A High-Resolution Time-Resolved Single-Cell Transcriptomic Landscape of Human Osteoarthritic Cartilage

Project Description: This dataset contains the processed single-cell RNA sequencing (scRNA-seq) gene expression matrices for a comparative study of transcriptomic profiles between normal and diseased states. The study focuses on characterizing cellular heterogeneity and identifying key molecular markers in human knee articular cartilage. Study Design: A total of 8 biological samples were collected and sequenced: Normal Control (NC) Group: 4 samples (NC1, NC2, NC3, NC4) Osteoarthritis (OA) Group: 4 samples (OA1, OA2, OA3, OA4) Data Content: For each sample, we provide the standard filtered feature-barcode matrices generated by the Cell Ranger pipeline, consisting of: barcodes.tsv.gz: Cellular barcodes identified in the sample. features.tsv.gz: List of genes/features (gene IDs and symbols). matrix.mtx.gz: Sparse matrix containing the counts of unique molecular identifiers (UMIs) for each gene in each cell. Technical Details: The data is ready for downstream analysis using standard bioinformatics tools such as Seurat (R) or Scanpy (Python). All files are fully compressed in GZIP format (.gz) to ensure data integrity and optimize storage efficiency for high-throughput sequencing data.