2019
DOI: 10.1101/642744
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Reconstructing wells from high density regions extracted from super-resolution single particle trajectories

Abstract: Large amount of super-resolution single particle trajectories has revealed that the cellular environment is enriched in heterogenous regions of high density, which remain unexplained. The biophysical properties of these regions are characterized by a drift and their extension (a basin of attraction) that can be estimated from an ensemble of trajectories. We develop here two statistical methods to recover the dynamics and local potential wells (field of force and boundary) using as a model a truncated Ornstein-… Show more

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Cited by 2 publications
(3 citation statements)
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“…From this local map, we kept only the ensemble that has a density of >80% of the central bin value. We collected the trajectory points falling into these bins and from which we computed the corresponding ellipse through their covariance matrix (41). Last, when ellipses overlap, we applied an iterative procedure that merges two overlapping ellipses by computing the ellipse based on the ensemble of points falling in each ellipse.…”
Section: Lysosome Velocitymentioning
confidence: 99%
“…From this local map, we kept only the ensemble that has a density of >80% of the central bin value. We collected the trajectory points falling into these bins and from which we computed the corresponding ellipse through their covariance matrix (41). Last, when ellipses overlap, we applied an iterative procedure that merges two overlapping ellipses by computing the ellipse based on the ensemble of points falling in each ellipse.…”
Section: Lysosome Velocitymentioning
confidence: 99%
“…Data from SPT allowed us to reconstruct the lysosome motion density. The high-density regions were extracted from the trajectories using a procedure derived from ( 34 ): the number of points falling into square bins of width Δx = 480 nm was counted to construct the density map. From this map, high-density regions, called seeds, were defined as the fraction with 5% higher-density bins.…”
Section: Methodsmentioning
confidence: 99%
“…From this local map, we kept only the ensemble that have a density > 80% of the central bin value. We collected the trajectory points falling into these bins and from which we computed the corresponding ellipse through their covariance matrix ( 34 ). Finally, when ellipses overlap, we applied an iterative procedure that merge two overlapping ellipses by computing the ellipse based on the ensemble of points falling in each ellipse.…”
Section: Methodsmentioning
confidence: 99%