Kidney fibrosis is the hallmark of chronic kidney disease progression, however, currently no antifibrotic therapies exist. This is largely because the origin, functional heterogeneity and regulation of scar-forming cells during human kidney fibrosis remains poorly understood. Here, using single cell RNA-seq, we profiled the transcriptomes of proximal tubule and non-proximal tubule cells in healthy and fibrotic human kidneys to map the entire human kidney in an unbiased approach. This enabled mapping of all matrix-producing cells at high resolution, revealing distinct subpopulations of pericytes and fibroblasts as the major cellular sources of scar forming myofibroblasts during human kidney fibrosis. We used genetic fate-tracing, time-course single cell RNA-seq and ATAC-seq experiments in mice, and spatial transcriptomics in human kidney fibrosis to functionally interrogate these findings, shedding new light on the origin, heterogeneity and differentiation of human kidney myofibroblasts and their fibroblast and pericyte precursors at unprecedented resolution. Finally, we used this strategy to facilitate target discovery, identifying Nkd2 as a myofibroblast-specific target in human kidney fibrosis.

Myocardial infarction is a leading cause of death worldwide 1 . Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality 2 . Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.

Under physiologic conditions, significant amounts of plasma protein pass the renal filter and are reabsorbed by proximal tubular cells, but it is not clear whether the endocytosed protein, particularly albumin, is degraded in lysosomes or returned to the circulatory system intact. To resolve this question, a transgenic mouse with podocyte-specific expression of doxycycline-inducible tagged murine albumin was developed. To assess potential glomerular backfiltration, two types of albumin with different charges were expressed. On administration of doxycycline, podocytes expressed either of the two types of transgenic albumin, which were secreted into the primary filtrate and reabsorbed by proximal tubular cells, resulting in serum accumulation. Renal transplantation experiments confirmed that extrarenal transcription of transgenic albumin was unlikely to account for these results. Genetic deletion of the neonatal Fc receptor (FcRn), which rescues albumin and IgG from lysosomal degradation, abolished transcytosis of both types of transgenic albumin and IgG in proximal tubular cells. In summary, we provide evidence of a transcytosis within the kidney tubular system that protects albumin and IgG from lysosomal degradation, allowing these proteins to be recycled intact. 24: 196624: -198024: , 201324: . doi: 10.1681 The main function of the kidney is to filter plasma, while at the same time, retain the majority of plasma proteins. However, a certain fraction of plasma proteins inevitably passes the glomerular filtration barrier. The most abundant and most studied plasma protein, albumin, is produced at a rate of about 15 g per day in humans. 1 In the renal glomerulus, the albumin sieving coefficient (i.e., the transport rate of albumin across the glomerular filter in relation to water) has been estimated to be below 0.001. [2][3][4] In more recent studies using intravital microscopy, the albumin sieving coefficient has been estimated to be significantly higher, although this result is still controversial. 5-7 Thus, even when assuming a tight renal filtration barrier, significant amounts of albumin pass the glomerular filterroughly in the range of 1 g per day in healthy humans, J Am Soc Nephrol

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.