ArticleScientific reports2022
WARS1, TYMP and GBP1 display a distinctive microcirculation pattern by immunohistochemistry during antibody-mediated rejection in kidney transplantation.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 12 citations in OpenAlex.
- A Subset of Pro-inflammatory CXCL10+ LILRB2+ Macrophages Derives From Recipient Monocytes and Drives Renal Allograft Rejection.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Identification of RNA-binding protein genes associated with renal rejection and graft survival.Renal failure · 2024Article
- The Use of Machine Learning in the Diagnosis of Kidney Allograft Rejection: Current Knowledge and Applications.Diagnostics (Basel, Switzerland) · 2024Review
- Landscape of the immune infiltration and identification of molecular diagnostic markers associated with immune cells in patients with kidney transplantation.Scientific reports · 2024Article
- Key genes and immune pathways in T-cell mediated rejection post-liver transplantation identified via integrated RNA-seq and machine learning.Scientific reports · 2024Article
- Article
- Transcriptional and spatial profiling of the kidney allograft unravels a central role for FcyRIII+ innate immune cells in rejection.Nature communications · 2023Article
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13 authors at 4 institutions in 1 country.
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Abstract
Antibody-mediated rejection (ABMR) is the leading cause of allograft failure in kidney transplantation. Defined by the Banff classification, its gold standard diagnosis remains a challenge, with limited inter-observer reproducibility of the histological scores and efficient immunomarker availability. We performed an immunohistochemical analysis of 3 interferon-related proteins, WARS1, TYMP and GBP1 in a cohort of kidney allograft biopsies including 17 ABMR cases and 37 other common graft injuries. Slides were interpreted, for an ABMR diagnosis, by four blinded nephropathologists and by a deep learning framework using convolutional neural networks. Pathologists identified a distinctive microcirculation staining pattern in ABMR with all three antibodies, displaying promising diagnostic performances and a substantial reproducibility. The deep learning analysis supported the microcirculation staining pattern and achieved similar diagnostic performance from internal validation, with a mean area under the receiver operating characteristic curve of 0.89 (± 0.02) for WARS1, 0.80 (± 0.04) for TYMP and 0.89 (± 0.04) for GBP1. The glomerulitis and peritubular capillaritis scores, the hallmarks of histological ABMR, were the most highly correlated Banff scores with the deep learning output, whatever the C4d status. These novel immunomarkers combined with a CNN framework could help mitigate current challenges in ABMR diagnosis and should be assessed in larger cohorts.
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