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Machine learning based analytics for bacteria cell cycle characterization using super resolution microscopy

Descrizione del progetto

Caratterizzazione del ciclo cellulare batterico attraverso la microscopia a super risoluzione

Il ciclo cellulare (CC) nei batteri implica un orologio interno associato, in particolare, all’omeostasi di dimensioni cellulari a livello di popolazione. Sono disponibili per le cellule eurocariote alcuni strumenti analitici dedicati alla caratterizzazione del CC, tuttavia essi sono inadeguati per lo studio delle cellule batteriche. L’aumento della resistenza agli antibiotici richiede piattaforme quantitative affidabili dedicate al CC batterico che consentano analisi ad alto rendimento. Il progetto BALTIC, finanziato dall’UE, si propone di sviluppare un nuovo approccio per l’indagine del CC batterico. La metodologia si affiderà a una microscopia a super risoluzione e ad alto rendimento di avanguardia per costruire modelli dinamici del CC batterico utilizzando cellule fisse. La tecnologia rappresenta un passo in avanti considerevole, beneficiando di una risoluzione spaziale di 10 nm e di informazioni quantitative sulla cellula batterica.

Obiettivo

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.

Coordinatore

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Contribution nette de l'UE
€ 191 149,44
Indirizzo
BATIMENT CE 3316 STATION 1
1015 Lausanne
Svizzera

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Regione
Schweiz/Suisse/Svizzera Région lémanique Vaud
Tipo di attività
Higher or Secondary Education Establishments
Collegamenti
Costo totale
€ 191 149,44