Thirty years of the International Banff Classification for Allograft Pathology: the past, present, and future of kidney transplant diagnostics

Loupy, Alexandre; Mengel, Michael; Haas, Mark

doi:10.1016/j.kint.2021.11.028

Cited by 98 publications

(75 citation statements)

References 122 publications

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“…Several seminal studies have demonstrated expansion of intra-graft lymphatics during allograft rejection in rodent models (Palin et al 2013; Vass et al 2012; Lin et al 2021; Motallebzadeh et al 2012) and in humans (Adair et al 2007; Kerjaschki et al 2004; Stuht et al 2007; Tsuchimoto et al 2017), suggesting that these vessels are either insufficient to resolve allograft inflammation or themselves partake in the process of rejection (Wong 2020). To probe this further, we used 3D lymphatic imaging and scRNA-seq to study kidneys with chronic rejection, which frequently involves both T cell and antibody-mediated alloimmune responses, leading to allograft injury (Loupy et al 2022; Callemeyn et al 2022). In three allograft explants with chronic mixed (T cell- and antibody-mediated) rejection ( Table S1B ) and using organ donor tissues as controls, we found disorganisation of the lymphatic network, with loss of structural hierarchy ( Fig.3A ), a seven-fold increase in lymphatic density (95.12 ± 49.21 vs .…”

Section: Resultsmentioning

confidence: 99%

Three-dimensional imaging and single-cell transcriptomics of the human kidney implicate perturbation of lymphatics in alloimmunity

Jafree¹,

Stewart

Kolatsi-Joannou³

et al. 2022

Preprint

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Studies of the structural and molecular features of the lymphatic vasculature, which clears fluid, macromolecules and leukocytes from the tissue microenvironment, have largely relied on animal models, with limited information in human organs beyond traditional immunohistochemical assessment. Here, we use three-dimensional imaging and single-cell RNA-sequencing to study lymphatics in the human kidney. We found a hierarchical arrangement of lymphatic vessels within human kidneys, initiating along specialised nephron epithelium in the renal cortex and displaying a distinct, kidney-specific transcriptional profile. In chronic transplant rejection we found kidney allograft lymphatic expansion alongside a loss of structural hierarchy, with human leukocyte antigen-expressing lymphatic vessels infiltrating the medulla, presenting a putative target for alloreactive antibodies. This occurred concurrently with lymphatic vessels invading and interconnecting tertiary lymphoid structures at early stages of lymphocyte colonisation. Analysis of intercellular signalling revealed upregulation of co-inhibitory molecule-mediated CD4+ T cell-lymphatic crosstalk in rejecting kidneys, potentially acting to limit local alloimmune responses. Overall, we delineate novel structural and molecular features of human kidney lymphatics and reveal perturbations to their phenotype and transcriptome in the context of alloimmunity.

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Section: Resultsmentioning

confidence: 99%

Three-dimensional imaging and single-cell transcriptomics of the human kidney implicate perturbation of lymphatics in alloimmunity

Jafree¹,

Stewart

Kolatsi-Joannou³

et al. 2022

Preprint

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“…Recipients who had a serum creatinine level >400 μmol/l after post-transplant 7 days and/or needed hemodialysis during the first week after transplantation were diagnosed with DGF [ 22 , 23 ]. Acute rejection was assessed via Banff criteria by allograft biopsy [ 24 ].…”

Section: Methodsmentioning

confidence: 99%

Dynamic changes of neutrophil-to-lymphocyte ratio in brain-dead donors and delayed graft function in kidney transplant recipients

et al. 2022

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Objectives Neutrophil-to-lymphocyte ratio (NLR) is a simple parameter implying the inflammatory status. We aimed to explore the association of brain-dead donor NLR change with delayed graft function (DGF) in kidney transplant recipients. Methods We retrospectively analyzed the data on 102 adult brain-dead donors and their corresponding 199 kidney transplant recipients (2018 − 2021). We calculated ΔNLR by subtracting the NLR before evaluating brain death from the preoperative NLR. Increasing donor NLR was defined as ΔNLR > 0. Results Forty-four (22%) recipients developed DGF after transplantation. Increasing donor NLR was significantly associated with the development of DGF in recipients (OR 2.8, 95% CI 1.2 − 6.6; p = .018), and remained significant (OR 2.6, 95% CI 1.0 − 6.4; p = .040) after adjustment of confounders including BMI, hypertension, diabetes, and the occurrence of cardiac arrest. When acute kidney injury (AKI) was included in the multivariable analysis, increasing donor NLR lost its independent correlation with DGF, while AKI remained an independent risk factor of recipient DGF (OR 4.5, 95% CI 2.7 − 7.6; p < .001). The area under the curve of combined increasing NLR and AKI in donors (0.873) for predicting DGF was superior to increasing donor NLR (0.625, p = .015) and AKI alone (0.859, p < .001). Conclusions Dynamic changes of donor NLR are promising in predicting post-transplant DGF. It will assist clinicians in the early recognition and management of renal graft dysfunction. Validation of this new biomarker in a large study is needed.

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“…Supervised learning requires prior knowledge of the output values; therefore, the goal is to determine a function that best approximates the relationship between input and output, given a sample of data and the desired outputs (labels). Since kidney allograft biopsy contextualization will be based on ML in the upcoming Banff classifications [ 16 ], we will explain the concepts of supervised and unsupervised learning using similar examples. For example, to train the machine to classify a given image from kidney allograft biopsy, we input multiple specimens with known labels.…”

Section: Core Conceptsmentioning

confidence: 99%

Toward generalizing the use of artificial intelligence in nephrology and kidney transplantation

et al. 2022

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With its robust ability to integrate and learn from large sets of clinical data, artificial intelligence (AI) can now play a role in diagnosis, clinical decision making, and personalized medicine. It is probably the natural progression of traditional statistical techniques. Currently, there are many unmet needs in nephrology and, more particularly, in the kidney transplantation (KT) field. The complexity and increase in the amount of data, and the multitude of nephrology registries worldwide have enabled the explosive use of AI within the field. Nephrologists in many countries are already at the center of experiments and advances in this cutting-edge technology and our aim is to generalize the use of AI among nephrologists worldwide. In this paper, we provide an overview of AI from a medical perspective. We cover the core concepts of AI relevant to the practicing nephrologist in a consistent and simple way to help them get started, and we discuss the technical challenges. Finally, we focus on the KT field: the unmet needs and the potential role that AI can play to fill these gaps, then we summarize the published KT-related studies, including predictive factors used in each study, which will allow researchers to quickly focus on the most relevant issues.

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Thirty years of the International Banff Classification for Allograft Pathology: the past, present, and future of kidney transplant diagnostics

Cited by 98 publications

References 122 publications

Three-dimensional imaging and single-cell transcriptomics of the human kidney implicate perturbation of lymphatics in alloimmunity

Three-dimensional imaging and single-cell transcriptomics of the human kidney implicate perturbation of lymphatics in alloimmunity

Dynamic changes of neutrophil-to-lymphocyte ratio in brain-dead donors and delayed graft function in kidney transplant recipients

Toward generalizing the use of artificial intelligence in nephrology and kidney transplantation

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