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Description The supplied python code is used to quantify the normalized fluorescence recovery ratio used to quantify liposome scission and hemiscission events, as well as events in which no scission occurred based on FRAP data. The code accompanies the publication de Franceschi et al., Nature Nanotechnology, 2023 # Data preparation ## Individual frames Some data was manually processed and the approximate centroid position of the lobes were marked in Fiji/ImageJ by a point ROI and exported with the following extensions: 1. BC: control lobe before FRAP 2. AC: control lobe right after FRAP 3. RC: control lobe after recovery is complete 4. BB: background before FRAP 5. AB: background right after FRAP 6. RB: background after recovery is complete 7. BF: FRAPper lobe before FRAP 8. AF: FRAPped lobe right after FRAP 9. RF: FRAPped lobe after recovery is complete These files are included for three FRAP experiments in this repository under .\sample_data. Note that the point ROIs must be only approximate as an automated circle optimization algorithm is applied to each frame. The manual annotation thus provides only a reasonable initial guess. ## Time traces Three files are required for the FRAP recovery quantification: 1. Background: A constant circular ROI should be placed in a region without fluorescence 2. CTRL: the center of the lobe is marked by a point ROI in every n'th frame 3. FRAP: the center of the lobe is marked by a point ROI in every n'th frame In this study, we marked every second frame (n=2) only and the circle optimization result served of the previous frames served as initial guesses of the not-annotated frames. Larger n might be applied, however, the algorithm was not tested with n>2. The coordinates of the marked centroids are then exported for the '_CTRL.tif' and '_FRAP.tif' files in Fiji/ImageJ via File -> Save as -> XY Coordinates with the same name as .txt file. These are files are included for one FRAP timecourse experiment in this repository under .\sample_data_time. Note that the point ROIs have to be only approximate as an automated circle optimization algorithm is applied to each frame. The manual annotation thus provides only a reasonable initial guess. # Code execution Two files are supplied which can be executed as python scripts: - LiposomeFRAPanalysis.py quantifies the normalized FRAP recovery based on individual frames. An excel file will be saved containing all raw and normalized intensity values and optimized lobe radii of the files in the supplied file directories. - LiposomeFRAPanalysis_time.py quantifies the normalized FRAP recovery over time. Excel files and plots of the time traces are saved in the directory of the supplied file. Users are requested to fill in the file paths to process as well as the microscope pixel size in (section '### USER INPUT').

The ParABS system is essential for prokaryotic chromosome segregation. After loading at parS on the genome, ParB (partition protein B) proteins rapidly redistribute to distances of ~15 kilobases from the loading site. It has remained puzzling how this large-distance spreading can occur along DNA loaded with hundreds of proteins. Using in vitro single-molecule fluorescence imaging, we show that ParB from Bacillus subtilis can load onto DNA distantly of parS, as loaded ParB molecules themselves are found to be able to recruit additional ParB proteins from bulk. Notably, this recruitment can occur in cis but also in trans, where, at low tensions within the DNA, newly recruited ParB can bypass roadblocks as it gets loaded to spatially proximal but genomically distant DNA regions. The data are supported by molecular dynamics simulations, which show that cooperative ParB-ParB recruitment can enhance spreading. ParS-independent recruitment explains how ParB can cover substantial genomic distance during chromosome segregation, which is vital for the bacterial cell cycle.

Review history.

Contains details on the conversion from flow fields to trajectories, a detailed discussion on the distinction between confined and anomalous diffusion and a comparison of Single Particle Tracking and Optical Flow for reconstruction of motion in intermediate to high labelling density scenarios, and further discussion on the comparison of Hi-D and iMSD. It also contains Figures S1-S13.

Additional file 1. Review history.