Nephron morphometry in mice and rats using tomographic microscopy.

The aim was to quantify the glomerular capillary surface area, the segmental tubular radius, length, and area of single nephrons in mouse and rat kidneys. Multiple 2.5-µm-thick serial Epon sections were obtained from three mouse and three rat kidneys for three-dimensional reconstruction of the nephron tubules. Micrographs were aligned for each kidney, and 359 nephrons were traced and their segments localized. Thirty mouse and thirty rat nephrons were selected for further investigation. The luminal radius of each segment was determined by two methods. The luminal surface area was estimated from the radius and length of each segment. High-resolution micrographs were recorded for five rat glomeruli, and the capillary surface area determined. The capillary volume and surface area were corrected for glomerular shrinkage. A positive correlation was found between glomerular capillary area and proximal tubule area. The thickest part of the nephron, i.e., the proximal tubule, was followed by the thinnest part of the nephron, i.e., the descending thin limb, and the diameters of the seven identified nephron segments share the same rank in the two species. The radius and length measurements from mouse and rat nephrons generally share the same pattern; rat tubular radius-to-mouse tubular radius ratio ≈ 1.47, and rat tubular length-to-mouse tubular length ratio ≈ 2.29, suggesting relatively longer tubules in the rat. The detailed tables of mouse and rat glomerular capillary area and segmental radius, length, and area values may be used to enhance understanding of the associated physiology, including existing steady-state models of the urine-concentrating mechanism.

Towards Automated Three-Dimensional Tracking of Nephrons through Stacked Histological Image Sets.

An automated approach for tracking individual nephrons through three-dimensional histological image sets of mouse and rat kidneys is presented. In a previous study, the available images were tracked manually through the image sets in order to explore renal microarchitecture. The purpose of the current research is to reduce the time and effort required to manually trace nephrons by creating an automated, intelligent system as a standard tool for such datasets. The algorithm is robust enough to isolate closely packed nephrons and track their convoluted paths despite a number of nonideal, interfering conditions such as local image distortions, artefacts, and interstitial tissue interference. The system comprises image preprocessing, feature extraction, and a custom graph-based tracking algorithm, which is validated by a rule base and a machine learning algorithm. A study of a selection of automatically tracked nephrons, when compared with manual tracking, yields a 95% tracking accuracy for structures in the cortex, while those in the medulla have lower accuracy due to narrower diameter and higher density. Limited manual intervention is introduced to improve tracking, enabling full nephron paths to be obtained with an average of 17 manual corrections per mouse nephron and 58 manual corrections per rat nephron.