arXiv subjects

Thibault Lagache

Publications and source records attributed to Thibault Lagache.

6 recordsLinked to original sources

arXiv2026

Tracking Intermittent Particles with Self-Learned Visual Features

In time-lapse fluorescence imaging, single-particle-tracking is a powerful tool to monitor the dynamics of objects of interest, and extract information about biological processes. However, tracked particles can be subject to occlusion and intermittent detectability. When these phenomena persist over a few frames, tracking algorithms tend to produce multiple tracklets for the same particle. In this work, we introduce self-supervised learning of visual features to compare tracked particles, and we exploit both visual and positional distances to robustly stitch tracklets representing the same particle. We demonstrate the performance of our stitching framework on time-lapse fluorescence sequences of Hydra vulgaris neurons. Results show high stitching precision, and reduction of errors made by previous algorithms on the same data by a factor of two.

eess.IV↗

arXiv2024

SINETRA: a Versatile Framework for Evaluating Single Neuron Tracking in Behaving Animals

Accurately tracking neuronal activity in behaving animals presents significant challenges due to complex motions and background noise. The lack of annotated datasets limits the evaluation and improvement of such tracking algorithms. To address this, we developed SINETRA, a versatile simulator that generates synthetic tracking data for particles on a deformable background, closely mimicking live animal recordings. This simulator produces annotated 2D and 3D videos that reflect the intricate movements seen in behaving animals like Hydra Vulgaris. We evaluated four state-of-the-art tracking algorithms highlighting the current limitations of these methods in challenging scenarios and paving the way for improved cell tracking techniques in dynamic biological systems.

cs.CV↗

arXiv2016

Narrow escape to small windows on a small ball modeling the viral entry into the cell nucleus

A certain class of viruses replicates inside a cell if they can enter the nucleus through one of many small target pores, before being permanently trapped or degraded. We adopt for viral motion a switching stochastic process model and we estimate here the probability and the conditional mean first passage time for a viral particle to attain alive the nucleus. The cell nucleus is covered with thousands of small absorbing nuclear pores and the minimum distance between them defines the smallest spatial scale that limits the efficiency of stochastic simulations. Using the Neuman-Green's function method to solve the steady-state Fokker-Planck equation, we derive asymptotic formula for the probability and mean arrival time to a small window for various pores' distributions, that agree with stochastic simulations. These formulas reveal how key geometrical parameters defines the cytoplasmic stage of viral infection.

q-bio.SC↗

arXiv2015

Stochastic model of endosomal escape of Influenza virus

Influenza viruses enter a cell via endocytosis after binding to the surface. During the endosomal journey, acidification triggers a conformational change of the virus spike protein hemagglutinin (HA) that results in escape of the viral genome from the endosome to the cytoplasm. A quantitative understanding of the processes involved in HA mediated fusion with the endosome is still missing. We develop here a stochastic model to estimate the change of conformation of HAs inside the endosome nanodomain. Using a Markov-jump process to model the arrival of protons to HA binding sites, we compute the kinetics of their accumulation and the mean first time for HAs to be activated. This analysis reveals that HA proton binding sites possess a high chemical barrier, ensuring a stability of the spike protein at sub-acidic pH. Finally, we predict that activating more than 3 adjacent HAs is necessary to prevent a premature fusion.

q-bio.SC↗

arXiv2011

Modeling the early steps of cytoplasmic trafficking in viral infection and gene delivery

Gene delivery of nucleic acid to the cell nucleus is a fundamental step in gene therapy. In this review of modeling drug and gene delivery, we focus on the particular stage of plasmid DNA or virus cytoplasmic trafficking. A challenging problem is to quantify the success of this limiting stage. We present some models and simulations of plasmid trafficking and of the limiting phase of DNA-polycation escape from an endosome and discuss virus cytoplasmic trafficking. The models can be used to assess the success of viral escape from endosomes, to quantify the early step of viral-cell infection, and to propose new simulation tools for designing new hybrid-viruses as synthetic vectors.

q-bio.SC↗

arXiv2007

Effective Motion of a Virus Trafficking Inside a Biological Cell

Virus trafficking is fundamental for infection success and plasmid cytosolic trafficking is a key step of gene delivery. Based on the main physical properties of the cellular transport machinery such as microtubules, motor proteins, our goal here is to derive a mathematical model to study cytoplasmic trafficking. Because experimental results reveal that both active and passive movement are necessary for a virus to reach the cell nucleus, by taking into account the complex interactions of the virus with the microtubules, we derive here an estimate of the mean time a virus reaches the nucleus. In particular, we present a mathematical procedure in which the complex viral movement, oscillating between pure diffusion and a deterministic movement along microtubules, can be approximated by a steady state stochastic equation with a constant effective drift. An explicit expression for the drift amplitude is given as a function of the real drift, the density of microtubules and other physical parameters. The present approach can be used to model viral trafficking inside the cytoplasm, which is a fundamental step of viral infection, leading to viral replication and in some cases to cell damage.

q-bio.QM↗