Single cell transcriptomics in human osteoarthritis synovium and in silico deconvoluted bulk RNA sequencing.
Journal Article (Journal Article)
OBJECTIVES: To reveal the heterogeneity of different cell types of osteoarthritis (OA) synovial tissues at a single-cell resolution, and determine by novel methodology whether bulk-RNA-seq data could be deconvoluted to create in silico scRNA-seq data for synovial tissue analyses. METHODS: OA scRNA-seq data (102,077 synoviocytes) were provided by 17 patients undergoing total knee arthroplasty; 9 tissues with matched scRNA-seq and bulk RNA-seq data were used to evaluate six in silico gene deconvolution tools. Predicted and observed cell types and proportions were compared to identify the best deconvolution tool for synovium. RESULTS: We identified seven distinct cell types in OA synovial tissues. Gene deconvolution identified three (of six) platforms as suitable for extrapolating cellular gene expression from bulk RNA-seq data. Using paired scRNA-seq and bulk RNA-seq data, an "arthritis" specific signature matrix was created and validated to have a significantly better predictive performance for synoviocytes than a default signature matrix. Use of the machine learning tool, Cell-type Identification By Estimating Relative Subsets of RNA Transcripts x (CIBERSORTx), to analyze rheumatoid arthritis (RA) and OA bulk RNA-seq data yielded proportions of T cells and fibroblasts that were similar to the gold standard observations from RA and OA scRNA-seq data, respectively. CONCLUSION: This novel study revealed heterogeneity of synovial cell types in OA and the feasibility of gene deconvolution for synovial tissue.
- Huang, ZY; Luo, ZY; Cai, YR; Chou, C-H; Yao, ML; Pei, FX; Kraus, VB; Zhou, ZK
- March 2022
Volume / Issue
- 30 / 3
Start / End Page
- 475 - 480
Electronic International Standard Serial Number (EISSN)
Digital Object Identifier (DOI)