Diffusion State Transitions in Single-Particle Trajectories of MET Receptor Tyrosine Kinase Measured in Live Cells

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.

Receptor tyrosine kinase MET ligand-interaction classified via machine learning from single-particle tracking data

“…, 2004 ) and it is also likely that receptors are confined before immobilization. MET receptors become immobilized due to interactions with the actin cytoskeleton and before endocytosis ( Shen et al. , 2000 ; Li et al.…”

“…If it is not possible to learn the transition probabilities reliably from the experimental data, it is also possible to apply the proposed method using only the parameters from the diffusion mixture model. For the MET SPT data set analyzed in this study, it was shown that changes in mobility modes in single-molecule trajectories can be observed by a combined application of segmentation and mean squared displacement analysis ( Rahm et al. , 2021 ).…”

“…The calculation of jump distance trajectories from localization-based trajectories was performed using the “preprocess_swift_data” function of the ermine package. Jump distance trajectories with less than four jumps were filtered from the data by setting the “min_track_length” parameter to 4, as short trajectories seldom exhibit state transitions ( Rahm et al. , 2021 ).…”

MPTHub: An Open-Source Software for Characterizing the Transport of Particles in Biorelevant Media

“…While the ability to analyze motion at the single-particle level provides a valuable body of information that is not possible to attain with purely ensemble techniques, manual handling of generated large datasets constitutes a time-consuming and prone to error process [ 24 ]. Various available software has been shown useful in aiding this task [ 25 , 26 , 27 , 28 , 29 , 30 ]. However, these solutions offer limited possibilities for data processing customization, need intermediate to advanced computing skills for installing and use, or require payment of a license (self or that of an ancillary software such as MATLAB).…”

ExTrack characterizes transition kinetics and diffusion in noisy single-particle tracks

“…Furthermore, tracks are often short as particles can bleach or diffuse out of the field of view or focal plane (17). In such situations, probabilistic methods are better suited to determine global parameters such as diffusion coefficients and transition rates (3, 7, 18–26). Some of these methods can also predict the motion states of individual molecules at every time point (3, 7, 26, 27), which can reveal the locations of binding sites, spatial correlations, and complex, potentially non-Markovian dynamics (28).…”