Title

Longitudinal Intravital Imaging to Quantify the “Race for the Surface” Between Host Immune Cell and Bacteria for Orthopaedic Implants with S. aureus Colonization in a Murine Model

Serial section electron microscopy (SSEM) was initially developed to map the neural connections in the brain. SSEM eventually led to the term ‘Connectomics’ to be coined to describe process of following a cell or structure through a volume of tissue. This permits the true three-dimensionality to be appreciated and relationships between cells and structures. The purpose of this study was to utilize this methodology to interrogate S. aureus infected bone.

Bone samples were harvested from mice tibia infected with S. aureus and were fixed, decalcified, and osmicated. The samples were paraffin embedded and 5-micron sections were cut to identify regions of bacterial invasion into the osteocyte-lacuna-canalicular-network (OLCN). This area was cut from the paraffin block, deparaffinized, post-fixed and reprocessed into epoxy resin. Serial sections were cut at 60nm and collected onto Kapton tape utilizing the Automated Tape-collecting Ultramicrotome (ATUMtome) system. Samples were mounted onto 4” silicon wafers and post-stained with 2% uranyl acetate followed by 0.3% lead citrate and carbon coated. A ZEISS GeminiSEM 450 scanning electron microscope fitted with an electron backscatter diffusion detector was used to image the sections. The image stack was aligned and segmented using the open-source software, VASTlite.

264 serial sections were imaged, representing approximately 40 × 45 × 15-micron (x, y, z) volume of tissue. 70% of the canaliculi demonstrated infiltration by S. aureus.

This study demonstrates that SSEM can be applied to the skeletal system and provide a new solution to investigate the OLCN system. It is feasible that this methodology could be implemented to investigate why some canaliculi are resistant to colonization and potentially opens up a new direction for the prevention of chronic osteomyelitis. In order to make this a realistic target, automated segmentation methodologies utilizing machine learning must be developed and applied to the bone tissue datasets.

INTRODUCTION

Staphylococci species account for ∼80 % of osteomyelitis cases. While the most severe infections are caused by Staphylococcus aureus (S. aureus), the clinical significance of coagulase negative Staphylococcus epidermidis (S. epidermidis) infections remain controversial. In general, S. epidermidis was known to be a protective commensal bacterium. However, recent studies have shown that intra-operative low-grade S. epidermidis contamination prevents bone healing. Thus, the purpose of this study is to compare the pathogenic features of S. aureus and S. epidermidis in an established murine model of implant-associated osteomyelitis.