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

Identification and quantification of explosives in nanolitre solution volumes by Raman spectroscopy in suspended core optical fibers

A novel approach for identifying explosive species is reported, using Raman spectroscopy in suspended core optical fibers. Numerical simulations are presented that predict the strength of the observed signal as a function of fiber geometry, with the calculated trends verified experimentally and used to optimize the sensors. This technique is used to identify hydrogen peroxide in water solutions at volumes less than 60 nL and to quantify microgram amounts of material using the solvent’s Raman signature as an internal calibration standard. The same system, without further modifications, is also used to detect 1,4-dinitrobenzene, a model molecule for nitrobenzene-based explosives such as 2,4,6-trinitrotoluene (TNT).Georgios Tsiminis, Fenghong Chu, Stephen C. Warren-Smith, Nigel A. Spooner and Tanya M. Monr

Role of the podocyte in proteinuria

In recent years, the podocyte, with its elaborate cytoarchitecture and slit diaphragm, has been the focus of extensive research, yet its precise role in the glomerular filtration barrier is still debated. There are puzzling observations indicating that a comprehensive mechanistic model for glomerular filtration is still necessary. There is no doubt that podocytes are essential for glomerular filtration barrier integrity. However, most albumin never reaches the podocyte because it is prevented from entering the glomerular filter at the endothelium level. Another puzzling observation is that the glomerular filter never clogs despite its high load of several kilograms of plasma proteins per day. Recently, we proposed a novel model in which an electrical potential difference is generated across the glomerular filtration barrier by filtration. The model offers novel potential solutions to some of the riddles regarding the glomerular filter

Brief Exposure to Sensory Cues Elicits Stimulus-Nonspecific General Sensitization in an Insect

The effect of repeated exposure to sensory stimuli, with or without reward is well known to induce stimulus-specific modifications of behaviour, described as different forms of learning. In recent studies we showed that a brief single pre-exposure to the female-produced sex pheromone or even a predator sound can increase the behavioural and central nervous responses to this pheromone in males of the noctuid moth Spodoptera littoralis. To investigate if this increase in sensitivity might be restricted to the pheromone system or is a form of general sensitization, we studied here if a brief pre-exposure to stimuli of different modalities can reciprocally change behavioural and physiological responses to olfactory and gustatory stimuli. Olfactory and gustatory pre-exposure and subsequent behavioural tests were carried out to reveal possible intra- and cross-modal effects. Attraction to pheromone, monitored with a locomotion compensator, increased after exposure to olfactory and gustatory stimuli. Behavioural responses to sucrose, investigated using the proboscis extension reflex, increased equally after pre-exposure to olfactory and gustatory cues. Pheromone-specific neurons in the brain and antennal gustatory neurons did, however, not change their sensitivity after sucrose exposure. The observed intra- and reciprocal cross-modal effects of pre-exposure may represent a new form of stimulus-nonspecific general sensitization originating from modifications at higher sensory processing levels

Mouse models of kidney fibrosis

Chronic kidney disease (CKD) affects over 10% of the worldwide population and kidney fibrosis is a main driver of CKD and considered a therapeutic target. The mechanisms leading to kidney fibrosis are highly complexed and can be best studied in rodent models. Here we describe the most commonly used kidney fibrosis models in mice, the unilateral ureteral obstruction (UUO) model and the ischemia reperfusion injury (IRI) model. Both models are easy to learn and can be applied in animals of different age, sex, and strain

Differences in Appearance-Based Commentary, Body Dissatisfaction, and Eating Disturbance among College Women of Varying Weight Groups

This study examined appearance-related commentary, body dissatisfaction, and eating disturbance in 924 undergraduate females. Significant group differences were found in type of appearance-related commentary received across weight groups. Overweight and obese women experienced negative weight and shape-related comments at greater frequencies and positive weight and shape-related comments at lower frequencies compared to underweight and normal weight women. A higher frequency of positive weight and shape-related commentary was associated with less body dissatisfaction for all women and less shape and weight concerns for obese women. These findings suggest that the weight status of young women likely influences the appearance-related commentary that they receive and the manner in which such commentary affects their body image and eating behaviors

Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen

A major drawback of single-cell ATAC-seq (scATAC-seq) is its sparsity, i.e., open chromatin regions with no reads due to loss of DNA material during the scATAC-seq protocol. Here, we propose scOpen, a computational method based on regularized non-negative matrix factorization for imputing and quantifying the open chromatin status of regulatory regions from sparse scATAC-seq experiments. We show that scOpen improves crucial downstream analysis steps of scATAC-seq data as clustering, visualization, cis-regulatory DNA interactions, and delineation of regulatory features. We demonstrate the power of scOpen to dissect regulatory changes in the development of fibrosis in the kidney. This identifies a role of Runx1 and target genes by promoting fibroblast to myofibroblast differentiation driving kidney fibrosis