Single-cell RNA sequencing of immune cells in patients with acute gout

被引:0
作者
Jan-Gowth Chang
Siang-Jyun Tu
Chung-Ming Huang
Yu-Chia Chen
Hui-Shan Chiang
Ya-Ting Lee
Ju-Chen Yen
Chia-Li Lin
Chin-Chun Chung
Ta-Chih Liu
Ya-Sian Chang
机构
[1] China Medical University Hospital,Center for Precision Medicine
[2] China Medical University Hospital,Epigenome Research Center
[3] China Medical University,School of Medicine
[4] Asia University,Department of Bioinformatics and Medical Engineering
[5] China Medical University Hospital,Department of Laboratory Medicine
[6] China Medical University Hospital,Division of Immunology and Rheumatology, Department of Internal Medicine
[7] Chang Bing Show Chwan Memorial Hospital,Department of Hematology
来源
Scientific Reports | / 12卷
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摘要
Cell subpopulations in the blood and joint fluid of patients with gout are poorly understood. Single-cell RNA sequencing and bioinformatic tools were used to identify cell subsets and their gene signatures in blood and synovial fluid (SF) cells, determine their relationships, characterize the diversity, and evaluate interactions among specific cell types. We identified 34 subpopulations (5 types of B cells, 16 types of T and natural killer cells, 9 types of monocytes, and 4 other cell types) in the blood of five healthy subjects and seven patients with acute gouty, and the SF of three patients with acute gout. We found that naïve CD4 T cells and classical monocytes cell populations were enriched in patients with gout, whereas plasmacytoid dendritic cells and intermediate monocytes were more abundant in healthy subjects. SF was enriched in Th1/Th17 cells, effector memory CD8 T cells, mucosal-associated invariant T cells, and macrophages. Subclusters of these cell subpopulations showed different compositions between healthy subjects and those with acute gout, according to blood and SF samples. At the cellular level, the inflammation score of a subpopulation or subcluster was highest in SF, following by the blood of acute gout patients and healthy person, whereas energy score showed the opposite trend. We also detected specific cell–cell interactions for interleukin-1, tumor necrosis factor-α, and transforming growth factor-β1 expression in the cells of patients with acute gout. Our study reveals cellular and molecular insights on inflammatory responses to hyperuricemia or uric crystal and may provide therapeutic guidance to improve treatments for gout.
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[1]  
Dalbeth N(2016)Gout Lancet 388 2039-2052
[2]  
Merriman TR(2020)The biology of urate Semin. Arthritis Rheum. 50 S2-S10
[3]  
Stamp LK(2020)Advances in our understanding of gout as an auto-inflammatory disease Semin. Arthritis Rheum. 50 1089-1100
[4]  
Keenan RT(2018)Relationship between serum urate concentration and clinically evident incident gout: An individual participant data analysis Ann. Rheum. Dis. 77 1048-1052
[5]  
Bodofsky S(2019)Contemporary prevalence of gout and hyperuricemia in the United States and decadal trends: The National Health and Nutrition Examination Survey, 2007–2016 Arthritis Rheumatol. 71 991-999
[6]  
Merriman TR(2019)Gout: State of the art after a decade of developments Rheumatology 58 27-44
[7]  
Thomas TJ(2020)Gout: a disease involved with complicated immunoinflammatory responses: A narrative review Clin. Rheumatol. 39 2849-2859
[8]  
Schlesinger N(2021)Autoinflammatory features in gouty arthritis J. Clin. Med. 10 1880-768
[9]  
Dalbeth N(2017)Molecular pathophysiology of gout Trends Mol. Med. 23 756-86
[10]  
Phipps-Green A(2020)Asymptomatic hyperuricaemia: A silent activator of the innate immune system Nat. Rev. Rheumatol. 16 75-105