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 border="0" cellpadding="0" cellspacing="0" style="width:944px;" width="944">
	<tbody>
		<tr height="40">
			<td height="40" style="height:40px;width:241px;">FM 4-64</td>
			<td style="width:135px;">ThermoFisher Scientific</td>
			<td style="width:384px;">T13320</td>
			<td style="width:184px;">Plasma membrane dye</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:241px;">Dimethyl sulfoxide</td>
			<td style="width:135px;">Sigma-Aldrich</td>
			<td style="width:384px;">D4540 Sigma</td>
			<td style="width:184px;">Dye solvent</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:241px;">Ordinary equipment (microscopic slides, pipettes, tips, tubes)</td>
			<td style="width:135px;"></td>
			<td></td>
			<td style="width:184px;">Equipment for cell labelling and microscopy</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:241px;">Confocal laser scanning microscope</td>
			<td style="width:135px;"></td>
			<td style="width:384px;"></td>
			<td style="width:184px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:241px;">Ordinary computer</td>
			<td style="width:135px;"></td>
			<td style="width:384px;"></td>
			<td style="width:184px;"></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. ...

Watch this Scientific Journal Video about Determination of Plasma Membrane Partitioning for Peripherally-associated Proteins at JoVE.com

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

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 ...

Crystallizing Membrane Proteins for Structure Determination using Lipidic Mesophases

A detailed protocol for crystallizing membrane proteins by using lipidic mesophases is described. This method has variously been referred to as the lipidic cubic phase or in meso method. The method has been shown to be quite versatile in that it has been used to solve X-ray crystallographic structures of prokaryotic and eukaryotic proteins, proteins that are monomeric, homo- and hetero-multimeric, chromophore-containing and chromophore-free, and alpha-helical and beta-barrel proteins. Recent successes using in meso crystallization are the human engineered beta2-adrenergic and adenosine A2a G protein-coupled receptors. Protocols are presented for reconstituting the membrane protein into the monoolein-based mesophase, and for setting up crystallizations in the manual mode. Additional steps in the overall process, such as crystal harvesting, are to be addressed in future video articles. The time required to prepare the protein-loaded mesophase and to set up a crystallization plate manually is about one hour.

Herein is described the procedure implemented in the Caffrey Membrane Structural and Functional Biology Group to set up manually crystallization trials of membrane proteins in lipidic mesophases.

A detailed protocol for crystallizing membrane proteins by using lipidic mesophases is described. This method has variously been referred to as the ...

Crystallization of Membrane Proteins in Lipidic Mesophases

Membrane proteins perform critical functions in living cells related to signal transduction, transport and energy transformations, and, as such, are implicated in a multitude of malfunctions and diseases. However, a structural and functional understanding of membrane proteins is strongly lagging behind that of their soluble partners, mainly, due to difficulties associated with their solubilization and generation of diffraction quality crystals. Crystallization in lipidic mesophases (also known as in meso or LCP crystallization) is a promising technique which was successfully applied to obtain high resolution structures of microbial rhodopsins, photosynthetic proteins, outer membrane beta barrels and G protein-coupled receptors. In meso crystallization takes advantage of a native-like membrane environment and typically produces crystals with lower solvent content and better ordering as compared to traditional crystallization from detergent solutions. The method is not difficult, but requires an understanding of lipid phase behavior and practice in handling viscous mesophase materials. Here we demonstrate a simple and efficient way of making LCP and reconstituting a membrane protein in the lipid bilayer of LCP using a syringe mixer, followed by dispensing nanoliter portions of LCP into an assay or crystallization plate, conducting pre-crystallization assays and harvesting crystals from the LCP matrix. These protocols provide a basic guide for approaching in meso crystallization trials; however, as with any crystallization experiment, extensive screening and optimization are required, and a successful outcome is not necessarily guaranteed.

The protocols describe the essential steps for obtaining diffraction quality crystals of a membrane protein starting from reconstitution of the protein in a lipidic cubic phase (LCP), finding initial conditions with LCP-FRAP pre-crystallization assays, setting up LCP crystallization trials and harvesting crystals.

Membrane proteins perform critical functions in living cells related to signal transduction, transport and energy transformations, and, as such, are ...

Quantification of Proteins Using Peptide Immunoaffinity Enrichment Coupled with Mass Spectrometry

There is a great need for quantitative assays in measuring proteins. Traditional sandwich immunoassays, largely considered the gold standard in quantitation, are associated with a high cost, long lead time, and are fraught with drawbacks (e.g. heterophilic antibodies, autoantibody interference, 'hook-effect').1 An alternative technique is affinity enrichment of peptides coupled with quantitative mass spectrometry, commonly referred to as SISCAPA (Stable Isotope Standards and Capture by Anti-Peptide Antibodies).2 In this technique, affinity enrichment of peptides with stable isotope dilution and detection by selected/multiple reaction monitoring mass spectrometry (SRM/MRM-MS) provides quantitative measurement of peptides as surrogates for their respective proteins. SRM/MRM-MS is well established for accurate quantitation of small molecules 3, 4 and more recently has been adapted to measure the concentrations of proteins in plasma and cell lysates.5-7 To achieve quantitation of proteins, these larger molecules are digested to component peptides using an enzyme such as trypsin. One or more selected peptides whose sequence is unique to the target protein in that species (i.e. "proteotypic" peptides) are then enriched from the sample using anti-peptide antibodies and measured as quantitative stoichiometric surrogates for protein concentration in the sample. Hence, coupled to stable isotope dilution (SID) methods (i.e. a spiked-in stable isotope labeled peptide standard), SRM/MRM can be used to measure concentrations of proteotypic peptides as surrogates for quantification of proteins in complex biological matrices. The assays have several advantages compared to traditional immunoassays. The reagents are relatively less expensive to generate, the specificity for the analyte is excellent, the assays can be highly multiplexed, enrichment can be performed from neat plasma (no depletion required), and the technique is amenable to a wide array of proteins or modifications of interest.8-13 In this video we demonstrate the basic protocol as adapted to a magnetic bead platform.

Stable Isotope Standards and Capture by Anti-Peptide Antibodies (SISCAPA) couples affinity enrichment of peptides with stable isotope dilution mass spectrometry (MRM-MS) to provide quantitative measurement of peptides as surrogates for their respective proteins. Here we describe the protocol using magnetic particles in a partially automated format.

There is a great need for quantitative assays in measuring proteins. Traditional sandwich immunoassays, largely considered the gold standard in ...

High-throughput Crystallization of Membrane Proteins Using the Lipidic Bicelle Method

Membrane proteins (MPs) play a critical role in many physiological processes such as pumping specific molecules across the otherwise impermeable membrane bilayer that surrounds all cells and organelles. Alterations in the function of MPs result in many human diseases and disorders; thus, an intricate understanding of their structures remains a critical objective for biological research. However, structure determination of MPs remains a significant challenge often stemming from their hydrophobicity. 
MPs have substantial hydrophobic regions embedded within the bilayer. Detergents are frequently used to solubilize these proteins from the bilayer generating a protein-detergent micelle that can then be manipulated in a similar manner as soluble proteins. Traditionally, crystallization trials proceed using a protein-detergent mixture, but they often resist crystallization or produce crystals of poor quality. These problems arise due to the detergent&prime;s inability to adequately mimic the bilayer resulting in poor stability and heterogeneity. In addition, the detergent shields the hydrophobic surface of the MP reducing the surface area available for crystal contacts. To circumvent these drawbacks MPs can be crystallized in lipidic media, which more closely simulates their endogenous environment, and has recently become a de novo technique for MP crystallization.
Lipidic cubic phase (LCP) is a three-dimensional lipid bilayer penetrated by an interconnected system of aqueous channels1. Although monoolein is the lipid of choice, related lipids such as monopalmitolein and monovaccenin have also been used to make LCP2. MPs are incorporated into the LCP where they diffuse in three dimensions and feed crystal nuclei. A great advantage of the LCP is that the protein remains in a more native environment, but the method has a number of technical disadvantages including high viscosity (requiring specialized apparatuses) and difficulties in crystal visualization and manipulation3,4. Because of these technical difficulties, we utilized another lipidic medium for crystallization-bicelles5,6 (Figure 1). Bicelles are lipid/amphiphile mixtures formed by blending a phosphatidylcholine lipid (DMPC) with an amphiphile (CHAPSO) or a short-chain lipid (DHPC). Within each bicelle disc, the lipid molecules generate a bilayer while the amphiphile molecules line the apolar edges providing beneficial properties of both bilayers and detergents. Importantly, below their transition temperature, protein-bicelle mixtures have a reduced viscosity and are manipulated in a similar manner as detergent-solubilized MPs, making bicelles compatible with crystallization robots. 
Bicelles have been successfully used to crystallize several membrane proteins5,7-11 (Table 1). This growing collection of proteins demonstrates the versatility of bicelles for crystallizing both alpha helical and beta sheet MPs from prokaryotic and eukaryotic sources. Because of these successes and the simplicity of high-throughput implementation, bicelles should be part of every membrane protein crystallographer&prime;s arsenal. In this video, we describe the bicelle methodology and provide a step-by-step protocol for setting up high-throughput crystallization trials of purified MPs using standard robotics.

Bicelles are lipid/amphiphile mixtures that maintain membrane proteins (MPs) within a lipid bilayer but have unique phase behavior that facilitates high-throughput screening by crystallization robots. This technique has successfully produced a number of high-resolution structures from both prokaryotic and eukaryotic sources. This video describes protocols for generating the lipidic bicelle mixture, incorporating MPs into the bicelle mixture, setting up crystallizations trials (manually as well as robotically) and harvesting crystals from the medium.

Membrane proteins (MPs) play a critical role in many physiological processes such as pumping specific molecules across the otherwise impermeable membrane ...

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. ...

Use of a Robot for High-throughput Crystallization of Membrane Proteins in Lipidic Mesophases

Structure-function studies of membrane proteins greatly benefit from having available high-resolution 3-D structures of the type provided through macromolecular X-ray crystallography (MX). An essential ingredient of MX is a steady supply of ideally diffraction-quality crystals. The in meso or lipidic cubic phase (LCP) method for crystallizing membrane proteins is one of several methods available for crystallizing membrane proteins. It makes use of a bicontinuous mesophase in which to grow crystals. As a method, it has had some spectacular successes of late and has attracted much attention with many research groups now interested in using it. One of the challenges associated with the method is that the hosting mesophase is extremely viscous and sticky, reminiscent of a thick toothpaste. Thus, dispensing it manually in a reproducible manner in small volumes into crystallization wells requires skill, patience and a steady hand. A protocol for doing just that was developed in the Membrane Structural &amp; Functional Biology (MS&amp;FB) Group1-3. JoVE video articles describing the method are available1,4. 
The manual approach for setting up in meso trials has distinct advantages with specialty applications, such as crystal optimization and derivatization. It does however suffer from being a low throughput method. Here, we demonstrate a protocol for performing in meso crystallization trials robotically. A robot offers the advantages of speed, accuracy, precision, miniaturization and being able to work continuously for extended periods under what could be regarded as hostile conditions such as in the dark, in a reducing atmosphere or at low or high temperatures. An in meso robot, when used properly, can greatly improve the productivity of membrane protein structure and function research by facilitating crystallization which is one of the slow steps in the overall structure determination pipeline. 
In this video article, we demonstrate the use of three commercially available robots that can dispense the viscous and sticky mesophase integral to in meso crystallogenesis. The first robot was developed in the MS&amp;FB Group5,6. The other two have recently become available and are included here for completeness.	
An overview of the protocol covered in this article is presented in Figure 1. All manipulations were performed at room temperature (~20 &deg;C) under ambient conditions.

Herein is described a robotic approach to high-throughput crystallization of membrane proteins in lipidic mesophases for use in structure determination using macromolecular X-ray crystallography. Three robots capable of handling the viscous and sticky protein-laden mesophase integral to the method are introduced.

Structure-function studies of membrane proteins greatly benefit from having available high-resolution 3-D structures of the type provided through ...

Harvesting and Cryo-cooling Crystals of Membrane Proteins Grown in Lipidic Mesophases for Structure Determination by Macromolecular Crystallography

An important route to understanding how proteins function at a mechanistic level is to have the structure of the target protein available, ideally at atomic resolution. Presently, there is only one way to capture such information as applied to integral membrane proteins (Figure 1), and the complexes they form, and that method is macromolecular X-ray crystallography (MX). To do MX diffraction quality crystals are needed which, in the case of membrane proteins, do not form readily. A method for crystallizing membrane proteins that involves the use of lipidic mesophases, specifically the cubic and sponge phases1-5, has gained considerable attention of late due to the successes it has had in the G protein-coupled receptor field6-21 (<a href="http://www.mpdb.tcd.ie" target="_blank">www.mpdb.tcd.ie</a>). However, the method, henceforth referred to as the in meso or lipidic cubic phase method, comes with its own technical challenges. These arise, in part, due to the generally viscous and sticky nature of the lipidic mesophase in which the crystals, which are often micro-crystals, grow. Manipulating crystals becomes difficult as a result and particularly so during harvesting22,23. Problems arise too at the step that precedes harvesting which requires that the glass sandwich plates in which the crystals grow (Figure 2)24,25 are opened to expose the mesophase bolus, and the crystals therein, for harvesting, cryo-cooling and eventual X-ray diffraction data collection.
The cubic and sponge mesophase variants (Figure 3) from which crystals must be harvested have profoundly different rheologies4,26. The cubic phase is viscous and sticky akin to a thick toothpaste. By contrast, the sponge phase is more fluid with a distinct tendency to flow. Accordingly, different approaches for opening crystallization wells containing crystals growing in the cubic and the sponge phase are called for as indeed different methods are required for harvesting crystals from the two mesophase types. Protocols for doing just that have been refined and implemented in the Membrane Structural and Functional Biology (MS&amp;FB) Group, and are described in detail in this JoVE article (Figure 4). Examples are given of situations where crystals are successfully harvested and cryo-cooled. We also provide examples of cases where problems arise that lead to the irretrievable loss of crystals and describe how these problems can be avoided. In this article the Viewer is provided with step-by-step instructions for opening glass sandwich crystallization wells, for harvesting and for cryo-cooling crystals of membrane proteins growing in cubic and in sponge phases.

Herein is described procedures implemented in the Caffrey Membrane Structural and Functional Biology Group to harvest and cryo-cool membrane protein crystals grown in lipidic cubic and sponge phases for use in structure determination using macromolecular X-ray crystallography.

An important route to understanding how proteins function at a mechanistic level is to have the structure of the target protein available, ideally at ...

Intravital Microscopy for Imaging Subcellular Structures in Live Mice Expressing Fluorescent Proteins

Here we describe a procedure to image subcellular structures in live rodents that is based on the use of confocal intravital microscopy. As a model organ, we use the salivary glands of live mice since they provide several advantages. First, they can be easily exposed to enable access to the optics, and stabilized to facilitate the reduction of the motion artifacts due to heartbeat and respiration. This significantly facilitates imaging and tracking small subcellular structures. Second, most of the cell populations of the salivary glands are accessible from the surface of the organ. This permits the use of confocal microscopy that has a higher spatial resolution than other techniques that have been used for in vivo imaging, such as two-photon microscopy. Finally, salivary glands can be easily manipulated pharmacologically and genetically, thus providing a robust system to investigate biological processes at a molecular level.
In this study we focus on a protocol designed to follow the kinetics of the exocytosis of secretory granules in acinar cells and the dynamics of the apical plasma membrane where the secretory granules fuse upon stimulation of the beta-adrenergic receptors. Specifically, we used a transgenic mouse that co-expresses cytosolic GFP and a membrane-targeted peptide fused with the fluorescent protein tandem-Tomato. However, the procedures that we used to stabilize and image the salivary glands can be extended to other mouse models and coupled to other approaches to label in vivo cellular components, enabling the visualization of various subcellular structures, such as endosomes, lysosomes, mitochondria, and the actin cytoskeleton.

Intravital microscopy is a powerful tool that enables imaging various biological processes in live animals. In this article, we present a detailed method for imaging the dynamics of subcellular structures, such as the secretory granules, in the salivary glands of live mice.

Here we describe a procedure to image subcellular structures in live rodents that is based on the use of confocal intravital microscopy. As a model organ, ...

The Cell-based L-Glutathione Protection Assays to Study Endocytosis and Recycling of Plasma Membrane Proteins

Membrane trafficking involves transport of proteins from the plasma membrane to the cell interior (i.e. endocytosis) followed by trafficking to lysosomes for degradation or to the plasma membrane for recycling. The cell based L-glutathione protection assays can be used to study endocytosis and recycling of protein receptors, channels, transporters, and adhesion molecules localized at the cell surface. The endocytic assay requires labeling of cell surface proteins with a cell membrane impermeable biotin containing a disulfide bond and the N-hydroxysuccinimide (NHS) ester at 4 &#186;C -&#160;a temperature at which membrane trafficking does not occur. Endocytosis of biotinylated plasma membrane proteins is induced by incubation at 37 &#186;C. Next, the temperature is decreased again to 4 &#186;C to stop endocytic trafficking and the disulfide bond in biotin covalently attached to proteins that have remained at the plasma membrane is reduced with L-glutathione. At this point, only proteins that were endocytosed remain protected from L-glutathione and thus remain biotinylated. After cell lysis, biotinylated proteins are isolated with streptavidin agarose, eluted from agarose, and the biotinylated protein of interest is detected by western blotting. During the recycling assay, after biotinylation cells are incubated at 37 &#176;C to load endocytic vesicles with biotinylated proteins and the disulfide bond in biotin covalently attached to proteins remaining at the plasma membrane is reduced with L-glutathione at 4 &#186;C as in the endocytic assay. Next, cells are incubated again at 37 &#176;C to allow biotinylated proteins from endocytic vesicles to recycle to the plasma membrane. Cells are then incubated at 4 &#186;C, and the disulfide bond in biotin attached to proteins that recycled to the plasma membranes is reduced with L-glutathione. The biotinylated proteins protected from L-glutathione are those that did not recycle to the plasma membrane.

Membrane trafficking involves transport of proteins from the plasma membrane to the cell interior (i.e. endocytosis) followed by trafficking to lysosomes for degradation or to the plasma membrane for recycling. Methods described in this article are designed to study endocytosis and recycling of plasma membrane proteins.

Membrane trafficking involves transport of proteins from the plasma membrane to the cell interior (i.e. endocytosis) followed by trafficking to lysosomes ...