Accurate quantification of the bacterial inoculum and tissue load in a preclinical model of implant-related infection

Aims: Orthopaedic device-related infections are a major and growing clinical complication of joint arthroplasty surgery. Preclinical models are vital for developing strategies for prevention and treatment of these infections but suffer from imprecise determination of pathogen load during the experimental infection. We sought to establish a protocol for the accurate measurement of pathogen load using genome-based quantification in a preclinical implant-related infection model. Methods: strain attachment to stainless steel pin implants was quantified using both colony-forming unit (CFU) counting and genome copy counting, utilizing droplet digital polymerase chain reaction (ddPCR). An optimized protocol was then applied to a trans-tibial periprosthetic joint infection (PJI) mouse model. Pathological peri-implant bone loss and residual bacteria were monitored by in vivo micro-CT and immunostaining. Fixed and demineralized bone histological sections were used to measure bacterial tissue load by direct DNA isolation and ddPCR quantification, and host gene expression by reverse transcription ddPCR analysis. Results: Attachment studies of strain ATCC25923 to pins revealed that bacterial load peaked and stabilized after 20 minutes, with ddPCR analysis showing superior reproducibility and higher yields than CFU counting: ddPCR (mean 46,532 genome copies/mm² (SD 14,413; 33,904 to 62,235), CFU: 3,751 CFU/mm² (SD 4,936; 743.1 to 9,448) . Similar findings were recorded for three other strains tested, demonstrating the inaccuracy of CFU analysis. Pins coated with ATCC25923 for 20 minutes were selected for the mouse model. At 18 days post-infection, ddPCR analysis of 20 mm tibial histological sections from individual mice demonstrated residual bacterial loads (mean bacterial genomes/µm) between mice of 1.50 to 3.46 (0.36 to 6.73) and 8.64 to 39.14 (5.27 to 47.86). The potential for performing transcriptomic analysis was also demonstrated using the above tissue section preparation. Conclusion: Our approach of ddPCR quantification from histological sections offers improved accuracy and reproducibility for readouts of bacterial load from input to endpoint, independent of bacterial culturability, in a preclinical model of PJI.