Pro-inflammatory alveolar macrophages associated with allograft dysfunction after lung transplantation
Bibliographic record
Abstract
Abstract Purpose Lung transplant (LT) recipients experience episodes of immune-mediated acute lung allograft dysfunction (ALAD). We have applied single-cell RNA sequencing (scRNAseq) to bronchoalveolar lavage (BAL) cells of stable and ALAD patients to determine key cellular elements in dysfunctional lung allografts. Our particular focus here is on studying alveolar macrophages (AMs) as scRNAseq enables us to elucidate their heterogeneity and possible association with ALAD where our knowledge from cytometry-based assays is very limited. Methods Fresh bronchoalveolar lavage (BAL) cells from 6 LT patients, 3 with stable lung function (3044 ± 1519 cells) and 3 undergoing an episode of ALAD (2593 ± 904 cells) were used for scRNAseq. R Bioconductor and Seurat were used to perform QC, dimensionality reduction, annotation, pathway analysis, and trajectory. Donor and recipient deconvolution was performed using single nucleotide variations. Results Our data revealed that AMs are highly heterogeneous (12 transcriptionally distinct subsets in stable). We identified two AM subsets uniquely represented in ALAD. Based on pathway analysis and the top differentially expressed genes in BAL we annotated them as pro-inflammatory interferon-stimulated genes (ISG) and metallothioneins-mediated inflammatory (MT). Pseudotime analysis suggested that ISG AMs represent an earlier stage of differentiation which may suggest them as monocyte drive macrophages. Our functional analysis on an independent set of BAL samples shows that ALAD samples have significantly higher expression of CXCL10, a marker of ISG AM, as we as higher secretion of pro-inflammatory cytokines. Single nucleotide variation calling algorithm has allowed us to identify macrophages of donor origin and demonstrated that donor AMs are lost with time post-transplant. Conclusion Using scRNAseq, we observed AMs heterogeneity and identified specific subsets that may be associated with allograft dysfunction. Further exploration with scRNAseq will shed light on LT immunobiology and the role of AMs in allograft injury and dysfunction.
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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".