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Unraveling asthma through single-cell RNA sequencing in understanding disease mechanisms

医学 哮喘 疾病 遗传学 核糖核酸 计算生物学 生物信息学 免疫学 基因 内科学 生物
作者
Xingxing Yuan,Chaofan Li,Liuxin Yang,Jiawei Gao,Bingyu Wang,Zhuying Li
出处
期刊:Journal of Asthma [Taylor & Francis]
卷期号:62 (8): 1257-1265 被引量:1
标识
DOI:10.1080/02770903.2025.2472358
摘要

OBJECTIVE: To elucidate the fundamental principles of single-cell RNA sequencing (scRNA-seq) and summarize its application in asthma research, aiming to enhance understanding of asthma pathophysiology and guide future research directions. DATASOURCE: Recent advances and emerging research in scRNA-seq and its role in the pathogenesis of asthma. STUDY SELECTIONS: This review incorporates studies that analyzed the heterogeneity of asthma cell types and their functional states using scRNA-seq, with particular emphasis on immune cells and airway remodeling. The selection of specific cell types and markers was based on their relevance to asthma pathogenesis, and we discuss the rationale for favoring certain scRNA-seq technologies in these investigations. RESULTS: ScRNA-seq technology has provided insights into the key mechanisms underlying inflammation and airway remodeling in asthma. It has uncovered the diversity of immune cell subtypes and their specific roles in asthma pathogenesis, revealing critical pathways that contribute to disease progression. These findings offer a theoretical foundation for the development of targeted therapeutic strategies, paving the way for personalized medicine and improved patient outcomes. CONCLUSION: ScRNA-seq reveals the complex heterogeneity and functional roles of immune cells in asthma, offering key insights into disease mechanisms and the potential for targeted therapies. However, challenges remain, such as the need for further refinement of data integration methods and addressing the limited clinical applicability of current findings. Future research should focus on overcoming these limitations, improving cell type annotation, and expanding studies to include longitudinal and clinical data to better understand disease dynamics and therapy responses.
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