Epithelial Injury Patterns Induced by Acute T Cell-Mediated Rejection in Kidney Transplants
Bibliographic record
Abstract
Background: Acute T-cell mediated rejection (TCMR) remains a major challenge after kidney transplantation, posing risks for the long-term outcome of the transplant. Previous research highlighted the importance of TCMR-induced renal epithelial injury for transplant outcome. Yet, the detailed cellular origin of these injury responses and the associated gene expression profiles remain poorly understood. Methods: To induce acute rejection, we used two different mouse models (C57BL/6 and BALB/c) for allogeneic kidney transplantation and syngeneic controls. We analyzed the molecular changes in renal gene expression during TCMR by using single nucleus RNA sequencing (snRNA-seq) and spatial transcriptomics on the kidneys 7 days post-transplant. Differentially expressed genes between allogeneic and syngeneic kidneys were analyzed and a published gene set predictive of allograft outcomes was investigated per cell type. All results were compared to our snRNA-seq data from three human TCMR kidney biopsies and three stable allografts. Results: Mouse kidneys from allogeneic transplantation showed all histological hallmarks of TCMR. SnRNA-seq revealed a strong gene expression response, especially in C57BL/6 kidneys transplanted into BALB/c mice, most pronounced in kidney epithelial cells, particularly in the proximal tubules (PT) and thick ascending limbs (TAL), inducing distinct injury-associated cell states. Spatial transcriptomics identified a heterogeneous spatial distribution of these cell states between cortex and medulla. Published genes indicative of allograft outcome were mostly expressed in injured PT and TAL but showed heterogeneous differential expression in the different injured PT and TAL cell states. Cross-species analysis revealed a substantial overlap of epithelial cell states between mouse and human TCMR. Conclusion: Our study offers a detailed exploration of cell type-specific gene expression changes during TCMR in humans and mice. The analysis of allograft outcome-associated genes revealed their origin from various injured epithelial cell states. This insight may help identify injured cell states most responsible for reduced graft function, potentially enabling targeted therapeutic interventions.
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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 | 0.001 | 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 | 0.000 |
| 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".