For ATAC-seq, each cell type was compared with all other cell types, and cell type-specific peaks were filtered with |log2 fold change| > 4, p < 0.001, FDR < 0.01 and the average of log2(peak counts) > 3 across all samples. For each cell type, a pairwise comparison among HD, OA, and RA samples was performed, and disease-specific peaks were filtered with |log2 fold change| > 1, p < 0.001, and FDR < 0.1. An unpaired Student’s t-test and Benjamini-Hochberg multiple test were used to calculate the p and FDR values between any pair of samples. Unsupervised clustering of differential peaks is performed using K-means algorithm. For ATAC-seq stimulated by CRP in vitro, we aligned the fastq data to the hg19 genome according to the ATAC-pipe described above. We then used the ATAC-seq peaks of monocytes from the RA, OA, and HD samples as input and calculated the peak intensity in the CRP-stimulated samples. Differential peaks were filtered by fold change > 1.5, p < 0.01. P is calculated using paired t-test. For RNA-seq, we used raw read counts mapped to each gene as input and used DEseq2 [53] to obtain a standardized expression matrix. The differential genes were filtered as fold change > 1.5, p < 0.05, and the sum of counts across all samples is greater than 5. P is calculated using independent sample unpaired t-test.

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