High-Dimensional Analysis of Immune Cell Composition Predicts Periprosthetic Joint Infections and Dissects Its Pathophysiology.

Accurate diagnosis of periprosthetic joint infections (PJI) is one of the most widely researched areas in modern orthopedic endoprosthesis. However, our understanding of the immunological basis of this severe complication is still limited. In this study, we developed a flow cytometric approach to precisely characterize the immune cell composition in periprosthetic joints. Using high-dimensional multi-parametric data, we defined, for the first time, the local immune cell populations of artificial joints. We identified significant differences in the cellular distribution between infected and non-infected samples, and revealed that myeloid-derived suppressor cells (MDSCs) act as potential regulators of infiltrating immune cells in PJI. Further, we developed an algorithm to predict septic and aseptic samples with high sensitivity and specificity, that may serve as an indispensable addition to the current criteria of the Musculoskeletal Infection Society. This study describes a novel approach to flow cytometrically analyze the immune cell infiltrate of joint fluid that not only improves our understanding of the pathophysiology of PJI, but also enables the development of a novel screening tool to predict infection status. Our data further suggest that pharmacological targeting of MDSCs represents a novel strategy for addressing PJI.

PMID: 32957521