scholarly journals Biophysical Models of PAR Cluster Transport by Cortical Flow in C. elegans Early Embryogenesis

2021 ◽  
Author(s):  
Cole Zmurchok ◽  
William R. Holmes

The clustering of membrane-bound proteins facilitates their transport by cortical actin flow in early Caenorhabditis elegans embryo cell polarity. PAR-3 clustering is critical for this process, yet the biophysical processes that couple protein clusters to cortical flow remain unknown. We develop a discrete, stochastic agent-based model of protein clustering and test four hypothetical models for how clusters may interact with the flow. Results show that the canonical way to assess transport characteristics from single particle tracking data used thus far in this area, the Péclet number, is insufficient to distinguish these hypotheses and that all models can account for transport characteristics quantified by this measure. However, using this model, we demonstrate that these different cluster-cortex interactions may be distinguished using a different metric, namely, the scalar projection of cluster displacement on to the flow displacement vector. Our results thus provide a testable way to use existing single particle tracking data to test how endogenous protein clusters may interact with the cortical flow to localize during polarity establishment. To facilitate this investigation, we also develop both improved simulation and semi-analytic methodologies to quantify motion summary statistics (e.g., Péclet number and scalar projection) for these stochastic models as a function of biophysical parameters.

2014 ◽  
Vol 16 (17) ◽  
pp. 7686-7691 ◽  
Author(s):  
Dominique Ernst ◽  
Jürgen Köhler ◽  
Matthias Weiss

We introduce a versatile method to extract the type of (transient) anomalous random walk from experimental single-particle tracking data.


Data in Brief ◽  
2016 ◽  
Vol 7 ◽  
pp. 1665-1669 ◽  
Author(s):  
Jan Peter Siebrasse ◽  
Ivona Djuric ◽  
Ulf Schulze ◽  
Marc A. Schlüter ◽  
Hermann Pavenstädt ◽  
...  

2021 ◽  
Vol 3 ◽  
Author(s):  
Johanna V. Rahm ◽  
Sebastian Malkusch ◽  
Ulrike Endesfelder ◽  
Marina S. Dietz ◽  
Mike Heilemann

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.


2018 ◽  
Author(s):  
Martin Lindén ◽  
Johan Elf

Single particle tracking offers a non-invasive high-resolution probe of biomolecular reactions inside living cells. However, efficient data analysis methods that correctly account for various noise soures are needed to realize the full quantitative potential of the method. We report new algorithms for hidden Markov based analysis of single particle tracking data, which incorporate most sources of experimental noise, including heterogeneuous localization errors and missing positions. Compared to previous implementations, the algorithms offer significant speed-ups, support for a wider range of inference methods, and a simple user interface. This will enable more advanced and exploratory quantitative analysis of single particle tracking data.


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