ArticleNature medicine2024
Decoding the hallmarks of allograft dysfunction with a comprehensive pan-organ transcriptomic atlas.
Article in Nature medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Single Cell Multi-Omics Revealing the Important Role of MR1 Mediated MAIT Cells in Maintaining Rejection for Liver Transplantation.Cell proliferation · 2026Article
- Emerging applications of traditional and generative artificial intelligence in liver transplantation.Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society · 2026Article
- Transforming lung transplantation with artificial intelligence: a narrative review from organ allocation to post-transplant management.Journal of thoracic disease · 2026Review
- Integration of intragraft transcriptomics and urinary cytokines identifies CXCL10 and FasL signature in subclinical acute rejection.Scientific reports · 2026Article
- Artificial Intelligence in Post-Liver Transplantation: A Scoping Review of Comparative Model Performance.Journal of clinical medicine · 2026Review
- A Subset of Pro-inflammatory CXCL10+ LILRB2+ Macrophages Derives From Recipient Monocytes and Drives Renal Allograft Rejection.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Targeted Expansion of Treg Cells to Induce Immune Tolerance after Kidney Transplantation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial intelligence optimizes immune rejection prediction and management in heart transplantation: a structured narrative review.Frontiers in cardiovascular medicine · 2026Review
- Timing matters: tacrolimus intra-patient variability within the initial seven months forecastsFrontiers in immunology · 2026Article
- Integrated cross-organ transcriptomic analysis uncovers conserved gene signatures predictive of allograft rejection.PloS one · 2026Article
- Sensitization in Transplantation Assessment of Risk 2025 innate working group: The potential role of innate allorecognition in kidney allograft damage.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2025Review
- From reactive to proactive: Continuous protein monitoring for preventive health care.Science (New York, N.Y.) · 2025Review
- Single cell transcriptome profiling of immune tissues from germ-free and specific pathogen-free piglet.Scientific data · 2025Article
- Digital twins as global learning health and disease models for preventive and personalized medicine.Genome medicine · 2025Review
- Tertiary lymphoid structure drives allograft rejection via IFN-γ-JAK-STAT-dependent atypical memory B cell differentiation.Frontiers in immunology · 2025Article
- Identification and characterization of senescent macrophages in renal allograft rejection: a cross-species MultiOmics study.Frontiers in immunology · 2025Article
- Editorial: Multi-omics assessment for the discovery of promising novel molecules in the treatment of transplant organ injury.Frontiers in immunology · 2025Article
- Decoding immune cell interactions during cardiac allograft vasculopathy: insights derived from bioinformatic strategies.Frontiers in cardiovascular medicine · 2025Review
- Gene expression profiling in PBMCs for acute rejection in lung transplant recipients reveals myeloid responses.Frontiers in transplantation · 2024Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The pathogenesis of allograft (dys)function has been increasingly studied using 'omics'-based technologies, but the focus on individual organs has created knowledge gaps that neither unify nor distinguish pathological mechanisms across allografts. Here we present a comprehensive study of human pan-organ allograft dysfunction, analyzing 150 datasets with more than 12,000 samples across four commonly transplanted solid organs (heart, lung, liver and kidney, n = 1,160, 1,241, 1,216 and 8,853 samples, respectively) that we leveraged to explore transcriptomic differences among allograft dysfunction (delayed graft function, acute rejection and fibrosis), tolerance and stable graft function. We identified genes that correlated robustly with allograft dysfunction across heart, lung, liver and kidney transplantation. Furthermore, we developed a transfer learning omics prediction framework that, by borrowing information across organs, demonstrated superior classifications compared to models trained on single organs. These findings were validated using a single-center prospective kidney transplant cohort study (a collective 329 samples across two timepoints), providing insights supporting the potential clinical utility of our approach. Our study establishes the capacity for machine learning models to learn across organs and presents a transcriptomic transplant resource that can be employed to develop pan-organ biomarkers of allograft dysfunction.
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