EVENT : C3BI Seminars
Main speaker : Charles KERVRANN, from Inria Rennes – Bretagne Atlantique / CNRS-UMR 144 Paris Inria
Institut Curie CNRS UPMC PSL Research University SERPICO Project Team
Date : 27-02-2020 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) , Institut Pasteur, Paris
The characterization of biomolecule dynamics and interactions in living cells is essential to decipher biological mechanisms and processes. This topic is usually addressed in fluorescent video-microscopy from particle trajectories computed by object tracking algorithms. However, classifying individual trajectories into predefined diffusion classes (e.g. sub-diffusion, free diffusion (or Brownian motion), super-diffusion), estimating diffusion model parameters, or detecting diffusion mode switches, is a difficult task in most cases. Meanwhile, colocalization is generally applied to detect interactions between two biomolecules observed in an image pair. Colocalization aims at characterizing spatial associations between two fluorescently-tagged biomolecules by quantifying the co-occurrence and correlation between the two channels acquired in fluorescence microscopy. This problem remains an open issue in diffraction-limited microscopy and raises new challenges with the emergence of super-resolution imaging.
To address these challenging issues, we propose a computational framework based on statistical tests to both classify biomolecule trajectories and to detect spatially-varying colocalization in single molecule imaging (PALM, STORM). The methodological approach is well-grounded in statistics and is more robust than previous techniques. In this talk, I will present the underlying concepts and methods. The resulting algorithms are flexible in most cases, with a minimal number of control parameters to be tuned (p-values). They can be applied to a large range of problems in cell imaging and can be integrated in generic image-based workflows, including for high content screening applications.
1. V. Briane, C. Kervrann, M. Vimond. Statistical analysis of particle trajectories in living cells, Phys. Rev. E 97, 062121
2. V. Briane, M. Vimond, C. Valades Cruz, A. Salomon, C. Wunder, C. Kervrann. A sequential algorithm to detect diffusion switching along intracellular particle trajectories, Bioinformatics, btz489, 2019.
3. V. Briane, M. Vimond, C. Kervrann. An overview of diffusion models for intracellular dynamics analysis, Briefings in Bioinformatics, bbz052, 2019
4. V. Briane, M. Vimond, A. Salomon, C. Kervrann. A computational approach for detecting micro-domains and confinement domains in cells: a simulation study, 2019.
5. F. Lavancier, T. Pécot, L. Zengzhen, C. Kervrann, Testing independence between two random sets for the analysis of colocalization in bio-imaging. Biometrics, doi:10.1111/BIOM.13115, 2019.
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