Deciphering the cellular and molecular landscape of pulmonary fibrosis through single-cell sequencing and machine learning.

ABSTRACT: Pulmonary fibrosis is characterized by progressive lung scarring, leading to a decline in lung function and an increase in morbidity and mortality. This study leverages single-cell sequencing and machine learning to unravel the complex cellular and molecular mechanisms underlying pulmonary fibrosis, aiming to improve diagnostic accuracy and uncover potential therapeutic targets. By analyzing lung tissue samples from pulmonary fibrosis patients, we identified distinct cellular phenotypes and gene expression patterns that contribute to the fibrotic process. Notably, our findings revealed a significant enrichment of activated B cells, CD4 T cells, macrophages, and specific fibroblast subpopulations in fibrotic versus normal lung tissue. Machine learning analysis further refined these observat

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC11697757 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature