New microscopy techniques require the development of adequate tools for the correct comprehension of the biological processes that are studied. This protocol describes the image processing steps required for single-molecule tracking, including estimation of lateral diffusion parameters and quantification of spot size over its entire trajectory at the cell membrane. This protocol combines the use of several software pieces, including ImageJ, MATLAB, and uTrack, as well as some proteins that have been developed specifically for this protocol.
This method is illustrated with the tracking of membrane receptors under light microscopy, but it can be applied to any particle-like structure whose trajectories can be tracked over time in a video sequence. This technique relates to basic research and has no direct application in a clinical setting. However, it can be used to aid in the characterization of biological events in both in different diseases and therefore in the identification of new therapeutic targets.
This protocol is relevant for the tracking of particles in cellular membranes, as shown in this video, but it can also be applied for the tracking of full cells, bacteria, nanoparticles in a fluid, or whichever other object whose center can be well determined. This protocol is user-friendly and only requires knowledge of cell culture and microscopy techniques. The different techniques and informatic tools employed in this protocol may intimidate a new user.
The visual demonstration helps one to use this technique with confidence. To begin, grow Jurkat cells, transfect them with a monomeric GFP-labeled chemokine receptor vector, and select cells with low expression levels of the labeled vector, as described in the accompanying text protocol. Add media containing the CXCL12 ligand or control media to each fibronectin-coated 35-millimeter glass-bottom microwell dishes that have been coated with fibronectin, and incubate for one hour at 37 degrees Celsius.
Next, add sorted cells to each dish, and incubate for 20 minutes prior to imaging the cells. Following incubation, transfer the first dish of cells to a TIRF microscope stage and turn to the 100x oil immersion objective. Locate and focus on the cells using bright-field to minimize photobleaching effects.
Then switch to TIRF mode and perform a fine-focus adjustment using low laser intensity. Acquire movies of approximately 50 seconds in length, minimizing the time interval between frames. For each experimental condition video file, create a new folder following the file structure directions described in this video's text protocol.
Open the video with Fiji or ImageJ by dragging and dropping the file on the Fiji menu bar, and click on OK to import the LIF file using bioformats. In order to design a mask, also import the corresponding multi-channel image. Next, create a mask by first creating a single image with the channels useful for the design of the mask.
Select Image in the bar menu, and click Color followed by Split Channels to show the different channels as separate images. Merge again the three channels in a single image by selecting Image in the bar menu, going to Color, and selecting Merge Channels. Make sure to select the appropriate channels and then press OK to generate a new non-stacked image.
Synchronize the two windows by going to the bar menu, selecting Analyze, and then going to Tools and selecting Sync Windows. A new window with the synchronized image possibilities will be shown. Now, with the two windows synchronized, the same region in both windows can be cropped.
Go to Image in the bar menu and select Crop. With the rectangular selection tool, draw the region of interest. The two cropped images will show individually.
Next, unsynchronize both windows. If no mask has been created, draw the region of interest with the selection tool and crop. After that, save the video as an image sequence in the directory Videosec.
Then select the multi-channel image, go to Plugins in the menu, and select Segmentation Editor to open the segmentation editor plugin. Choose the freehand selection tool, and use it to select a green label and design the outermost mask. Once designed, press the Plus button in selection option of composite window, and the selected mask will be displayed on the viewer.
Repeat this step for all labels. Once all of the masks are designed, save the mask with the same file name as the video, with the name:mask.tif. Open MATLAB and add the uTrack directory to the path by going to Set Path, and selecting Add With.
Then, change the Working Directory to the directory containing the series to be analyzed. Invoke uTrack by typing Movie Selector GUI in the console and pressing Enter. This will cause the movie selection window to be opened.
Press on the New Movie button, and wait for the Movie Addition window to appear. Press on Add Channel to choose the directory with the video and fill out the movie information parameters. Set the output directory for the results of uTrack to Results, then, press on the Advanced Channel Settings, fill the parameters related to the acquisition, and save.
After creating the movie, press on Continue in the movie selection window. Here, uTrack will ask about the type of object to be analyzed. Choose Single Particles, and then the control panel window will appear.
Next, select the first step, Step 1:Detection, and click on Setting. The Setting Gaussian Mixture Model Fitting window will appear. Enter the settings as shown here, and then press Apply.
In the control panel, click on Run to run the detection step. After a few minutes, check the results by pressing the Result button. The movie shows red circles on the detected particles.
If no red circle is shown, then this step has not worked correctly and you should try again. Now, merge the particles that were just detected into tracks that span multiple frames by first setting up the parameters as shown here. Then press Run in the control panel.
Next, perform Track Analysis. Define the settings, as shown here, and then press Apply and Run. Verify the results by first pressing on the Result button, then, click on Show Track Number of the movie options window and check frame-by-frame that each track has been correctly identified, manually marking particles that are not true particles.
In MATLAB, enter the command as shown here. This command will cause the program to read all of the trajectories and compute the diffusion coefficients. Next, exclude trajectories corresponding to incorrectly identified spots or trajectories by giving the list of spots to exclude.
Calculate the instantaneous diffusion coefficients for each one of the tracks of this cell by following along in the accompanying text protocol. In this case, calculate the diffusion coefficient for a time-lag equals 4 called D1 through 4. When finished, decompose the trajectories into short and long trajectories.
To analyze long trajectories, type the command as shown here to classify the type of motion through their moment scaling spectrum. Analyze the intensity of each particle along their trajectory. Configure this basic behavior in many different ways by following along in the accompanying text protocol.
Then, gather the diffusion and intensity information for all the trajectories. Gather only the diffusion and intensity information for short trajectories. Finally, gather the moment spectrum scaling and the intensity information by typing the following, where long is the suffix used previously.
The use of the techniques described in this protocol allows for the automated tracking of particles detected in fluorescence microscopy movies and the analysis of their dynamic characteristics. From this analysis, one can obtain different characteristics based on variations in stimuli. This includes, the percentage of immobile spots based on four different stimuli, the percentage of long trajectories of greater than 50 frames, and the types of movement along the trajectories as broken up by directed, free, or confined movements.
In addition, the diffusion coefficient, mean spot intensities, and number of receptors per particle can be determined. This shows the distribution of the short diffusion coefficient values for each spot in response to different stimuli. The red line represents the median value, this similar graph shows the mean spot intensities for each spot along its first 20 frames in response to the same stimuli.
Again, the red line represents the mean intensity value. As a spot's mean corrected intensity is related with the number of fluorescent proteins present in this spot, one can directly calculate the number of receptors per particle, as shown here. MATLAB is a case-sensitive programming language.
You may vary the variable names with respect to the ones described in this protocol, but be sure to be consistent in your naming. The names of the functions cannot be changed, and commas, colons, and semi-colons are important for a correct syntax. Single-molecule microscopy allows for the visualization of individual membrane proteins with unprecedented spatio-temporal resolution and provides unique opportunities to reveal unanticipated aspects of cell biology.
This protocols open new doors for the quantitative analysis of microscopy videos in cells and molecular biology.
