Z Y Huang1, Z Y Luo2, Y R Cai3, C-H Chou4, M L Yao5, F X Pei6, V B Kraus7, Z K Zhou8. 1. Department of Orthopedic Surgery, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan Province, PR China; Department of Orthopaedic Surgery, School of Medicine, Duke University, Durham, NC, USA. Electronic address: zey.huang@gmail.com. 2. Department of Orthopedic Surgery, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan Province, PR China. Electronic address: dr_zeyu@163.com. 3. Department of Orthopedic Surgery, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan Province, PR China. Electronic address: caiyongrui96@163.com. 4. Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA. Electronic address: chouchingheng@gmail.com. 5. Institute of Respiratory Health, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan Province, PR China. Electronic address: yaomenglin@wchscu.cn. 6. Department of Orthopedic Surgery, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan Province, PR China. Electronic address: peifuxing1951@163.com. 7. Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA; Division of Rheumatology, Department of Medicine, Duke University School of Medicine, Durham, NC, USA. Electronic address: vbk@duke.edu. 8. Department of Orthopedic Surgery, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan Province, PR China. Electronic address: zhouzongke@scu.edu.cn.
Abstract
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.
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.