Fig. 5: Murine model glomerulus feature analysis—utility study.

Feature analysis from glomeruli segmented from renal tissue whole slide images (WSIs) from three murine models: a is an aging model and b, c are two type 2 diabetic nephropathy (DN) models (KKAy and Db/Db). In each panel, the left plot shows an unsupervised uniform manifold approximation and projection for dimension reduction (UMAP) representations of 315 engineered image features extracted from the murine glomeruli, where the glomeruli were segmented using the glomerulus model. Here each dot is a glomerulus and the red and blue colors differentiate the disease from the control. Definitions and quantification strategy of the 315 engineered image features are available in our prior work⁵. The right plot shows the highest differentially expressed feature as predicted using the Seurat software³⁷. The representative glomeruli from each murine class depicting this differentially expressed feature, and the feature value, are shown on the right for each murine model. Each dot in the UMAP and violin plots in [a–c] represents a WSI. d shows a K-nearest neighbors (KNN) classifier performance plotting the Cohen’s Kappa measure as a function of K neighbors for classifying the unsupervised UMAP features with respect to disease vs control status for the murine models. This analysis was done using tenfold cross-validation using a similar method as formalized in a previous work³⁵. Definitions of the 315 features are provided in Supp. Table 2. This study suggests that the seamless segmentation of glomeruli from large WSIs using our tool facilitates conducting deep glomerular feature analysis to study novel murine models.