Single-cell RNA sequencing reveals that an imbalance in monocyte subsets rather than changes in gene expression patterns is a feature of postmenopausal osteoporosis.

Abstract

The role of monocytes in postmenopausal osteoporosis is widely recognized; however, the mechanisms underlying monocyte reprogramming remain unknown. In this study, single-cell RNA sequencing (scRNA-seq) was conducted on CD14+ bone marrow monocytes obtained from 3 postmenopausal women with normal BMD and 3 women with postmenopausal osteoporosis (PMOP). Monocle2 was used to classify the monocytes into 7 distinct clusters. The proportion of cluster 1 significantly decreased in PMOP patients, while the proportion of cluster 7 increased. Further analysis via GSEA, transcription factor activity analysis, and sc-metabolic analysis revealed significant differences between clusters 1 and 7. Cluster 7 exhibited upregulated pathways associated with inflammation, immunity, and osteoclast differentiation, whereas cluster 1 demonstrated the opposite results. Monocle2, TSCAN, VECTOR, and scVelo data indicated that cluster 1 represented the initial subset and that cluster 7 represents one of the terminal subsets. BayesPrism and ssGSEA were employed to analyze the bulk transcriptome data obtained from the GEO database. The observed alterations in the proportions of 1 and 7 were validated and found to have diagnostic significance. CD16 serves as the marker gene for cluster 7, thus leading to an increased proportion of CD16+ monocytes in women with PMOP. Flow cytometry was used to assess the consistency of outcomes with those of the bioinformatic analysis. Subsequently, an additional scRNA-seq analysis was conducted on bone marrow mononuclear cells obtained from 3 patients with PMOP and 3 postmenopausal women with normal BMD. The differential proportions of cluster 1 and cluster 7 were once again confirmed, with the pathological effect of cluster 7 may attribute to cell-cell communication. The scRNA-seq findings suggest that an imbalance in monocyte subsets is a characteristic feature of PMOP. These findings elucidate the limitations of utilizing bulk transcriptome data for detecting alterations in monocytes, which may influence novel research inquiries.