Monocyte-derived macrophages aggravate pulmonary vasculitis via cGAS/STING/IFN-mediated nucleic acid sensing.

Autoimmune vasculitis is a group of life-threatening diseases, whose underlying pathogenic mechanisms are incompletely understood, hampering development of targeted therapies. Here, we demonstrate that patients suffering from anti-neutrophil cytoplasmic antibodies (ANCA)-associated vasculitis (AAV) showed increased levels of cGAMP and enhanced IFN-I signature. To identify disease mechanisms and potential therapeutic targets, we developed a mouse model for pulmonary AAV that mimics severe disease in patients. Immunogenic DNA accumulated during disease onset, triggering cGAS/STING/IRF3-dependent IFN-I release that promoted endothelial damage, pulmonary hemorrhages, and lung dysfunction. Macrophage subsets played dichotomic roles in disease. While recruited monocyte-derived macrophages were major disease drivers by producing most IFN-ß, resident alveolar macrophages contributed to tissue homeostasis by clearing red blood cells and limiting infiltration of IFN-ß-producing macrophages. Moreover, pharmacological inhibition of STING, IFNAR-I, or its downstream JAK/STAT signaling reduced disease severity and accelerated recovery. Our study unveils the importance of STING/IFN-I axis in promoting pulmonary AAV progression and identifies cellular and molecular targets to ameliorate disease outcomes.

Deep Learning-Based Segmentation and Quantification in Experimental Kidney Histopathology.

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.

Trapping of drops by wetting defects.

Controlling the motion of drops on solid surfaces is crucial in many natural phenomena and technological processes including the collection and removal of rain drops, cleaning technology and heat exchangers. Topographic and chemical heterogeneities on solid surfaces give rise to pinning forces that can capture and steer drops in desired directions. Here we determine general physical conditions required for capturing sliding drops on an inclined plane that is equipped with electrically tunable wetting defects. By mapping the drop dynamics on the one-dimensional motion of a point mass, we demonstrate that the trapping process is controlled by two dimensionless parameters, the trapping strength measured in units of the driving force and the ratio between a viscous and an inertial time scale. Complementary experiments involving superhydrophobic surfaces with wetting defects demonstrate the general applicability of the concept. Moreover, we show that electrically tunable defects can be used to guide sliding drops along actively switchable tracks-with potential applications in microfluidics.

Mesenchymal stem cells from rats with chronic kidney disease exhibit premature senescence and loss of regenerative potential.

Mesenchymal stem cell (MSC) transplantation has the potential for organ repair. Nevertheless, some factors might lessen the regenerative potential of MSCs, e.g. donor age or systemic disease. It is thus important to carefully assess the patient's suitability for autologous MSC transplantation. Here we investigated the effects of chronic kidney disease (CKD) on MSC function. We isolated bone marrow MSCs from remnant kidney rats (RK) with CKD (CKD-RK-MSC) and found signs of premature senescence: spontaneous adipogenesis, reduced proliferation capacity, active senescence-associated-ß-galactosidase, accumulation of actin and a modulated secretion profile. The functionality of CKD-RK-MSCs in vivo was tested in rats with acute anti-Thy1.1-nephritis, where healthy MSCs have been shown to be beneficial. Rats received healthy MSCs, CKD-RK-MSC or medium by injection into the left renal artery. Kidneys receiving healthy MSCs exhibited accelerated healing of glomerular lesions, whereas CKD-RK-MSC or medium exerted no benefit. The negative influence of advanced CKD/uremia on MSCs was confirmed in a second model of CKD, adenine nephropathy (AD). MSCs from rats with adenine nephropathy (CKD-AD-MSC) also exhibited cellular modifications and functional deficits in vivo. We conclude that CKD leads to a sustained loss of in vitro and in vivo functionality in MSCs, possibly due to premature cellular senescence. Considering autologous MSC therapy in human renal disease, studies identifying uremia-associated mechanisms that account for altered MSC function are urgently needed.