Mass-Sensitive Particle Tracking to Characterize Membrane-Associated Macromolecule Dynamics

With mass-sensitive particle tracking, we can observe individual biomolecules diffusing on lipid membranes and quantify their dynamics in real time, all completely label free. Since no labels are required, there is no danger of disturbing the dynamic behavior of the biomolecules. Another advantage of this method is its mass sensitivity, which allows us to determine membrane-bound oligomeric states.

Due to its versatility, we can apply this method to any membrane-associated system. In particular, it can be used to study the formation of ligand-induced receptor complexes. Begin by distributing an equal number of microscope slides in PTFE holders.

Transfer the PTFE holders into the beakers. Add ultrapure water and sonicate them for 15 minutes at room temperature. Use tweezers to transfer the holders to clean beakers and add ultrapure isopropanol.

Sonicate again for 15 minutes, then again replace the isopropanol with ultrapure water, and sonicate the beaker containing the holders for 15 minutes. Remove the holders from the beakers and blow dry the microscope slides in the holder under a steady stream of nitrogen gas or compressed air. Place the dried holder containing the large microscope slides into the plasma cleaner and switch it on.

Wrap the flat cardboard with aluminum foil. Spread the cleaned 24 millimeters by 24 millimeters microscope slides on the aluminum foil with sufficient distance between each other. Then attach double-sided tape strips to the upper and lower edges of the slides.

Excise each microscope slide with a scalpel such that it can be removed from the aluminum foil. Each slide should have stripes of double-sided tape attached to the upper and lower edges of the slide. Then attach the slide with the two double-sided tape strips to the hydrophilized slide.

This will form a flow path between the smaller and bigger microscope slides. Dilute the freshly extruded small unilamellar vesicles, or SUVs, to a final concentration of 0.4 milligrams per milliliter in the required reaction buffer. Optionally, to promote vesicle rupture, add two millimolar calcium chloride to the vesicle suspension.

Mount a flow chamber onto the mass photometer stage. Flush 25 microliters of the vesicle suspension into the flow chamber and incubate the chamber for two minutes, then remove the unfused vesicles through repeated washing of the flow chamber with 200 microliters of the reaction buffer each time. Once the lipid membrane is free of unfused vesicles, add 50 microliters of the protein of interest to the sample chamber.

Next set the imaging conditions such as the size of the field of view, exposure time, frame rate, and acquisition time in the acquisition software. Adjust the focus automatically. If necessary use the lateral control to move the field of view to a position with a homogenous membrane.

Create a project folder and start recording the movie. After completion of the recording, specify a file name in the dialogue prompted by the acquisition software. The movie will be saved automatically to the project folder as an MP file for subsequent analysis.

To analyze the recorded videos, launch the Jupyter notebook app. In the appearing folder list, navigate to the location where the MSPT analysis. ipy notebook file is stored and click on the file.

In Jupyter notebooks, code is organized in cells and can be executed step by step. Click on the little Play button next to the cell or select a cell and press Shift Enter to execute the cell. Start by importing all the required packages for the analysis.

Execute the next cell to launch a file prompt. Choose a folder containing one or multiple MP video files to be analyzed and press Select Folder. A list of the chosen files is printed below the cell.

To remove the dominant static scattering of light with the pixel-wise background estimation algorithm, choose the continuous median option for the parameter mode and set an appropriate length for the sliding median window. Optionally, save the movies after background removal by setting save_processed_movies True to use them for particle detection and trajectory linking. Make sure to set parallel True and GPU False for processing on the CPU or vice versa for processing on a GPU.

Particles and their respective position are identified and localized throughout the movie. Tune the sensitivity of the particle detection with a threshold parameter which is used to highlight the candidate spots by image binarization. Examine the effect of varying threshold parameters on the spot detection sensitivity in a separate notebook called Movie visualization.

ipy notebook. After loading the movie into the frame viewer use the threshold slider to adjust the particle detection threshold and find an appropriate setting. Back to the other notebook, choose three parameters for linking particles in consecutive frames into trajectories using the Python package trackpy.

Set the maximum displacement of particles from one frame to the next according to the maximum expected diffusion speed. Select the maximum number of frames a particle may vanish and reappear but still be considered the same particle. Trajectories with too few points may be removed using the parameter minimum_trajectory_length for a more robust determination of the diffusion coefficients.

Fix all parameters in the cell by executing it. Execute the next cell to analyze all the selected videos with the chosen parameters. Progress bars for each processing step will appear below the cell.

This may take a while, depending on the video length, parameter settings, and hardware. In the next step the software determines the diffusion coefficients for the trajectories found in the previous section, based on both jump distance distribution and mean square displacement analysis. Start by specifying the movie frame_rate and pixel_size.

Fix them by executing the cell, then run the next cell and select the parent directory containing all CSV files generated by trackpy. A list of the found CSV files is printed below the cell. In the next cell choose a name for the HDF5 container where the results are stored and execute it.

Finally execute cell C.4 to perform the diffusion analysis. In cell C.5 enter the contrast to mass calibration line parameters, which means the slope and offset determined using samples of known mass and execute it to convert all the particle contrasts into the molecular mass. Evaluate the apparent particle density on the membrane by executing cell C.6, which returns the median density value in terms of detected particles and present trajectories during each frame as additional columns in the data frame.

To generate the final plot for the correlation of mass and diffusion coefficient, execute cell D.1 to load a file prompt and specify the HDF5 file containing the MSPT results, then select either the data set from a single video to plot in cell D.2 or combine the data from multiple videos into a single data frame by executing the cell D.3. In this example, since all the videos are replicates of identical samples, the data set is pooled. Finally plot the 2D kernel density by executing the cell D.4.

If satisfied, specify a location to save the plot into a PDF file in cell D.5 and execute it. Representative images of the surface roughness of a glass cover slide during the formation of a supported lipid bilayer, with an intact supported lipid bilayer, and of exemplary proteins reconstituted on an SLB are shown here. All four examples are displayed in the native mode, which can be accessed during the measurement itself, and as processed ratiometric images after median-based background removal.

A calibration that translates contrast into molecular mass can be achieved by attaching biomolecules of known mass to an SLB via a biotin-streptavidin-biotin complex. As an exemplary strategy, one can use biotinylated variants of bovine serum albumin, protein A, alkaline phosphatase, and fibronectin which bind to streptavidin that itself is bound to biotin-containing lipids in the membrane. The increasingly pronounced contrast of these exemplary macromolecules reflects the increasing molecular weight of the respective biotinylated standards.

By assigning each peak of the contrast histograms to the corresponding mass of the standard protein's oligomer state, a linear relationship between contrast and mass is revealed and can subsequently be used for the analysis of unknown macromolecule systems. 2D kernel density estimations of both mass and diffusion coefficient of tetravalent streptavidin in complex with biotinylated aldolase or with a biotin-modified goat anti-Rabbit IgG antibody are shown here. The representative images show a comparison of determined oligomer masses for the complex of tetravalent streptavidin with biotin-modified aldolase or IgG and expected molecular weights.

With MSPT, we can track biomolecules directly on lipid membranes, determine which mass they have, how they move, and how they interact. I am confident that this technique will transform our understanding of biological processes on and with membranes.