Évènement

Jules Vanaret
I2M

Date(s) : 12/05/2025   iCal
9h30 - 12h30

Le jury sera composé de :
– M. David ROUSSEAU, Univ. d’Angers, IRHS, Rapporteur
– M. Nicolas LOMENIE, Univ. Paris Cité, LIPADE, Rapporteur
– M. Hervé ISAMBERT, CNRS, Institut Curie, Examinateur
– M. Daniel SAGE, EPFL, Laboratoire d’imagerie biomédicale, Examinateur
– M. Frédéric RICHARD, Aix-Marseille Univ., I2M, Directeur de thèse
– M. Philippe ROUDOT, Aix-Marseille Univ., Institut Fresnel, Co-directeur de thèse
– Mme. Sham TLILI, Aix-Marseille Univ., IBDM, Co-encadrante de thèse

Abstract:

This thesis explores the biophysical and computational modeling of self-organization in gastruloids, three-dimensional in vitro models of embryonic development. It investigates how cellular decisions emerge from mechanogenetic interactions at the single-cell level, leveraging advanced microscopy, image analysis, and tracking methodologies to quantify cellular behaviors and micro-environmental cues.

The first part presents a methodological pipeline to quantify mechanical and genetic properties in deep gastruloid tissues. A deep-learning-based segmentation strategy tailored for heterogeneous nuclei shapes is then developed, integrating contrast enhancement and dual-view fusion to improve imaging depth and accuracy. Building on these segmentation  advances, the study probes the mechanogenetic landscape of gastruloids, integrating quantitative analyses of gene expression and cellular deformation. A dedicated Python-based  computational pipeline is introduced, enabling multiscale analysis and user-friendly visualization.

In the second part, the challenges of cell tracking in gastruloids are assessed, presenting how stochasticity and measurement uncertainty complicate lineage reconstruction. Various tracking methodologies applied in the context of cell tracking or developmental biology are reviewed, leading to the development of a Bayesian filtering framework for the multiscale quantification of tracking uncertainty.

The final sections apply these probabilistic methods to validate segmentation and tracking algorithms, demonstrating how the Bayesian filtering algorithm can guide unsupervised parameter tuning and segmentation algorithm ranking. Through extensive simulations and experimental applications, the study provides rigorous methods to aid cellular dynamics analysis in complex gastruloid datasets, with implications for developmental biology and tissue engineering.

Keywords: embryonic organoid, cell tracking, live imaging, cellular heterogeneity, deep-learning, bayesian filtering

Emplacement
Luminy - Auditorium de l'Hexagone

Catégories

• Soutenance de thèse