The Banff Digital Pathology Working Group (DPWG) was established with the goal to establish a digital pathology repository; develop, validate, and share models for image analysis; and foster... Show moreThe Banff Digital Pathology Working Group (DPWG) was established with the goal to establish a digital pathology repository; develop, validate, and share models for image analysis; and foster collaborations using regular videoconferencing. During the calls, a variety of artificial intelligence (AI)-based support systems for transplantation pathology were presented. Potential collaborations in a competition/trial on AI applied to kidney transplant specimens, including the DIAGGRAFT challenge (staining of biopsies at multiple institutions, pathologists' visual assessment, and development and validation of new and pre-existing Banff scoring algorithms), were also discussed. To determine the next steps, a survey was conducted, primarily focusing on the feasibility of establishing a digital pathology repository and identifying potential hosts. Sixteen of the 35 respondents (46%) had access to a server hosting a digital pathology repository, with 2 respondents that could serve as a potential host at no cost to the DPWG. The 16 digital pathology repositories collected specimens from various organs, with the largest constituent being kidney (n = 12,870 specimens). A DPWG pilot digital pathology repository was established, and there are plans for a competition/trial with the DIAGGRAFT project. Utilizing existing resources and previously established models, the Banff DPWG is establishing new resources for the Banff community. Show less
Background Histopathological assessment of transplant biopsies is currently the standard method to diagnose allograft rejection and can help guide patient management, but it is one of the most... Show moreBackground Histopathological assessment of transplant biopsies is currently the standard method to diagnose allograft rejection and can help guide patient management, but it is one of the most challenging areas of pathology, requiring considerable expertise, time, and effort. We aimed to analyse the utility of deep learning to preclassify histology of kidney allograft biopsies into three main broad categories (ie, normal, rejection, and other diseases) as a potential biopsy triage system focusing on transplant rejection.Methods We performed a retrospective, multicentre, proof-of-concept study using 5844 digital whole slide images of kidney allograft biopsies from 1948 patients. Kidney allograft biopsy samples were identified by a database search in the Departments of Pathology of the Amsterdam UMC, Amsterdam, Netherlands (1130 patients) and the University Medical Center Utrecht, Utrecht, Netherlands (717 patients). 101 consecutive kidney transplant biopsies were identified in the archive of the Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany. Convolutional neural networks (CNNs) were trained to classify allograft biopsies as normal, rejection, or other diseases. Three times cross-validation (1847 patients) and deployment on an external real-world cohort (101 patients) were used for validation. Area under the receiver operating characteristic curve (AUROC) was used as the main performance metric (the primary endpoint to assess CNN performance).Findings Serial CNNs, first classifying kidney allograft biopsies as normal (AUROC 0.87 [ten times bootstrapped CI 0.85-0.88]) and disease (0.87 [0.86-0.88]), followed by a second CNN classifying biopsies classified as disease into rejection (0.75 [0.73-0.76]) and other diseases (0.75 [0.72-0.77]), showed similar AUROC in cross-validation and deployment on independent real-world data (first CNN normal AUROC 0.83 [0.80-0.85], disease 0.83 [0.73-0.91]; second CNN rejection 0.61 [0.51-0.70], other diseases 0.61 [0.50-4.74]). A single CNN classifying biopsies as normal, rejection, or other diseases showed similar performance in cross-validation (normal AUROC 0.80 [0.73-0.84], rejection 0.76 [0.66-0.80], other diseases 0.50 [0.36-0.57]) and generalised well for normal and rejection classes in the real-world data. Visualisation techniques highlighted rejection-relevant areas of biopsies in the tubulointerstitium.Interpretation This study showed that deep learning-based classification of transplant biopsies could support pathological diagnostics of kidney allograft rejection. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd. Show less
Background Crumbs2 is expressed at embryonic stages as well as in the retina, brain, and glomerular podocytes. Recent studies identified CRB2 mutations as a novel cause of steroid-resistant... Show moreBackground Crumbs2 is expressed at embryonic stages as well as in the retina, brain, and glomerular podocytes. Recent studies identified CRB2 mutations as a novel cause of steroid-resistant nephrotic syndrome (SRNS).Methods To study the function of Crb2 at the renal filtration barrier, mice lacking Crb2 exclusively in podocytes were generated. Gene expression and histologic studies as well as transmission and scanning electron microscopy were used to analyze these Crb2(podKO) knockout mice and their littermate controls. Furthermore, high-resolution expansion microscopy was used to investigate Crb2 distribution in murine glomeruli. For pull-down experiments, live cell imaging, and transcriptome analyses, cell lines were applied that inducibly express fluorescent protein-tagged CRB2 wild type and mutants.Results Crb2(podKO) mice developed proteinuria directly after birth that preceded a prominent development of disordered and effaced foot processes, upregulation of renal injury and inflammatory markers, and glomerulosclerosis. Pull-down assays revealed an interaction of CRB2 with Nephrin, mediated by their extracellular domains. Expansion microscopy showed that in mice glomeruli, Crb2 and Nephrin are organized in adjacent clusters. SRNS-associated CRB2 protein variants and a mutant that lacks a putative conserved O-glycosylation site were not transported to the cell surface. Instead, mutants accumulated in the ER, showed altered glycosylation pattern, and triggered an ER stress response.Conclusions Crb2 is an essential component of the podocyte's slit diaphragm, interacting with Nephrin. Loss of slit diaphragm targeting and increasing ER stress are pivotal factors for onset and progression of CRB2-related SRNS. Show less
