Description du projet
Caractérisation du cycle cellulaire bactérien par microscopie à super-résolution
Le cycle cellulaire (CC) dans les bactéries implique une horloge interne associée, en particulier, à l’homéostasie de la taille des cellules au niveau de la population. Certains outils analytiques dédiés à la caractérisation des CC sont disponibles pour les cellules eucaryotes, mais ils s’avèrent insuffisants pour étudier des cellules bactériennes. L’augmentation de la résistance aux antibiotiques nécessite des plateformes quantitatives fiables dédiées au CC bactérien et permettant une analyse à haut débit. Le projet BALTIC financé par l’UE vise à développer une nouvelle approche pour l’étude des CC bactériens. La méthodologie s’appuiera sur une microscopie de pointe, à super-résolution et haut débit, pour construire des modèles dynamiques du CC bactérien à l’aide de cellules fixes. La technologie représente un progrès considérable, bénéficiant d’une résolution spatiale de 10 nm et d’informations quantitatives sur les cellules bactériennes.
Objectif
The cellular life cycle, or cell cycle (CC), is the fundamental backbone of the cellular machinery: it orchestrates processes over multiple scales, in space and time. In bacteria, it consists of an internal clock associated with, for instance, cell-size homeostasis at a population level. Although some analytical tools dedicated to characterizing CC are available for eukaryotic cells, such approaches are still lacking when it comes to bacteria cells study. Moreover, existing eukaryotic cell cyclers are highly limited in terms of both resolution (spatial or temporal) and applications. However, with the rise of antibiotic resistance, there is a real need for reliable quantitative platforms dedicated to bacteria CC and allowing for high throughput comparison studies. This research proposal aims at producing a novel approach for bacteria CC investigation, whilst over-passing the drawbacks associated with existing tools. The developed methodology will rely on cutting edge high throughput super resolution microscopy. We will firstly explore proteins contribution to characterizing CC at the nano-scale, taking a step back from the unreliable and limited size or time dependent estimation. Relying on state of the art machine learning strategies and the identified CC reporting features, I will develop tactics to circumvent the trade-off between temporal and spatial resolution constraining fluorescence nanoscopy when it comes to the study of dynamic processes such as CC. I will implement a methodology to extract, for the first time, dynamic models of bacteria CC from fixed cells super resolved images. It is a considerable step forward: enabling to benefit from a spatial resolution around 10 nm, whilst inferring live-cell akin quantitative information. The highly innovative approaches to bacteria CC quantification developed here will be made generalizable across cell types and applications, providing a unique platform for complex studies, and therapeutics development.
Champ scientifique
- natural sciencesbiological sciencesmicrobiologybacteriology
- natural sciencesphysical sciencesopticsmicroscopysuper resolution microscopy
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteins
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- medical and health sciencesbasic medicinepharmacology and pharmacydrug resistanceantibiotic resistance
Programme(s)
Régime de financement
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinateur
1015 Lausanne
Suisse