The Macrophage Landscape Across the Lifespan of a Human Cardiac Allograft
Bibliographic record
Abstract
BACKGROUND: Much of our knowledge of organ rejection after transplantation is derived from rodent models. METHODS: We used single-nucleus RNA sequencing to investigate the inflammatory myocardial microenvironment in human pediatric cardiac allografts at different stages after transplantation. We distinguished donor- from recipient-derived cells using naturally occurring genetic variants embedded in single-nucleus RNA sequencing data. RESULTS: Donor-derived tissue resident macrophages, which accompany the allograft into the recipient, are lost over time after transplantation. In contrast, monocyte-derived macrophages from the recipient populate the heart within days after transplantation and form 2 macrophage populations: recipient MP1 and recipient MP2. Recipient MP2s have cell signatures similar to donor-derived resident macrophages; however, they lack signatures of pro-reparative phagocytic activity typical of donor-derived resident macrophages and instead express profibrotic genes. In contrast, recipient MP1s express genes consistent with hallmarks of cellular rejection. Our data suggest that recipient MP1s activate a subset of natural killer cells, turning them into a cytotoxic cell population through feed-forward signaling between recipient MP1s and natural killer cells. CONCLUSIONS: Our findings reveal an imbalance of donor-derived and recipient-derived macrophages in the pediatric cardiac allograft that contributes to allograft failure.
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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) | 0.001 | 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".