Journal of autoimmunity · 2020
Integrated bulk and single-cell RNA-sequencing identified disease-relevant monocytes and a gene network module underlying systemic sclerosis.
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Kobayashi Satomi, Nagafuchi Yasuo, Okubo Mai, Sugimori Yusuke, Shirai Harumi, Hatano Hiroaki, Junko Maeda, Yanaoka Haruyuki, Takeshima Yusuke, Ota Mineto, Iwasaki Yukiko, Sumitomo Shuji, Okamura Tomohisa, Yamamoto Kazuhiko, Shoda Hirofumi, Fujio Keishi
Аннотация
OBJECTIVE: Immunological disturbances have been reported in systemic sclerosis (SSc). This study assessed the transcriptome disturbances in immune cell subsets in SSc and characterized a disease-related gene network module and immune cell cluster at single cell resolution.
METHODS: Twenty-one Japanese SSc patients were enrolled and compared with 13 age- and sex-matched healthy controls (HC). Nineteen peripheral blood immune cell subsets were sorted by flow cytometry and bulk RNA-seq analysis was performed for each. Differential expression and pathway analyses were conducted. Iterative weighted gene correlation network analysis (iWGCNA) of each subset revealed clustered co-expressed gene network modules. Random forest analysis prioritized a disease-related gene module. Single cell RNA-seq analysis of 878 monocytes was integrated with bulk RNA-seq analysis and with a public database for single cell RNA-seq analysis of SSc patients.
RESULTS: Inflammatory pathway genes were differentially expressed in widespread immune cell subsets of SSc. An inflammatory gene module from CD16+ monocytes, which included KLF10, PLAUR, JUNB and JUND, showed the greatest discrimination between SSc and HC. One of the clusters of SSc monocytes identified by single-cell RNA-seq analysis characteristically expressed these inflammatory co-expressed genes and was similar to lung infiltrating FCN1hi monocytes expressing IL1B.
CONCLUSIONS: Our integrated analysis of bulk and single cell RNA-seq analysis identified an inflammatory gene module and a cluster of monocytes that are relevant to SSc pathophysiology. They could serve as candidate novel therapeutic targets in SSc.
METHODS: Twenty-one Japanese SSc patients were enrolled and compared with 13 age- and sex-matched healthy controls (HC). Nineteen peripheral blood immune cell subsets were sorted by flow cytometry and bulk RNA-seq analysis was performed for each. Differential expression and pathway analyses were conducted. Iterative weighted gene correlation network analysis (iWGCNA) of each subset revealed clustered co-expressed gene network modules. Random forest analysis prioritized a disease-related gene module. Single cell RNA-seq analysis of 878 monocytes was integrated with bulk RNA-seq analysis and with a public database for single cell RNA-seq analysis of SSc patients.
RESULTS: Inflammatory pathway genes were differentially expressed in widespread immune cell subsets of SSc. An inflammatory gene module from CD16+ monocytes, which included KLF10, PLAUR, JUNB and JUND, showed the greatest discrimination between SSc and HC. One of the clusters of SSc monocytes identified by single-cell RNA-seq analysis characteristically expressed these inflammatory co-expressed genes and was similar to lung infiltrating FCN1hi monocytes expressing IL1B.
CONCLUSIONS: Our integrated analysis of bulk and single cell RNA-seq analysis identified an inflammatory gene module and a cluster of monocytes that are relevant to SSc pathophysiology. They could serve as candidate novel therapeutic targets in SSc.