Literature DB >> 31100034

Computational deconvolution of synovial tissue cellular composition: presence of adipocytes in synovial tissue decreased during arthritis pathogenesis and progression.

Wenhui Wang1,2, Li Wang1,2,3, Percio S Gulko4, Jun Zhu1,2,3.   

Abstract

Osteoarthritis (OA) and rheumatoid arthritis (RA) are the most common forms of arthritis. The synovial tissue is the major site of inflammation of OA and RA and consists of diverse cells. Synovial tissue cell composition changes during arthritis pathogenesis and progression have not been systematically characterized and may provide critical insights into disease processes. In this study we aimed at systematically examining cellular changes in synovial tissue. Publicly available synovial tissue transcriptomic data sets were used. We computationally estimated cell compositions in synovial tissue based on transcriptomic data and compared cell compositions in different diseases or at different disease stages. Synovial fibroblasts, macrophages, adipocytes, and immune cells were the major cell types in all synovial tissue. Both OA and RA patients had a significantly lower adipocyte fraction compared with healthy controls. The decrease trend was also observed during OA and RA progression. The fraction of monocytes was also increased in both OA and RA arthritis patients, consistent with the observations that inflammation involved in both OA and RA. But the monocyte fraction in RAs was much higher than the ones in healthy controls and OAs. The M2 macrophage fraction was reduced in RA compared with OA, the reduction trend continued during RA progression from the early- to the late-stage. There were consistent cell composition differences between different types or stages of arthritis. Both in RA and OA, the new discovery of changes in the adipocyte and M2 macrophage fractions has potential leading to novel therapeutic development.

Entities:  

Keywords:  osteoarthritis; rheumatoid arthritis; synovial adipocyte; synovial fibroblast; synovial macrophage

Mesh:

Year:  2019        PMID: 31100034      PMCID: PMC6620645          DOI: 10.1152/physiolgenomics.00009.2019

Source DB:  PubMed          Journal:  Physiol Genomics        ISSN: 1094-8341            Impact factor:   3.107


  60 in total

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