Projektbeschreibung
Charakterisierung des bakteriellen Zellzyklus mittels hochauflösender Mikroskopie
Der Zellzyklus von Bakterien beinhaltet eine innere Uhr, die vor allem mit der Homöostase der Zellgröße auf Populationsebene in Verbindung steht. Es gibt zwar einige Analyseinstrumente zur Charakterisierung des Zellzyklus eukaryotischer Zellen, zur Untersuchung von Bakterienzellen sind sie jedoch nicht geeignet. Da Antibiotikaresistenzen stetig zunehmen, sind zuverlässige quantitative Plattformen für den bakteriellen Zellzyklus erforderlich, die Hochdurchsatzanalysen ermöglichen. Das EU-finanzierte Projekt BALTIC will einen neuen Ansatz für die Untersuchung des bakteriellen Zellzyklus entwickeln. Mit dieser Methode, die auf hochmoderner, hochauflösender Hochdurchsatz-Mikroskopie basiert, sollen dynamische Modelle des bakteriellen Zellzyklus anhand von fixierten Zellen entwickelt werden. Die Technik ist ein bedeutender Fortschritt und profitiert von einer räumlichen Auflösung von 10 nm sowie quantitativen Informationen zur Bakterienzelle.
Ziel
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.
Wissenschaftliches Gebiet
Not validated
Not validated
- 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
Schlüsselbegriffe
Programm/Programme
Thema/Themen
Aufforderung zur Vorschlagseinreichung
Andere Projekte für diesen Aufruf anzeigenFinanzierungsplan
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Koordinator
1015 Lausanne
Schweiz