Abstract
Single-cell RNA-seq (scRNA-seq) technologies have been broadly utilized to reveal the molecular mechanisms of respiratory diseases and physiology at single-cell resolution. Here, we constructed a cigarette smoking lung atlas by integrating data from 8 public datasets, including 104 lung scRNA-seq samples with patient state information. The cigarette smoking lung atlas generated by this single-cell meta-analysis (scMeta-analysis) revealed early carcinogenesis events and defined the alterations of single-cell gene expression, cell population, fundamental properties of biological pathways, and cell–cell interactions induced by cigarette smoking. In addition, we developed two novel scMeta-analysis methods incorporating clinical metadata: VARIED (Visualized Algorithms of Relationships In Expressional Diversity) and AGED (Aging-related Gene Expressional Differences). VARIED analysis revealed the expressional diversity associated with smoking carcinogenesis in each cell population. AGED analysis revealed differences in gene expression related to both aging and smoking states. Our scMeta-analysis provided new insights into the effects of smoking and into cellular diversity in the human lung at single-cell resolution.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Lead contact: Yusuke Yamamoto (yuyamamo{at}ncc.go.jp) ORCID ID: 0000-0001-8844-4295 (Jun Nakayama), 0000-0002-5262-8479 (Yusuke Yamamoto)
https://github.com/JunNakayama/scMeta-analysis-of-cigarette-smoking