Fibroblast Subpopulations in Systemic Sclerosis: Functional Implications of Individual Subpopulations and Correlations with Clinical Features

成纤维细胞 多发性硬化 生物 免疫学 医学 细胞培养 遗传学
作者
Honglin Zhu,Hui Luo,Brian Skaug,Tracy Tabib,Yinan Li,Yongguang Tao,Alexandru‐Emil Matei,Marka A. Lyons,Georg Schett,Robert Lafyatis,Shervin Assassi,Jörg H. W. Distler
出处
期刊:Journal of Investigative Dermatology [Elsevier]
卷期号:144 (6): 1251-1261.e13 被引量:39
标识
DOI:10.1016/j.jid.2023.09.288
摘要

Fibroblasts constitute a heterogeneous population of cells. Here, we integrated scRNA-seq and bulk RNA-seq data as well as clinical information to study the role of individual fibroblast populations in Systemic Sclerosis (SSc). SSc skin demonstrated increased abundance of COMP+, COL11A1+, MYOC+, CCL19+, SFRP4/SFRP2+ and PRSS23/SFRP2+ fibroblasts signatures, and decreased proportions of CXCL12+ and PI16+ fibroblast signatures in the PRESS and GENISOS cohorts. Numerical differences were confirmed by multicolor immunofluorescence for selected fibroblast populations. COMP+, COL11A1+, SFRP4/SFRP2+, PRSS23/SFRP2+ and PI16+ fibroblasts were similarly altered between normal wound healing and SSc patients. The proportions of profibrotic COMP+, COL11A1+, SFRP4/SFRP2+ and PRSS23/SFRP2+, and proinflammatory CCL19+ fibroblast signatures were positively correlated with clinical and histopathological parameters of skin fibrosis, whereas signatures of CXCL12+ and PI16+ fibroblasts were inversely correlated. Incorporating the proportions of COMP+, COL11A1+, SFRP4/SFRP2+ and PRSS23/SFRP2+ fibroblast signatures into machine learning models improved the classification of SSc patients into those with progressive versus stable skin fibrosis. In summary, the profound imbalance of fibroblast subpopulations in SSc may drive progression of skin fibrosis. Specific targeting of disease-relevant fibroblast populations may offer the opportunities for the treatment of SSc and other fibrotic diseases.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
qxqy6678发布了新的文献求助10
刚刚
天天快乐应助荻野千寻采纳,获得10
刚刚
黄伟凯发布了新的文献求助10
1秒前
研友_VZG7GZ应助HMUBIN采纳,获得10
1秒前
2秒前
无奈冬寒发布了新的文献求助10
2秒前
manyi1972发布了新的文献求助10
2秒前
2秒前
3秒前
3秒前
3秒前
4秒前
4秒前
文具盒完成签到,获得积分10
4秒前
4秒前
4秒前
舒心冷珍发布了新的文献求助10
4秒前
852应助杜若采纳,获得10
4秒前
4秒前
蔚蓝绽放发布了新的文献求助20
4秒前
张军发布了新的文献求助20
5秒前
晓汐完成签到,获得积分20
5秒前
潮汐发布了新的文献求助10
5秒前
小小鹤鹤发布了新的文献求助10
5秒前
Ava应助蒹葭苍苍采纳,获得30
6秒前
英姑应助朴若琛采纳,获得10
6秒前
子车茗应助怕黑墨镜采纳,获得30
6秒前
6秒前
7秒前
传奇3应助shijie采纳,获得10
7秒前
酷波er应助dudu采纳,获得20
7秒前
7秒前
xiaowan发布了新的文献求助10
7秒前
7秒前
何必在乎发布了新的文献求助10
7秒前
8秒前
wwwwnr发布了新的文献求助10
8秒前
科研人发布了新的文献求助10
8秒前
9秒前
9秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
The Cambridge History of China: Volume 4, Sui and T'ang China, 589–906 AD, Part Two 1000
The Composition and Relative Chronology of Dynasties 16 and 17 in Egypt 1000
Russian Foreign Policy: Change and Continuity 800
Real World Research, 5th Edition 800
Qualitative Data Analysis with NVivo By Jenine Beekhuyzen, Pat Bazeley · 2024 800
Superabsorbent Polymers 700
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5711035
求助须知:如何正确求助?哪些是违规求助? 5202070
关于积分的说明 15263091
捐赠科研通 4863454
什么是DOI,文献DOI怎么找? 2610771
邀请新用户注册赠送积分活动 1561017
关于科研通互助平台的介绍 1518534