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. The 'da_tracker' (which stands for "the tracker") 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). Its 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.

The Streptococcus pyogenes stand-alone regulator RofA exhibits characteristics of a PRD-containing virulence regulator.

Streptococcus pyogenes [group A streptococcus (GAS)] is a human pathogen capable of infecting diverse tissues. To successfully infect these sites, GAS must detect available nutrients and adapt accordingly. The phosphoenolpyruvate transferase system (PTS) mediates carbohydrate uptake and metabolic gene regulation to adapt to the nutritional environment. Regulation by the PTS can occur through phosphorylation of transcriptional regulators at conserved PTS-regulatory domains (PRDs). GAS has several PRD-containing stand-alone regulators with regulons encoding both metabolic genes and virulence factors [PRD-containing virulence regulators (PCVRs)]. One is RofA, which regulates the expression of virulence genes in multiple GAS serotypes. It was hypothesized that RofA is phosphorylated by the PTS in response to carbohydrate levels to coordinate virulence gene expression. In this study, the RofA regulon of M1T1 strain 5448 was determined using RNA sequencing. Two operons were consistently differentially expressed across growth in the absence of RofA; the pilus operon was downregulated, and the capsule operon was upregulated. This correlated with increased capsule production and decreased adherence to keratinocytes. Purified RofA-His was phosphorylated in vitro by PTS proteins EI and HPr, and phosphorylated RofA-FLAG was detected in vivo when GAS was grown in low-glucose C medium. Phosphorylated RofA was not observed when C medium was supplemented 10-fold with glucose. Mutations of select histidine residues within the putative PRDs contributed to the in vivo phosphorylation of RofA, although phosphorylation of RofA was still observed, suggesting other phosphorylation sites exist in the protein. Together, these findings support the hypothesis that RofA is a PCVR that may couple sugar metabolism with virulence regulation.

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.