Understanding individual heterogeneity in ageing from stochastic dynamics of sigma factor regulatory network in bacteria

Informations projet

ASNet

N° de convention de subvention: 101069035

DOI

10.3030/101069035

Projet clôturé

Date de signature de la CE 30 Juin 2022

Date de début 15 Decembre 2022

Date de fin 14 Decembre 2024

Financé au titre de

• Marie Skłodowska-Curie Actions (MSCA)

Coût total

Aucune donnée

Contribution de l’UE

€ 189 687,36

Coordonné par

FREIE UNIVERSITAET BERLIN
Germany

Periodic Reporting for period 1 - ASNet (Understanding individual heterogeneity in ageing from stochastic dynamics of sigma factor regulatory network in bacteria)

Période du rapport: 2022-12-15 au 2024-12-14

Résumé du contexte et des objectifs généraux du projet

Ageing is a fundamental biological process that affects all living organisms, including bacteria. Despite being genetically identical and living in the same environment, individual bacterial cells display high variability in ageing rates and survival. This individual heterogeneity in ageing remains poorly understood at the molecular level. The ASNet project aimed to uncover the link between stochastic gene expression and bacterial ageing, focusing on the role of sigma factors in bacteria. By integrating single-cell microscopy, deep learning-based image analysis, and mathematical modelling, the project sought to:
1. Collect gene expression and cellular ageing data in individual E. coli cells using time-lapse fluorescence microscopy.
2. Utilise deep learning algorithms for high-throughput bacterial cell segmentation and tracking.
3. Construct mathematical models to understand how cellular damage and gene regulation influence bacterial demography and population dynamics

Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent

Throughout the project, ASNet successfully combined cutting-edge experimental, theoretical and computational approaches to achieve its objectives:

- Single-Cell Microscopy of Bacterial Ageing
• Tracked rpoS (a major sigma factor in stress response and stationary phase regulation) at the single-cell level.
• Captured thousands of individual bacterial life histories, linking gene expression fluctuations to cellular fate.

- Deep Learning-Based Image Analysis
• Utilised a machine learning cell segmentation and tracking pipeline, enabling automated high-throughput analysis of bacterial ageing dynamics.

- Mathematical Modelling of Bacterial Ageing
• Developed stochastic models describing damage accumulation in single-cell bacteria, a key factor influencing ageing and survival.
• Built matrix projection models to estimate population-level consequences of cellular heterogeneity.

- Dissemination & Exploitation of Results
• Published findings in peer-reviewed journals, with additional manuscripts under review.
• Presented research at international conferences, contributing to the global understanding of microbial ageing and resistance.
• Contributing with datasets and computational tools, ensuring the project’s impact extends beyond its duration.

Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent)

The project pushed the boundaries of microbial ageing research by integrating experimental, computational, and theoretical approaches in a novel way. It advanced the understanding of stochastic ageing in bacteria. It demonstrated that the noise in cellular damage and gene expression in sigma factor regulation influences ageing variability. It established a quantitative framework for linking cellular damage and gene expression to demographic outcomes, providing new insights

Single-Cell Bacteria