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.

中文翻译：

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人骨关节炎滑膜中的单细胞转录组学和计算机解卷积大量 RNA 测序

目标

以单细胞分辨率揭示骨关节炎 (OA) 滑膜组织不同细胞类型的异质性，并通过新方法确定是否可以对 bulk-RNA-seq 数据进行去卷积以创建用于滑膜组织分析的计算机 scRNA-seq数据。

方法

OA scRNA-seq 数据（102,077 个滑膜细胞）由 17 名接受全膝关节置换术的患者提供；具有匹配的 scRNA-seq 和大量 RNA-seq 数据的 9 个组织用于评估六个计算机基因反卷积工具。比较预测和观察到的细胞类型和比例，以确定滑膜的最佳去卷积工具。

结果

我们在 OA 滑膜组织中鉴定了七种不同的细胞类型。基因反卷积确定了三个（共六个）平台适合从大量 RNA-seq 数据中推断细胞基因表达。使用配对的 scRNA-seq 和大量 RNA-seq 数据，创建并验证了“关节炎”特定特征矩阵，其对滑膜细胞的预测性能明显优于默认特征矩阵。使用机器学习工具，通过估计 RNA 转录本 x (CIBERSORTx) 的相对子集进行细胞类型识别，分析类风湿性关节炎 (RA) 和 OA 大量 RNA-seq 数据产生的 T 细胞和成纤维细胞比例与黄金相似分别来自 RA 和 OA scRNA-seq 数据的标准观察结果。

结论

这项新研究揭示了 OA 中滑膜细胞类型的异质性和滑膜组织基因去卷积的可行性。