Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry
By Fan Zhang, Kevin Wei, Kamil Slowikowski, C. Fonseka, Deepak A. Rao, S. Kelly, S. Goodman, Darren Tabechian, L. Hughes, K. Salomon-Escoto and 23 more. Nature Immunology, 2019
1,117 citations recorded by the source corpus.
To define the cell populations that drive joint inflammation in rheumatoid arthritis (RA), we applied single-cell RNA sequencing (scRNA-seq), mass cytometry, bulk RNA sequencing (RNA-seq) and flow cytometry to T cells, B cells, monocytes, and fibroblasts from 51 samples of synovial tissue from patients with RA or osteoarthritis (OA). Utilizing an integrated strategy based on canonical correlation analysis of 5,265 scRNA-seq profiles, we identified 18 unique cell populations. Combining mass cytometry and transcriptomics revealed cell states expanded in RA synovia: THY1(CD90)+HLA-DRAhi sublining fibroblasts, IL1B+ pro-inflammatory monocytes, ITGAX+TBX21+ autoimmune-associated B cells and PDCD1+ peripheral helper T (TPH) cells and follicular helper T (TFH) cells. We defined distinct subsets of CD8+ T cells characterized by GZMK+, GZMB+, and GNLY+ phenotypes. We mapped inflammatory mediators to their source cell populations; for example, we attributed IL6 expression to THY1+HLA-DRAhi fibroblasts and IL1B production to pro-inflammatory monocytes. These populations are potentially key mediators of RA pathogenesis. Defining cell types and their activation status in rheumatoid arthritis (RA) is critical to understanding this disease. Raychaudhuri and colleagues leverage several single-cell -omics approaches to define the cellular processes and pathways in the human RA joint.
Subfields: Immunology, Oncology, Rheumatology
View Paper: https://www.semanticscholar.org/paper/3e23dd9f2cc1684f5f7df5911f88b284690842b1
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Citations in the corpus
- Fast, sensitive, and accurate integration of single cell data with Harmony (2019, methodology): “In such a situation, most neighborhoods can be dominated by a single dataset and LISI values become difficult to interpret (Supplementary Note 2).”