da_Tracker: Automated workflow for high throughput single cell and single phagosome tracking in infected cells

Time-lapse microscopy has emerged as a crucial tool in cell biology, facilitating a deeper understanding of dynamic cellular processes. While existing tracking tools have proven effective in detecting and monitoring objects over time, the quantification of signals within these tracked objects often faces implementation constraints. In the context of infectious diseases, the quantification of signals at localized compartments within the cell and around intracellular pathogens can provide even deeper insight into the interactions between the pathogen and host cell organelles. Existing quantitative analysis at a single-phagosome level remains limited and dependent on manual tracking methods. We developed a near-fully automated workflow that performs with limited bias, high-throughput cell segmentation and quantitative tracking of both single cell and single bacterium/phagosome within multi-channel, z-stack, time-lapse confocal microscopy videos. We took advantage of the PyImageJ library to bring Fiji functionality into a Python environment and combined deep-learning-based segmentation from Cellpose with tracking algorithms from Trackmate. Our workflow provides a versatile toolkit of functions for measuring relevant signal parameters at the single-cell level (such as velocity or bacterial burden) and at the single-phagosome level (i.e. assessment of phagosome maturation over time). It's capabilities in both single-cell and single-phagosome quantification, its flexibility and open-source nature should assist studies that aim to decipher for example the pathogenicity of bacteria and the mechanism of virulence factors that could pave the way for the development of innovative therapeutic approaches.