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Single-particle tracking reports on the mobility of biomolecules in living cells with high spatial and temporal resolution. From single-particle trajectories, information such as the diffusion coefficient and diffusion state can be derived. Changes in particle dynamics within single trajectories can be extracted by segmentation, which provides information on transitions between different functional states of a biomolecule. However, such analyses of single-particle tracking data is complex and time-consuming. Here, we present a pipeline that enables a straightforward and rapid analysis of single-particle tracking data. It incorporates mean-squared displacement analysis of trajectories that distinguishes between immobile, confined, and free diffusion states, as well as the analysis of diffusion state transitions within a trajectory with transition counts and hidden Markov modeling. We apply this analysis to single-molecule trajectories of un-activated Fab-bound and internalin B-bound MET receptors in the plasma membrane of live HeLa cells. We found that ligand activated receptors move slower and more confined and exhibit more transitions from free to confined diffusion states than un-activated receptors. This suggests that the confined diffusion state functions as an intermediate between free and immobile, as this state is most likely changing the diffusion type in the following segment. Hidden Markov modeling reported three diffusion states with increased transition probabilities towards the less mobile and immobile states upon ligand activation. The less mobile state operates as an intermediate state, as it has the highest transition probabilities. The analysis pipeline can be readily applied to single-particle tracking data of other membrane proteins and provides rapid access to information that can be associated with functional states.
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