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

Descripción del proyecto

Caracterización del ciclo celular bacteriano mediante microscopía de superresolución

El ciclo celular (CC) en las bacterias incluye un reloj interno relacionado, en particular, con la homeostasis del tamaño celular a escala poblacional. Existen algunas herramientas analíticas de caracterización del CC para las células eucariotas, pero no resultan adecuadas para el estudio de células bacterianas. El aumento de la resistencia a los antibióticos requiere plataformas cuantitativas fiables centradas en el CC bacteriano que permitan un análisis de alto rendimiento. El proyecto BALTIC, financiado con fondos europeos, pretende desarrollar un método novedoso para el estudio del CC bacteriano. La metodología se basará en una avanzada microscopía de superresolución y alto rendimiento para construir modelos dinámicos del CC bacteriano mediante células fijas. La tecnología constituye un gran avance y ofrece una resolución espacial de 10 nm e información cuantitativa sobre las células bacterianas.

Objetivo

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.

Coordinador

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Aportación neta de la UEn
€ 191 149,44
Dirección
BATIMENT CE 3316 STATION 1
1015 Lausanne
Suiza

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Región
Schweiz/Suisse/Svizzera Région lémanique Vaud
Tipo de actividad
Higher or Secondary Education Establishments
Enlaces
Coste total
€ 191 149,44