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      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

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          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.

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          Most cited references2

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          The Biophysics of Infection.

          Our understanding of the processes involved in infection has grown enormously in the past decade due in part to emerging methods of biophysics. This new insight has been enabled through advances in interdisciplinary experimental technologies and theoretical methods at the cutting-edge interface of the life and physical sciences. For example, this has involved several state-of-the-art biophysical tools used in conjunction with molecular and cell biology approaches, which enable investigation of infection in living cells. There are also new, emerging interfacial science tools which enable significant improvements to the resolution of quantitative measurements both in space and time. These include single-molecule biophysics methods and super-resolution microscopy approaches. These new technological tools in particular have underpinned much new understanding of dynamic processes of infection at a molecular length scale. Also, there are many valuable advances made recently in theoretical approaches of biophysics which enable advances in predictive modelling to generate new understanding of infection. Here, I discuss these advances, and take stock on our knowledge of the biophysics of infection and discuss where future advances may lead.
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            Designing a single-molecule biophysics tool for characterizing DNA damage for techniques that kill infectious pathogens through DNA damage effects

            Antibiotics such as the quinolones and fluoroquinolones kill bacterial pathogens ultimately through DNA damage. They target the essential type IIA topoisomerases in bacteria by stabilising the normally transient double strand break state which is created to modify the supercoiling state of the DNA. Here we discuss the development of these antibiotics and their method of action. Existing methods for DNA damage visualisation, such as the comet assay and immunofluorescence imaging can often only be analysed qualitatively and this analysis is subjective. We describe a putative single-molecule fluorescence technique for quantifying DNA damage via the total fluorescence intensity of a DNA origami tile fully saturated with an intercalating dye, along with the optical requirements for how to implement these into a light microscopy imaging system capable of single-molecule millisecond timescale imaging. This system promises significant improvements in reproducibility of the quantification of DNA damage over traditional techniques.
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              Author and article information

              Journal
              2016-10-20
              Article
              10.1088/1478-3975/13/5/055002
              1610.06361
              182bbd6d-f70e-4f80-9108-7bfa1a56fe65

              http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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              arXiv admin note: text overlap with arXiv:1603.00695
              physics.bio-ph

              Biophysics
              Biophysics

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