2.3 Single-cell data analysis

Quality control of datasets was performed using the Seurat package (version 4.4.0) in R software (version 4.3.1). Samples were first converted to Seurat objects using the “CreateSeuratObject”. Cells with mitochondrial gene percentages < 25% and unique gene counts between 200 and 6,000 were used. The data were normalized using the “NormalizeData” function, scaled for all genes using the ScaleData function, and subjected to principal component analysis (PCA). Hypervariable genes were identified by the FindVariableFeatures function and used for downstream analysis. Since the data were obtained from different samples, batch correction was performed using the R package “Harmony” (1.0.3) to avoid any batch effects interfering with downstream analysis. Cell clustering and classification were performed by using the FindClusters function. The SingleR package (2.2.0) was then used to match the single-cell RNA-seq data to a known reference dataset and manually calibrated to improve the accuracy and reliability of cell type annotation. Marker genes are shown in Supplementary Figures S1A, S1B. Monocyte subpopulations were categorized as classical monocytes (CCR2, SELL, S100A8, S100A9, LYZ, SERPINB2, CD14), non-classical monocytes (NAP1L1, FCGR3A, FCGR3B, CSTA, CX3CR1, ITGAL), and intermediate monocytes (HLA-DRA, HLA-DPB1, EVL) based on markers genes listed in literature. Dot plots show the proportion and average expression of cell clusters expressing the marker gene in monocytes from COVID-19, gout flare and healthy samples (Supplementary Figure S1C), and the marker gene in monocytes from COVID-19, gout remission and healthy samples (Supplementary Figure S1D). The correspondence between cell clusters, cell marker and cell types from COVID-19, gout flare and healthy samples are shown in Supplementary Figure S2A, and cell types from COVID-19, gout remission and healthy samples are shown in Supplementary Figure S2B. The FindAllMarkers function was used to find differentially expressed genes between monocyte and other clusters. The communication between monocytes and other cells was determined by analyzing the ligand-receptor pairs using CellChat (version 1.6.1) R package, with CellChatDB.human as the reference database. The R package scMetabolism was used to quantify the metabolic activities of the different types of cells at the single-cell level.