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Alport syndrome is an inherited kidney disease, which can lead to glomerulosclerosis and fibrosis, as well as end-stage kidney disease in children and adults. Platelet-derived growth factor-D (PDGF-D) mediates glomerulosclerosis and interstitial fibrosis in various models of kidney disease, prompting investigation of its role in a murine model of Alport syndrome. In vitro, PDGF-D induced proliferation and profibrotic activation of conditionally immortalized human parietal epithelial cells. In Col4a3 -/- mice, a model of Alport syndrome, PDGF-D mRNA and protein were significantly up-regulated compared with non-diseased wild-type mice. To analyze the therapeutic potential of PDGF-D inhibition, Col4a3 -/- mice were treated with a PDGF-D neutralizing antibody. Surprisingly, PDGF-D antibody treatment had no effect on renal function, glomerulosclerosis, fibrosis, or other indices of kidney injury compared with control treatment with unspecific IgG. To characterize the role of PDGF-D in disease development, Col4a3 -/- mice with a constitutive genetic deletion of Pdgfd were generated and analyzed. No difference in pathologic features or kidney function was observed in Col4a3 -/- Pdgfd -/- mice compared with Col4a3 -/- Pdgfd +/+ littermates, confirming the antibody treatment data. Mechanistically, lack of proteolytic PDGF-D activation in Col4a3 -/- mice might explain the lack of effects in vivo. In conclusion, despite its established role in kidney fibrosis, PDGF-D, without further activation, does not mediate the development and progression of Alport syndrome in mice., (Copyright © 2024 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.)

Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions., (© 2023. The Author(s).)

Significance Statement: We hypothesized that triple therapy with inhibitors of the renin-angiotensin system (RAS), sodium-glucose transporter (SGLT)-2, and the mineralocorticoid receptor (MR) would be superior to dual RAS/SGLT2 blockade in attenuating CKD progression in Col4a3 -deficient mice, a model of Alport syndrome. Late-onset ramipril monotherapy or dual ramipril/empagliflozin therapy attenuated CKD and prolonged overall survival by 2 weeks. Adding the nonsteroidal MR antagonist finerenone extended survival by 4 weeks. Pathomics and RNA sequencing revealed significant protective effects on the tubulointerstitium when adding finerenone to RAS/SGLT2 inhibition. Thus, triple RAS/SGLT2/MR blockade has synergistic effects and might attenuate CKD progression in patients with Alport syndrome and possibly other progressive chronic kidney disorders., Background: Dual inhibition of the renin-angiotensin system (RAS) plus sodium-glucose transporter (SGLT)-2 or the mineralocorticoid receptor (MR) demonstrated additive renoprotective effects in large clinical trials. We hypothesized that triple therapy with RAS/SGLT2/MR inhibitors would be superior to dual RAS/SGLT2 blockade in attenuating CKD progression., Methods: We performed a preclinical randomized controlled trial (PCTE0000266) in Col4a3 -deficient mice with established Alport nephropathy. Treatment was initiated late (age 6 weeks) in mice with elevated serum creatinine and albuminuria and with glomerulosclerosis, interstitial fibrosis, and tubular atrophy. We block-randomized 40 male and 40 female mice to either nil (vehicle) or late-onset food admixes of ramipril monotherapy (10 mg/kg), ramipril plus empagliflozin (30 mg/kg), or ramipril plus empagliflozin plus finerenone (10 mg/kg). Primary end point was mean survival., Results: Mean survival was 63.7±10.0 days (vehicle), 77.3±5.3 days (ramipril), 80.3±11.0 days (dual), and 103.1±20.3 days (triple). Sex did not affect outcome. Histopathology, pathomics, and RNA sequencing revealed that finerenone mainly suppressed the residual interstitial inflammation and fibrosis despite dual RAS/SGLT2 inhibition., Conclusion: Experiments in mice suggest that triple RAS/SGLT2/MR blockade may substantially improve renal outcomes in Alport syndrome and possibly other progressive CKDs because of synergistic effects on the glomerular and tubulointerstitial compartments., (Copyright © 2023 by the American Society of Nephrology.)

Pathology diagnostics relies on the assessment of morphology by trained experts, which remains subjective and qualitative. Here we developed a framework for large-scale histomorphometry (FLASH) performing deep learning-based semantic segmentation and subsequent large-scale extraction of interpretable, quantitative, morphometric features in non-tumour kidney histology. We use two internal and three external, multi-centre cohorts to analyse over 1000 kidney biopsies and nephrectomies. By associating morphometric features with clinical parameters, we confirm previous concepts and reveal unexpected relations. We show that the extracted features are independent predictors of long-term clinical outcomes in IgA-nephropathy. We introduce single-structure morphometric analysis by applying techniques from single-cell transcriptomics, identifying distinct glomerular populations and morphometric phenotypes along a trajectory of disease progression. Our study provides a concept for Next-generation Morphometry (NGM), enabling comprehensive quantitative pathology data mining, i.e., pathomics., (© 2023. The Author(s).)

Convolutional neural network (CNN)-based image analysis applications in digital pathology (eg, tissue segmentation) require a large amount of annotated data and are mostly trained and applicable on a single stain. Here, a novel concept based on stain augmentation is proposed to develop stain-independent CNNs requiring only one annotated stain. In this benchmark study on stain independence in digital pathology, this approach is comprehensively compared with state-of-the-art techniques including image registration and stain translation, and several modifications thereof. A previously developed CNN for segmentation of periodic acid-Schiff-stained kidney histology was used and applied to various immunohistochemical stainings. Stain augmentation showed very high performance in all evaluated stains and outperformed all other techniques in all structures and stains. Without the need for additional annotations, it enabled segmentation on immunohistochemical stainings with performance nearly comparable to that of the annotated periodic acid-Schiff stain and could further uphold performance on several held-out stains not seen during training. Herein, examples of how this framework can be applied for compartment-specific quantification of immunohistochemical stains for inflammation and fibrosis in animal models and patient biopsy specimens are presented. The results show that stain augmentation is a highly effective approach to enable stain-independent applications of deep-learning segmentation algorithms. This opens new possibilities for broad implementation in digital pathology., (Copyright © 2023 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.)

Artificial intelligence (AI) solutions that automatically extract information from digital histology images have shown great promise for improving pathological diagnosis. Prior to routine use, it is important to evaluate their predictive performance and obtain regulatory approval. This assessment requires appropriate test datasets. However, compiling such datasets is challenging and specific recommendations are missing. A committee of various stakeholders, including commercial AI developers, pathologists, and researchers, discussed key aspects and conducted extensive literature reviews on test datasets in pathology. Here, we summarize the results and derive general recommendations on compiling test datasets. We address several questions: Which and how many images are needed? How to deal with low-prevalence subsets? How can potential bias be detected? How should datasets be reported? What are the regulatory requirements in different countries? The recommendations are intended to help AI developers demonstrate the utility of their products and to help pathologists and regulatory agencies verify reported performance measures. Further research is needed to formulate criteria for sufficiently representative test datasets so that AI solutions can operate with less user intervention and better support diagnostic workflows in the future., (© 2022. The Author(s).)

Background: Considerable inter- and intra-laboratory stain variability exists in pathology, representing a challenge in development and application of deep learning (DL) approaches. Since tackling all sources of stain variability with manual annotation is not feasible, we here investigated and compared unsupervised DL approaches to reduce the consequences of stain variability in kidney pathology., Methods: We aimed to improve the applicability of a pretrained DL segmentation model to 3 external multi-centric cohorts with large stain variability. In contrast to the traditional approach of training generative adversarial networks (GAN) for stain normalization, we here propose to tackle stain variability by data augmentation. We augment the training data of the pretrained model by the stain variability using CycleGANs and then retrain the model on the stain-augmented dataset. We compared the performance of i/ the unmodified pretrained segmentation model with ii/ CycleGAN-based stain normalization, iii/ a feature-preserving modification to ii/ for improved normalization, and iv/ the proposed stain-augmented model., Results: The proposed stain-augmented model showed highest mean segmentation accuracy in all external cohorts and maintained comparable performance on the training cohort. However, the increase in performance was only marginal compared to the pretrained model. CycleGAN-based stain normalization suffered from encoded imperceptible information into the normalizations that confused the pretrained model and thus resulted in slightly worse performance., Conclusions: Our findings suggest that stain variability can be tackled more effectively by augmenting data by it than by following the commonly used approach of normalizing the stain. However, the applicability of this approach providing only a rather slight performance increase has to be weighted against an additional carbon footprint., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.     , (© 2022 The Authors.)

Although underlying mechanisms and the clinical course of kidney disease progression are well described, less is known about potential disease reversibility. Therefore, to analyze kidney recovery, we adapted a commonly used murine chronic kidney disease (CKD) model of 2,8- dihydroxyadenine (2,8-DHA) crystal-induced nephropathy to study disease recovery and efficacy of disease-modifying interventions. The recovery phase after CKD was characterized by improved kidney function after two weeks which remained stable thereafter. By contrast, even after eight weeks recovery, tubular injury and inflammation were only partially reduced, and fibrosis persisted. Deep-learning-based histologic analysis of 8,604 glomeruli and 596,614 tubular cross sections revealed numerous tubules had undergone either prominent dilation or complete atrophy, leading to atubular glomeruli and irreversible nephron loss. We confirmed these findings in a second CKD model, reversible unilateral ureteral obstruction, in which a rapid improvement of glomerular filtration rate during recovery also did not reflect the permanent histologic kidney injury. In 2,8-DHA nephropathy, increased drinking volume was highly effective in disease prevention. However, in therapeutic approaches, high fluid intake was only effective in moderate but not severe CKD and established tissue injury was again poorly reflective of kidney function parameters. The injury was particularly localized in the medulla, which is often not analyzed. Thus, recovery after crystal- or obstruction-induced CKD is characterized by ongoing tissue injury, fibrosis, and nephron loss, but not reflected by standard measures of kidney function. Hence, our data might aid in designing kidney recovery studies and suggest the need for biomarkers specifically monitoring intra-kidney tissue injury., (Copyright © 2022 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.)

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., (© 2022 The Authors.)

Whole slide images contain a magnitude of quantitative information that may not be fully explored in qualitative visual assessments. We propose: (1) a novel pipeline for extracting a comprehensive set of visual features, which are detectable by a pathologist, as well as sub-visual features, which are not discernible by human experts and (2) perform detailed analyses on renal images from mice with experimental unilateral ureteral obstruction. An important criterion for these features is that they are easy to interpret, as opposed to features obtained from neural networks. We extract and compare features from pathological and healthy control kidneys to learn how the compartments (glomerulus, Bowman's capsule, tubule, interstitium, artery, and arterial lumen) are affected by the pathology. We define feature selection methods to extract the most informative and discriminative features. We perform statistical analyses to understand the relation of the extracted features, both individually, and in combinations, with tissue morphology and pathology. Particularly for the presented case-study, we highlight features that are affected in each compartment. With this, prior biological knowledge, such as the increase in interstitial nuclei, is confirmed and presented in a quantitative way, alongside with novel findings, like color and intensity changes in glomeruli and Bowman's capsule. The proposed approach is therefore an important step towards quantitative, reproducible, and rater-independent analysis in histopathology., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

Background: The field of cancer immunology is rapidly moving towards innovative therapeutic strategies, resulting in the need for robust and predictive preclinical platforms reflecting the immunological response to cancer. Well characterized preclinical models are essential for the development of predictive biomarkers in the oncology as well as the immune-oncology space. In the current study, gold standard preclinical models are being refined and combined with novel image analysis tools to meet those requirements., Methods: A panel of 14 non-small cell lung cancer patient-derived xenograft models (NSCLC PDX) was propagated in humanized NOD/Shi-scid/IL-2Rnull mice. The models were comprehensively characterized for relevant phenotypic and molecular features, including flow cytometry, immunohistochemistry, histology, whole exome sequencing and cytokine secretion., Results: Models reflecting hot (>5% tumor-infiltrating lymphocytes/TILs) as opposed to cold tumors (<5% TILs) significantly differed regarding their cytokine profiles, molecular genetic aberrations, stroma content, and programmed cell death ligand-1 status. Treatment experiments including anti cytotoxic T-lymphocyte-associated protein 4, anti-programmed cell death 1 or the combination thereof across all 14 models in the single mouse trial format showed distinctive tumor growth response and spatial immune cell patterns as monitored by computerized analysis of digitized whole-slide images. Image analysis provided for the first time qualitative evaluation of the extent to which PDX models retain the histological features from their original human donors., Conclusions: Deep phenotyping of PDX models in a humanized setting by combinations of computational pathology, immunohistochemistry, flow cytometry and proteomics enables the exhaustive analysis of innovative preclinical models and paves the way towards the development of translational biomarkers for immuno-oncology drugs., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)