Molecular features and diagnostic modeling of synovium- and IPFP-derived OA macrophages in the inflammatory microenvironment via scRNA-seq and machine learning.

Abstract

Osteoarthritis (OA) is the leading cause of degenerative joint disease, with total joint replacement as the only definitive cure. However, no disease-modifying therapy is currently available. Inflammation and fibrosis in the infrapatellar fat pad (IPFP) contribute to OA onset and progression. However, the cellular composition and molecular mechanisms in the IPFP microenvironment remain unclear. This study investigates the functions of OA-macrophages and their clinical significance. We analyzed single-cell RNA sequencing (scRNA-seq) data from normal and OA patients. Enrichment analysis revealed differences in biological pathways across cell types. Pseudotime and cell-cell communication analyses revealed the developmental trajectory and interactions of OA-macrophages with other cell types. Machine learning (ML) algorithms identified feature genes of OA-macrophages. An OAMGS diagnostic score was developed, and CIBERSORT was used to analyze immune infiltration and its association with immune cells. Rat OA and normal models were established, and feature gene expression was validated using immunofluorescence (IF) staining and quantitative reverse transcription PCR (RT-qPCR). OA-macrophages play a central role in inflammation and fibrosis, enhancing leukocyte recruitment, chondrocyte apoptosis, and angiogenesis. They interact with chondrocytes, endothelial cells, and fibroblasts via CXCL and NF-κB signaling. High-dimensional weighted gene co-expression network analysis (hdWGCNA) identified 352 module genes linked to OA-macrophages. Machine learning developed a four-gene-based OAMGS score that accurately identifies OA-macrophages, with an AUC of 1 in the discovery cohort and 0.990 in an external cohort. Gene expression was validated in the OA model using RT-qPCR and IF. This study identifies a macrophage subcluster elevated in OA patients. OA-macrophages play an immunoregulatory role and may serve as diagnostic markers. The OAMGS score, based on four genes, provides an accurate diagnostic tool and potential therapeutic target for OA.