Single-cell approach dissecting agr quorum sensing dynamics in Staphylococcus aureus

Training data for the two StarDist2D models and the DeLTA 2.0 2D tracking model used in the publication. The trained stardist models are included in the respective zip files of the training data. mm: mother-machine; cc: connected chamber. Each of them contains two folders, img and seg_label. They contain matching pairs of phasecontrast images (img) and label images (seg_label). 

tracking_set_subset.zip contains the training data for the DeLTA tracking model following the default folder structure. We used custom weight functions to create the training weight maps in the folder wei. The folder wei_bck contains weights generated with the original function.

The unet_pads_tracking.hdf5 is the retrained tracking model used in the associated publication.

See associated GitHub repository for example code on how to use the models for segmentation and tracking.

Abstract:

Staphylococcus aureus both colonizes humans and causes severe virulent infections. Virulence is regulated by the agr quorum sensing system and its autoinducing peptide (AIP), with dynamics at the single-cell level across four agr-types – each defined by distinct AIP sequences and capable of cross-inhibition – remaining elusive. Employing microfluidics, time-lapse microscopy, and deep-learning image analysis, we uncovered significant differences in AIP sensitivity among agr-types. We observed bimodal agr activation, attributed to intergenerational phenotypic stability and influenced by AIP concentration. Upon AIP stimulation, agr‑III showed AIP insensitivity, while agr‑II exhibited increased sensitivity and prolonged generation time. Beyond expected cross-inhibition of agr‑I by heterologous AIP‑II and ‑III, the presumably cross-activating AIP‑IV also inhibited agr‑I. Community interactions across different agr-type pairings revealed four main patterns: stable or switched dominance, and delayed or stable dual activation, influenced by community characteristics. These insights underscore the potential of personalized treatment strategies considering virulence and genetic diversity.