MM3

MM3 processes and analyzes high-throughput time-lapse images from mother machine microfluidic experiments to extract single-cell measurements such as elongation rate and birth size.

Key Features:

• Modular design: Modular pipeline that handles both phase contrast and fluorescence time-lapse images.
• Data processing: Transforms raw mother machine images into structured, cell-resolved data containing features such as elongation rate and birth size.
• Algorithmic approach: Integrates a combination of machine learning and non-learning algorithms for segmentation and tracking of microbial cells.
• Implementation: Implemented in Python.

Scientific Applications:

• Microbial growth dynamics: Quantifies single-cell growth rates and size control by extracting elongation rates and birth sizes over time.
• Cell cycle progression: Analyzes division timing and cell cycle-related phenotypes in bacteria cultured in mother machine devices.
• High-throughput time-lapse phenotyping: Enables large-scale, quantitative phenotyping of microbial populations from time-lapse image series.

Methodology:

Ingests raw time-lapse images, performs segmentation and tracking using a combination of machine learning and non-learning algorithms, and outputs structured per-cell feature data (e.g., elongation rate, birth size).