Characterizing smoking-induced transcriptional heterogeneity in the human bronchial epithelium at single-cell resolution
Bibliographic record
Abstract
ABSTRACT The human bronchial epithelium is composed of multiple, distinct cell types that cooperate to perform functions, such as mucociliary clearance, that defend against environmental insults. While studies have shown that smoking alters bronchial epithelial function and morphology, the precise effects of this exposure on specific cell types are not well-understood. We used single-cell RNA sequencing to profile bronchial epithelial cells from six never- and six current smokers. Unsupervised analyses identified thirteen cell clusters defined by unique combinations of nineteen distinct gene sets. Expression of a set of toxin metabolism genes localized to ciliated cells from smokers. Smoking-induced airway remodeling was characterized by a loss of club cells and extensive goblet cell hyperplasia. Finally, we identified a novel peri-goblet epithelial subpopulation in smokers that expressed a marker of bronchial premalignant lesions. Our data demonstrates that smoke exposure drives a complex landscape of cellular and molecular alterations in the human bronchial epithelium that may contribute to the onset of smoking-associated lung diseases.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".