Automated image segmentation and division plane detection in single live Staphylococcus aureus cells

TL;DR

This paper introduces an automated image analysis framework for detecting and segmenting Staphylococcus aureus cells and their division planes, aiding research on bacterial division and potential antimicrobial targets.

Contribution

The study presents a novel automated method for analyzing S. aureus cell division, specifically detecting cells and their division planes in fluorescent images, with potential broader applications.

Findings

Successfully detects and segments S. aureus cells in clusters.

Identifies cell division planes using GFP-labelled EzrA protein.

Framework applicable to other fluorescently-labelled cellular structures.

Abstract

Staphylococcus aureus is a coccal bacterium, which divides by binary fission. After division the cells remain attached giving rise to small clusters, with a characteristic 'bunch of grapes' morphology. S. aureus is an important human pathogen and this, combined with the increasing prevalence of antibiotic-resistant strains, such as Methicillin Resistant S. aureus (MRSA), make it an excellent subject for studies of new methods of antimicrobial action. Many antibiotics, such as penicillin, prevent S. aureus cell division and so an understanding of this fundamental process may pave the way to the identification of novel drugs. We present here a novel image analysis framework for automated detection and segmentation of cells in S. aureus clusters, and identification of their cell division planes. We demonstrate the technique on GFP labelled EzrA, a protein that localises to a mid-cell plane during division and is involved in regulation of cell size and division. The algorithms may have wider applicability in detecting morphologically complex structures of fluorescently-labelled proteins within cells in other cell clusters.