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\u27s 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

Vascular CXCR4 Limits Atherosclerosis by Maintaining Arterial Integrity Evidence From Mouse and Human Studies

BACKGROUND: The CXCL12/CXCR4 chemokine ligand/receptor axis controls (progenitor) cell homeostasis and trafficking. So far, an atheroprotective role of CXCL12/CXCR4 has only been implied through pharmacological intervention, in particular, because the somatic deletion of the CXCR4 gene in mice is embryonically lethal. Moreover, cell-specific effects of CXCR4 in the arterial wall and underlying mechanisms remain elusive, prompting us to investigate the relevance of CXCR4 in vascular cell types for atheroprotection. METHODS: We examined the role of vascular CXCR4 in atherosclerosis and plaque composition by inducing an endothelial cell (BmxCreERT2-driven)-specific or smooth muscle cell (SMC, SmmhcCreERT2-or TaglnCre-driven)-specific deficiency of CXCR4 in an apolipoprotein E-deficient mouse model. To identify underlying mechanisms for effects of CXCR4, we studied endothelial permeability, intravital leukocyte adhesion, involvement of the Akt/WNT/beta-catenin signaling pathway and relevant phosphatases in VE-cadherin expression and function, vascular tone in aortic rings, cholesterol efflux from macrophages, and expression of SMC phenotypic markers. Finally, we analyzed associations of common genetic variants at the CXCR4 locus with the risk for coronary heart disease, along with CXCR4 transcript expression in human atherosclerotic plaques. RESULTS: The cell-specific deletion of CXCR4 in arterial endothelial cells (n=1215) or SMCs (n=13-24) markedly increased atherosclerotic lesion formation in hyperlipidemic mice. Endothelial barrier function was promoted by CXCL12/\CXCR4, which triggered Akt/WNT/beta-catenin signaling to drive VE-cadherin expression and stabilized junctional VE-cadherin complexes through associated phosphatases. Conversely, endothelial CXCR4 deficiency caused arterial leakage and inflammatory leukocyte recruitment during atherogenesis. In arterial SMCs, CXCR4 sustained normal vascular reactivity and contractile responses, whereas CXCR4 deficiency favored a synthetic phenotype, the occurrence of macrophage-like SMCs in the lesions, and impaired cholesterol efflux. Regression analyses in humans (n=259 796) identified the C-allele at rs2322864 within the CXCR4 locus to be associated with increased risk for coronary heart disease. In line, C/C risk genotype carriers showed reduced CXCR4 expression in carotid artery plaques (n=188), which was furthermore associated with symptomatic disease. CONCLUSIONS: Our data clearly establish that vascular CXCR4 limits atherosclerosis by maintaining arterial integrity, preserving endothelial barrier function, and a normal contractile SMC phenotype. Enhancing these beneficial functions of arterial CXCR4 by selective modulators might open novel therapeutic options in atherosclerosis

Effects of Anacetrapib in Patients with Atherosclerotic Vascular Disease

BACKGROUND: Patients with atherosclerotic vascular disease remain at high risk for cardiovascular events despite effective statin-based treatment of low-density lipoprotein (LDL) cholesterol levels. The inhibition of cholesteryl ester transfer protein (CETP) by anacetrapib reduces LDL cholesterol levels and increases high-density lipoprotein (HDL) cholesterol levels. However, trials of other CETP inhibitors have shown neutral or adverse effects on cardiovascular outcomes. METHODS: We conducted a randomized, double-blind, placebo-controlled trial involving 30,449 adults with atherosclerotic vascular disease who were receiving intensive atorvastatin therapy and who had a mean LDL cholesterol level of 61 mg per deciliter (1.58 mmol per liter), a mean non-HDL cholesterol level of 92 mg per deciliter (2.38 mmol per liter), and a mean HDL cholesterol level of 40 mg per deciliter (1.03 mmol per liter). The patients were assigned to receive either 100 mg of anacetrapib once daily (15,225 patients) or matching placebo (15,224 patients). The primary outcome was the first major coronary event, a composite of coronary death, myocardial infarction, or coronary revascularization. RESULTS: During the median follow-up period of 4.1 years, the primary outcome occurred in significantly fewer patients in the anacetrapib group than in the placebo group (1640 of 15,225 patients [10.8%] vs. 1803 of 15,224 patients [11.8%]; rate ratio, 0.91; 95% confidence interval, 0.85 to 0.97; P=0.004). The relative difference in risk was similar across multiple prespecified subgroups. At the trial midpoint, the mean level of HDL cholesterol was higher by 43 mg per deciliter (1.12 mmol per liter) in the anacetrapib group than in the placebo group (a relative difference of 104%), and the mean level of non-HDL cholesterol was lower by 17 mg per deciliter (0.44 mmol per liter), a relative difference of -18%. There were no significant between-group differences in the risk of death, cancer, or other serious adverse events. CONCLUSIONS: Among patients with atherosclerotic vascular disease who were receiving intensive statin therapy, the use of anacetrapib resulted in a lower incidence of major coronary events than the use of placebo. (Funded by Merck and others; Current Controlled Trials number, ISRCTN48678192 ; ClinicalTrials.gov number, NCT01252953 ; and EudraCT number, 2010-023467-18 .)

Treatment of Renal Fibrosis-Turning Challenges into Opportunities

Current treatment modalities are not effective in halting the progression of most CKD. Renal fibrosis is a pathological process common to all CKD and thereby represents an excellent treatment target. A large number of molecular pathways involved in renal fibrosis were identified in preclinical studies, some of them being similar among different organs and some with available drugs in various phases of clinical testing. Yet only few clinical trials with antifibrotic drugs are being conducted in CKD patients. Here we review those clinical trials, focusing on agents with direct antifibrotic effects, with particular focus on pirfenidone and neutralizing antibodies directed against profibrotic growth factors and cell connection proteins. We discuss the potential reasons for the poor translation in treatment of renal fibrosis and propose possible approaches and future developments to improve it, eg, patient selection and companion diagnostics, specific and sensitive biomarkers as novel end points for clinical trials, and drug-targeting and theranostics

Treatment of Renal Fibrosis—Turning Challenges into Opportunities

Current treatment modalities are not effective in halting the progression of most CKD. Renal fibrosis is a pathological process common to all CKD and thereby represents an excellent treatment target. A large number of molecular pathways involved in renal fibrosis were identified in preclinical studies, some of them being similar among different organs and some with available drugs in various phases of clinical testing. Yet only few clinical trials with antifibrotic drugs are being conducted in CKD patients. Here we review those clinical trials, focusing on agents with direct antifibrotic effects, with particular focus on pirfenidone and neutralizing antibodies directed against profibrotic growth factors and cell connection proteins. We discuss the potential reasons for the poor translation in treatment of renal fibrosis and propose possible approaches and future developments to improve it, eg, patient selection and companion diagnostics, specific and sensitive biomarkers as novel end points for clinical trials, and drug-targeting and theranostics

Developmental stages of tertiary lymphoid tissue reflect local injury and inflammation in mouse and human kidneys

腎臓病の予後を予測する新規障害マーカーの発見 --腎障害と共に三次リンパ組織は成熟する--. 京都大学プレスリリース. 2020-05-29.Tertiary lymphoid tissues (TLTs) are inducible ectopic lymphoid tissues in chronic inflammatory states and function as sites of priming local immune responses. We previously demonstrated that aged but not young mice exhibited multiple TLTs after acute kidney injury and that TLTs were also detected in human aged and diseased kidneys. However, the forms of progression and the implication for kidney injury remain unclear. To clarify this we analyzed surgically resected kidneys from aged patients with or without chronic kidney disease as well as kidneys resected for pyelonephritis, and classified TLTs into three distinct developmental stages based on the presence of follicular dendritic cells and germinal centers. In injury-induced murine TLT models, the stages advanced with the extent of kidney injury, and decreased with dexamethasone accompanied with improvement of renal function, fibrosis and inflammation. Kidneys from aged patients with chronic kidney disease consistently exhibited more frequent and advanced stages of TLTs than those without chronic kidney disease. Kidneys of patients with pyelonephritis exhibited more frequent TLTs with more advanced stages than aged kidneys. Additionally, TLTs in both cohorts shared similar locations and components, suggesting that TLT formation may not be a disease-specific phenomenon but rather a common pathological process. Thus, our findings provide the insights into biological features of TLT in the kidney and implicate TLT stage as a potential marker reflecting local injury and inflammation

Improving unsupervised stain-to-stain translation using self-supervision and meta-learning

Background: In digital pathology, many image analysis tasks are challenged by the need for large and time-consuming manual data annotations to cope with various sources of variability in the image domain. Unsupervised domain adaptation based on image-to-image translation is gaining importance in this field by addressing variabilities without the manual overhead. Here, we tackle the variation of different histological stains by unsupervised stain-to-stain translation to enable a stain-independent applicability of a deep learning segmentation model. Methods: We use CycleGANs for stain-to-stain translation in kidney histopathology, and propose two novel approaches to improve translational effectivity. First, we integrate a prior segmentation network into the CycleGAN for a self-supervised, application-oriented optimization of translation through semantic guidance, and second, we incorporate extra channels to the translation output to implicitly separate artificial meta-information otherwise encoded for tackling underdetermined reconstructions. Results: The latter showed partially superior performances to the unmodified CycleGAN, but the former performed best in all stains providing instance-level Dice scores ranging between 78% and 92% for most kidney structures, such as glomeruli, tubules, and veins. However, CycleGANs showed only limited performance in the translation of other structures, e.g. arteries. Our study also found somewhat lower performance for all structures in all stains when compared to segmentation in the original stain. Conclusions: Our study suggests that with current unsupervised technologies, it seems unlikely to produce “generally” applicable simulated stains