Single-cell sequencing reveals cellular landscape alterations in the airway mucosa of patients with pulmonary long COVID
Bibliographic record
Abstract
AIM: To elucidate the important cellular and molecular drivers of pulmonary long COVID, we generated a single-cell transcriptomic map of the airway mucosa using bronchial brushings from patients with long COVID who reported persistent pulmonary symptoms. METHOD: Adults with and without long COVID were recruited from the general community in Greater Vancouver, Canada. The cohort was divided into those with pulmonary long COVID, which was defined as persons with new or worsening respiratory symptoms following ≥12 weeks from their initial acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (n=9); and control subjects defined as SARS-CoV-2 infected persons whose acute respiratory symptoms had fully resolved or individuals who had no history of acute coronavirus disease 2019 (COVID-19) (n=9). These participants underwent bronchoscopy from which a single cell suspension was created from bronchial brush samples and then sequenced. RESULTS: A total of 56 906 cells were recovered for the downstream analysis, with 34 840 cells belonging to the pulmonary long COVID group, which strikingly showed a unique cluster of neutrophils in the pulmonary long COVID group (p<0.05). Ingenuity Pathway Analysis revealed that the neutrophil degranulation pathway was enriched across epithelial cell clusters. Differential gene expression analysis between the pulmonary long COVID and control groups demonstrated upregulation of inflammatory chemokines and epithelial barrier dysfunction across epithelial cell clusters, as well as over-expression of mucin genes across secretory cell clusters. CONCLUSION: A single-cell transcriptomic landscape of the small airways suggest that neutrophils may play a significant role in mediating the chronic small airway inflammation driving pulmonary symptoms of long COVID.
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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 | 0.001 | 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".