Zusammenfassung Hintergrund In der Nephropathologie werden häufig semiquantitative histologische Scoringsysteme verwendet. In der computergestützten Nephropathologie liegt der Fokus auf der Generierung von quantitativen Daten aus der Histologie (sog. Pathomics). Mehrere aktuelle Studien haben mittels Next Generation Morphometry (NGM) solche Daten basierend auf Segmentierungen durch Künstliche Neuronale Netzwerke erhoben und deren Nutzbarkeit für verschiedene klinische oder diagnostische Zwecke untersucht. Ziel der Arbeit Die Arbeit gibt einen Überblick über die aktuelle Studienlage hinsichtlich Pathomics von Nierengewebe und identifiziert aktuelle Herausforderungen und potenzielle Lösungen. Material und Methoden Aufgrund der Literaturrestriktion (maximal 30 Referenzen) wurden Studien, die möglichst große Datenmengen verarbeiten, innovative Methodiken verwenden und/oder idealerweise multizentrisch konzipiert sind, basierend auf einer Datenbankrecherche ausgewählt. Ergebnisse und Diskussion Pathomics-Studien der Niere haben eindrucksvoll gezeigt, dass morphometrische Daten klinisch (beispielsweise zur Prognoseabschätzung) und auch translational nützlich sind. Zur weiteren Entwicklung von NGM ist die Bewältigung einiger Herausforderungen notwendig, darunter insbesondere eine bessere Standardisierung und die Generierung prospektiver Evidenz.

Cholesterol crystal embolism (CCE) implies immunothrombosis, tissue necrosis, and organ failure but no specific treatments are available. As CCE involves complement activation, we speculated that inhibitors of the C5a/C5aR axis would be sufficient to attenuate the consequences of CCE like that with systemic vasculitis. Cholesterol microcrystal injection into the kidney artery of wildtype mice initiated intra-kidney immunothrombosis within a few hours followed by a sudden drop of glomerular filtration rate and ischemic kidney necrosis after 24 hours. Genetic deficiency of either C3 or C5aR prevented immunothrombosis, glomerular filtration rate drop, and ischemic necrosis at 24 hours as did preemptive treatment with inhibitors of either C5a or C5aR. Delayed C5a blockade after crystal injection still resolved crystal clots and prevented all consequences. Thus, selective blockade of C5a or C5aR is sufficient to attenuate the consequences of established CCE and prospective inhibition in high-risk patients may be clinically feasible and safe.

Acute kidney injury is still associated with high morbidity and mortality. Reichardt et al. investigated DNA-binding protein-A ( Ybx3 ) in acute kidney injury induced by ischemia-reperfusion injury and found that mice lacking Ybx3 have altered mitochondrial function and increased antioxidant activity, making them more resistant to ischemia-reperfusion injury-acute - acute kidney injury. The study highlights a new role of the multifaceted protein DNA-binding protein-A, which could be potentially therapeutically exploited.

Background Semiquantitative histological scoring systems are frequently used in nephropathology. In computational nephropathology, the focus is on generating quantitative data from histology (so-called pathomics). Several recent studies have collected such data using next-generation morphometry (NGM) based on segmentations by artificial neural networks and investigated their usability for various clinical or diagnostic purposes. Aim To present an overview of the current state of studies regarding renal pathomics and to identify current challenges and potential solutions. Materials and methods Due to the literature restriction (maximum of 30 references), studies were selected based on a database search that processed as much data as possible, used innovative methodologies, and/or were ideally multicentric in design. Results and discussion Pathomics studies in the kidney have impressively demonstrated that morphometric data are useful clinically (for example, for prognosis assessment) and translationally. Further development of NGM requires overcoming some challenges, including better standardization and generation of prospective evidence.

Background Pathomics facilitates automated, reproducible and precise histopathology analysis and morphological phenotyping. Similar to molecular omics, pathomics datasets are high-dimensional, but also face large outlier variability and inherent data missingness, making quick and comprehensible data analysis challenging. To facilitate pathomics data analysis and interpretation as well as support a broad implementation we developed tRigon (Toolbox foR InteGrative (path-)Omics data aNalysis), a Shiny application for fast, comprehensive and reproducible pathomics analysis. Results tRigon is available via the CRAN repository ( https://cran.r-project.org/web/packages/tRigon ) with its source code available on GitLab ( https://git-ce.rwth-aachen.de/labooratory-ai/trigon ). The tRigon package can be installed locally and its application can be executed from the R console via the command ‘tRigon::run_tRigon()’. Alternatively, the application is hosted online and can be accessed at https://labooratory.shinyapps.io/tRigon . We show fast computation of small, medium and large datasets in a low- and high-performance hardware setting, indicating broad applicability of tRigon. Conclusions tRigon allows researchers without coding abilities to perform exploratory feature analyses of pathomics and non-pathomics datasets on their own using a variety of hardware.

BACKGROUND:Heart diseases are among the leading causes of death worldwide, many of which lead to pathological cardiomyocyte hypertrophy and capillary rarefaction in both patients and animal models, the quantification of which is both technically challenging and highly time-consuming. Here we developed a semiautomated pipeline for quantification of the size of cardiomyocytes and capillary density in cardiac histology, termed HeartJ, by generating macros in ImageJ, a broadly used, open-source, Java-based software.METHODS:We have used modified Gomori silver staining, which is easy to perform and digitize in high throughput, or Fluorescein-labeled lectin staining. The latter can be easily combined with other stainings, allowing additional quantitative analysis on the same section, e.g., the size of cardiomyocyte nuclei, capillary density, or single-cardiomyocyte protein expression. We validated the pipeline in a mouse model of cardiac hypertrophy induced by transverse aortic constriction, and in autopsy samples of patients with and without aortic stenosis.RESULTS:In both animals and humans, HeartJ-based histology quantification revealed significant hypertrophy of cardiomyocytes reflecting other parameters of hypertrophy and rarefaction of microvasculature and enabling the analysis of protein expression in individual cardiomyocytes. The analysis also revealed that murine and human cardiomyocytes had similar diameters in health and extent of hypertrophy in disease confirming the translatability of our murine cardiac hypertrophy model. HeartJ enables a rapid analysis that would not be feasible by manual methods. The pipeline has little hardware requirements and is freely available.CONCLUSIONS:In summary, our analysis pipeline can facilitate effective and objective quantitative histological analyses in preclinical and clinical heart samples.

Cardiac remodeling occurs frequently in chronic kidney disease patients and affects quality of life and survival. Current treatment options are highly inadequate. As kidney function declines, numerous metabolic pathways are disturbed. Kidney and heart functions are highly connected by organ crosstalk. Among others, altered volume and pressure status, ischemia, accelerated atherosclerosis and arteriosclerosis, disturbed mineral metabolism, renal anemia, activation of the renin-angiotensin system, uremic toxins, oxidative stress and upregulation of cytokines stress the sensitive interplay between different cardiac cell types. The fatal consequences are left-ventricular hypertrophy, fibrosis and capillary rarefaction, which lead to systolic and/or diastolic left-ventricular failure. Furthermore, fibrosis triggers electric instability and sudden cardiac death. This review focuses on established and potential pathophysiological cardiorenal crosstalk mechanisms that drive uremia-induced senescence and disease progression, including potential known targets and animal models that might help us to better understand the disease and to identify novel therapeutics.

Objectives: Investigation of the effect of SGLT2 inhibition by empagliflozin on left ventricular function in a model of diabetic cardiomyopathy. Background: SGLT2 inhibition is a new strategy to treat diabetes. In the EMPA-REG Outcome trial empagliflozin treatment reduced cardiovascular and overall mortality in patients with diabetes presumably due to beneficial cardiac effects, leading to reduced heart failure hospitalization. The relevant mechanisms remain currently elusive but might be mediated by a shift in cardiac substrate utilization leading to improved energetic supply to the heart. Methods: We used db/db mice on high-fat western diet with or without empagliflozin treatment as a model of severe diabetes. Left ventricular function was assessed by pressure catheter with or without dobutamine stress. Results: Treatment with empagliflozin significantly increased glycosuria, improved glucose metabolism, ameliorated left ventricular diastolic function and reduced mortality of mice. This was associated with reduced cardiac glucose concentrations and decreased calcium/calmodulin-dependent protein kinase (CaMKII) activation with subsequent less phosphorylation of the ryanodine receptor (RyR). No change of cardiac ketone bodies or branched-chain amino acid (BCAA) metabolites in serum was detected nor was cardiac expression of relevant catabolic enzymes for these substrates affected. Conclusions: In a murine model of severe diabetes empagliflozin-dependent SGLT2 inhibition improved diastolic function and reduced mortality. Improvement of diastolic function was likely mediated by reduced spontaneous diastolic sarcoplasmic reticulum (SR) calcium release but independent of changes in cardiac ketone and BCAA metabolism.

BACKGROUND:Nephropathologic 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. METHODS:We 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. RESULTS:Multiclass 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. CONCLUSIONS:We 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.