescent fusion proteins, such as green fluorescent protein (GFP), to determine their expression, localization, and function. The subcellular localization of target proteins is important for identification, characterization, and functional analyses. Internalization is one of the predominant mechanisms controlling G protein-coupled receptor (GPCR) signaling to ensure the appropriate cellular responses to stimuli. Here, we describe an experimental method to detect the subcellular localization and internalization of GPCR in HEK293 cells with confocal microscopy. In addition, this experiment provides some details about cell culture and transfection. This protocol is compatible with a variety of widely available fluorescent markers and is applicable to the visualization of the subcellular localization of a majority of proteins, as well as of the internalization of GPCR. This technique should enable researchers to efficiently manipulate GPCR gene expression in mammalian cell lines and should facilitate studies on GPCR subcellular localization and internalization.

This protocol describes confocal microscopy detection of G protein-coupled receptor (GPCR) internalization in mammalian cells. It includes the basic cell culture, transfection, and confocal microscopy procedure and provides an efficient and easily interpretable method to detect the subcellular localization and internalization of fusion-expressed GPCR.

Confocal laser scanning microscopy (CLSM) is an optical imaging technique for high-contrast imaging. It is a powerful approach to visualize fluorescent ...

Reconstruction of Single-Cell Innate Fluorescence Signatures by Confocal Microscopy

Described here is confocal reflection microscopy-assisted single-cell innate fluorescence analysis (CRIF), a minimally invasive method for reconstructing the innate cellular fluorescence signature from each individual live cell in a population distributed in a three-dimensional (3D) space. The innate fluorescence signature of a cell is a collection of fluorescence signals emitted by various biomolecules within the cell. Previous studies established that innate fluorescence signatures reflect various cellular properties and differences in physiological status and are a rich source of information for cell characterization and identification. Innate fluorescence signatures have been traditionally analyzed at the population level, necessitating a clonal culture, but not at the single-cell level. CRIF is particularly suitable for studies that require 3D resolution and/or selective extraction of fluorescence signals from individual cells. Because the fluorescence signature is an innate property of a cell, CRIF is also suitable for tag-free prediction of the type and/or physiological status of intact and single cells. This method may be a powerful tool for streamlined cell analysis, where the phenotype of each single cell in a heterogenous population can be directly assessed by its autofluorescence signature under a microscope without cell tagging.

Here, a protocol is presented for optically extracting and cataloging innate cellular fluorescence signatures (i.e., cellular autofluorescence) from every individual live cell distributed in a three-dimensional space. This method is suitable for studying the innate fluorescence signature of diverse biological systems at a single-cell resolution, including cells from bacteria, fungi, yeasts, plants, and animals.

Described here is confocal reflection microscopy-assisted single-cell innate fluorescence analysis (CRIF), a minimally invasive method for reconstructing ...

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 ...

Quantification of Mitochondrial Membrane Potential and Superoxide Levels

Fluorescence-Based Quantification of Mitochondrial Membrane Potential and Superoxide Levels Using Live Imaging in HeLa Cells

Mitochondria are dynamic organelles critical for metabolic homeostasis by controlling energy production via ATP synthesis. To support cellular metabolism, various mitochondrial quality control mechanisms cooperate to maintain a healthy mitochondrial network. One such pathway is mitophagy, where PTEN-induced kinase 1 (PINK1) and Parkin phospho-ubiquitination of damaged mitochondria facilitate autophagosome sequestration and subsequent removal from the cell via lysosome fusion. Mitophagy is important for cellular homeostasis, and mutations in Parkin are linked to Parkinson's disease (PD). Due to these findings, there has been a significant emphasis on investigating mitochondrial damage and turnover to understand the molecular mechanisms and dynamics of mitochondrial quality control. Here, live-cell imaging was used to visualize the mitochondrial network of HeLa cells, to quantify the mitochondrial membrane potential and superoxide levels following treatment with carbonyl cyanide m-chlorophenyl hydrazone (CCCP), a mitochondrial uncoupling agent. In addition, a PD-linked mutation of Parkin (ParkinT240R) that inhibits Parkin-dependent mitophagy was expressed to determine how mutant expression impacts the mitochondrial network compared to cells expressing wild-type Parkin. The protocol outlined here describes a simple workflow using fluorescence-based approaches to quantify mitochondrial membrane potential and superoxide levels effectively.

Author Spotlight: Fluorescence-Based Quantification of Mitochondrial Membrane Potential and Superoxide Levels Using Live Imaging in HeLa Cells  

This technique describes an effective workflow to visualize and quantitatively measure mitochondrial membrane potential and superoxide levels within HeLa cells using fluorescence-based live imaging.

Mitochondria are dynamic organelles critical for metabolic homeostasis by controlling energy production via ATP synthesis. To support cellular metabolism, ...