Determining individual glomerular proteinuria and periglomerular infiltration in a cleared murine kidney by a 3D fast-marching algorithm.

Abstract

Three-dimensional imaging has advanced basic research and clinical medicine. However, limited resolution and imperfections of real-world 3D image material often preclude algorithmic image analysis. Here, we present a methodological framework for such imaging and analysis for functional and spatial relations in experimental nephritis. First, optical tissue clearing protocols were optimized to preserve fluorescence signals for light-sheet-fluorescence-microscopy and compensated attenuation effects using adjustable 3D correction fields. Next, we adapted the Fast — Marching — Algorithm to conduct backtracking in 3D environments and developed a tool to determine local concentrations of extractable objects. As a proof-of-concept application, we used this framework to determine in a glomerulonephritis model the individual proteinuria and periglomerular immune cell infiltration for all glomeruli of half a mouse kidney. A correlation between these parameters surprisingly did not support the intuitional assumption that the most inflamed glomeruli are the most proteinuric. Instead, the spatial density of adjacent glomeruli positively correlated with the proteinuria of a given glomerulus. Since proteinuric glomeruli appear clustered, this suggests that the exact location of a kidney biopsy may affect the observed severity of glomerular damage. Thus, our algorithmic pipeline described here allows analysis of various parameters of various organs composed of functional subunits, such as the kidney, and can theoretically be adapted to processing other image modalities.