A CD8+ CD27+ TIM-3+ T cell population expanded in chronically rejected human lung allografts 4126
Bibliographic record
Abstract
Abstract Description Long-term survival in lung transplantation is limited by the eventual development of chronic lung allograft dysfunction (CLAD), resulting in the loss of the transplanted lung. We hypothesized that analysis of the pulmonary T cell transcriptome would reveal novel T cell populations that drive CLAD pathogenesis. Human lung tissue samples (9 CLAD, 5 donor as healthy control) were obtained and processed into single cell suspensions. T cells were enriched through magnetic selection of CD45+ leukocytes followed by fluorescence-activated cell sorting. Sorted cells then underwent 5’ single cell RNA sequencing (scRNAseq) using 10x Genomics. scRNAseq data was analyzed using Seurat. T cell clusters of interest were validated at the protein level using spectral flow cytometry Comparison between lung samples revealed a cluster that was only present in CLAD samples (6/9 CLAD lungs vs. 0/5 donor lungs, Fig A), characterized by RNA expression of CD8, CD27 and HAVCR2 (TIM-3, Fig B). Flow cytometry analysis of T cells from CLAD lungs (n = 3) and donor lungs (n = 4) similarly showed that CD8+ CD27+TIM-3+ T cells were uniquely present in CLAD lungs (Fig C). Utilizing pseudotime trajectroy analysis to examine cellular changes, this cluster appears to be terminally differentiated (Fig D). We conclude that a CD8+ T cell population expressing CD27 and TIM-3 is enriched in CLAD lung T cells. This phenotype is suggestive of a memory/exhaustion phenotype that could play an important role in CLAD. Funding Sources New Frontiers in Research Fund - Transformation Grant Topic Categories Transplantation Immunology (TRAN)
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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 | 0.001 |
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".