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

The overall goal of this procedure is to measure diffusion coefficients of membrane proteins in live cells. This is accomplished by first growing cells expressing the GFP tagged protein, and then acquiring a time lapse image sequence on a spinning disc microscope with focus set to the plasma membrane. The second step is to select a crop in the image stack in which the membrane is flat and there are no moving organelles.

Next, the crop is analyzed using case-based image correlation spectroscopy software. The final step is to average the diffusion plots in Excel. Ultimately, live cell microscopy combined with kicks is used to measure the diffusion coefficient of a membrane protein.

This method can help answer key questions in the membrane biophysics field, such as finding the diffusion coefficients of membrane proteins at different conditions. The first time the kicks code is used, open MATLAB and click file. Then set path, then click add with sub folders and find the folder on the computer in which the kicks code is located.

Click okay. Before saving and closing MATLAB during acquisition, it is important to keep the membrane in focus throughout the duration of the imaging. Select a rectangular region of interest or ROI at the flat part of the membrane where fluorescence is uniformly distributed.

Exclude moving cell organelles and protruding membrane regions. The crop size is chosen so that enough statistics are generated for good K squared plots. 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 such as stack one crop, do tiff, stack one, crop two, tif, et cetera. To perform the analysis first open mat lab and type ICS gooey in the command window and press enter ICS.

GUI is the executable name for the image Correlation spectroscopy graphical user interface program in the ICS Gooey. Click the kicks tab. Find the folder with the folder name, MATLAB code.

Scroll to the file with the file name scripts, and open this by double clicking it. This file is called editor. In.The editor.

Scroll to load kicks data sets. Then copy or type the file name and folder location into the command line. In the command line below the folder location.

Set the number of frames to be analyzed. 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 in the editor. Click save, and then click evaluate. Sell the dataset is now loaded into the analysis software in the kicks analysis window in the gui.

Enter the settings for the analysis by first unclicking T-cell boxes. Then enter the number of timelag to be analyzed. Enter the maximum K squared.

It is usually between 20 and 50 Initially. The analysis should be repeated until the best values are determined, and then the setting should be used for all consecutive movies in the data series. Always check that the selected settings give a good fit for the data as evidenced by a good case square plots and a linear diffusion plot.

Next, click store settings and proceed. Then click yes to show all graphs. Click load image series, and then click workspace.

Select image series crop before clicking import from workspace. Now enter the imaging system collection settings in the box pixel size. Enter the projected pixel size for the imaging system at which the image stacks are acquired.

Click select ROIs and enter one. Then click okay and use whole image. Click do kicks analysis and click yes to do immobile filtering.

Results will open automatically as images. 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 align 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 K squared settings. The data points in the K squared plots must be clustered around the line for the given time. Lags tick box, save open figures as images, and then click save open figures as images to save result files.

Save in a new folder and then click clear current data and continue with analysis of the next crop. Using the names of the crops as set before, enter the individual diffusion coefficients from each ROI in a spreadsheet and make sure to note the number of time lags and K squared values. Calculate the average diffusion coefficient and standard deviation using a spreadsheet program.

Perform a MOV Smirnoff test or student's T-test to evaluate statistical differences. Using a 5%significance level, quantitative comparisons between cells can be made 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 average diffusion plot for presenting in papers or presentations. The results presented here are a time-lapse image sequence of Aquaporin three tagged with EGFP. In live MDCK cells, which are renal epithelial cells, imaged on a spinning disc microscope with focus set to the plasma membrane.

Shown here is an image of the cell with example ROI crops 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.

This representative crop was excluded from analysis and kicks since there are moving vesicles, which can contribute to the diffusion coefficient. Another example of an excluded crop in which there is a hole in the membrane is shown here. The diffusion plot of one crop generated with kicks is presented here.

This method can be used to find the diffusion coefficient of a protein tagged with fluorescent protein, a dye or quantum dots. The slope of the curve in the diffusion plot is simply the negative of the diffusion coefficient shown here for Aquaporin three tagged with EGFP shown here 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.

A typical case squared plot is presented here. This is the radially averaged and logarithm transformed correlation curve for a selected time lag from these K squared plots. The slope is plotted versus time lags, and the result is the previously shown diffusion plot.

When inspecting the K squared plots, it is important that the fit is evaluated from this K squared plot. It can be concluded that the crop is too small and could not be analyzed as it should have a negative slope and to decay linearly to a noise floor. This K squared plot needs to be reanalyzed with a reduced maximum K squared value since the fit is no longer good as judged by the increasing residuals at larger K squared values.

In this case, the analysis must be run again, entering a lower maximum K squared value. An average diffusion plot averaged over 10 crops is shown here. The diffusion plot was found in the Excel file from the analysis and from it.

The AQUAPORIN three EGFP diffusion coefficient is calculated Following this procedure. Other methods like measuring the diffusion coefficient of membrane proteins after the addition of different drugs can be performed in order to answer additional questions like the diffusion coefficient changes after drug treatment, and if it's likely that there are changes in the lipid environments and or protein protein interactions in the membrane.