Pulmonary macrophage subsets associated with lung allograft dysfunction revealed by single-cell RNA sequencing
Bibliographic record
Abstract
Lung transplant (LT) recipients experience a low survival rate due to chronic lung allograft dysfunction (CLAD). Acute lung allograft dysfunction (ALAD) is a risk factor for CLAD. The contribution of lung macrophages (Macs) to ALAD and CLAD is not clear. Determine the role of Macs in dysfunctional lung allografts using single-cell RNA sequencing (scRNAseq) during quiescence, ALAD, and CLAD. Fresh BAL cells from 6 LT patients - 3 stable and 3 ALAD - and cells from 4 explanted CLAD lungs underwent scRNAseq. R Bioconductor and Seurat were used to perform QC, annotation, and pathway analysis. Donor and recipient deconvolution was performed using single nucleotide variations. We identified two Mac subsets uniquely present in ALAD compared to stable BAL (Fig 1A). Using pathway analysis, we annotated these as pro-inflammatory interferon-stimulated gene (ISG) and metallothionein-mediated inflammatory (MT) Macs. Using publicly available BAL scRNAseq datasets, we found that ISG and MT Macs are associated with severe inflammation in COVID-19 patients (Fig 1B). Analysis of cells from CLAD lungs revealed similar Mac populations (Fig 1C). Deconvolution demonstrated that donor-derived Macs are lost with time post-transplant (Fig 1D). Using scRNAseq, we identified specific Macs that may be associated with allograft dysfunction, raising the possibility that these cells may represent important therapeutic targets.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".