Biology subjects

Sarder, P.

Publications and source records attributed to Sarder, P..

18 recordsLinked to original sources

bioRxiv2026

DigitAb: Domain-Adaptive Cell Type Prediction Method from Light Microscopy Images

Light microscopy imaging with histological stains is central to disease diagnosis and research. It is enhanced with immunostaining to reveal cellular composition and complexity linked to clinical utility and biological mechanisms. Emerging multiplex imaging technologies like Phenocycler markedly increase the coverage to capture the cellular diversity but are costly, technically demanding, and inaccessible to most clinical laboratories. We developed DigitAb, a deep learning framework that classifies cell types directly from hematoxylin and eosin (H&E) stained slides, eliminating the need for specialized assays. Using Phenocycler imaging, we generated highlZlresolution ground truths for [~]3.5 million cells from 29 human kidney samples across four multi-institutional datasets to train a semantic segmentation model for 10 cell types, achieving a balanced accuracy of 0.78. By employing an integrated adversarial domain adaptation module, we tested DigitAb on unlabeled and untested biopsy samples from kidney transplant and diabetic samples. We were able to predict several cell types just from histology images, without using any special technology or immunostains, and demonstrate high concordance with clinical gold-standard Banff schema in kidney transplant rejection, and clinical characteristics of diabetic nephropathy. Our cloudlZlbased tool, DigitAb, provides scalable, accessible, labellZlfree cellular segmentation for research and clinical pathology.

pathology↗

bioRxiv2026

Spatially Resolved Banff Tubulitis and Glomerulitis Scoring in Kidney Allograft Biopsies via Artificial Intelligent-Based Structure Segmentation and Spatial Transcriptomics

BackgroundTubulitis is a defining histologic feature of T cell-mediated rejection (TCMR), while glomerulitis is often characteristic of antibody mediated rejection (AMR). Histologic quantification of tubulitis and glomerulitis using Banff criteria is subject to interobserver variability. Bulk transcriptomic assays (e.g., MMDx) have introduced molecular correlations of tubulitis with TCMR and glomerulitis with AMR, but lack spatial resolution. MethodsWe applied a web-based platform, FUSION (Functional Unit State Identification in Whole Slide Images), to a cohort of 8 cases (n=2 per condition) with kidney allograft biopsy samples acute TCMR, active AMR, chronic active AMR, and no rejection (control). The machine-learning (ML) platform enabled integrated visualization and analysis of spatial transcriptomics (10x Genomics Visium v2) together with high-resolution whole-slide histology. ResultsTranscriptomics-derived immune cell proportions within AI-segmented tubular and glomerular regions were used to generate spatial Banff t- and g-scores. Derived t-scores showed full concordance with pathologist scores in both acute TCMR cases; g-scores showed concordance in 2 of 4 AMR cases, with discordant cases characterized by low absolute immune signal near the classification boundary. ConclusionsWe demonstrate the feasibility of using AI-based FTU segmentation integrated with spatial transcriptomics-derived immune cell proportions to generate spatially informed t- and g-scores aligned with Banff criteria, with full concordance in severe rejection and partial concordance in mild rejection. This approach lays the foundation for validated, spatial transcriptomics-augmented t-scores and g-scores that enhance diagnostic precision, reduces inter-observer variability among renal pathologists, and support potential clinical adoption.

pathology↗

bioRxiv2024

Integration of spatial multiplexed protein imaging and transcriptomics in the human kidney tracks the regenerative potential timeline of proximal tubules

The organizational principles of nephronal segments are based on longstanding anatomical and physiological attributes that are closely linked to the homeostatic functions of the kidney. Novel molecular approaches have recently uncovered layers of deeper signatures and states in tubular cells that arise at various timepoints on the spectrum between health and disease. For example, a dedifferentiated state of proximal tubular cells with mesenchymal stemness markers is frequently seen after injury. The persistence of such a state is associated with failed repair. Here, we introduce a novel analytical pipeline applied to highly multiplexed spatial protein imaging to characterize proximal tubular subpopulations and neighborhoods in reference and disease human kidney tissue. The results were validated and extended through integration with spatial and single cell transcriptomics. We demonstrate that, in reference tissue, a large proportion of S1 and S2 proximal tubular epithelial cells express THY1, a mesenchymal stromal and stem cell marker that regulates differentiation. Kidney disease is associated with loss of THY1 and transition towards expression of PROM1, another stem cell marker shown recently to be linked to failed repair. We demonstrate that the trajectory of proximal tubular cells to THY1 expression is clearly distinct from that of PROM1, and that a state with PROM1 expression is associated with niches of inflammation. Our data support a model in which the interplay between THY1 and PROM1 expression in proximal tubules associates with their regenerative potential and marks the timeline of disease progression.

bioinformatics↗

bioRxiv2024

A MEF2C transcripton factor network regulates proliferation of glomerular endothelial cells in diabetic kidney disease

The maintenance of a healthy epithelial-endothelial juxtaposition requires cross-talk within glomerular cellular niches. We sought to understand the spatially-anchored regulation and transition of endothelial and mesangial cells from health to injury in DKD. From 74 human kidney samples, an integrated multi-omics approach was leveraged to identify cellular niches, cell-cell communication, cell injury trajectories, and regulatory transcription factor (TF) networks in glomerular capillary endothelial (EC-GC) and mesangial cells. Data were culled from single nucleus RNA and ATAC sequencing and three orthogonal spatial transcriptomic technologies for correlation with histopathological and clinical trial data. We identified a cellular niche in diabetic glomeruli enriched in a proliferative endothelial cell subtype (prEC) and altered vascular smooth muscle cells (VSMCs). Cellular communication within this niche maintained pro-angiogenic signaling with loss of anti-angiogenic factors. We identified a TF network of MEF2C, MEF2A, and TRPS1 which regulated SEMA6A and PLXNA2, a receptor-ligand pair opposing angiogenesis. In silico knockout of the TF network accelerated the transition from healthy EC-GCs toward a degenerative (injury) endothelial phenotype, with concomitant disruption of EC-GC and prEC expression patterns. Glomeruli enriched in the prEC niche had histologic evidence of neovascularization. MEF2C activity was increased in diabetic glomeruli with nodular mesangial sclerosis. The gene regulatory network (GRN) of MEF2C was dysregulated in EC-GCs of patients with DKD, but sodium glucose transporter-2 inhibitor (SGLT2i) treatment reversed the MEF2C GRN effects of DKD. The MEF2C, MEF2A, and TRPS1 TF network carefully balances the fate of the EC-GC in DKD. When the TF network is "on" or over-expressed in DKD, EC-GCs may progress to a prEC state, while TF suppression leads to cell death. SGLT2i therapy may restore the balance of MEF2C activity.

cell biology↗

bioRxiv2024

FUSION: A web-based application for in-depth exploration of multi-omics data with brightfield histology

Spatial -OMICS technologies facilitate the interrogation of molecular profiles in the context of the underlying histopathology and tissue microenvironment. Paired analysis of histopathology and molecular data can provide pathologists with otherwise unobtainable insights into biological mechanisms. To connect the disparate molecular and histopathologic features into a single workspace, we developed FUSION (Functional Unit State IdentificatiON in WSIs [Whole Slide Images]), a web-based tool that provides users with a broad array of visualization and analytical tools including deep learning-based algorithms for in-depth interrogation of spatial -OMICS datasets and their associated high-resolution histology images. FUSION enables end-to-end analysis of functional tissue units (FTUs), automatically aggregating underlying molecular data to provide a histopathology-based medium for analyzing healthy and altered cell states and driving new discoveries using "pathomic" features. We demonstrate FUSION using 10x Visium spatial transcriptomics (ST) data from both formalin-fixed paraffin embedded (FFPE) and frozen prepared datasets consisting of healthy and diseased tissue. Through several use-cases, we demonstrate how users can identify spatial linkages between quantitative pathomics, qualitative image characteristics, and spatial --omics

pathology↗

bioRxiv2024

Ontology-based modeling, integration, and analysis of heterogeneous clinical, pathological, and molecular kidney data for precision medicine

Many resources are now generating, processing, storing, or providing kidney-related molecular, pathological, and clinical data. Reference ontologies offer an opportunity to support knowledge and data organization and integration. The Kidney Precision Medicine Project (KPMP) team contributed to the representation and addition of 329 kidney phenotype terms to the Human Phenotype Ontology (HPO) and identified many subcategories of acute kidney injury (AKI) or chronic kidney disease (CKD). The Kidney Tissue Atlas Ontology (KTAO) imports and integrates kidney-related terms from existing ontologies (e.g., HPO, CL, and Uberon) and represents 259 kidney-related biomarkers. We also developed a precision medicine metadata ontology (PMMO) to integrate 50 variables from KPMP and CellxGene resources and applied PMMO for integrative analysis. The gene expression profiles of kidney gene biomarkers were specifically analyzed in healthy controls or AKI/CKD disease states. This work demonstrates how ontology-based approaches support multi-domain data and knowledge organization and integration to advance precision medicine.

bioinformatics↗

bioRxiv2024

ComPRePS: An Automated Cloud-based Image Analysis tool to democratize AI in Digital Pathology

Digital pathology using whole slide imaging (WSI) and artificial intelligence (AI) has the potential to transform diagnostic workflows, but adoption remains limited by technical complexity and scalability. We developed the Computational Renal Pathology Suite (ComPRePS), a scalable cloud-based platform that automates WSI ingestion, compartmental segmentation, feature extraction, and AI-assisted interpretation through an integrated high-performance architecture. ComPRePS was evaluated in two use cases. First, using 213 procurement biopsies, we compared conventional assessments with automated AI analyses and a hybrid AI-assisted expert workflow. ComPRePS AI-assisted methods achieved higher precision and significantly improved interobserver agreement for key lesions, including global glomerulosclerosis, interstitial fibrosis and tubular atrophy, and arterial intimal thickening. Second, ComPRePS enabled high-throughput quantitative profiling of glomerular and tubular features across minimal change disease, diabetic nephropathy, and amyloid nephropathy revealing disease-specific phenotypic patterns inaccessible to manual evaluation. Overall, ComPRePS improves reproducibility, interpretability, and objectivity in renal pathology, bridging computation with clinical practice.

pathology↗

bioRxiv2023

Correlating Deep Learning-Based Automated Reference Kidney Histomorphometry with Patient Demographics and Creatinine

BackgroundReference histomorphometric data of healthy human kidneys are largely lacking due to laborious quantitation requirements. Correlating histomorphometric features with clinical parameters through machine learning approaches can provide valuable information about natural population variance. To this end, we leveraged deep learning, computational image analysis, and feature analysis to investigate the relationship of histomorphometry with patient age, sex, and serum creatinine (SCr) in a multinational set of reference kidney tissue sections. MethodsA panoptic segmentation neural network was developed and used to segment viable and sclerotic glomeruli, cortical and medullary interstitia, tubules, and arteries/arterioles in the digitized images of 79 periodic acid-Schiff-stained human nephrectomy sections showing minimal pathologic changes. Simple morphometrics (e.g., area, radius, density) were quantified from the segmented classes. Regression analysis aided in determining the relationship of histomorphometric parameters with age, sex, and SCr. ResultsOur deep-learning model achieved high segmentation performance for all test compartments. The size and density of nephrons and arteries/arterioles varied significantly among healthy humans, with potentially large differences between geographically diverse patients. Nephron size was significantly dependent on SCr. Slight, albeit significant, differences in renal vasculature were observed between sexes. Glomerulosclerosis percentage increased, and cortical density of arteries/arterioles decreased, as a function of age. ConclusionsUsing deep learning, we automated precise measurements of kidney histomorphometric features. In the reference kidney tissue, several histomorphometric features demonstrated significant correlation to patient demographics and SCr. Deep learning tools can increase the efficiency and rigor of histomorphometric analysis. Significance statementAlthough the importance of kidney morphometry is well explored in disease contexts, the definition of variance in reference tissue is not. Advancements in digital and computational pathology have rendered quantitative analysis of unprecedented tissue volumes via the single press of a button. The authors leverage the unique benefits of panoptic segmentation to perform the largest ever quantitation of reference kidney morphometry. Regression analysis identified several kidney morphometric features that varied significantly with patient age and sex, and the results suggested that the set size of nephrons might depend more intricately on creatinine than previously thought.

cell biology↗

bioRxiv2023

APOL1 kidney risk variants in glomerular diseases modeled in transgenic mice

APOL1 high-risk variants partially explain the high kidney disease prevalence among African ancestry individuals. Many mechanisms have been reported in cell culture models, but few have been demonstrated in mouse models. Here we characterize two models: (1) HIV- associated nephropathy (HIVAN) Tg26 mice crossed with bacterial artificial chromosome (BAC)/APOL1 transgenic mice and (2) interferon-{psi} administered to BAC/APOL1 mice. Both models showed exacerbated glomerular disease in APOL1-G1 compared to APOL1-G0 mice. HIVAN model glomerular bulk RNA-seq identified synergistic podocyte-damaging pathways activated by the APOL1-G1 allele and by HIV transgenes. Single-nuclear RNA-seq revealed podocyte-specific patterns of differentially-expressed genes as a function of APOL1 alleles. Eukaryotic Initiation factor-2 pathway was the most activated pathway in the interferon-{psi} model and the most deactivated pathway in the HIVAN model. HIVAN mouse model podocyte single-nuclear RNA-seq data showed similarity to human focal segmental glomerulosclerosis (FSGS) glomerular bulk RNA-seq data. Furthermore, single-nuclear RNA-seq data from interferon-{psi} mouse model podocytes (in vivo) showed similarity to human FSGS single-cell RNA- seq data from urine podocytes (ex vivo) and from human podocyte cell lines (in vitro) using bulk RNA-seq. These data highlight differences in the transcriptional effects of the APOL1-G1 risk variant in a model specific manner. Shared differentially expressed genes in podocytes in both mouse models suggest possible novel glomerular damage markers in APOL1 variant-induced diseases. Transcription factor Zbtb16 was downregulated in podocytes and endothelial cells in both models, possibly contributing to glucocorticoid-resistance. In summary, these findings in two mouse models suggest both shared and distinct therapeutic opportunities for APOL1 glomerulopathies. Significance statementCoding variants in APOL1, encoding apolipoprotein L1, contribute to kidney disease in individuals with African ancestry. The mechanisms for glomerular injury remain incompletely understood. We studied two transgenic mouse models, HIV-associated nephropathy and interferon-{psi} administration. Using glomerular and single-nuclear RNA sequencing, we identified genes differentially expressed among mice with kidney risk alleles (G1) and the common variant (G0). Both models exhibited up-regulation of genes that indicated podocyte damage with risk alleles compared to the common variant. One gene down-regulated in both models was Zbtb16, encoding a transcription factor, that may contribute to glucocorticoid-resistance. Overall, the findings suggest both shared and distinct alterations in the two disease models.

pathology↗

bioRxiv2022

PKR activation-induced mitochondrial dysfunction in HIV-transgenic mice with nephropathy

HIV disease remains prevalent in the USA and chronic kidney disease remains a major cause of morbidity in HIV-1-positive patients. Host double-stranded RNA (dsRNA)-activated protein kinase (PKR) is a sensor for viral dsRNA, including HIV-1. We show that PKR inhibition by compound C16 ameliorates the HIV-associated nephropathy (HIVAN) kidney phenotype in the Tg26 transgenic mouse model, with reversal of mitochondrial dysfunction. Combined analysis of single-nucleus RNA-seq and bulk RNA-seq data revealed that oxidative phosphorylation was one of the most downregulated pathways and identified signal transducer and activator of transcription (STAT3) as a potential mediating factor. We identified in Tg26 mice a novel proximal tubular cell cluster enriched in mitochondrial transcripts. Podocytes showed high levels of HIV-1 gene expression and dysregulation of cytoskeleton-related genes; and these cells dedifferentiated. In injured proximal tubules, cell-cell interaction analysis indicated activation of the profibrogenic PKR-STAT3-platelet derived growth factor (PDGF)-D pathway. These findings suggest that PKR inhibition and mitochondrial rescue are potential novel therapeutic approaches for HIVAN. Translational StatementThis work identified mitochondrial dysfunction in transgenic mice manifesting HIV-associated nephropathy mice kidney, using combination of single-nuclear and bulk RNA-seq analysis. Kidney damage was ameliorated by the PKR inhibitor C16, and mitochondrial rescue was shown by transcriptomic profiling and functional assay. These findings suggest that PKR inhibition and mitochondrial rescue are potential therapeutic approaches for HIV-associated nephropathy.

pathology↗

bioRxiv2021

NAD metabolism modulates mitochondrial function and inflammation and prevents progression of diabetic kidney disease

Diabetes mellitus is the leading cause of cardiovascular and renal disease in the United States. In spite of the beneficial interventions available for patients with diabetes, there remains a need for additional therapeutic targets and therapies in diabetic kidney disease (DKD). Inflammation and oxidative stress are increasingly recognized as important causes of renal diseases. Inflammation is closely associated with mitochondrial damage. The molecular connection between inflammation and mitochondrial metabolism remains to be elucidated. Recently, nicotinamide adenine nucleotide (NAD+) metabolism has been found to regulate immune function and inflammation. In the present studies we tested the hypothesis that enhancing NAD metabolism could prevent inflammation in and progression of DKD. We found that treatment of db/db mice with type 2 diabetes with nicotinamide riboside (NR) prevented several manifestations of kidney dysfunction (i.e., albuminuria, increased urinary kidney injury marker-1 (KIM1) excretion and pathologic changes). These effects were associated with decreased inflammation, at least in part via inhibiting the activation of the cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) signaling pathway. An antagonist of the serum stimulator of interferon genes (STING) and whole-body STING deletion in diabetic mice showed similar renoprotection. Further analysis found that NR increased SIRT3 activity and improved mitochondrial function, which led to decreased mitochondrial DNA damage, a trigger for mitochondrial DNA leakage which activates the cGAS-STING pathway. Overall, these data show that NR supplementation boosted NAD metabolism to augment mitochondrial function, reducing inflammation and thereby preventing progression of diabetic kidney disease.

pathology↗

bioRxiv2021

Structural Knowledge Transfer of Panoptic Kidney Segmentation to Other Stains, Organs, and Species

BackgroundPanoptic segmentation networks are a newer class of image segmentation algorithms that are constrained to understand the difference between instance-type objects (objects that are discrete countable entities, such as renal tubules) and group-type objects (uncountable, amorphous regions of texture such as renal interstitium). This class of deep networks has unique advantages for biological datasets, particularly in computational pathology. MethodsWe collected 126 periodic acid Schiff whole slide images of native diabetic nephropathy, lupus nephritis, and transplant surveillance kidney biopsies, and fully annotated them for the following micro-compartments: interstitium, glomeruli, globally sclerotic glomeruli, tubules, and arterial tree (arteries/arterioles). Using this data, we trained a panoptic feature pyramid network. We compared performance of the network against a renal pathologists annotations, and the methods transferability to other computational pathology domain tasks was investigated. ResultsThe panoptic feature pyramid networks showed high performance as compared to renal pathologist for all of the annotated classes in a testing set of transplant kidney biopsies. The network was not only able to generalize its object understanding across different stains and species of kidney data, but also across several organ types. ConclusionsPanoptic networks have unique advantages for computational pathology; namely, these networks internally model structural morphology, which aids bootstrapping of annotations for new computational pathology tasks.

pathology↗

bioRxiv2021

Generative modeling of histology tissue reduces human annotation effort for segmentation model development.

Segmentation of histology tissue whole side images is an important step for tissue analysis. Given enough annotated training data modern neural networks are capable accurate reproducible segmentation, however, the annotation of training datasets is time consuming. Techniques such as human in the loop annotation attempt to reduce this annotation burden, but still require a large amount of initial annotation. Semi-supervised learning, a technique which leverages both labeled and unlabeled data to learn features has shown promise for easing the burden of annotation. Towards this goal, we employ a recently published semi-supervised method: datasetGAN for the segmentation of glomeruli from renal biopsy images. We compare the performance of models trained using datasetGAN and traditional annotation and show that datasetGAN significantly reduces the amount of annotation required to develop a highly performing segmentation model. We also explore the usefulness of using datasetGAN for transfer learning and find that this greatly enhances the performance when a limited number of whole slide images are used for training.

pathology↗

bioRxiv2021

Endothelial Cell-Specific Molecule-1 Inhibits Albuminuria in Diabetic Mice

Diabetic kidney disease (DKD) is the most common cause of kidney failure in the world, and novel predictive biomarkers and molecular mechanisms of disease are needed. Endothelial cell-specific molecule-1 (Esm-1) is a secreted proteoglycan that attenuates inflammation. We previously identified that a glomerular deficiency of Esm-1 associates with more pronounced albuminuria and glomerular inflammation in DKD-susceptible relative to DKD-resistant mice, but its contribution to DKD remains unexplored. In this study, we show that lower circulating Esm-1 predicts progressive stages of albuminuria in patients with diabetes. In DKD-susceptible mice, Esm-1 inversely correlates with albuminuria and glomerular leukocyte infiltration. Using hydrodynamic tail-vein injection, we show that over-expression of either mouse or human Esm-1 reduces diabetes-induced albuminuria relative to saline-injected controls independent of leukocyte infiltration. Using a complementary approach, we find that constitutive deletion of Esm-1 in DKD-resistant mice increases the degree of diabetes-induced albuminuria versus wild-type controls. Mechanistically, over-expression of Esm-1 attenuates diabetes-induced podocyte injury. By glomerular RNAseq, we identify that Esm-1 attenuates diabetes-induced up-regulation of interferon-stimulated genes, and Esm-1 inhibits expression of kidney disease-promoting and interferon-related genes, including Ackr2 and Cxcl11. In conclusion, we demonstrate that Esm-1 protects against diabetes-induced albuminuria, and podocytopathy, possibly through select interferon signaling.

immunology↗

bioRxiv2021

A tool for federated training of segmentation models on whole slide images.

The largest bottleneck to the development of convolutional neural network (CNN) models in the computational pathology domain is the collection and curation of diverse training datasets. Training CNNs requires large cohorts of image data, and model generalizability is dependent on training data heterogeneity. Including data from multiple centers enhances the generalizability of CNN based models, but this is hindered by the logistical challenges of sharing medical data. In this paper we explore the feasibility of training our recently developed cloud-based segmentation tool (Histo-Cloud) using federated learning. We show that a federated trained model to segment interstitial fibrosis and tubular atrophy (IFTA) using datasets from three institutions is comparable to a model trained by pooling the data on one server when tested on a fourth (holdout) institutions data. Further, training a model to segment glomeruli for a federated dataset (split by staining) demonstrates similar performance.

pathology↗

bioRxiv2021

A user-friendly tool for cloud-based whole slide image segmentation, with examples from renal histopathology

BackgroundImage-based machine learning tools hold great promise for clinical applications in nephropathology and kidney research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often face prohibitive challenges in using these tools to their full potential, including the lack of technical expertise, suboptimal user interface, and limited computation power. MethodsWe have developed Histo-Cloud, a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis. ResultsBy segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in murine models of aging, diabetic nephropathy, and HIV associated nephropathy. ConclusionThe ability to access this tool over the internet will facilitate widespread use by computational non-experts. Histo-Cloud is open source and adaptable for segmentation of any histological structure regardless of stain. Histo-Cloud will greatly accelerate and facilitate the generation of datasets for machine learning in the analysis of kidney histology, empowering computationally novice end-users to conduct deep feature analysis of tissue slides.

pathology↗

bioRxiv2021

PodoCount: A robust, fully automated whole-slide podocyte quantification tool

BackgroundPodocyte depletion is an established indicator of glomerular injury and predicts clinical outcomes. The semi-quantitative nature of existing podocyte estimation methods or podometrics hinders incorporation of such analysis into experimental and clinical pathologic workflows. Computational image analysis offers a robust approach to automate podometrics through objective quantification of cell and tissue structure. Toward this goal, we developed PodoCount, a computational tool for quantitative analysis of podocytes, and validated the generalizability of the tool across a diverse dataset. MethodsPodocyte nuclei and glomerular boundaries were labeled in murine whole kidney sections, n = 135, from six disease models and human kidney biopsies, n = 45, from diabetic nephropathy (DN) patients. Digital whole slide images (WSIs) of tissues were then acquired. Classical image analysis was applied to obtain podocyte nuclear and glomerular morphometrics. Statistically significant morphometric features, which correlated with each murine disease, were identified. Engineered features were also assessed for their ability to predict outcomes in human DN. PodoCount has been disbursed for other researchers as an open-source, cloud-based computational tool. ResultsPodoCount offers highly accurate quantification of podocytes. Engineered podometric features were benchmarked against routine glomerular histopathology and were found to be significant predictors of disease diagnosis, proteinuria level, and clinical outcomes. ConclusionsPodoCount offers high quantification performance in diverse murine disease models as well as in human DN. Resultant podometric features offers significant correlation with associated metadata as well as outcome. Our cloud-based end-user tool will provide a standardized approach for podometric analysis from gigapixel size WSIs in basic research and clinical practice.

pathology↗

bioRxiv2019

ERR agonism reverses mitochondrial dysfunction and inflammation in aging

BackgroundA gradual decline in renal function occurs even in healthy aging individuals. In addition to aging per se, concurrent metabolic syndrome and hypertension, which are common in the aging population, can induce mitochondrial dysfunction and inflammation, which collectively contribute to age-related kidney dysfunction and disease. Here we studied the role of the nuclear hormone receptors, the estrogen-related receptors (ERRs) in regulation of age-related mitochondrial dysfunction and inflammation. ERRs were decreased in aging human and mouse kidneys and were preserved in aging mice with lifelong caloric restriction (CR). MethodsA pan-ERR agonist was used to treat 21-month-old mice for 8-weeks. In addition, 21-month-old mice were treated with a STING inhibitor for 3 weeks. ResultsRemarkably, only an 8-week treatment with a pan-ERR agonist reversed the age-related increases in albuminuria, podocyte loss, mitochondrial dysfunction and inflammatory cytokines, including the cGAS-STING and STAT3 signaling pathways. A 3-week treatment of 21-month-old mice with a STING inhibitor reversed the increases in inflammatory cytokines and the senescence marker p21 but also unexpectedly reversed the age-related decreases in PGC-1, ERR, mitochondrial complexes and MCAD expression. ConclusionsOur studies identified ERRs as important modulators of age-related mitochondrial dysfunction and inflammation. These findings highlight novel druggable pathways that can be further evaluated to prevent progression of age-related kidney disease. Significance StatementThere is an increasing need for prevention and treatment strategies for age-related kidney disease. The hallmarks of aging kidneys are decreased mitochondrial function and increased inflammation. The expression of the nuclear hormone receptors estrogen-related receptors (ERRs) are decreased in aging human and mouse kidneys. This paper investigates the role of ERRs in the aging kidney. Treatment of aging mice with a pan-ERR agonist reversed the age-related increases in albuminuria and podocyte loss, mitochondrial dysfunction and inflammatory cytokines, including the cGAS-STING signaling pathways. Treatment of aging mice with a STING inhibitor decreased inflammation and increased mitochondrial gene expression. These findings identify ERRs as important modulators of age-related mitochondrial dysfunction and inflammation.

pathology↗