Single-cell RNA sequencing of bronchoalveolar lavage fluid reveals potential increase in antigen presentation pathways and cellular immunity in airways of pulmonary long COVID
Bibliographic record
Abstract
Many patients continue to suffer residual respiratory symptoms several months following a SARS-CoV-2 infection, known as “pulmonary long COVID” (PLC), but the mechanisms remain unknown. We performed single-cell RNA-sequencing on bronchoalveolar lavage (BAL) fluid to characterize the immune cell population in PLC relative to healthy controls. BAL was obtained via bronchoscopy and washed twice with 0.4% bovine serum albumin in Dulbecco’s phosphate-buffered saline. Bioinformatic analysis was performed with Rsubread, SoupX, Scanpy, Scrublet, and Gseapy software. We characterized 116,856 cells from 5 PLC patients (4 females, age 43±10 years) and in two control groups: 1) 3 post-COVID patients without pulmonary symptoms (1 female, age 53 ±15 years), and 2) 3 never-infected controls (3 females, age 38±23 years). No residual SARS-CoV-2 mRNA was detected in any of the samples. Macrophages made up the majority of cells among all three groups (~70%). The proportions of other cell types were not significantly different between groups. However, gene set enrichment analysis revealed upregulated pathways in antigen presentation and cell cycles in the dendritic cells (p<0.001) and enrichment of cytokine- and neutrophil-mediated immune pathways in T-cells (p<0.001) from PLC samples. On the other hand, T-cells, and dendritic cells in the control groups showed enrichment in protein targeting (p<0.001) and viral pathways (p<0.001). These data suggest that the airways of PLC patients are demonstrate an ongoing heightened cellular immune response, and increased antigen presentation, even in the absence of detectable viral mRNA.
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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) | 0.002 | 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".