Single molecule tracking and analysis framework including theory-predicted parameter settings

Abstract

Imaging, tracking and analyzing individual biomolecules in living systems is a powerful technology to obtain quantitative kinetic and spatial information such as reaction rates, diffusion coefficients and localization maps. Common tracking tools often operate on single movies and require additional manual steps to analyze whole data sets or to compare different experimental conditions. We report a fast and comprehensive single molecule tracking and analysis framework (TrackIt) to simultaneously process several multi-movie data sets. A user-friendly GUI offers convenient tracking visualization, multiple state-of-the-art analysis procedures, display of results, and data im- and export at different levels to utilize external software tools. We applied our framework to quantify dissociation rates of a transcription factor in the nucleus and found that tracking errors, similar to fluorophore photobleaching, have to be considered for reliable analysis. Accordingly, we developed an algorithm, which accounts for both tracking losses and suggests optimized tracking parameters when evaluating reaction rates. Our versatile and extensible framework facilitates quantitative analysis of single molecule experiments at different experimental conditions.

Transcription factor residence time dominates over concentration in transcription activation

“…We analyzed the single molecule microscopy data with TrackIt 103 to obtain fluorescence survival time distributions of bound TALE-TF. We adjusted the tracking settings for a nearest neighbour tracking algorithm to minimize false-positive connections due to nearby binding events and to obtain an equal probability for tracking losses due to tracking errors and photobleaching for all time-lapse conditions.…”

Stress induced TDP-43 mobility loss independent of stress granules

“…To reduce the bias towards slower moving and bound molecules, only the first 5 jumps of every tracked single-molecule time-trace were used for further analysis (Hansen et al 2018). Diffusion coefficients and the respective fractions can be computed by fitting the cumulative displacement histograms with a multi-exponential fit function (Kuhn et al 2021; Mazza et al 2012, see Material and Methods). For the extraction of the apparent diffusion coefficients, a three-exponential fit function was used, since it fitted best (compared to mono- and double exponential) to the cumulative jump-distance histogram (Supplementary fig.…”

“…3 B), confirmed an insoluble fraction with increasing stress exposure. To study the spatial distribution of the different TDP-43 mobility regimes, we performed TALM analysis (tracking and localization microscopy (Niewidok et al 2018; Appelhans et al 2012; Manley et al 2008)) and investigated local jump distances (distance mapping, DM) as a means to obtain a super-resolved diffusivity map (Kuhn et al 2021; Xiang et al 2020).…”

“…More frequent localization of TDP-43 at a given location, indicates binding to cellular structures or aggregation events. Frame to frame position jumps of localized molecules result in average displacements for each pixel in the image (Kuhn et al 2021), indicating regions of local high or low mobility. The combination of these two methods allows for a correlation between binding hotspots or regions with increased TDP-43 localization and local mobility patterns.…”

“…Spot detection, tracking and diffusion analysis was performed using TrackIt (Kuhn et al 2021). Spot detection was performed with a threshold factor of two.…”