← BioTransfer GEO Dataset Finder
GEO series

High Dimensional Analysis of Sputum Cell Transcriptomes Identifies novel pathways associated with Clinical Phenotypes of Asthma

GSE270863 Homo sapiens Expression profiling by high throughput sequencing 24 samples 2025/06/24 GPL20301
Summary
Background: Asthma is a chronic inflammatory disease of the airways driven by interactions among structural cells of the airway, the immune system, and the environment. Characterization of cells isolated from the sputum has been used to study the pathogenesis of asthma and optimize treatment regimens, but the cellular transcriptomes and intercellular communication changes in the sputum of asthma are not well understood. Methods: Single cell RNA sequencing was applied to cells isolated from the sputum from 16 patients with asthma and 8 non-asthmatic normal controls. Cell identity was determined by analysis of curated cell marker genes combined with SingleR annotation. Cell specific gene expression and communication networks were compared between asthma and controls and were correlated with asthma phenotypes. Findings: 15 different cell populations were identified, including several types of macrophages, monocytes, dendritic cells, and lymphocytes as well as several rare (<2%) cell types such as mast cells, innate lymphoid cells, and bronchial epithelial cells. Analysis of the intercellular communications demonstrated that signaling pathways are generally more active in asthma compared to control. CD4+ cells and dendritic cells had the largest increase in outgoing signals to other cell types. One of the strongest signal was the ADAM12 and CCL22 ligand-receptor pathways showing the strongest shift between asthma and controls. Interpretation: Analysis of cellular transcriptomes in the sputum demonstrates innate and adaptive mechanisms are increased in asthma. Increased expression of ADAM12 suggests a critical role in the pathogenesis of asthma.
Download
NCBI GEO page ↗ Paper (PMID 40236103) ↗ {# Names what the click gives you. "Open in finder" meant nothing to a visitor who arrived from a search engine and has never seen the tool. #} Find more human RNA-seq datasets →
Similar datasets

Search all human RNA-seq datasets in GEO →

Share this dataset

Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.