Single-cell multi-omics show disruption of blood and airway T-cells in pulmonary long COVID
Bibliographic record
Abstract
Abstract Background Approximately 10% of individuals who recover from COVID-19 experience residual respiratory symptoms impacting their quality of life, but the mechanisms behind pulmonary long COVID (PLC) are largely unknown. Objectives We characterized airway and circulating immune cells in patients with and without PLC. Methods Participants were recruited and allocated into two groups: 1) PLC, defined by a St. George’s Respiratory Questionnaire (SGRQ) total score of >10 at least three months following an acute SARS-CoV-2 infection with self-reported new or worsening symptoms, and 2) controls, defined by SGRQ =<10 with or without a prior history of COVID. We performed research bronchoscopy and obtained bronchoalveolar lavage (BAL) in seven PLC patients and seven age- and sex-matched control subjects. Single-cell RNA sequencing (scRNAseq) was performed on the BAL cells. Peripheral blood mononuclear cells (PBMCs) were cryopreserved in 30 participants (17 PLC, 13 controls) for proteomic analysis. Serum was submitted for microarray detection of auto-IgG antibodies. Methods We annotated 105,836 cells using scRNAseq and found that CD4+ T-cells were credibly increased in participants with PLC. scRNAseq also revealed up-regulation of anti-viral pathways including those related to interferon signaling in T-cells as well as antigen presenting cells. In PBMCs, T-cells expressing both CD4 and CD8 were elevated in PLC participants. Autoantibodies targeting genomic DNA, collagen II, and TIF-gamma were significantly increased in PLC patients. Conclusions PLC is associated with dysregulation of T-cell mediated immunity, which may be related to autoimmunity. These cells represent potential novel therapeutic targets in patients suffering from PLC.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".