Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy

The Imaging the Right Mean Square Displacement Method'is able to simultaneously extract both the structure and dynamic average properties of a target biological object in a living sample. Imaging the Right Mean Square Displacement, is a fast and robust procedure, with no need to extract single object trajectories and no need for complex labeling. It just requires a standard optical setup and a fluorescently-labelled object of interest.

The method paves the way to the large-scale, quantitative screening of the structural and dynamic alterations found at the level of subcellular nano-structures in several pathological conditions, such as cancer, diabetes, or neuro-degenerative disorders. Demonstrating the procedure will be Fabio Azzarello, a PhD student from my laboratory. To begin sub-culturing the cells, start by washing a 10 centimeter tissue culture-treated dish of confluent hela cells, twice, with 0.01 molar PBS.

Then, add 1 milliliter of 0.05%trypsin-EDTA and incubate the dish at 37 degrees Celsius and 5%carbon dioxide for five minutes. Re-suspend the detached cells in 9 milliliters of complete DMEM medium and collect 10 milliliters of total medium with trypsin in the centrifuge tube. Seed approximately 0.2 million cells in each 35 millimeter by 10 millimeter dish with the final medium volume of 1 milliliter, and then incubate the cells.

Depending on the subcellular structure of interest, a specific labeling method is required, and once the labeling solution is ready, wash the cells twice with 0.01 molar PBS. After replacing PBS with the dye re-agent containing medium, incubate the dish at 37 degrees Celsius and 5%carbon dioxide, as per the manufacturer's recommendation. At the end of the incubation, wash the cells twice with a fresh medium before the experiment.

Before performing the time-lapsed series acquisition, turn on the microscope incubator controlling system and let the microscope equilibrate at 37 degrees Celsius and 5%carbon dioxide, for about two hours. Use a 488 nanometer argon laser for excitation of EGFP in transfected cells, and fluorescene-labeled Macropinesomes, and collect the fluorescence emission between 500 to 600 nanometers, using a standard photo multiplier tube detector. Use a 543 nanometer helium neon laser to excite the fluorescent dye, and collect the emission between 555 to 655 nanometers.

Set the diameter of the detection pinhole to the size of one Eri. And, for each acquisition, collect a series of 1000 sequential frames. Set the pixel dwell time to 2 microseconds per pixel for a frame time of 129 milliseconds.

To properly initialize the instrumental parameters for the acquisitions, open iMSD. M with the software text editor. Set the parameters by typing values for N'as the number of frames in the time series, pixel size in micrometers, and f'as the temporal resolution in seconds.

Further, set filter background correction input to zero, to process raw images, or one, to perform a threshold-based background subtraction. Next, set AV toll threshold for background correction. If the filter value is set as one, pixels with an intensity lower than the threshold value will be set as zero.

Next, set bit as the integer number determining the intensity sampling. Save and run the edited iMSD. M script file.

Check the script execution on the command window, and if any problems occur, the interrupted warning message will be displayed. After successful script execution, import the image stack and subtract the background, if required. Then, calculate the spacio-temporal correlation function, using the Fourier method.

Fit the spatio-temporal correlation function with a 2D Gaussian function. Next, check the graphical output with the IMSD curve and the corresponding fitting curves, displayed in three separate panels for different types of the fitting equation used. The R-square values are provided in the graph legend.

Then, check the text output. The image acquisition of Lysosome as a test organelle, was performed in live cells at the appropriate temporal resolution, and very low temporal resolution. The artifactual deformation of the Lysosome due to organelle motion, was visible in the low resolution image.

The intensity profile of the spot was derived using a line tool in the image analysis software. Then, the spot diameter estimation was performed, by plotting and interpolating the intensity by a Gaussian function. The acquisition at high speed, yielded a comparable average structure size close to the fixed sample.

Instead, the acquisition at a slow speed, increased the structure size, owing to the natural structure dynamics during imaging. The structural and dynamic properties of Macropinesomes, were observed during trafficking. The observations revealed a decrease in the average size of the Macropinosomes.

The concomitant increase in the sub-diffusive motion, was denoted by decreased alpha'values. For each acquisition, the increase in the number of Macropinesomes with time, was observed. By contrast, similar measurements performed on ISGs, suggested a very different behavior of these latter organelles compared to Macropinesomes.

In fact, insulin granules show unchanged, average structural, or dynamic properties, suggesting they were probed in a stationary state. The method can be easily applied in dual color mode, if two distinct subcellular organelles are labeled differently. In this case, cross-correlation analysis will highlight potential dynamic interactions of the two objects within the cell.