Summary
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.
Keywords
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Article info
Publication history
Published online: December 28, 2021
Accepted:
December 20,
2021
Received:
September 3,
2021
Identification
Copyright
© 2021 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.
