Determination of Plasma Membrane Partitioning for Peripherally-associated Proteins

Here, we present a protocol to perform a quantitative analysis of the level of plasma-membrane association for fluorescently-tagged peripherally-associated protein. The method is based on the computational decomposition of membrane and cytoplasmic component of signal observed in cells labeled with plasma membrane fluorescent marker.

This method can help answer key questions in cell biology, including unraveling cell signaling pathways. So the main advantage of this technique is that it actually doesn't require any sophisticated, microscopical approaches. It's also very fast and therefore usable for realtime observation of protein distribution in cells.

The evaluation and quantification of membrane partitioning of peripherally associated protein is complicated due to light diffraction that causes a mixing of plasma membrane and cytoplasmic fluorescence under the microscope. Begin this procedure with preparation of biological material as described in the text protocol. Stain the prepared material with FM 4-64 dye by applying a staining protocol that is appropriate for the studied material.

For tobacco BY-2 cells, stain 200 microliters of cell suspension with 0.2 microliters of 10 millimolar FM 4-64 solution in dimethyl sulfoxide. To capture one equatorial confocal section per one cell, set a sequential two-channel scanning for the chromophore used as the protein tag and for FM 4-64. Also set a higher optical resolution.

An example setup is a 63 times oil immersion objective with a numerical aperture of 1.3 and an image size of 1, 024 by 1, 024 pixels. Install Fiji ImageJ Distribution and the required macro peripheral. In Fiji, select plugins, then macros, then install in the main menu and specify the path to the downloaded file with the ImageJ macro peripheral.

Install the R Project software and required packages. Run the R Graphical User Interface, select packages, then install packages in the main menu, and specify the nearest repository as well as package ggplot2 for installation. Download the R Package peripheral.

Install them by selecting packages then install packages from local files. Process the confocal images of the cytoplasmic marker. To do so, import the images to Fiji using plugins then bioformats, then bioformats importer from the Fiji menu with default settings.

Run the import options macro for an analysis setup. Activate the macro by selecting the same item after clicking the menu tool peripheral protein menu. Define the appropriate value in the field gaussian blur radius pixel.

This influences picture smoothing and noise reduction. A low value is sufficient and does not cause any image information loss. Now set a value in the field profile line width pixel.

A thicker line causes higher smoothing of the profile curves. A default value of 10 pixels is recommended. Set an image title, parsing by the definition of sample delimiters and exported items.

Click OK.Check the title parsing in the next dialog window and click OK or back to reset. Next, perform a linear selection across the plasma membrane region in the processed images and measure the fluorescence profiles. To do so, select the Fiji tool's straight line from the Fiji toolbar.

Click the image and drag a line across the plasma membrane region. The line must start in the extracellular space and should be perpendicular to the plasma membrane. Choose regions with a thicker and more homogenous layer of cortical cytoplasm.

The optimal length of the linear selection is five to 10 microns. Run the take profile macro by clicking the take profile tool in the Fiji toolbar. The automatic measurement of fluorescence profiles in both channels will be performed, and the data will be displayed in the results window.

According to individual signal variability, take representative numbers of the profiles for each cell. Run the plot profile data macro for graphical visualization of the measured profiles. Set the parameter in the invoked macro dialog window.

Keep the check box use filtering for input data unselected. Specify if the plot should be created from recent data in the result window from a single CSV file or from all CSV files in a specified directory by clicking the appropriate radio button. Specify which grouping factors will be used for the plotting by selecting the appropriate check boxes.

Select the factor I if all profiles should be displayed in unique plots. Keep the check boxes unselected if all data should be plotted in a single plot. Click OK.Specify the path and file name for saving the results or for eventual data import in the next dialog window.

Confirm analysis by clicking OK in the dialog windows, showing the performed R code. Run the profile filtering setup macro if some plotted profiles have excessive signals in the extracellular or the intracellular space. Follow by setting the parameters in the invoked macro dialog window.

For each channel, set the appropriate intensity threshold in the file, remove measurements with an excessive extracellular or intracellular signal. Specify the region where the intensity threshold will be applied in the field at X coordinates lower or greater than and then click OK.Run the plot profile data macro again with the check box use filtering for data input selected. Now add the data from the previously saved file.

Ensure that all aberrant profiles were successfully removed, otherwise, improve the filtering parameters. Run the create model macro to create a model of the plasma membrane and the cytoplasm fluorescence distribution based on the calibration data. After setting the parameters in the invoked macro dialog window as detailed in the text protocol, click OK.Specify the input file path and name in the next dialog windows and confirm the analysis by clicking OK in the dialog window showing the performed R code.

Next, process the images of the cells expressing the protein of interest. Import the images and set up the analysis as before. The smoothing and line width should be identical.

Proceed to measure the profiles. Check the accuracy of the profiles by plotting, and set the filtering if desired as described earlier. Run the calculate distribution macro for the calculation of a protein partitioning between the plasma membrane and in the cytoplasm.

Specify input data and model sources and eventually set a data filtering. Keep the check box activated for remove results with residual variability greater than if a result filtering is desired. Specify the threshold of the maximum allowed residual variability that can be unexplained by the signal decomposition of the individual profile measurement.

Select sample grouping factors, which may be used for creating a box plot. Click OK, specify the input or output paths, and confirm the analysis by clicking OK in the dialog window showing the performed R code. Shown here is an image of an FM 4-64 stained cell expressing a plant protein, which is associated with the plasma membrane via N-myristoylation and electrostatic interaction to phosphoinositides.

These interactions were experimentally disturbed by wortmannin. The importance of N-myristoylation was tested by mutation in glycine on the second amino acid position which serves as N-myristoylation site. The effect of a removing of both plasma membrane binding abilities was tested by truncation of the N-terminal domain of the protein.

The resulting box plot shows a decrease of a plasma membrane affinity after mutation of N-myristoylation site and after drug treatment. Their mutual effect is comparable to the effect of removing the N-terminal domain. After watching this video, you should have a good understanding of how to carry out the main steps of the analysis in a proper order and how to set their parameters.

Following this analysis, statistical method like ANOVA can be performed for statistical evaluation of obtained data. Though this method was developed for plant suspension cells, it can also be applied to other cells or tissues with smooth and clearly distinguishable plasma membranes.