This work was supported by a Lundbeck Junior Group Leader Fellowship to LNN. PWW acknowledges grant funding support from the Natural Sciences and Engineering Research Council of Canada (NSERC). We also thank the Danish Molecular Bioimaging Center at University of Southern Denmark for access to spinning disk microscopy.

Acquisition of EGFP-tagged proteins in the plasma membrane for kICS analysis can be done on an epifluorescence microscope, a TIRF setup or on a spinning disk microscope set to acquire images of the membrane of interest. The camera pixel size as well as the time between image frames is needed for the analysis. Image sequences of 100-1,200 frames at a frame rate of 4-30 Hz can be used for the analysis. During the acquisition, it is important to keep the membrane in focus throughout the duration of the imaging and focus on a fraction of the flat membrane in which the fluorescence is uniformly distributed. Moving vesicles, protruding membrane regions and cell organelles can be cropped out later in the analysis process. The acquisition time should be chosen so that there is no drift or movement of the cell.
To get the kICS code scripts for MATLAB, contact Paul Wiseman (Paul.Wiseman@McGill.ca). The first time the kICS code is used, open MATLAB&#160;and click file, then set path, then click add with subfolders and find the folder on the computer in which the kICS code is located. Then click ok, save and close. Now continue with the analysis of crops described in the next points.
1. Import the Image Sequence of Interest into an Imaging Analysis Program as a TIFF Stack
Save the file with the name: stack1.tif.
<ol>
	<li>Select a rectangular region of interest (ROI) at the flat part of the membrane excluding moving cell organelles and protruding membrane regions. The crop size is chosen so that enough statistics are generated for good k2 plots.</li>
	<li>Crop the region and save the crop as a TIFF file in an appropriate folder. If there are several crops from the same movie, use a number increment, e.g. &#8220;stack1crop.tif&#8221;, &#8220;stack1crop2.tif&#8221;, &#8221; stack1crop3.tif&#8221;.</li>
	<li>Place the folder containing the crops locally on the computer, not on a server, while doing kICS analysis.</li>
</ol>
2. Import the Crops into the Analysis Software One by One to Perform the Analysis
<ol>
	<li>Open MATLAB and type &#8221;icsgui&#8221; in the command window and press ENTER. ICS GUI is the executable name for the image correlation spectroscopy graphical user interface program.</li>
	<li>In the ICS GUI click the &#8220;kICS&#8221; tab. A screen shot of the analysis window is shown in Figure 1.</li>
	<li>Find the folder with the folder name &#8220;MATLAB code&#8221; and scroll to the file with the file name &#8220;Scripts&#8221; and open this by double clicking it. Alternatively, right click the file and select &#8220;open with MATLAB&#8221; or go to file, open in MATLAB and open the scripts file. This file is called Editor.</li>
	<li>In the Editor scroll to &#8220;Load kICS data sets&#8221;, then copy or type file name and folder location into the command line which begins with img = e.g. &#8220;C:\Users\Documents\AQP3dynamics\stack1crop.tif&#8221;. In the command line below the folder location set the number of frames to be analyzed &#8211; e.g. [1:500]. Use the same settings for all consecutive movies in the data series. If there is focus drift in later frames, these should be excluded from the analysis.</li>
	<li>In the editor click &#8220;save&#8221; and then click &#8220;evaluate cell&#8221;. The dataset is now loaded into into the analysis software.</li>
	<li>In the kICS Analysis window in the GUI, enter the settings for the analysis:
	<ol>
		<li>Unclick &#8220;T cell&#8221; boxes.</li>
		<li>Enter the number of time lags to be analyzed. The number of time lags (&#964;) will have effect on the diffusion plot and must be chosen so that the diffusion plot is linear. Start with entering 25 and then decrease to where the diffusion plot is linear. &#964; is the number of time-lags the kICS analysis compares and a time lag is the time between one frame and the next. The smallest value which gives a linear plot should be selected. The maximum number of time lags is chosen so that a linear fit is obtained, choosing a higher value will produce data which cannot be line fitted.</li>
		<li>Enter the maximum k2 value (see circle 1 in Figure 1), it is usually 20-50. A good k2 plot is a plot in which there are many data points which are distributed along a line. Start with entering 70 and then decrease after having evaluated the k2 and diffusion plots and select the lowest value where the data points cluster around a straight line. The k2 value is the squared magnitude of the spatial k-vector in Fourier space. 
		Initially, the analysis should be repeated until the best values are determined, then use the same settings for all consecutive movies in the data series but always check that the selected settings give a good fit to the data: good k2 plots and a linear diffusion plot.</li>
		<li>Click &#8220;Store settings and proceed&#8221;.</li>
		<li>Click &#8220;yes&#8221; to show all graphs.</li>
		<li>Click &#8220;Load image series&#8221; (see circle 2 in Figure 1), then click &#8220;workspace&#8221;.</li>
		<li>Select &#8220;imgSerCrop&#8221;, then click &#8220;Import from Workspace&#8221;.</li>
		<li>Enter the imaging system collection settings. In the box &#8220;pixel size&#8221; enter the projected pixel size for the imaging system at which the image stacks are acquired. For AQP3-EGFP, 0.111 was used. In the box &#8220;Enter the time (s) between frames&#8221;, 0.1150 was used for AQP3-EGFP.</li>
		<li>Click &#8220;select ROIs&#8221;, enter &#8220;1&#8221;, click ok and &#8220;use whole image&#8221;.</li>
		<li>Click &#8220;Do kICS analysis&#8221; and click yes to do &#8220;immobile filtering&#8221;. Results will open automatically as images.</li>
		<li>Minimize the settings window, minimize the cell info window, and minimize the photophysics window. In the dynamics window, check that the dynamics plot shows a linear fit of the data to a line with a negative slope meaning that the data points correlate and free diffusion can be assumed. Record the diffusion coefficient for the specific time lag and k2 settings (it is not necessary to note the standard deviation of the fit). The data points in the k2 plots must be clustered around the line for the given time lags.</li>
		<li>Tick box &#8220;Save open figures as images&#8221;, then click &#8220;Save open figures as images&#8221; to save result files. Save in a new folder under C:\Users\Documents\AQP3diffusion. Click &#8220;clear current data&#8221; and continue with analysis of the next crop.</li>
	</ol>
	</li>
</ol>
3. Average of the Calculated Diffusion Coefficient is Calculated to Get an Overview of the Analyzed Data
<ol>
	<li>Enter the individual diffusion coefficients from each ROI in a spreadsheet (make sure to note the &#964; and k2 values), use the names of the crops as set above.</li>
	<li>Calculate the average diffusion coefficient and standard deviation using a spreadsheet program.</li>
	<li>Perform a Kolmogorov-Smirnov test or students t-test to evaluate statistical differences using a 5% significance level. Quantitave comparisons between cells (for example treated vs. nontreated) can be made.</li>
	<li>Open the spreadsheet version of the diffusion plots which was generated by the analysis software for each crop, and average the data for each condition for each time lag. Generate a new averaged diffusion plot for presenting in papers or presentations.</li>
</ol>

<ol>
	<li>Kolin, D. L., Ronis, D., Wiseman, P. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=k-Space+image+correlation+spectroscopy:+A+method+for+accurate+transport+measurements+independent+of+fluorophore+photophysics.">k-Space image correlation spectroscopy: A method for accurate transport measurements independent of fluorophore photophysics.</a> Biophysical Journal. 91, 3061-3075 (2006).</li><li>Saxton, M. J., Jacobson, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-particle+tracking:+Applications+to+membrane+dynamics.">Single-particle tracking: Applications to membrane dynamics.</a> Annual Review of Biophysics and Biomolecular Structure. 26, 373-399 (1997).</li><li>Bannai, H., Levi, S., Schweizer, C., Dahan, M., Triller, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Imaging+the+lateral+diffusion+of+membrane+molecules+with+quantum+dots.">Imaging the lateral diffusion of membrane molecules with quantum dots.</a> Nat Protoc. 1, 2628-2634 (2006).</li><li>Jaqaman, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Robust+single-particle+tracking+in+live-cell+time-lapse+sequences.">Robust single-particle tracking in live-cell time-lapse sequences.</a> Nat Methods. 5, 695-702 (2008).</li><li>Pinaud, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamic+partitioning+of+a+glycosyl-phosphatidylinositol-anchored+protein+in+glycosphingolipid-rich+microdomains+imaged+by+single-quantum+dot+tracking.">Dynamic partitioning of a glycosyl-phosphatidylinositol-anchored protein in glycosphingolipid-rich microdomains imaged by single-quantum dot tracking.</a> Traffic. 10, 691-712 (2009).</li><li>Wieser, S., Axmann, M., Schutz, G. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Versatile+analysis+of+single-molecule+tracking+data+by+comprehensive+testing+against+Monte+Carlo+simulations.">Versatile analysis of single-molecule tracking data by comprehensive testing against Monte Carlo simulations.</a> Biophys J. 95, 5988-6001 (2008).</li><li>Sbalzarini, I. F., Koumoutsakos, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Feature+point+tracking+and+trajectory+analysis+for+video+imaging+in+cell+biology.">Feature point tracking and trajectory analysis for video imaging in cell biology.</a> Journal of Structural Biology. 151, 182-195 (2005).</li><li>Durisic, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detection+and+correction+of+blinking+bias+in+image+correlation+transport+measurements+of+quantum+dot+tagged+macromolecules.">Detection and correction of blinking bias in image correlation transport measurements of quantum dot tagged macromolecules.</a> Biophys J. 93, 1338-1346 (2007).</li><li>Umenishi, F., Verbavatz, J. M., Verkman, A. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=cAMP+regulated+membrane+diffusion+of+a+green+fluorescent+protein-aquaporin+2+chimera.">cAMP regulated membrane diffusion of a green fluorescent protein-aquaporin 2 chimera.</a> Biophys J. 78, 1024-1035 (2000).</li><li>Levin, M. H., Haggie, P. M., Vetrivel, L., Verkman, A. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Diffusion+in+the+endoplasmic+reticulum+of+an+aquaporin-2+mutant+causing+human+nephrogenic+diabetes+insipidus.">Diffusion in the endoplasmic reticulum of an aquaporin-2 mutant causing human nephrogenic diabetes insipidus.</a> The Journal of biological chemistry. 276, 21331-21336 (2001).</li><li>Semrau, S., Schmidt, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Particle+image+correlation+spectroscopy+(PICS):+retrieving+nanometer-scale+correlations+from+high-density+single-molecule+position+data.">Particle image correlation spectroscopy (PICS): retrieving nanometer-scale correlations from high-density single-molecule position data.</a> Biophys J. 92, 613-621 (2007).</li><li>Petersen, N. O., Hoddelius, P. L., Wiseman, P. W., Seger, O., Magnusson, K. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitation+of+membrane+receptor+distributions+by+image+correlation+spectroscopy:+concept+and+application.">Quantitation of membrane receptor distributions by image correlation spectroscopy: concept and application.</a> Biophys J. 65, 1135-1146 (1993).</li><li>Marlar, S., Arnspang, E. C., Koffman, J. S., L&#248;cke, E. M., Christensen, B. M., Nejsum, L. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Elevated+cAMP+increases+aquaporin-3+plasma+membrane+diffusion.">Elevated cAMP increases aquaporin-3 plasma membrane diffusion.</a> Am J Physiol Cell Physiol. 306, (2014).</li></ol>

The authors declare that they have no competing financial interests.

This paper presented a detailed step-by-step overview of how to apply the kICS analysis method to determine diffusion coefficients from microscopy images of proteins tagged with fluorescent proteins. The analysis is independent of probe choice with a very broad dynamic range in labeling density and can thus also be applied to proteins labeled with quantum dots as well as dyes and fluorescent proteins such as EGFP.
To calculate valid diffusion coefficients for the proteins, there are several critical steps, which have been optimized. It is essential to image labeled proteins on the membrane only. If there are additional objects like moving vesicles or cell organelles in the image sequence to be analyzed, these will contribute to the calculated diffusion coefficient which results in under- or overestimates of the diffusion coefficient.
Following the steps listed above, the GUI code for kICS is user friendly and analysis is rapid. It includes several averaging steps: including both the circular averaging and the calculation of the slope of slopes diffusion plot as function of time lags, which facilitates quick generation of an average and statistically significant value for the diffusion coefficient. Thus, compared to the extensive analysis needed to calculate diffusion coefficients from SPT experiments, kICS is user friendly, rapid and can be performed without specialized training in biophysics.
If the analysis results in a negative diffusion coefficient or a horizontal diffusion plot, trouble-shooting includes visual inspection of the crop to see if it fulfills all requirements described here. Crops which are too small can yield unphysical negative diffusion coefficients and must be discarded. In order to calculate a diffusion coefficient, it is often possible to make a new crop elsewhere in the dataset which is suitable for analysis or simply increase the crop size if possible.
This technique can be applied to most datasets but currently, kICS analysis can only calculate an average diffusion coefficient and cannot distinguish different protein migration patterns or generate trajectories. Using kICS, diffusion coefficients can easily be extracted and diffusion of a protein can be compared for example between treated and non-treated cells or between a wild-type and a mutant protein, which will reveal if treatment of mutations induce differences in diffusion and hence potentially in protein-protein and/or protein-lipid interactions. Plasma membrane diffusion of AQP2 measured by FRAP was shown to decrease upon addition of forskolin, a cAMP agonist10, and moreover, in the ER, a mutated version of AQP2 causing nephrogenic diabetes insipidus, diffused differently than wildtype AQP2&#160;9.
Although it is possible to calculate diffusion coefficients from single particle data using mean squared displacement, step size analysis or particle image correlation spectroscopy2,11,12, these analysis methods can be computationally demanding and often require custom written computer algorithms. The advantage of the kICS protocol presented here is that it is relatively independent of labeling density and computationally easy. This walk through of kICS analysis will thus be useful for many cell biologists, who can easily apply it to their membrane protein of interest.

The four dimensional spatiotemporal organization and lateral mobility of plasma membrane proteins are tightly regulated and may play a role in protein function, activity and protein-protein interactions. Lateral diffusion of plasma membrane proteins, has traditionally been studied by calculating diffusion coefficients for single particles from time-lapse imaging of quantum dot or dye labeled plasma membrane proteins2-4. This approach requires insertion of an extracellular tag in the plasma membrane protein for quantum dot or dye labeling, which can compromise protein folding and function and thus, cannot be achieved for some proteins. The steric volume of a quantum dot has been shown to slow down diffusion of the protein5 and moreover, only a tiny fraction of the proteins in the population are labeled with quantum dots, and it is not known if this fraction is representative for the total pool of plasma membrane proteins. Single particle tracking (SPT) measurements of diffusion coefficients from image series of quantum dot labeled proteins involves mapping the imaged peak positions of the particles with two dimensional Gaussian fits followed by extensive analysis algorithms. The analysis is computationally very intensive, requiring extensive non-linear curve fitting in each image frame of a time-lapse sequence to approximations of the microscope point spread function (typically two dimensional spatial Gaussians) and subsequent linking of particle positions into particle trajectories describing the motion of single molecules6,7.
A recently developed image correlation technique, k-Space Image Correlation Spectroscopy (kICS) enables relative simple measurements of diffusion coefficients of fluorescently tagged plasma membrane proteins. The possibility to routinely calculate diffusion coefficients of membrane proteins labeled with fluorescent proteins by kICS is a unique tool which holds several advantages over traditional quantum dot SPT analysis: No insertion of extracellular tags and time consuming extracellular labeling is required (cell lines expressing fluorescent proteins may be used); diffusion coefficients are extracted from the total pool of fluorescent proteins compared to a subset labeled with quantum dots; analysis is simple without the need to track single proteins and the analysis can be performed using an existing code with no requirement for additional user programming. The method is rapid because it is an averaging technique which enables fast computation and calculation of diffusion coefficients. These rapid diffusion measurements for the protein population complement the more detailed molecular transport information obtained for a subset of the population by the painstaking SPT methods.
kICS time correlates fluorescence microscopy image sequences that have first been transformed to Fourier space, and thereby separates fluorescence fluctuations due to photophysics from those due to molecular transport1. As a result, kICS can determine the number density, flow speed, and diffusion of fluorescently labeled molecules while being unbiased by complex photobleaching or blinking of the fluorophores. This makes kICS a useful tool for quickly determining the diffusion dynamics of fluorescently-labeled cell membrane proteins without the need to custom write algorithms. Besides fluorescently labeled proteins, kICS can also be applied to quantum dot-labeled membrane proteins8.
This paper provides a step-by-step introduction of how to use kICS to extract diffusion coefficients of EGFP-tagged plasma membrane proteins by demonstrating how to place the crop, how to use the code and how to evaluate the generated plots, from which diffusion coefficients are extracted. As an example, data obtained by spinning disk-microscopy set in semi-total internal reflection fluorescence (TIRF) mode, of a renal water channel protein aquaporin-3 (AQP3) tagged with EGFP is presented.

<table border="0" cellpadding="0" cellspacing="0" width="406">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">DMEM</td>
			<td style="width:71px;">Gibco</td>
			<td style="width:119px;">31600-083</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">FBS</td>
			<td style="width:71px;">Invitrogen</td>
			<td>10082147</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Penicilin&#160;&#160;</td>
			<td style="width:71px;">Sigma</td>
			<td><a href="http://www.sigmaaldrich.com/catalog/product/sigma/13752?lang=en&amp;region=DK">13752</a></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Kanamycin</td>
			<td style="width:71px;">Gibco</td>
			<td>15160070</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Streptomycin</td>
			<td style="width:71px;">Gibco</td>
			<td>11860038</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Phenol red free medium</td>
			<td style="width:71px;">Gibco</td>
			<td>11880-028</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">DMSO</td>
			<td style="width:71px;">Sigma</td>
			<td><a href="http://www.sigmaaldrich.com/catalog/product/sigma/d8418?lang=en&amp;region=DK">D8418&#160;</a></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">HEPES</td>
			<td style="width:71px;">Gibco</td>
			<td>15630056</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Apparatus</td>
			<td style="width:71px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Spinning Disk Microscope</td>
			<td style="width:71px;">Nikon</td>
			<td style="width:119px;">Ti Eclipse</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">EMCCD Camera</td>
			<td style="width:71px;">Andor</td>
			<td style="width:119px;">Ixon+</td>
		</tr>
	</tbody>
</table>

easy measurement of diffusion coefficients of egfp-tagged plasma membrane proteins using k-space image correlation spectroscopy

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

To acquire suitable image stacks to be analyzed with kICS, different microscope systems can be used. It is possible to analyze image sequences from an epi-fluorescence microscope as well as a TIRF&#160;or spinning disk setup. The membrane must be flat without any large moving cell organelles or moving vesicles/objects. This paper presents a time lapse image sequence of AQP3 tagged with EGFP in live MDCK cells imaged on a spinning disk microscope with focus set to the plasma membrane&#160;at a frame rate of 9.2 Hz. The focus was set at the basal (bottom) plasma membrane. The data has recently been published in AJP Cell13.
Figure 2A&#160;shows an image of the cell. The scalebar is 10 mm and example ROI crops are highlighted in rectangles. For the selection of crops it is important to crop at the periphery of the cell where the membrane is uniform and flat so only a two-dimensional diffusion is visualized. Cell organelles, vesicles and membrane projections should be avoided and it is important for the analysis that there is no drift during the time lapse. Examples of crops which would be excluded from analysis are presented in Figures 2B&#160;and 2C. Moving vesicles contribute to the correlation (Figure 2B) and holes in the membrane which in this case move slightly (Figure 2C) obviously should not be included in the analysis. It is also important that the ROI crop is large enough to have sufficient spatial sampling for the analysis (for imaging with a high NA objective lens a minimum linear size of 32 pixels is a reasonable guideline). For this dataset a 60X&#160;(NA 1.40) was used.
Figure 3A&#160;shows the diffusion plot of one crop generated with kICS. This method can be used to find the diffusion coefficient of a protein tagged with EGFP (or another fluorescent protein), a dye or quantum dots. The slope of the curve in the diffusion plot is simply the negative of the diffusion coefficient. In this case, the diffusion coefficient of AQP3-GFP was&#160;0.0104 &#177;&#160;0.0040 &#181;m2/s. Figure 3B&#160;is a calculated diffusion plot with too many time lags, in this case the analysis was redone but with fewer time lags so that the diffusion plot is linear.
Figure 4A&#160;presents a typical k2 plot. This is the radially averaged and ln transformed correlation curve for a selected time lag. From these k2 plots the slope is plotted versus time lags and the result is the diffusion plot as shown in Figure 3A. When inspecting the k2 plots, it is important that the fit is evaluated. Figure 4B&#160;is an example of a k2 plot for which it can be concluded that the crop is too small and could not be analyzed as it should have a negative slope and decay linearly to a noise floor. Figure 4C&#160;is an example of a k2 plot which needs to be reanalyzed with a reduced maximum k2 value as the fit is no longer good (as judged by the increasing residuals at larger k2 values). In this case the analysis must be run again entering a lower maximum k2 value, in this case 25.
Figure 5 shows an averaged diffusion plot averaged over 10 crops. The diffusion plot was found in the excel file from the analysis, and all the diffusion plots can now be averaged for all crops from the same experiment. In the resulting diffusion plot different treatments of the cell can be combined in the same figure. Parallel trend lines are equal diffusion coefficients and non parallel trend lines indicate unequal diffusion. In this figure, the AQP3-EGFP diffusion coefficient is calculated (data from AJP Cell13).
<img alt="Figure 1" fo:content-width="5in" src="/files/ftp_upload/51074/51074fig1highres.jpg" width="300" /> 
Figure 1.&#160;Screen shot of the kICS analysis window. In this window the kiCS analysis is done. The circles indicate which buttons to press for the individual steps in the protocol. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/51074/51074fig1highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Figure 2" fo:content-width="3in" src="/files/ftp_upload/51074/51074fig2highres.jpg" width="300" /> 
Figure 2.&#160;Spinning disk image of a cell expressing GFP. (A) Representative image of an MDCK cell stably expressing AQP3-GFP and representative crops shown. The image is acquired on a spinning disk microscope with focus close to the basal plasma membrane. (B) Representative crop, which was excluded from analysis in kICS since there are moving vesicles, which can contribute to the diffusion coefficient. (C) An example of a crop in which there is a hole in the membrane, thus, this crop was excluded from analysis. All scale bars are 10 &#956;m. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/51074/51074fig2highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Figure 3" fo:content-width="5in" src="/files/ftp_upload/51074/51074fig3highres.jpg" width="500" /> 
Figure 3.&#160;Diffusion plots. (A) A representative diffusion plot in which all point are close to the line fit. This is a good diffusion plot because the data are linear. (B) An example of a diffusion plot for which the analysis must be redone using a lower number of timelags. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/51074/51074fig3highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Figure 4" fo:content-width="3in" src="/files/ftp_upload/51074/51074fig4highres.jpg" width="300" /> 
Figure 4.&#160;Examples of k2 plots. (A) An example of a good k2 plot in which the data points are equally distributed on either side of the line fit. (B) An example of a k2 plot for a crop smaller than the optimal size. In this k2 plot, the slope of the line fit is positive, it should be negative and therefore the crop is excluded from the analysis. &#160;(C) This k2 plot shows that &#160;the analysis must be redone with a lower maximum k2 value in order to select the part of the k2 plot were the data are equally distributed on either side of the line fit. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/51074/51074fig4highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Figure 5" fo:content-width="5in" src="/files/ftp_upload/51074/51074fig5highres.jpg" width="500" /> 
Figure 5.&#160;Averaged diffusion plot. An averaged diffusion plot that was averaged over 10 crops.

Watch this Scientific Journal Video about Easy Measurement of Diffusion Coefficients of EGFP-tagged Plasma Membrane Proteins Using k-Space Image Correlation Spectroscopy at JoVE.com

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

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

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

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Nanyang Technological University

Research

JoVE Journal

Biology

10.7K Views.  Aarhus University. This paper provides a step by step guide to the fluctuation analysis technique k-Space Image Correlation Spectroscopy (kICS) for measuring diffusion coefficients of fluorescently labeled plasma membrane proteins in live mammalian cells.

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

Measuring Diffusion Coefficients via Two-photon Fluorescence Recovery After Photobleaching

Multi-fluorescence recovery after photobleaching is a microscopy technique used to measure the diffusion coefficient (or analogous transport parameters) of macromolecules, and can be applied to both in vitro and in vivo biological systems. Multi-fluorescence recovery after photobleaching is performed by photobleaching a region of interest within a fluorescent sample using an intense laser flash, then attenuating the beam and monitoring the fluorescence as still-fluorescent molecules from outside the region of interest diffuse in to replace the photobleached molecules. We will begin our demonstration by aligning the laser beam through the Pockels Cell (laser modulator) and along the optical path through the laser scan box and objective lens to the sample. For simplicity, we will use a sample of aqueous fluorescent dye. We will then determine the proper experimental parameters for our sample including, monitor and bleaching powers, bleach duration, bin widths (for photon counting), and fluorescence recovery time. Next, we will describe the procedure for taking recovery curves, a process that can be largely automated via LabVIEW (National Instruments, Austin, TX) for enhanced throughput. Finally, the diffusion coefficient is determined by fitting the recovery data to the appropriate mathematical model using a least-squares fitting algorithm, readily programmable using software such as MATLAB (The Mathworks, Natick, MA).

In this article we will describe the procedure for measuring diffusion coefficients using multi-photon fluorescence recovery after photobleaching. We will begin by aligning the laser along the optical path to the sample and determining the proper experimental parameters, then continue generating and finally fitting fluorescence recovery curves.

Multi-fluorescence recovery after photobleaching is a microscopy technique used to measure the diffusion coefficient (or analogous transport parameters) ...

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 ...

Quantitative Measurement of GLUT4 Translocation to the Plasma Membrane by Flow Cytometry

Glucose is the main source of energy for the body, requiring constant regulation of its blood concentration. Insulin release by the pancreas induces glucose uptake by insulin-sensitive tissues, most notably the brain, skeletal muscle, and adipocytes. Patients suffering from type-2 diabetes and/or obesity often develop insulin resistance and are unable to control their glucose homeostasis. New insights into the mechanisms of insulin resistance may provide new treatment strategies for type-2 diabetes.
The GLUT family of glucose transporters consists of thirteen members distributed on different tissues throughout the body1. Glucose transporter type 4 (GLUT4) is the major transporter that mediates glucose uptake by insulin sensitive tissues, such as the skeletal muscle. Upon binding of insulin to its receptor, vesicles containing GLUT4 translocate from the cytoplasm to the plasma membrane, inducing glucose uptake. Reduced GLUT4 translocation is one of the causes of insulin resistance in type-2 diabetes2,3.
The translocation of GLUT4 from the cytoplasm to the plasma membrane can be visualized by immunocytochemistry, using fluorophore-conjugated GLUT4-specific antibodies.
Here, we describe a technique to quantify total amounts of GLUT4 translocation to the plasma membrane of cells during a chosen duration, using flow cytometry. This protocol is rapid (less than 4 hours, including incubation with insulin) and allows the analysis of as few as 3,000 cells or as many as 1 million cells per condition in a single experiment. It relies on anti-GLUT4 antibodies directed to an external epitope of the transporter that bind to it as soon as it is exposed to the extracellular medium after translocation to the plasma membrane.

This protocol describes a rapid technique to quantify the translocation of GLUT4 from the cytoplasm to the plasma membrane of cells by flow cytometry.

Glucose is the main source of energy for the body, requiring constant regulation of its blood concentration. Insulin release by the pancreas induces ...

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 ...

Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing (MTT)

Our goal is to obtain a comprehensive description of molecular processes occurring at cellular membranes in different biological functions. We aim at characterizing the complex organization and dynamics of the plasma membrane at single-molecule level, by developing analytic tools dedicated to Single-Particle Tracking (SPT) at high density: Multiple-Target Tracing (MTT)1. Single-molecule videomicroscopy, offering millisecond and nanometric resolution1-11, allows a detailed representation of membrane organization12-14 by accurately mapping descriptors such as cell receptors localization, mobility, confinement or interactions.
We revisited SPT, both experimentally and algorithmically. Experimental aspects included optimizing setup and cell labeling, with a particular emphasis on reaching the highest possible labeling density, in order to provide a dynamic snapshot of molecular dynamics as it occurs within the membrane. Algorithmic issues concerned each step used for rebuilding trajectories: peaks detection, estimation and reconnection, addressed by specific tools from image analysis15,16. Implementing deflation after detection allows rescuing peaks initially hidden by neighboring, stronger peaks. Of note, improving detection directly impacts reconnection, by reducing gaps within trajectories. Performances have been evaluated using Monte-Carlo simulations for various labeling density and noise values, which typically represent the two major limitations for parallel measurements at high spatiotemporal resolution.
The nanometric accuracy17 obtained for single molecules, using either successive on/off photoswitching or non-linear optics, can deliver exhaustive observations. This is the basis of nanoscopy methods17 such as STORM18, PALM19,20, RESOLFT21 or STED22,23, which may often require imaging fixed samples. The central task is the detection and estimation of diffraction-limited peaks emanating from single-molecules. Hence, providing adequate assumptions such as handling a constant positional accuracy instead of Brownian motion, MTT is straightforwardly suited for nanoscopic analyses. Furthermore, MTT can fundamentally be used at any scale: not only for molecules, but also for cells or animals, for instance. Hence, MTT is a powerful tracking algorithm that finds applications at molecular and cellular scales.

Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing MTT

Multiple-Target Tracing is a homemade algorithm developed for tracking individually labeled molecules within the plasma membrane of living cells. Efficiently detecting, estimating and tracing molecules over time at high-density provide a user-friendly, comprehensive tool to investigate nanoscale membrane dynamics.

Our goal is to obtain a comprehensive description of molecular processes occurring at cellular membranes in different biological functions. We aim at ...

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 ...

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 ...

Imaging Plasma Membrane Deformations With pTIRFM

To gain novel insights into the dynamics of exocytosis, our group focuses on the changes in lipid bilayer shape that must be precisely regulated during the fusion of vesicle and plasma membranes. These rapid and localized changes are achieved by dynamic interactions between lipids and specialized proteins that control membrane curvature. The absence of such interactions would not only have devastating consequences for vesicle fusion, but a host of other cellular functions that involve control of membrane shape. In recent years, the identity of a number of proteins with membrane-shaping properties has been determined. What remains missing is a roadmap of when, where, and how they act as fusion and content release progress.
Our understanding of the molecular events that enable membrane remodeling has historically been limited by a lack of analytical methods that are sensitive to membrane curvature or have the temporal resolution to track rapid changes. PTIRFM satisfies both of these criteria. We discuss how pTIRFM is implemented to visualize and interpret rapid, submicron changes in the orientation of chromaffin cell membranes during dense core vesicle (DCV) fusion. The chromaffin cells we use are isolated from bovine adrenal glands. The membrane is stained with a lipophilic carbocyanine dye,1,1&#39;-dioctadecyl-3,3,3&#39;,3&#39;-tetramethylindodicarbocyanine, 4-chlorobenzenesulfonate, or&#160;diD. DiD intercalates in the membrane plane with a &#34;fixed&#34; orientation and is therefore sensitive to the polarization of the evanescent field. The diD-stained cell membrane is sequentially excited with orthogonal polarizations of a 561&#160;nm laser (p-pol, s-pol). A 488&#160;nm laser is used to visualize vesicle constituents and time the moment of fusion. Exocytosis is triggered by locally perfusing cells with a depolarizing KCl solution. Analysis is performed offline using custom-written software to understand how diD emission intensity changes relate to fusion pore dilation.

Polarization-based Total Internal Reflection Fluorescence Microscopy (pTIRFM) enables real-time detection of cell membrane dynamics. This article describes the implementation of pTIRFM for the study of membrane remodeling during regulated exocytosis. The technique is generalizable to other processes in cell biology that directly or indirectly involve changes in membrane shape.

To gain novel insights into the dynamics of exocytosis, our group focuses on the changes in lipid bilayer shape that must be precisely regulated during ...

Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens

Characterization of the mechanical behavior of biological and engineered soft tissues is a central component of fundamental biomedical research and product development. Stress-strain relationships are typically obtained from mechanical testing data to enable comparative assessment among samples and in some cases identification of constitutive mechanical properties. However, errors may be introduced through the use of average strain measures, as significant heterogeneity in the strain field may result from geometrical non-uniformity of the sample and stress concentrations induced by mounting/gripping of soft tissues within the test system. When strain field heterogeneity is significant, accurate assessment of the sample mechanical response requires measurement of local strains. This study demonstrates a novel biomechanical testing protocol for calculating local surface strains using a mechanical testing device coupled with a high resolution camera and a digital image correlation technique. A series of sample surface images are acquired and then analyzed to quantify the local surface strain of a vascular tissue specimen subjected to ramped uniaxial loading. This approach can improve accuracy in experimental vascular biomechanics and has potential for broader use among other native soft tissues, engineered soft tissues, and soft hydrogel/polymeric materials. In the video, we demonstrate how to set up the system components and perform a complete experiment on native vascular tissue.

We describe the use of digital image correlation to characterize the local surface strain field on vascular tissue samples subjected to uniaxial tensile testing. These measurements facilitate precise quantification of the sample mechanical response and the generation of constitutive stress-strain relations.

Characterization of the mechanical behavior of biological and engineered soft tissues is a central component of fundamental biomedical research and ...