An automated image analysis framework for segmentation and division plane detection of single live Staphylococcus aureus cells which can operate at millisecond sampling time scales using bespoke Slimfield microscopy

TL;DR

This paper introduces an automated image analysis framework capable of detecting and segmenting live S. aureus cells and their division planes at millisecond sampling rates using bespoke microscopy, aiding antimicrobial research.

Contribution

The study presents a novel combination of analytical tools for real-time detection and segmentation of bacterial cells and division planes from high-speed microscopy images.

Findings

Achieved single-molecule detection precision in live cell imaging.

Successfully identified cell division planes using fluorescent protein markers.

Framework applicable to studying antimicrobial effects on cell division.

Abstract

Staphylococcus aureus is an important pathogen, giving rise to antimicrobial resistance in cell strains such as Methicillin Resistant S. aureus (MRSA). Here we report an image analysis framework for automated detection and image segmentation of cells in S. aureus cell clusters, and explicit identification of their cell division planes. We use a new combination of several existing analytical tools of image analysis to detect cellular and subcellular morphological features relevant to cell division from millisecond time scale sampled images of live pathogens at a detection precision of single molecules. We demonstrate this approach using a fluorescent reporter GFP fused to the protein EzrA that localises to a mid-cell plane during division and is involved in regulation of cell size and division. This image analysis framework presents a valuable platform from which to study candidate new antimicrobials which target the cell division machinery, but may also have more general application in detecting morphologically complex structures of fluorescently labelled proteins present in clusters of other types of cells.