Kidney fibrosis is the common histological end-point of progressive, chronic kidney diseases (CKDs) regardless of the underlying etiology. The hallmark of renal fibrosis, similar to all other organs, is pathological deposition of extracellular matrix (ECM). Renal ECM is a complex network of collagens, elastin, and several glycoproteins and proteoglycans forming basal membranes and interstitial space. Several ECM functions beyond providing a scaffold and organ stability are being increasingly recognized, for example, in inflammation. ECM composition is determined by the function of each of the histological compartments of the kidney, that is, glomeruli, tubulo-interstitium, and vessels. Renal ECM is a dynamic structure undergoing remodeling, particularly during fibrosis. From a clinical perspective, ECM proteins are directly involved in several rare renal diseases and indirectly in CKD progression during renal fibrosis. ECM proteins could serve as specific non-invasive biomarkers of fibrosis and scaffolds in regenerative medicine. The gold standard and currently only specific means to measure renal fibrosis is renal biopsy, but new diagnostic approaches are appearing. Here, we discuss the localization, function, and remodeling of major renal ECM components in healthy and diseased, fibrotic kidneys and the potential use of ECM in diagnostics of renal fibrosis and in tissue engineering.

Severe COVID-19 is linked to both dysfunctional immune response and unrestrained immunopathology, and it remains unclear whether T cells contribute to disease pathology. Here, we combined single-cell transcriptomics and single-cell proteomics with mechanistic studies to assess pathogenic T cell functions and inducing signals. We identified highly activated, CD16 + T cells with increased cytotoxic functions in severe COVID-19. CD16 expression enabled immune complex-mediated, T cell receptor-independent degranulation and cytotoxicity not found in other diseases. CD16 + T cells from COVID-19 patients promoted microvascular endothelial cell injury and release of neutrophil and monocyte chemoattractants. CD16 + T cell clones persisted beyond acute disease maintaining their cytotoxic phenotype. Increased generation of C3a in severe COVID-19 induced activated CD16 + cytotoxic T cells. Proportions of activated CD16 + T cells and plasma levels of complement proteins upstream of C3a were associated with fatal outcome of COVID-19, supporting a pathological role of exacerbated cytotoxicity and complement activation in COVID-19.

Keratins, the intermediate filaments of the epithelial cell cytoskeleton, are up-regulated and post-translationally modified in stress situations. Renal tubular epithelial cell stress is a common finding in progressive kidney diseases, but little is known about keratin expression and phosphorylation. Here, we comprehensively describe keratin expression in healthy and diseased kidneys. In healthy mice, the major renal keratins, K7, K8, K18, and K19, were expressed in the collecting ducts and K8, K18 in the glomerular parietal epithelial cells. Tubular expression of all 4 keratins increased by 20- to 40-fold in 5 different models of renal tubular injury as assessed by immunohistochemistry, Western blot, and quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). The up-regulation became significant early after disease induction, increased with disease progression, was found de novo in distal tubules and was accompanied by altered subcellular localization. Phosphorylation of K8 and K18 increased under stress. In humans, injured tubules also exhibited increased keratin expression. Urinary K18 was only detected in mice and patients with tubular cell injury. Keratins labeled glomerular parietal epithelial cells forming crescents in patients and animals. Thus, all 4 major renal keratins are significantly, early, and progressively up-regulated upon tubular injury regardless of the underlying disease and may be novel sensitive markers of renal tubular cell stress.

BackgroundNephropathologic analyses provide important outcomes-related data in experiments with the animal models that are essential for understanding kidney disease pathophysiology. Precision medicine increases the demand for quantitative, unbiased, reproducible, and efficient histopathologic analyses, which will require novel high-throughput tools. A deep learning technique, the convolutional neural network, is increasingly applied in pathology because of its high performance in tasks like histology segmentation.MethodsWe investigated use of a convolutional neural network architecture for accurate segmentation of periodic acid–Schiff-stained kidney tissue from healthy mice and five murine disease models and from other species used in preclinical research. We trained the convolutional neural network to segment six major renal structures: glomerular tuft, glomerulus including Bowman’s capsule, tubules, arteries, arterial lumina, and veins. To achieve high accuracy, we performed a large number of expert-based annotations, 72,722 in total.ResultsMulticlass segmentation performance was very high in all disease models. The convolutional neural network allowed high-throughput and large-scale, quantitative and comparative analyses of various models. In disease models, computational feature extraction revealed interstitial expansion, tubular dilation and atrophy, and glomerular size variability. Validation showed a high correlation of findings with current standard morphometric analysis. The convolutional neural network also showed high performance in other species used in research—including rats, pigs, bears, and marmosets—as well as in humans, providing a translational bridge between preclinical and clinical studies.ConclusionsWe developed a deep learning algorithm for accurate multiclass segmentation of digital whole-slide images of periodic acid–Schiff-stained kidneys from various species and renal disease models. This enables reproducible quantitative histopathologic analyses in preclinical models that also might be applicable to clinical studies.

Objective Infection with severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) can lead to severe pneumonia, but also thrombotic complications and non‐pulmonary organ failure. Recent studies suggest intravascular neutrophil activation and subsequent immune cell–triggered immunothrombosis as a central pathomechanism linking the heterogenous clinical picture of coronavirus disease 2019 (COVID‐19). We sought to study whether immunothrombosis is a pathognomonic factor in COVID‐19 or a general feature of (viral) pneumonia, as well as to better understand its upstream regulation. Approach and results By comparing histopathological specimens of SARS‐CoV‐2 with influenza‐affected lungs, we show that vascular neutrophil recruitment, NETosis, and subsequent immunothrombosis are typical features of severe COVID‐19, but less prominent in influenza pneumonia. Activated neutrophils were typically found in physical association with monocytes. To explore this further, we combined clinical data of COVID‐19 cases with comprehensive immune cell phenotyping and bronchoalveolar lavage fluid scRNA‐seq data. We show that a HLADR low CD9 low monocyte population expands in severe COVID‐19, which releases neutrophil chemokines in the lungs, and might in turn explain neutrophil expansion and pulmonary recruitment in the late stages of severe COVID‐19. Conclusions Our data underline an innate immune cell axis causing vascular inflammation and immunothrombosis in severe SARS‐CoV‐2 infection.

No abstract

Pathology diagnostics relies on the assessment of morphology by trained experts, which remains subjective and qualitative. Here we developed a framework for large-scale histomorphometry (FLASH) performing deep learning-based semantic segmentation and subsequent large-scale extraction of interpretable, quantitative, morphometric features in non-tumour kidney histology. We use two internal and three external, multi-centre cohorts to analyse over 1000 kidney biopsies and nephrectomies. By associating morphometric features with clinical parameters, we confirm previous concepts and reveal unexpected relations. We show that the extracted features are independent predictors of long-term clinical outcomes in IgA-nephropathy. We introduce single-structure morphometric analysis by applying techniques from single-cell transcriptomics, identifying distinct glomerular populations and morphometric phenotypes along a trajectory of disease progression. Our study provides a concept for Next-generation Morphometry (NGM), enabling comprehensive quantitative pathology data mining, i.e., pathomics.

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 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. MethodsWe 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