This project was supported by NPU I, LO1417 (Ministry of Education, Youth and Sports of the Czech Republic).

1. Preparation of Biological Material
<ol>
	<li>Prepare biological material expressing the fluorescently tagged protein of interest, as well as a cytoplasmic marker. Follow the procedures mentioned in the Introduction.</li>
</ol>
2. Confocal Laser Scanning Microscopy
<ol>
	<li>Stain the material prepared in section 1 with FM 4-64 dye7&#8288;.
	<ol>
		<li>Apply a staining protocol that is appropriate for the studied material. For tobacco BY-2 cells, stain 200 &#956;L of cell suspension with 0.2 &#956;L of 10 mM FM 4-64 solution in dimethylsulfoxide. 
		Note: The typical staining concentration of FM 4-64 dye is about 10 &#956;M using 10 mM stock solution in dimethylsulfoxide. Use the lowest possible concentration of FM 4-64 for proper staining and do not incubate cells on ice (FM 4-64 and low temperature can affect plasma membrane properties). Continue with observation using confocal microscopy immediately. The interval between the staining and the image acquisition should not be longer than 10 min; otherwise, endocytosis of the dye will affect the FM 4-64 staining.</li>
		<li>Process multiple samples consecutively, and do not stain all the samples at the beginning of the experiment.</li>
	</ol>
	</li>
	<li>Capture one equatorial confocal section per one cell.
	<ol>
		<li>Set a sequential two-channel scanning for the chromophore used as the protein tag (must be in the first channel) and FM 4-64 (must be in the second channel). Set a higher optical resolution (10&#8211;20 px/&#956;m). An example set-up: 63X oil immersion objective, NA 1.3, image size 1,024 x 1,024 px. Ensure that the plasma membrane plane is perpendicular to the confocal-section plane in the acquired images.</li>
		<li>Capture at least 20 cells per sample to have enough data for the statistic evaluation (depends on the signal variability in processed material).</li>
	</ol>
	</li>
</ol>
3. Required Software Installation
<ol>
	<li>Install Fiji ImageJ distribution8&#8288; and the required macro peripheral.
	<ol>
		<li>Download software from the site https://fiji.sc/#download, and install them by unpacking.</li>
		<li>Start Fiji by double-clicking on the &#8220;ImageJ(.exe)&#8221; file in the unpacked &#8220;Fiji.app&#8221; directory.</li>
		<li>Download a macro &#8220;peripheral&#8221; from the following site: 
		http://kfrserver.natur.cuni.cz/lide/vosolsob/Peripheral/source/Peripheral_.ijm.</li>
		<li>In Fiji, select Plugins | Macros | Install... in the main menu and specify the path to the downloaded file &#8220;Peripheral_.ijm.&#8221; 
		Note: Two new tools of the installed macro &#8220;peripheral&#8221; will appear in the Fiji toolbar: &#8220;Take profile Tool&#8221; and &#8220;Peripheral Protein Menu.&#8221;</li>
	</ol>
	</li>
	<li>Install the R-project software9&#8288; and required packages.
	<ol>
		<li>Follow the instructions on the site https://cloud.r-project.org/ for R-project installation. 
		Note: The overall analysis will be performed in Fiji. The R software will be called only internally by the Fiji macro &#8220;peripheral&#8221; during computation.</li>
		<li>Run R GUI, select Packages | Install package(s)... in the main menu, and specify the nearest repository and package &#8220;ggplot2&#8221; for installation. 
		Note: Alternatively, type to the R command-line: install.packages(&#8220;ggplot2&#8221;).</li>
		<li>Download an R package peripheral from the destination: http://kfrserver.natur.cuni.cz/lide/vosolsob/Peripheral/source/peripheral_latest.tar.gz. Install them by selecting Packages | Install package(s) from local files.... 
		Note: Alternatively, type to the R command-line this command only: 
		install.packages(&#8220;http://kfrserver.natur.cuni.cz/lide/vosolsob/Peripheral/source/peripheral_latest.tar.gz&#8221;, repo=NULL, type=&#8220;source&#8221;).</li>
	</ol>
	</li>
</ol>
4. Image Analysis in Fiji
<ol>
	<li>Process the confocal images of the cytoplasmic marker.
	<ol>
		<li>Import the images to Fiji using Plugins | Bio-Formats | Bio-Formats Importer from the Fiji menu with default settings.</li>
		<li>Check if the Bio-Formats Importer properly recognized the image calibration. The picture dimension shown in the upper left information field image window must be equal to the original picture dimensions.</li>
		<li>Treat improperly calibrated images with the incorrect dimensions individually, e.g., by setting the correct parameters in the dialog window Analyze | Set Scale... from the Fiji menu: fill the image width in pixels into the Distance in pixels field and the real width in the appropriate length unit into the Known distance field). 
		Note: If the Leica LCS LEI files are improperly calibrated, follow step 4.2.1.</li>
	</ol>
	</li>
	<li>Run the Import options macro for an analysis set-up. Activate the macro by one of three different ways: select Plugins | Macros | Import options [f1] in the main Fiji menu, select the same item after clicking the menu tool Peripheral Protein Menu, or press the keyboard short-cut F1. Set the parameter in the invoked macro dialog window.
	<ol>
		<li>In case of the Leica LCS LEI format, solve improper calibration by activating Leica.lei images check-box. This option properly imports the image dimensions from the file with the &#8220;lei&#8221; extension.</li>
		<li>Define the appropriate value in the field Gaussian blur radius (px). This influences picture smoothing and noise reduction. A low value (1 px) is sufficient and does not cause any image information loss.</li>
		<li>Set a value in the field Profile line width (px). A thicker line causes higher smoothing of the profile curves. A default value 10 px is recommended.</li>
		<li>Set an image title&#160;parsing by the definition of &#8220;Sample delimiters&#8221; and &#8220;Exported items.&#8221; 
		Note: An image title (filename without extension, e.g.: &#8220;Experiment-1_line-02_cell-11&#8221;) will be split around all the characters listed in the field &#8220;Sample delimiters&#8221; (e.g.: &#8220;-_&#8221;) into a set of items (&#8220;Experiment&#8221;, &#8220;1&#8221;, &#8220;line&#8221;, &#8220;02&#8221;, &#8220;cell&#8221;, and &#8220;11&#8221;) that can be used as grouping factors (sample, cell, line, or treatment identity) in the following analysis. Define which items will be used by typing their sequence number (&#8220;0&#8221; for the first item) into the filed &#8220;Exported items.&#8221;&#160;Use space-delimited format, e.g., &#8220;3 5.&#8221;&#160;In this example, items &#8220;02&#8221; and &#8220;11&#8221; will be referred to as grouping factors named &#8220;Fac_0&#8221; and &#8220;Fac_1.&#8221;&#160;Additionally, the identity of each measurement will be referred to as &#8220;i&#8221; factor.</li>
		<li>Alternatively, keep the Sample delimiters empty and enter 0 into the Exported items if the full image title may be retained.</li>
		<li>On the Windows operation system, check if the path to the R software will be correctly auto-detected in the field Path to R.exe. Otherwise, specify the full path to the R.exe file in the system (e.g., &#8220;C:\Program Files\R\bin\R.exe&#8221;).</li>
		<li>Click OK. Check the title parsing in the next dialog window and click OK or Back to reset. 
		Note: All pictures will be automatically smoothed and eventually recalibrated. A list of all exported grouping factors will be displayed in the Results window.</li>
	</ol>
	</li>
	<li>Perform a linear selection across the plasma membrane region in the processed images and measure the fluorescence profiles.
	<ol>
		<li>Select the Fiji tool 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 homogeneous layer of cortical cytoplasm. The optimal length of the linear selection is 5 - 10 &#956;m.</li>
		<li>Run the Take profile macro by analogical method as in the step 4.2 (shortcut &#8220;x&#8221;) or by clicking Take profile Tool in the Fiji toolbar. The automatic measurement of fluorescence profiles in both channels will be performed and data will be displayed in the Results window.</li>
		<li>If using the &#8220;x&#8221; key is considered user-unfriendly, open the macro source file Peripheral_.ijm in the text editor, replace the &#8220;x&#8221; symbol in the line &#8216;macro &#34;Take profile [x]&#34; {&#8217; with a more favorable symbol (that is not used in the Fiji environment as an active keyboard short-cut) and save the file. Reinstall the macro (step 3.1.4) and start the analysis from the image import.</li>
		<li>According to individual signal variability, take representative numbers of the profiles for each cell.</li>
		<li>Save the data via File | Save As&#8230;. Always use the &#8220;csv&#8221; extension; it is necessary for proper data format.</li>
	</ol>
	</li>
	<li>Run the Plot profile data macro (short-cut F5) for graphical visualization of the measured profiles. Set the parameter in the invoked macro dialog window.
	<ol>
		<li>Keep the check-box Use filtering for input data? unselected.</li>
		<li>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. 
		Note: If the plot is created from recent results, data will be saved first (Save As... dialog will be automatically activated).</li>
		<li>Specify which grouping factors will be used for the plotting by selecting the appropriate check-boxes. Select the factor &#8220;i.&#8221;&#160;if all profiles should be displayed in unique plots. Keep the check-boxes unselected, if all data should by plotted in a single plot. 
		CAUTION: Selecting of a grouping factor that does not exist in the processed results causes error during the plot creation.</li>
		<li>Specify the filename when saving a plot in the field Output file prefix and define the plot image dimensions in files Plot high and Plot width. 
		Note: The resulting plot will be saved in a source data directory as a PNG file.</li>
		<li>Select the Pdf output? check-box if an additional PDF output may be produced.</li>
		<li>Click OK. Specify the path for saving the results or eventually for data import in the next dialog window. Confirm analysis by clicking OK in the dialog windows showing the performed R code. 
		Note: The plot will be opened in a new image window and the R analysis output will be listed in text window.</li>
	</ol>
	</li>
	<li>Run the Profile filtering setup macro (short-cut F2) if some plotted profiles have excessive signals in the extracellular space (for the tagged protein) or in the intracellular space (for the FM 4-64 signal). Follow by setting the parameters in the invoked macro dialog window. 
	Note: Filtering enables the removal of poor profiles according to the maximum allowed intensity threshold at specified x-coordinates.
	<ol>
		<li>For each channel, set the appropriate intensity threshold in the file Remove measurements with an excessive extracellular (intracellular) signal. Specify the region where the intensity threshold will be applied in the field at x-coordinates lower (greater) than.</li>
		<li>Run the Plot profile data macro (step 4.4) again with the check-box Use filtering for input data? selected; ensure that all aberrant profiles were successfully removed. Otherwise, improve the filtering parameters (step 4.5.1).</li>
	</ol>
	</li>
	<li>Run the Create model macro (short-cut F3) to create a model of the plasma membrane and the cytoplasm fluorescence distribution based on the calibration data. Follow by setting the parameters in the invoked macro dialog window.
	<ol>
		<li>Specify if the input data should be filtered (step 4.5) by selecting the Use filtering for input data? check-box.</li>
		<li>Specify the source data analogically as in step 4.4.2.</li>
		<li>Specify an interval at which the model will be predicted by definition of &#8220;start,&#8221;&#160;&#8220;end,&#8221;&#160;and &#8220;step&#8221; values in &#956;m. 
		Note: All profile measurements should be longer than the specified interval.</li>
		<li>Specify the wavelengths of fluorescence excitation and emission maxima of the chromophores used, in the appropriate fields. Set the defaults for GFP and FM 4-64 combination. 
		Note: &#8220;GFP&#8221; indicates the chromophore used for the protein tagging (GFP, CFP, YFP, etc.), and &#8220;FM4&#8221; indicates the chromophore used for a plasma membrane staining (typically FM 4-64).</li>
		<li>Specify the Output file prefix field. The prefix will be used for saving a resulting model as the RData file to a source data directory.</li>
		<li>Select the check-box Plot model? If the graphical output is required. The plot will be saved as the PNG and also as the PDF file if the Pdf output? check-box is selected.</li>
		<li>Click OK. Specify the input or output paths in the next dialog windows and confirm the analysis by clicking OK in the dialog window showing the performed R code. 
		Note: The plot of the modeled cytoplasmic and membrane compound of the peripherally-associated protein fluorescence will be shown in the new image window, and the R analysis output will be listed in the text window.</li>
	</ol>
	</li>
	<li>Process the images of the cells expressing the protein of interest.
	<ol>
		<li>Import the images analogically as in step 4.1.</li>
		<li>Set up the analysis analogically as in step 4.2. The smoothing and line width should be identical.</li>
		<li>Measure the profiles analogically as in step 4.3.</li>
		<li>Check the accuracy of the profiles by plotting (step 4.4) and set the filtering if desired (step 4.5).</li>
	</ol>
	</li>
	<li>Run the Calculate distribution macro (short-cut F4) for the calculation of a protein partitioning between the plasma membrane and in the cytoplasm. Follow the parameter setting in the invoked macro dialog window.
	<ol>
		<li>Analogically with the previous step (step 4.4), specify the data source, filtering, and filename prefix.</li>
		<li>Specify if the model for protein distribution calculation will be loaded from the last model computation in the recent instance of the &#8220;Peripheral&#8221; macro on Fiji (select check-box Recent results), or from the RData file (select check-box &#8220;From file&#8221;).</li>
		<li>Keep the check-box Remove results with residual variability greater than activated 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. For example: A value of 0.200 indicates that all measurement with unexplained variability higher than 20% of the total variability of the fluorescence intensity in the profile will be discarded.</li>
		<li>Select sample grouping factors, which may be used for box-plot creating (step 4.4).</li>
		<li>Activate the check-box Pdf output? for saving the box-plot as the PDF file.</li>
		<li>Click OK, specify the input or output paths, and confirm the analysis by clicking OK in the dialog window showing the performed R code. 
		Note: The box-plot of peripherally-associated protein partitioning between the plasma membrane and the cytoplasm will be shown in a new image window, and the R analysis output will be listed in text window.</li>
		<li>For external processing, save the results displayed in the Results window via File | Save As... in the window menu.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

The method described here generates a more accurate estimation of plasma membrane partitioning for peripherally associated proteins compared to other approaches based on measuring fluorescence intensities5. The major improvement of this method is that it takes into account the light diffraction and superposition of the plasma-membrane and the cytoplasmic signals. Although these method results are in correlation with results of a simple method based on the comparison of fluorescence intensity at th...

An erratum was issued for: <a href="https://www.jove.com/video/57837/determination-plasma-membrane-partitioning-for-peripherally">Determination of Plasma Membrane Partitioning for Peripherally-associated Proteins</a>. The Affiliations section was updated.
One of the affiliations was updated from:
Department of Experimental Plant Biology, University of Science, Charles University
to:
Department of Experimental Plant Biology, Faculty of Science, Charles University

An erratum was issued for: <a href="https://www.jove.com/video/57837/determination-plasma-membrane-partitioning-for-peripherally">Determination of Plasma Membrane Partitioning for Peripherally-associated Proteins</a>. The Affiliations section was updated.

Erratum: Determination of Plasma Membrane Partitioning for Peripherally-associated Proteins

Peripherally-associated plasma-membrane proteins are the key components of cell signaling pathways. One of their fundamental roles is their transient plasma membrane association and dissociation, which is important for the signal transduction between plasma membrane and cytoplasm. Peripherally-associated plasma membrane proteins can be attached on plasma membrane by lipid anchors (N-myristoylation, S-acylation, or prenylation) or by lipid binding domains (interacting with phosphatidylinositol phosphates, phosphatidic acid, etc.).
Plasma-membrane binding properties of these proteins can be examined in vivo, e.g., when a fluorescently-tagged protein is modified by a site-directed mutagenesis of key amino acids, or when it is treated with various inhibitors affecting lipid signaling. The distributions of peripheral plasma membrane proteins are mostly being evaluated qualitatively, especially in cases, when protein re-distribution is obvious. The presented method is optimal for situations when protein re-distribution is only partial and quantitative evaluation is necessary. A frequently used approach of when plasma membrane association is estimated from confocal laser scanning microscopy images as a ratio of fluorescence intensities at the plasma-membrane and in the cytoplasm1,2, is simple, but not accurate. Fluorescence intensities at the plasma membrane reflect a superposition of the plasma-membrane and cytoplasm signal due to the light diffraction characteristic for the particular fluorescence microscopy technique and optical elements used3. Consequently, the cytoplasmic signal is included also in the membrane region. For this reason, FM 4-64 staining pattern cannot be used as a mask for a membrane signal selection4. Furthermore, simple measurements of membrane signal at the position defined by the FM 4-64 staining maximum always systematically overestimate the real plasma-membrane signal of peripherally-associated plasma-membrane protein due to the superposition of the membrane and cytoplasmic compound. The maximum of observed signals for fluorescently-tagged peripherally-associated proteins also does not co-localize with the maximum of the plasma membrane marker (i.e., FM 4-64 styryl dye), but is shifted towards the cytoplasm. Another limitation is based on the fact that the FM 4-64 emission peak is wider in comparison with the emission peaks for green fluorescent proteins such as GFP due to the wavelength-dependency of light diffraction3.
In the method described here, the tagged protein signal is fitted by two empirical functions describing a hypothetical distribution of the plasma membrane and cytoplasm signal, respectively. This signal decomposition is applied to linear fluorescence profiles that are applied to the cell surface perpendicularly to the plasma membrane in source images, which are regular, two-channel confocal sections of fluorescently-tagged protein expressing cells labeled with FM 4-64 dye.
The first function used for fitting describes a diffraction of a cytoplasm signal on the cell edge. It is obtained from previously acquired fluorescence profiles that were measured in cells expressing a cytoplasm protein marker tagged by the same chromophore as the plasma membrane peripherally-associated protein of interest. The second function describing a diffraction of a plasma-membrane signal is derived from the fluorescence of FM 4-64. This signal is firstly approximated by a Gaussian function that is being used for an approximate modeling of light diffraction of a point source. Secondly, this model, valid for red FM 4-64 emission, is mathematically transformed to the form that is relevant for an emission wavelength of the chromophore used for the tagging of peripherally-associated proteins of interest at the plasma membrane. Both functions are normalized by the maximal intensity and by the mean from 10% of the highest values for FM 4-64 signal and cytoplasmic protein signal, respectively. By this signal decomposition (non-linear least square fitting method), the ratio of the plasma membrane and the cytoplasm fraction of the examined protein can be estimated easily and accurately. The real physical dimension of computed partitioning coefficient is in the range of micrometer, because cytoplasmic volume concentration is compared with surface concentration on the plasma membrane. It defines the distance from the plasma membrane to the cytoplasm, within which the same amount of proteins is localized as in the adjacent area of the plasma membrane. This value is equivalent to the partitioning coefficient K2 introduced previously5. The method is very quick, requiring only single confocal sections acquired using routine confocal laser scanning microscope, and it is not computationally demanding. The analysis core has been implemented in a portable R package and an additional ImageJ macro was written to provide graphical user interface to run the analysis from the intuitive dialogs. Software and more detailed description of the method (published previously6) can be found at http://kfrserver.natur.cuni.cz/lide/vosolsob/Peripheral/.
The method is suitable for isolated cells, protoplasts, and tissues, where the plasma membrane of individual cells is clearly distinguishable, expressing a fluorescently-tagged construct of examined peripherally-associated protein. A chromophore compatible with FM 4-64 staining must be used. FM 4-64 emits red fluorescence; therefore, examined protein can be tagged by a fluorescence protein with blue, green, or yellow emission (e.g., GFP, CFP, YFP). Stable transformation of biological material is recommended because it enables less artificial and more reproducible observations of protein distribution. It is necessary that the examined protein has a relatively homogeneous cytoplasmic distribution. The localization of a protein in the endoplasmic reticulum or another intracellular membrane compartment can produce artificial results.
Additionally, the same biological material expressing a cytoplasmic marker must be used for the comparison. Cells can be transformed by a free chomophore (the same as used for peripheral protein tagging, e.g., free GFP) or by tagged protein of interest with abolished membrane binding capacity. Membrane binding capacity can be abolished, for example, by trimming of the membrane-binding domain or by site-directed mutagenesis of key amino acid residua (e.g., sites for N-myristoylation, S-acylation, or prenylation, etc.).
For confocal scanning microscopy, cells must be labeled by a membrane marker like FM 4-64 dye. If FM 4-64 staining is not suitable for the studied material (due to interfering autofluorescence, poor dye penetration, etc.), the plasma membrane can be labeled, for example, by integral plasma membrane protein tagged to an appropriate chomophore (mCherry, RFP, etc.). It is essential that the marker has negligible localization in the intracellular membrane compartments (endomembranes).
If working with fixed samples and antibodies, fixable analogue FM 4-64FX or plasma membrane labeling by antibody against an appropriate target can be used. In this case, it is essential to evaluate results very carefully because fixation procedures can lead to selective loss of proteins from both the cytoplasm and the plasma membrane.

<table><tbody><tr><td>FM 4-64</td><td>ThermoFisher Scientific</td><td>T13320</td><td>Plasma membrane dye</td></tr><tr><td>Dimethyl sulfoxide</td><td>Sigma-Aldrich</td><td>D4540 Sigma</td><td>Dye solvent</td></tr><tr><td>Ordinary equipment (microscopic slides, pipettes, tips, tubes)</td><td></td><td></td><td>Equipment for cell labelling and microscopy</td></tr><tr><td>Confocal laser scanning microscope</td><td></td><td></td><td></td></tr><tr><td>Ordinary computer</td><td></td><td></td><td></td></tr></tbody></table>

determination of plasma membrane partitioning for peripherally-associated proteins

This method provides a fast approach for the determination of plasma membrane partitioning of any fluorescently-tagged peripherally-associated protein using the profiles of fluorescence intensity across the plasma membrane. Measured fluorescence profiles are fitted by a model for membrane and cytoplasm fluorescence distribution along a line applied perpendicularly to the cell periphery. This model is constructed from the fluorescence intensity values in reference cells expressing a fluorescently-tagged marker for cytoplasm and with FM 4-64-labeled plasma membrane. The method can be applied to various cell types and organisms; however, only plasma membranes of non-neighboring cells can be evaluated. This fast microscopy-based method is suitable for experiments, where subtle and dynamic changes of plasma membrane-associated markers are expected and need to be quantified, e.g., in the analysis of mutant versions of proteins, inhibitor treatments, and signal transduction observations. The method is implemented in a multi-platform R package that is coupled with an ImageJ macro that serves as a user-friendly interface.

DREPP10 is a plant-specific peripherally-associated plasma-membrane protein that is associated with the plasma membrane via an N-myristoylation and an electrostatic interaction with phosphatidylinositol phosphates11,12. DREPP was described as a component of calcium-signaling machinery in the plant cell and also interacts with the cytoskeleton13,14. ...

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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 provides a fast approach for the determination of plasma membrane partitioning of any fluorescently-tagged peripherally-associated protein ...

determination-plasma-membrane-partitioning-for-peripherally

Research

JoVE Journal

Biology

8.3K Views.  Faculty of Science, Charles University. 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.

Video: Determination of Plasma Membrane Partitioning for Peripherally-associated Proteins

Method to Visualize and Analyze Membrane Interacting Proteins by Transmission Electron Microscopy

Monotopic proteins exert their function when attached to a membrane surface, and such interactions depend on the specific lipid composition and on the availability of enough area to perform the function. Nanodiscs are used to provide a membrane surface of controlled size and lipid content. In the absence of bound extrinsic proteins, sodium phosphotungstate-stained nanodiscs appear as stacks of coins when viewed from the side by transmission electron microscopy (TEM). This protocol is therefore designed to intentionally promote stacking; consequently, the prevention of stacking can be interpreted as the binding of the membrane-binding protein to the nanodisc. In a further step, the TEM images of the protein-nanodisc complexes can be processed with standard single-particle methods to yield low-resolution structures as a basis for higher resolution cryoEM work. Furthermore, the nanodiscs provide samples suitable for either TEM or non-denaturing gel electrophoresis. To illustrate the method, Ca2+-induced binding of 5-lipoxygenase on nanodiscs is presented.

Many proteins perform their function when attached to membrane surfaces. The binding of extrinsic proteins on nanodisc membranes can be indirectly imaged by transmission electron microscopy. We show that the characteristic stacking (rouleau) of nanodiscs induced by the negative stain sodium phosphotungstate is prevented by the binding of extrinsic protein.

Monotopic proteins exert their function when attached to a membrane surface, and such interactions depend on the specific lipid composition and on the ...

Biochemistry

Brain Membrane Fractionation: An Ex Vivo Approach to Assess Subsynaptic Protein Localization

Assessing the synaptic protein composition and function constitutes an important challenge in neuroscience. However, it is not easy to evaluate neurotransmission that occurs within synapses because it is highly regulated by dynamic protein-protein interactions and phosphorylation events. Accordingly, when any method is used to study synaptic transmission, a major goal is to preserve these transient physiological modifications. Here, we present a brain membrane fractionation protocol that represents a robust procedure to isolate proteins belonging to different synaptic compartments. In other words, the protocol describes a biochemical methodology to carry out protein enrichment from presynaptic, postsynaptic, and extrasynaptic compartments. First, synaptosomes, or synaptic terminals, are obtained from neurons that contain all synaptic compartments by means of a discontinuous sucrose gradient. Of note, the quality of this initial synaptic membrane preparation is critical. Subsequently, the isolation of the different subsynaptic compartments is achieved with light solubilization using mild detergents at differential pH conditions. This allows for separation by gradient and isopycnic centrifugations. Finally, protein enrichment at the different subsynaptic compartments (i.e., pre-, post- and extrasynaptic membrane fractions) is validated by means of immunoblot analysis using well-characterized synaptic protein markers (i.e., SNAP-25, PSD-95, and synaptophysin, respectively), thus enabling a direct assessment of the synaptic distribution of any particular neuronal protein.

Brain Membrane Fractionation: An Ex Vivo Approach to Assess Subsynaptic Protein Localization

Here, we present a brain membrane fractionation protocol that represents a robust procedure to isolate proteins belonging to different synaptic compartments.

Assessing the synaptic protein composition and function constitutes an important challenge in neuroscience. However, it is not easy to evaluate ...

Nitrogen Cavitation and Differential Centrifugation Allows for Monitoring the Distribution of Peripheral Membrane Proteins in Cultured Cells

Cultured cells are useful for studying the subcellular distribution of proteins, including peripheral membrane proteins. Genetically encoded fluorescently tagged proteins have revolutionized the study of subcellular protein distribution. However, it is difficult to quantify the distribution with fluorescent microscopy, especially when proteins are partially cytosolic. Moreover, it is often important to study endogenous proteins. Biochemical assays such as immunoblots remain the gold standard for quantification of protein distribution after subcellular fractionation. Although there are commercial kits that aim to isolate cytosolic or certain membrane fractions, most of these kits are based on extraction with detergents, which may be unsuitable for studying peripheral membrane proteins that are easily extracted from membranes. Here we present a detergent-free protocol for cellular homogenization by nitrogen cavitation and subsequent separation of cytosolic and membrane-bound proteins by ultracentrifugation. We confirm the separation of subcellular organelles in soluble and pellet fractions across different cell types, and compare protein extraction among several common non-detergent-based mechanical homogenization methods. Among several advantages of nitrogen cavitation is the superior efficiency of cellular disruption with minimal physical and chemical damage to delicate organelles. Combined with ultracentrifugation, nitrogen cavitation is an excellent method to examine the shift of peripheral membrane proteins between cytosolic and membrane fractions.

Here we present protocols for detergent-free homogenization of cultured mammalian cells based on nitrogen cavitation and subsequent separation of cytosolic and membrane-bound proteins by ultracentrifugation. This method is ideal for monitoring the partitioning of peripheral membrane proteins between soluble and membrane fractions.

Cultured cells are useful for studying the subcellular distribution of proteins, including peripheral membrane proteins. Genetically encoded fluorescently ...

Native Cell Membrane Nanoparticles System for Membrane Protein-Protein Interaction Analysis

Protein-protein interactions in cell membrane systems play crucial roles in a wide range of biological processes- from cell-to-cell interactions to signal transduction; from sensing environmental signals to biological response; from metabolic regulation to developmental control. Accurate structural information of protein-protein interactions is crucial for understanding the molecular mechanisms of membrane protein complexes and for the design of highly specific molecules to modulate these proteins. Many in vivo and in vitro approaches have been developed for the detection and analysis of protein-protein interactions. Among them the structural biology approach is unique in that it can provide direct structural information of protein-protein interactions at the atomic level. However, current membrane protein structural biology is still largely limited to detergent-based methods. The major drawback of detergent-based methods is that they often dissociate or denature membrane protein complexes once their native lipid bilayer environment is removed by detergent molecules. We have been developing a&#160;native cell membrane nanoparticle system for membrane protein structural biology. Here, we demonstrate the use of this system in the analysis of protein-protein interactions on the cell membrane with a case study of the oligomeric state of AcrB.

Presented here is a protocol for the determination of oligomeric state of membrane proteins that utilizes a native cell membrane nanoparticle system in conjunction with electron microscopy.

Protein-protein interactions in cell membrane systems play crucial roles in a wide range of biological processes- from cell-to-cell interactions to signal ...

Spot Variation Fluorescence Correlation Spectroscopy for Analysis of Molecular Diffusion at the Plasma Membrane of Living Cells

Dynamic biological processes in living cells, including those associated with plasma membrane organization, occur on various spatial and temporal scales, ranging from nanometers to micrometers and microseconds to minutes, respectively. Such a broad range of biological processes challenges conventional microscopy approaches. Here, we detail the procedure for implementing spot variation Fluorescence Correlation Spectroscopy (svFCS) measurements using a classical fluorescence microscope that has been customized. The protocol includes a specific performance check of the svFCS setup and the guidelines for molecular diffusion measurements by svFCS on the plasma membrane of living cells under physiological conditions. Additionally, we provide a procedure for disrupting plasma membrane raft nanodomains by cholesterol oxidase treatment and demonstrate how these changes in the lateral organization of the plasma membrane might be revealed by svFCS analysis. In conclusion, this fluorescence-based method can provide unprecedented details on the lateral organization of the plasma membrane with the appropriate spatial and temporal resolution.

This article aims to present a protocol on how to build a spot variation Fluorescence Correlation Spectroscopy (svFCS) microscope to measure molecular diffusion at the plasma membrane of living cells.

Dynamic biological processes in living cells, including those associated with plasma membrane organization, occur on various spatial and temporal scales, ...

Measuring Plasma Membrane Protein Endocytic Rates by Reversible Biotinylation

Plasma membrane proteins are a large, diverse group of proteins comprised of receptors, ion channels, transporters and pumps.  Activity of these proteins is responsible for a variety of key cellular events, including nutrient delivery, cellular excitability, and chemical signaling.  Many plasma membrane proteins are dynamically regulated by endocytic trafficking, which modulates protein function by altering protein surface expression.  The mechanisms that facilitate protein endocytosis are complex and are not fully understood for many membrane proteins.  In order to fully understand the mechanisms that control the endocytic trafficking of a given protein, it is critical that the protein s endocytic rate be precisely measured.  For many receptors, direct endocytic rate measurements are frequently achieved utilizing labeled receptor ligands.  However, for many classes of membrane proteins, such as transporters, pumps and ion channels, there is no convenient ligand that can be used to measure the endocytic rate.  In the present report, we describe a reversible biotinylation method that we employ to measure the dopamine transporter (DAT) endocytic rate.  This method provides a straightforward approach to measuring internalization rates, and can be easily employed for trafficking studies of most membrane proteins.

Regulated endocytosis governs the cell surface expression levels of the majority of membrane proteins. Here we utilize reducible, membrane impermeant biotinylation reagents to measure the endocytic rate of the dopamine transporter (DAT), a polytopic membrane protein. The method facilitates a straightforward approach to measuring the endocytic rate of most plasma membrane proteins.

Plasma membrane proteins are a large, diverse group of proteins comprised of receptors, ion channels, transporters and pumps.  Activity of these proteins ...

PIP-on-a-chip: A Label-free Study of Protein-phosphoinositide Interactions

Numerous cellular proteins interact with membrane surfaces to affect essential cellular processes. These interactions can be directed towards a specific lipid component within a membrane, as in the case of phosphoinositides (PIPs), to ensure specific subcellular localization and/or activation. PIPs and cellular PIP-binding domains have been studied extensively to better understand their role in cellular physiology. We applied a pH modulation assay on supported lipid bilayers (SLBs) as a tool to study protein-PIP interactions. In these studies, pH sensitive ortho-Sulforhodamine B conjugated phosphatidylethanolamine is used to detect protein-PIP interactions. Upon binding of a protein to a PIP-containing membrane surface, the interfacial potential is modulated (i.e. change in local pH), shifting the protonation state of the probe. A case study of the successful usage of the pH modulation assay is presented by using phospholipase C delta1 Pleckstrin Homology (PLC-&#948;1 PH) domain and phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2) interaction as an example. The apparent dissociation constant (Kd,app) for this interaction was 0.39 &#177; 0.05 &#181;M, similar to Kd,app values obtained by others. As previously observed, the PLC-&#948;1 PH domain is PI(4,5)P2 specific, shows weaker binding towards phosphatidylinositol 4-phosphate, and no binding to pure phosphatidylcholine SLBs. The PIP-on-a-chip assay is advantageous over traditional PIP-binding assays, including but not limited to low sample volume and no ligand/receptor labeling requirements, the ability to test high- and low-affinity membrane interactions with both small and large molecules, and improved signal to noise ratio. Accordingly, the usage of the PIP-on-a-chip approach will facilitate the elucidation of mechanisms of a wide range of membrane interactions. Furthermore, this method could potentially be used in identifying therapeutics that modulate protein&#39;s capacity to interact with membranes.

Here we present a supported lipid bilayer in the context of a microfluidic platform to study protein-phosphoinositide interactions using a label-free method based on pH modulation.

Numerous cellular proteins interact with membrane surfaces to affect essential cellular processes. These interactions can be directed towards a specific ...

Bioengineering

Determination of Lipid Raft Partitioning of Fluorescently-tagged Probes in Living Cells by Fluorescence Correlation Spectroscopy (FCS)

In the past fifteen years the notion that cell membranes are not homogenous and rely on microdomains to exert their functions has become widely accepted. Lipid rafts are membrane microdomains enriched in cholesterol and sphingolipids. They play a role in cellular physiological processes such as signalling, and trafficking1,2 but are also thought to be key players in several diseases including viral or bacterial infections and neurodegenerative diseases3.
Yet their existence is still a matter of controversy4,5. Indeed, lipid raft size has been estimated to be around 20 nm6, far under the resolution limit of conventional microscopy (around 200 nm), thus precluding their direct imaging. Up to now, the main techniques used to assess the partition of proteins of interest inside lipid rafts were Detergent Resistant Membranes (DRMs) isolation and co-patching with antibodies. Though widely used because of their rather easy implementation, these techniques were prone to artefacts and thus criticized7,8. Technical improvements were therefore necessary to overcome these artefacts and to be able to probe lipid rafts partition in living cells.
Here we present a method for the sensitive analysis of lipid rafts partition of fluorescently-tagged proteins or lipids in the plasma membrane of living cells. This method, termed Fluorescence Correlation Spectroscopy (FCS), relies on the disparity in diffusion times of fluorescent probes located inside or outside of lipid rafts. In fact, as evidenced in both artificial membranes and cell cultures, probes would diffuse much faster outside than inside dense lipid rafts9,10. To determine diffusion times, minute fluorescence fluctuations are measured as a function of time in a focal volume (approximately 1 femtoliter), located at the plasma membrane of cells with a confocal microscope (Fig. 1). The auto-correlation curves can then be drawn from these fluctuations and fitted with appropriate mathematical diffusion models11.
FCS can be used to determine the lipid raft partitioning of various probes, as long as they are fluorescently tagged. Fluorescent tagging can be achieved by expression of fluorescent fusion proteins or by binding of fluorescent ligands. Moreover, FCS can be used not only in artificial membranes and cell lines but also in primary cultures, as described recently12. It can also be used to follow the dynamics of lipid raft partitioning after drug addition or membrane lipid composition change12.

Determination of Lipid Raft Partitioning of Fluorescently-tagged Probes in Living Cells by Fluorescence Correlation Spectroscopy FCS

A technique to probe the lipid raft partitioning of fluorescent proteins at the plasma membrane of living cells is described. It takes advantage of the disparity in diffusion times of proteins located inside or outside of lipid rafts. Acquisition can be performed dynamically in control conditions or after drug addition.

In the past fifteen years the notion that cell membranes are not homogenous and rely on microdomains to exert their functions has become widely accepted. ...

High-throughput Measurement of Plasma Membrane Resealing Efficiency in Mammalian Cells

In their physiological environment, mammalian cells are often subjected to mechanical and biochemical stresses that result in plasma membrane damage. In response to these damages, complex molecular machineries rapidly reseal the plasma membrane to restore its barrier function and maintain cell survival. Despite 60 years of research in this field, we still lack a thorough understanding of the cell resealing machinery. With the goal of identifying cellular components that control plasma membrane resealing or drugs that can improve resealing, we have developed a fluorescence-based high-throughput assay that measures the plasma membrane resealing efficiency in mammalian cells cultured in microplates. As a model system for plasma membrane damage, cells are exposed to the bacterial pore-forming toxin listeriolysin O (LLO), which forms large 30-50 nm diameter proteinaceous pores in cholesterol-containing membranes. The use of a temperature-controlled multi-mode microplate reader allows for rapid and sensitive spectrofluorometric measurements in combination with brightfield and fluorescence microscopy imaging of living cells. Kinetic analysis of the fluorescence intensity emitted by a membrane impermeant nucleic acid-binding fluorochrome reflects the extent of membrane wounding and resealing at the cell population level, allowing for the calculation of the cell resealing efficiency. Fluorescence microscopy imaging allows for the enumeration of cells, which constitutively express a fluorescent chimera of the nuclear protein histone 2B, in each well of the microplate to account for potential variations in their number and allows for eventual identification of distinct cell populations. This high-throughput assay is a powerful tool expected to expand our understanding of membrane repair mechanisms via screening for host genes or exogenously added compounds that control plasma membrane resealing.

Here we describe a high-throughput fluorescence-based assay that measures the plasma membrane resealing efficiency through fluorometric and imaging analyses in living cells. This assay can be used for screening drugs or target genes that regulate plasma membrane resealing in mammalian cells.

In their physiological environment, mammalian cells are often subjected to mechanical and biochemical stresses that result in plasma membrane damage. In ...

Immunology and Infection

Metabolic Labeling and Membrane Fractionation for Comparative Proteomic Analysis of Arabidopsis thaliana Suspension Cell Cultures

Plasma membrane microdomains are features based on the physical properties of the lipid and sterol environment and have particular roles in signaling processes. Extracting sterol-enriched membrane microdomains from plant cells for proteomic analysis is a difficult task mainly due to multiple preparation steps and sources for contaminations from other cellular compartments. The plasma membrane constitutes only about 5-20% of all the membranes in a plant cell, and therefore isolation of highly purified plasma membrane fraction is challenging. A frequently used method involves aqueous two-phase partitioning in polyethylene glycol and dextran, which yields plasma membrane vesicles with a purity of 95% 1. Sterol-rich membrane microdomains within the plasma membrane are insoluble upon treatment with cold nonionic detergents at alkaline pH. This detergent-resistant membrane fraction can be separated from the bulk plasma membrane by ultracentrifugation in a sucrose gradient 2. Subsequently, proteins can be extracted from the low density band of the sucrose gradient by methanol/chloroform precipitation. Extracted protein will then be trypsin digested, desalted and finally analyzed by LC-MS/MS. Our extraction protocol for sterol-rich microdomains is optimized for the preparation of clean detergent-resistant membrane fractions from Arabidopsis thaliana cell cultures.
We use full metabolic labeling of Arabidopsis thaliana suspension cell cultures with K15NO3 as the only nitrogen source for quantitative comparative proteomic studies following biological treatment of interest 3. By mixing equal ratios of labeled and unlabeled cell cultures for joint protein extraction the influence of preparation steps on final quantitative result is kept at a minimum. Also loss of material during extraction will affect both control and treatment samples in the same way, and therefore the ratio of light and heave peptide will remain constant. In the proposed method either labeled or unlabeled cell culture undergoes a biological treatment, while the other serves as control 4.

Metabolic Labeling and Membrane Fractionation for Comparative Proteomic Analysis of Arabidopsis thaliana Suspension Cell Cultures

Here we describe a robust method for the fractionation of plant plasma membranes into detergent resistant and detergent soluble membranes based on a mixture of unlabeled and in vivo fully 15N labeled Arabidopsis thaliana cell cultures. The procedure is applied for comparative proteomic studies to understand signaling processes.

Plasma membrane microdomains are features based on the physical properties of the lipid and sterol environment and have particular roles in signaling ...

Determination of Plasma Membrane Partitioning for Peripherally-associated Proteins

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Chapters in this video

Method to Visualize and Analyze Membrane Interacting Proteins by Transmission Electron Microscopy

Brain Membrane Fractionation: An Ex Vivo Approach to Assess Subsynaptic Protein Localization

Nitrogen Cavitation and Differential Centrifugation Allows for Monitoring the Distribution of Peripheral Membrane Proteins in Cultured Cells

Native Cell Membrane Nanoparticles System for Membrane Protein-Protein Interaction Analysis

Spot Variation Fluorescence Correlation Spectroscopy for Analysis of Molecular Diffusion at the Plasma Membrane of Living Cells

Measuring Plasma Membrane Protein Endocytic Rates by Reversible Biotinylation

PIP-on-a-chip: A Label-free Study of Protein-phosphoinositide Interactions

Determination of Lipid Raft Partitioning of Fluorescently-tagged Probes in Living Cells by Fluorescence Correlation Spectroscopy (FCS)

High-throughput Measurement of Plasma Membrane Resealing Efficiency in Mammalian Cells

Metabolic Labeling and Membrane Fractionation for Comparative Proteomic Analysis of Arabidopsis thaliana Suspension Cell Cultures