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.

The heart is one of the most frequently affected organs in sepsis. Recent studies focused on lipopolysaccharide-induced mitochondrial dysfunction; however myocardial dysfunction is not restricted to gram-negative bacterial sepsis. The purpose of this study was to investigate circulating heparan sulfate (HS) as an endogenous danger associated molecule causing cardiac mitochondrial dysfunction in sepsis. We used an in vitro model with native sera (SsP) and sera eliminated from HS (HS-free), both of septic shock patients, to stimulate murine cardiomyocytes. As determined by extracellular flux analyzing, SsP increased basal mitochondrial respiration, but reduced maximum mitochondrial respiration, compared with unstimulated cells (P < 0.0001 and P < 0.0001, respectively). Cells stimulated with HS-free serum revealed unaltered basal and maximum mitochondrial respiration, compared with unstimulated cells (P = 0.1174 and P = 0.8992, respectively). Cellular ATP-level were decreased in SsP-stimulated cells but unaltered in cells stimulated with HS-free serum compared with unstimulated cells (P < 0.0001 and P = 0.1593, respectively). Live-cell imaging revealed an increased production of mitochondrial reactive oxygen species in cells stimulated with SsP compared with cells stimulated with HS-free serum (P < 0.0001). Expression of peroxisome proliferator-activated receptors (PPARα and PPARγ) and their co-activators PGC-1α, which regulate mitochondrial function, were studied using PCR. Cells stimulated with SsP showed downregulated PPARs and PGC-1α mRNA-levels compared with HS-free serum (P = 0.0082, P = 0.0128, and P = 0.0185, respectively). Blocking Toll-like receptor 4 revealed an inhibition of HS-dependent downregulation of PPARs and PGC-1α (all P < 0.0001). In conclusion, circulating HS in serum of septic shock patients cause cardiac mitochondrial dysfunction, suggesting that HS may be targets of therapeutics in septic cardiomyopathy.

No abstract