Periodic Reporting for period 1 - ASNet (Understanding individual heterogeneity in ageing from stochastic dynamics of sigma factor regulatory network in bacteria)
Reporting period: 2022-12-15 to 2024-12-14
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
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
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.
- 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.
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