Podocyte injury plays a crucial role in the progression of diabetic kidney disease (DKD). Injured podocytes demonstrate variations in nuclear shape and chromatin distribution. These morphometric changes have not yet been quantified in podocytes. Furthermore, the molecular mechanisms underlying these variations are poorly understood. Recent advances in omics have shed new lights into the biological mechanisms behind podocyte injury. However, there currently exists no study analyzing the biological mechanisms underlying podocyte morphometric variations during DKD. First, to study the importance of nuclear morphometrics, we performed morphometric quantification of podocyte nuclei from whole slide images of renal tissue sections obtained from murine models of DKD. Our results indicated that podocyte nuclear textural features demonstrate statistically significant difference in diabetic podocytes when compared to control. Additionally, the morphometric features demonstrated the existence of multiple subpopulations of podocytes suggesting a potential cause for their varying response to injury. Second, to study the underlying pathophysiology, we employed single cell RNA sequencing data from the murine models. Our results again indicated five subpopulations of podocytes in control and diabetic mouse models, validating the morphometrics-based results. Additionally, gene set enrichment analysis revealed epithelial to mesenchymal transition and apoptotic pathways in a subgroup of podocytes exclusive to diabetic mice, suggesting the molecular mechanism behind injury. Lastly, our results highlighted two distinct lineages of podocytes in control and diabetic cases suggesting a phenotypical change in podocytes during DKD. These results suggest that textural variations in podocyte nuclei may be key to understanding the pathophysiology behind podocyte injury.
In diabetic kidney disease (DKD), podocyte depletion, and the subsequent migration of parietal epithelial cells (PECs) to the tuft, is a precursor to progressive glomerular damage, but the limitations of brightfield microscopy currently preclude direct pathological quantitation of these cells. Here we present an automated approach to podocyte and PEC detection developed using kidney sections from mouse model emulating DKD, stained first for Wilms’ Tumor 1 (WT1) (podocyte and PEC marker) by immunofluorescence, then post-stained with periodic acid-Schiff (PAS). A generative adversarial network (GAN)-based pipeline was used to translate these PAS-stained sections into WT1-labeled IF images, enabling in silico label-free podocyte and PEC identification in brightfield images. Our method detected WT1-positive cells with high sensitivity/specificity (0.87/0.92). Additionally, our algorithm performed with a higher Cohen’s kappa (0.85) than the average manual identification by three renal pathologists (0.78). We propose that this pipeline will enable accurate detection of WT1- positive cells in research applications.
The primary purpose of the kidney, specifically the glomerulus, is filtration. Filtration is accomplished through the glomerular filtration barrier, which consists of the fenestrated endothelium, glomerular basement membrane, and specialized epithelial cells called podocytes. In pathologic states, such as Diabetes Mellitus (DM) and diabetic kidney disease (DKD), variable glomerular conditions result in podocyte injury and depletion, followed by progressive glomerular injury and DKD progression. In this work we quantified glomerulus and podocyte structural changes in histopathology image data derived from a murine model of DM. Using a variety of image processing techniques, we studied changes in podocyte morphology and intra-glomerular distribution across healthy, mild DM, and DM glomeruli. Our feature analysis provided feature trends which we believe are reflective of DKD pathology; while glomerular area peaked in mild DM, average podocyte number and distance from the urinary pole continued to increase throughout DM. Ultimately, this study aims to augment the set of quantifiable image biomarkers used for evaluation of DKD progression in digital pathology, as well as underscore the importance of engineering biologically inspired image features.
Chronic kidney disease (CKD) is associated with gradual bone loss that occurs from the failure of the kidneys to regulate bone mineralization. Degradation of bone structure can be quantified with the usage of Micro-CT. The current methods of quantitative imaging typically use a single region of interest (ROI) that segments the whole trabecular region and obtain bone parameters, which usually are not homogenous across such a large ROI. Here we introduce a novel method of quantifying bone parameters that can be used to determine overall bone health. This method analyzes sequential regions on the trabecular bone with multiple small ROIs and evaluates the gradients of bone parameters across these ROIs. Two C57Bl/6J mice femur groups were prepared: a control and CKD groups. All femurs were scanned with a Micro-CT system using tube voltage of 60 kV and current of 0.667 mA. Femur volumes were reconstructed with the Feldkamp-Davis-Kress algorithm and were imported into MicroView to perform bone analysis. Six different sequential ROIs were selected at different distances from the growth plate (0.5mm increments). The gradients of bone parameters along the ROI distance for the control and CKD group were compared. Significant differences were found between two groups in the gradients of bone volume density (P = 0.0002), connective density (P = 0.0003), trabecular spacing (P = 0.001), and trabecular number (P = 0.01). As a result, our method identified a sharp change in several parameters representing a novel and biologically significant strategy.
The adoption of deep learning techniques in medical applications has thus far been limited by the availability of the large labeled datasets required to robustly train neural networks, as well as difficulty interpreting these networks. However, recent techniques for unsupervised training of neural networks promise to address these issues, leveraging only structure to model input data. We propose the use of a variational autoencoder (VAE) which utilizes data from an animal model to augment the training set and non-linear dimensionality reduction to map this data to human sets. This architecture utilizes variational inference, performed on latent parameters, to statistically model the probability distribution of training data in a latent feature space. We show the feasibility of VAEs, using images of mouse and human renal glomeruli from various pathological stages of diabetic nephropathy (DN), to model the progression of structural changes which occur in DN. When plotted in a 2-dimentional latent space, human and mouse glomeruli, show separation with some overlap, suggesting that the data is continuous, and can be statistically correlated. When DN stage is plotted in this latent space, trends in disease pathology are visualized.
In diabetic nephropathy (DN), hyperglycemia drives a progressive thickening of and damage to the glomerular filtration surfaces, as well as mesangial expansion and a constriction of capillary lumens. This leads at first to high blood pressure, increased glomerular filtration and micro-proteinuria, and later (if untreated) to severe proteinuria and end-stage renal disease (ESRD). Though, it is well known that DN is accompanied by marked histopathological changes, the assessment of these structural changes is to a degree subjective and hence varies between pathologists. In this work, we make a first study of glomerular changes in DN from a graph-theoretical and distance-based standpoint, using minimal spanning trees (MSTs) and distance matrices to generate statistical distributions that can potentially provide a “fingerprint” of DN. We apply these tools to detect notable differences between normal and DN glomeruli in both human disease and in a streptozotocin-induced (STZ) mouse model. We also introduce an automated pipeline for rapidly generating MSTs and evaluating their properties with respect to DN, and make a first pass at three-dimensional MST structures. We envision these approaches may provide a better understanding not only of the processes underway in DN progression, but of key differences between actual human disease and current experimental models.
The glomerulus is the blood filtering unit of the kidney. Each human kidney contains ∼1 million glomeruli. Several renal conditions originate from structural damage to glomerular microcompartments, such as proteinuria, the excessive loss of blood proteins into urine. The gold standard for evaluating structural damage in renal pathology is histopathological and immunofluorescence examination of needle biopsies under a light microscope. This method is limited by qualitative or semiquantitative manual scoring approaches to the evaluation of glomerular structural features. Computational quantification of equivalent features promises to improve the precision of glomerular structural analysis. One large obstacle to the computational quantification of renal tissue is the identification of complex glomerular boundaries automatically. To mitigate this issue, we developed a computational pipeline capable of extracting and exactly defining glomerular boundaries. Our method, composed of Gabor filtering, Gaussian blurring, statistical F-testing, and distance transform, is able to accurately identify glomerular boundaries with mean sensitivity/specificity of 0.88/0.96 and accuracy of 0.92, on n=1000 glomeruli images stained with standard renal histological stains. Our method will simplify computational partitioning of glomerular microcompartments hidden within dense textural boundaries. Automatic quantification of glomeruli will streamline structural analysis in clinic and can pioneer real-time diagnoses and interventions for renal care.
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