• The early stages of biofilm formation by Staphylococcus epidermidis studied by XPS and AFM

      Smith, Graham; Bava, Radhika (University of Chester, 2019-09)
      Staphylococcus epidermidis is an opportunistic bacteria which forms pathogenic biofilms in medical implant environment. Biofilm formation is a complex multistage process within which the initial stages of adhesion are deemed the most critical target for preventing biofilms. This research involves the characterisation of S. epidermidis (ATCC35984 and NCTC13360) by using X-ray photoelectron spectroscopy (XPS) and atomic force microscopy (AFM) on model substrates including glass, muscovite mica, silicon (111) wafer, sputter-coated titanium and sputter-coated silver, focusing on the effect of chemical properties of the material on adhesion by using surfaces with minimal roughness. AFM was used to image the surface, from which bacterial coverage can be estimated. AFM was also used to probe adhesion forces and local mechanical properties of all samples through the use of force-distance curves. AFM images were also used to estimate the bacterial coverage. XPS was used to investigate the surface chemistry from the layer thicknesses, the percentage coverage and potential composition of the overlayer. The combination of these techniques allow the relationships between the surface chemistry of the substrate and the bacteria to be correlated with changes in coverage and properties of bacterial films. Data on incubated bacterial samples were compared with those from the reference substrates, both before and after autoclaving, and from samples prepared using protein rich growth medium (tryptic soy broth) in the absence of bacteria as well as a pure bacterial pellet in an assumed non-biofilm forming state. The research indicates the potential differences between biofilm and non-biofilm former strains, with both strains being covered by an organic layer with little influence of the growth media used to incubate the bacteria. This research also shows how XPS and AFM data can be combined and applied to bacterial adhesion.