A NEW approach for characterizing mouse urinary pathophysiologies

The void spot assay (VSA) is a cost-effective method for evaluating and quantifying mouse urinary voiding phenotypes. The VSA has been used to differentiate voiding behaviors between experimental groups, but not as a diagnostic assay. To build toward this goal, we used the VSA to define voiding patterns of male mice with diabetic diuresis (BTBR.Cg-Lep(ob) /WiscJ mice), irritative urinary dysfunction (E. coli UTI89 urinary tract infection), and obstructive urinary dysfunction (testosterone and estradiol slow-release implants) compared to their respective controls. Many studies compare individual VSA endpoints (urine spot size, quantity, or distribution) between experimental groups. Here, we consider all endpoints collectively to establish VSA phenomes of mice with three different etiologies of voiding dysfunction. We created an approach called normalized endpoint work through (NEW) to normalize VSA outputs to control mice, and then applied principal components analysis and hierarchical clustering to 12 equally weighted, normalized, scaled, and zero-centered VSA outcomes collected from each mouse (the VSA phenome). This approach accurately classifies mice based on voiding dysfunction etiology. We used principal components analysis and hierarchical clustering to show that some aged mice (>24 m old) develop an obstructive or a diabetic diuresis VSA phenotype while others develop a unique phenotype that does not cluster with that of diabetic, infected, or obstructed mice. These findings support use of the VSA to identify specific urinary phenotypes in mice and the continued use of aged mice as they develop urinary dysfunction representative of the various etiologies of LUTS in men.

Automated summary

The void spot assay is a cost-effective method for evaluating and quantifying mouse urinary voiding phenotypes. It can differentiate voiding behaviors between different experimental groups and accurately classify mice based on voiding dysfunction etiology. The approach of using principal components analysis and hierarchical clustering can help identify specific urinary phenotypes in mice.