Abstract
Single-particle tracking enables the analysis of the dynamics of biomolecules in living cells with nanometer spatial and millisecond temporal resolution. This technique reports on the mobility of membrane proteins and is sensitive to the molecular state of a biomolecule and to interactions with other biomolecules. Trajectories describe the mobility of single particles over time and provide information such as the diffusion coefficient and diffusion state. Changes in particle dynamics within single trajectories lead to segmentation, which allows to extract information on transitions of functional states of a biomolecule. Here, mean-squared displacement analysis is developed to classify trajectory segments into immobile, confined diffusing, and freely diffusing states, and to extract the occurrence of transitions between these modes. We applied this analysis to single-particle tracking data of the membrane receptor MET in live cells and analyzed state transitions in single trajectories of the un-activated receptor and the receptor bound to the ligand internalin B. We found that internalin B-bound MET shows an enhancement of transitions from freely and confined diffusing states into the immobile state as compared to un-activated MET. Confined diffusion acts as an intermediate state between immobile and free, as this state is most likely to change the diffusion state in the following segment. This analysis can be readily applied to single-particle tracking data of other membrane receptors and intracellular proteins under various conditions and contribute to the understanding of molecular states and signaling pathways.
Highlights
Cells sense their environment through membrane proteins, and extracellular stimuli are translated into intracellular signaling cascades and a cellular response
We applied this analysis to single-particle tracking data of MET receptors in live HeLa cells recorded using the uPAINT principle (Giannone et al, 2010)
The method is sensitive to report segments of free, confined, and immobile states within single trajectories, and transitions between these diffusion states. This allowed us to relate dynamic information on protein mobility to functional states of a protein in a membrane, e.g. the immobilization upon binding of a ligand to a receptor. This additional information from single-particle tracking data complements the available portfolio on analyzing mobility data of single proteins (Rossier et al, 2012; Calebiro et al, 2013; Ibach et al, 2015; Sungkaworn et al, 2017)
Summary
Cells sense their environment through membrane proteins, and extracellular stimuli are translated into intracellular signaling cascades and a cellular response. This process often begins with ligands that bind to membrane receptors, induce receptor oligomerization, and recruit other proteins such as co-receptors. SPT requires low molecular densities, in order to allow single-molecule detection and assignment of these into single-protein trajectories. Such low molecular densities can be achieved by substoichiometric labeling, by the introduction of a photoactivatable fluorophore, or by using transiently binding labels that target the membrane protein (Manley et al, 2008; Giannone et al, 2010). It may occur that a molecule switches between different diffusion states within a single trajectory; such transitions can be analyzed by comparing the experimental dataset to Monte Carlo simulations (Wieser et al, 2008), using hidden Markov models (Persson et al, 2013; Sungkaworn et al, 2017; Liu et al, 2019), analytic diffusion distribution analysis (Vink et al, 2020), local MSD exponent values (Hubicka and Janczura, 2020), and unsupervised Gibbs sampling (Karslake et al, 2021)
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