First-passage-probability analysis of active transport in live cells

Abstract

The first-passage-probability can be used as an unbiased method for determining the phases of motion of individual organelles within live cells. Using high speed microscopy, we observe individual lipid droplet tracks and analyze the motor protein driven motion. At short passage lengths (<10(-2)μm), a log-normal distribution in the first-passage-probability as a function of time is observed, which switches to a Gaussian distribution at longer passages due to the running motion of the motor proteins. The mean first-passage times (<t(FPT)>) as a function of the passage length (L), averaged over a number of runs for a single lipid droplet, follow a power law distribution <t(FPT)>~L(α), α>2, at short times due to a passive subdiffusive process. This changes to another power law at long times where 1<α<2, corresponding to sub-ballistic superdiffusive motion, an active process. Subdiffusive passive mean square displacements are observed as a function of time, <r(2)>~t(β), where 0<β<1 at short times again crossing over to an active sub-ballistic superdiffusive result 1<β<2 at longer times. Consecutive runs of the lipid droplets add additional independent Gaussian peaks to a cumulative first-passage-probability distribution indicating that the speeds of sequential phases of motion are independent and biochemically well regulated. As a result we propose a model for motor driven lipid droplets that exhibits a sequential run behavior with occasional pauses.

Particle tracking microrheology of purified gastrointestinal mucins

“…Videos of the spheres' Brownian motion were recorded using a FastCam 1024‐PCI fast camera and a bright CoolLED light source. This allowed the motion of the spheres to be followed at up to 27,000 frames per second and their two‐dimensional tracks were extracted using a parallel version of the MatLab based Polyparticletracker software . This is based on a polynomial fit of the intensity around each feature point, weighted by a Gaussian function of the distance from the centre .…”

“…This allowed the motion of the spheres to be followed at up to 27,000 frames per second and their two-dimensional tracks were extracted using a parallel version of the MatLab based Polyparticletracker software. 34 This is based on a polynomial fit of the intensity around each feature point, weighted by a Gaussian function of the distance from the centre. 35 Special care was taken to only track spheres away from the surfaces of the microscope slides to avoid detecting the hydrodynamic interactions between them (the Faxen's law corrections are negligible).…”

Non-Markovian intracellular transport with sub-diffusion and run-length dependent detachment rate

“…We focus on implications of non-Markovian dynamics for the physical properties of cargo transport. We show that in contrast to the Markovian model, the non-Markovian model is able to describe the process of super-diffusion previously reported in [ 32 ].…”

“…As a result, the movement of cargoes is non-Markovian and involves a memory. In this paper we analyze individual lipid droplet tracks [ 32 ] and compare with the non-Markovian and the Markovian models of cargo transport. We focus on implications of non-Markovian dynamics for the physical properties of cargo transport.…”

Unraveling intermittent features in single-particle trajectories by a local convex hull method

“…Most theoretical works remain focused on the simplest observable, the time-averaged mean square displacement (TA MSD), which has been studied for many anomalous diffusion models (see [10][11][12] and references therein). However, since this observable does not discriminate between various models, other observables have been proposed, e.g., first passage times [13,14], the maximum excursion [15], fundamental moments [16], and the fractal dimension of the explored space [17] (see also [18] and references therein). At the level of a single tra- ‡ International Joint Research Unit -UMI 2615 CNRS/ IUM/ IITP RAS/ Steklov MI RAS/ Skoltech/ HSE, Moscow, Russian Federation * Electronic address: yann.lanoiselee@polytechnique.edu † Electronic address: denis.grebenkov@polytechnique.edu jectory, elaborate statistical tools have been developed to recognize fractional Brownian motion (fBm) [19], to distinguish between fBm and Continuous Time Random Walk (CTRW) [20][21][22], and to reveal ergodicity breaking [23,24].…”