Single-cell RNA sequencing of small airways of young persons who use e-cigarettes
Bibliographic record
Abstract
BACKGROUND: Marketed as safer than smoking, electronic cigarettes (EC) raise global health concerns. We used single-cell RNA sequencing (scRNA-seq) to investigate the impact of the small airway epithelium. Hypothesizing EC alters its cellular composition. METHODS: Adults (≥18yrs) who were THC/nicotine EC users (n=9) or nonsmoking controls (n=8) underwent bronchoscopy. Bronchial brushings were analyzed with scRNA-seq (10x Chromium, Illumina® NextSeq 2000). Processing includes Cell Ranger, ambient RNA removal (SoupX), quality control (Scanpy), and downstream analysis (clustering and cell annotation). The cellular proportions (scCODA) correlated with ventilation abnormalities measured by 129Xe magnetic resonance imaging (MRI) using Spearman’s correlations. RESULTS: Groups were similar in age [EC 35yrs, control 44yrs] and sex, with normal lung function [FEV1: EC 106%, Control 117%; DLCO: EC 28%, Control 25%]. EC group vaped primarily nicotine (66.67%) for 5-7 days/week, inhaled an average of ~ 27 puffs/day. Analysis of 56,485 cells identified 15 epithelial and 17 immune cell clusters. Compared to controls, EC users had a higher proportion of CD8+γδT cells (49.1% vs 30.1%), neutrophils (0.36% vs 0.02%), goblet cells (16.5 vs 4.8%), and ionocytes (1.6% vs 0.3%), but lower CD4+γδT (8.6% vs 27.6%) and basal (11% vs 21%). Gene set enrichment analysis (GSEA) revealed immune activation via interferon-gamma and neutrophil signaling. Additionally,129XeMRI showed greater low-ventilation areas in EC, correlated with increased CD8+γδT cells. CONCLUSION: EC use is linked with immune alterations, and ventilation defects in the small airways, highlighting the potential harm of EC in the small airways of young persons
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".