GEOapplication/xmlftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE311nnn/GSE311208/primaryOK200TranscriptomicsMus musculusExpression profiling by high throughput sequencinghttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE311208GEOGSEfalseCombination of single-cell and bulk RNA-seq reveals changes in the immune landscape in osteomyelitisObjective: This study profiled the osteomyelitis immune micro-environment in depth, pinpointed driver genes and cell populations that fuel disease progression, and mined the data for actionable drug targets. Methods: We analyzed time-series transcriptomic sequencing data from mouse tibial osteomyelitis samples in dataset GSE168896. Fuzzy c-means clustering was applied to reveal gene sets linked to disease progression. Immune cell infiltration analysis was conducted through online tool ImmuCellAI-mouse. Furthermore, by leveraging single-cell sequencing data, we characterized immune cell subpopulations and pinpointed the key cell subtypes that exhibited in osteomyelitis mouse. Results: We identified six gene clusters exhibiting distinct temporal expression patterns and functional roles in osteomyelitis processes, such as leukocyte and lymphocyte activation, ossification. Single-cell sequencing analysis further revealed 7 distinct cellular subpopulations. Among these, M2-like macrophages demonstrated a significant increase following osteomyelitis. Arg+Sdc4+ Mac and Cxcl1+Ccl4+ Mac were new subpopulations of macrophages that emerged after osteomyelitis, the infiltration of Mif+Cd63+ Mac significantly increased. Besides, Cxcl2-Cxcr2 ligand-receptors contributed mostly in immune cells.2026/02/25GSE311208GSM9321145GSM9321143GSM932114419057311208Mus musculus[12982112]