Automated Two-dimensional Spatiotemporal Analysis of Mobile Single-molecule FRET Probes

Single-molecule FRET experiments using surface-bound probes have been performed almost exclusively on immobilized molecules in the past. However, many biomolecules diffuse and can be analyzed with our method. Combining single-molecule FRET with tracking makes it possible to analyze not only FRET deficiency time traces of moving probes, but also to investigate spatiotemporal aspects such as the diffusional behavior.

Our method is compatible with a variety of different FRET-based probes. For example, it could provide insights concerning molecular forces, conformational dynamics, and binding kinetics in live cell experiments. High-quality single-molecule FRET experiments are notoriously hard to perform.

For reliable quantification, it is important to record data at a good signal-to-noise ratio and single-molecule tracks of sufficient length. To begin sample measurement, excite the donor and accepter fluorophores for an appropriate illumination time while triggering the camera and wait until the camera readout is enabled. Repeat the excitation of the donor and accepter fluorophores alternatively.

Choose the number of repeats to be large enough to ensure photobleaching of at least one fluorophore per probe within the field of view permitting stepwise photobleaching analysis to discriminate single-molecule signals from aggregates. Specify the illumination sequence to permit the selection of donor and accepter excitation frames as well as frames for image segmentation from recorded image sequences. Simultaneously, analyze the datasets recorded using the same illumination settings.

To this end, assign an identifier and a pattern that matches the respective image sequence filenames to each dataset. Additionally, define specific datasets for special purposes such as recordings of fiducial markers for image registration, excitation light profiles for flat field correction, and optionally donor-only and accepter-only samples to determine correction factors. Next, select emission channels and raw images if both channels are recorded using a single camera.

For this, use the appropriate graphical widget to select appropriate regions for donor and accepter emission, then localize fiducial markers in both emission channels and perform image registration. Use the provided user interface to find the appropriate parameters for the localization algorithm for both donor and accepter emission channels. Next, set single-molecule localization parameters for FRET probes upon donor excitation in the sum of the images obtained from donor and acceptor emissions, then set localization parameters for probes upon acceptor excitation in the acceptor emission channel.

Localize the FRET probes upon donor and acceptor excitation independently in all frames. The results are merged into one table that contains the original frame number, two-dimensional coordinates, and an identifier referring to the source image file. To track and measure fluorescence intensity, choose appropriate options for the Trackpy algorithm to link FRET probe localizations into trajectories.

Next, using the analysis software functionality, process auxiliary image data from image sequences. Extract additional images recorded to facilitate segmentation marked by S in the excitation sequence. Finally, determine the donor and acceptor excitation light profiles across the field of view from images recorded on a densely labeled sample.

For the initial filtering steps, discard signals with overlapping point spread functions as it is difficult to determine their fluorescence intensities reliably. In the case of inhomogenous illumination, accept only signals located in well-illuminated regions within the field of view to ensure good signal-to-noise ratio. If studying intramolecular FRET, restrict the analysis to those trajectories present from the beginning of the image sequence.

Next, execute the flat field correction, which uses the excitation light source profiles obtained earlier to reverse the position-dependent fluorescence intensity variations caused by inhomogenous illumination, then compute the apparent FRET efficiency and the apparent stoichiometry. To perform stepwise analysis of photobleaching for discriminating between single molecular probes and aggregates, find appropriate parameters for the change point detection algorithm upon donor and accepter excitation independently. Then execute the change point detection algorithm.

To remove track showing ambiguous photobleaching behavior, define intensity thresholds below which a fluorophore is considered bleached, then select one of the following options. Option one wherein the acceptor fluorophore bleaches in a single step while the donor shows no partial bleaching. Option two wherein the donor bleaches in a single step while there is no partial acceptor bleaching.

Option three wherein either fluorophore bleaches in a single step while the other does not partially bleach. And option four wherein donor and acceptor fluorophores show single-step photobleaching or no photobleaching at all. Then calculate the correction factors for donor emission leakage into the acceptor channel, direct acceptor excitation, detection efficiencies and excitation efficiencies.

Next, use the correction factors to calculate FRET efficiency from apparent efficiency and stoichiometry from apparent stoichiometry. For further filtering, select only data points from before the first bleaching event in each trajectory. Additionally, to restrict the analysis to single-molecule probes, accept only trajectories with at least 75%of data points within the appropriate stoichiometry limits.

Then perform image segmentation via global or adaptive thresholding methods on the appropriate auxiliary images to restrict the analysis to distinct regions within in the field of view. Create efficiency versus stoichiometry plots to verify that signals of incorrect stoichiometry have been correctly identified and removed. Then plot histograms of FRET efficiencies to provide a well-established overview of FRET efficiency distributions and group the histograms for convenient comparison of results from different experiments.

Taking advantage of scientific Python libraries, it is possible to further evaluate the data within the notebook, performing, for instance, diffusion analysis. Visualization and tracking of single-molecule FRET event are shown here. Filtered FRET events are represented by efficiency versus stoichiometry plots in a FRET efficiency histogram.

Further, mobility parameters can be investigated by plotting an individual trajectory path in an XY plot or a mean square displacement plot. The output of our platform allows us to further identify transitions in FRET efficiency time traces of mobile probes. For example, to evaluate conformational changes of the biomolecules.

Using a FRET-based force sensor, the analysis platform allowed us to quantify single-molecule force events within the immunological synapse during early T-cell signaling.