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

Podsumowanie

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.

Streszczenie

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 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 "slope of slopes" 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.

Wprowadzenie

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 proteins^2-4. 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 molecules^6,7.

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.

Protokół

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 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.

1. 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.
2. 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. “stack1crop.tif”, “stack1crop2.tif”, ” stack1crop3.tif”.
3. Place the folder containing the crops locally on the computer, not on a server, while doing kICS analysis.

2. Import the Crops into the Analysis Software One by One to Perform the Analysis

1. Open MATLAB and type ”icsgui” in the command window and press ENTER. ICS GUI is the executable name for the image correlation spectroscopy graphical user interface program.
2. In the ICS GUI click the “kICS” tab. A screen shot of the analysis window is shown in Figure 1.
3. Find the folder with the folder name “MATLAB code” and scroll to the file with the file name “Scripts” and open this by double clicking it. Alternatively, right click the file and select “open with MATLAB” or go to file, open in MATLAB and open the scripts file. This file is called Editor.
4. In the Editor scroll to “Load kICS data sets”, then copy or type file name and folder location into the command line which begins with img = e.g. “C:\Users\Documents\AQP3dynamics\stack1crop.tif”. In the command line below the folder location set the number of frames to be analyzed – 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.
5. In the editor click “save” and then click “evaluate cell”. The dataset is now loaded into into the analysis software.
6. In the kICS Analysis window in the GUI, enter the settings for the analysis:
   1. Unclick “T cell” boxes.
   2. Enter the number of time lags to be analyzed. The number of time lags (τ) 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. τ 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.
   3. 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.
   4. Click “Store settings and proceed”.
   5. Click “yes” to show all graphs.
   6. Click “Load image series” (see circle 2 in Figure 1), then click “workspace”.
   7. Select “imgSerCrop”, then click “Import from Workspace”.
   8. 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. For AQP3-EGFP, 0.111 was used. In the box “Enter the time (s) between frames”, 0.1150 was used for AQP3-EGFP.
   9. Click “select ROIs”, enter “1”, click ok and “use whole image”.
   10. Click “Do kICS analysis” and click yes to do “immobile filtering”. Results will open automatically as images.
   11. 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.
   12. Tick box “Save open figures as images”, then click “Save open figures as images” to save result files. Save in a new folder under C:\Users\Documents\AQP3diffusion. Click “clear current data” and continue with analysis of the next crop.

3. Average of the Calculated Diffusion Coefficient is Calculated to Get an Overview of the Analyzed Data

1. Enter the individual diffusion coefficients from each ROI in a spreadsheet (make sure to note the τ and k² values), use the names of the crops as set above.
2. Calculate the average diffusion coefficient and standard deviation using a spreadsheet program.
3. 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.
4. 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.

Wyniki

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 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 at a frame rate of 9.2 Hz. The fo...

Dyskusje

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 cr...

Materiały

Name	Company	Catalog Number	Comments
DMEM	Gibco	31600-083
FBS	Invitrogen	10082147
Penicilin	Sigma	13752
Kanamycin	Gibco	15160070
Streptomycin	Gibco	11860038
Phenol red free medium	Gibco	11880-028
DMSO	Sigma	D8418
HEPES	Gibco	15630056
Apparatus
Spinning Disk Microscope	Nikon	Ti Eclipse
EMCCD Camera	Andor	Ixon+