Semi-automated quantification of hair cells in the mature mouse utricle.

• Quantification of hair cell density by counting a subset of hair cells (HCs) in regions of interest (ROIs) reduces the accuracy of HC counts and increases the variability compared to counting all HCs in the utricle. • Manually counting HCs from the entire utricle is time-consuming and labor-intensive. • Pou4f3 immunolabeling results in a highly specific and discrete nuclear signal that allows semi-automated quantification of HCs using an ImageJ macro. • The semi-automated method provides accurate HC counts while significantly reducing the time required to quantify HCs in the entire utricle. • It is essential to confirm HC viability using a secondary method (such as activated caspase 3/7 labeling), since dead HCs can retain both Myosin7a and Pou4f3 immunoreactivity, especially in cisplatin-treated tissue. The mouse utricle model system is the best-characterized ex vivo preparation for studies of mature mammalian hair cells (HCs). Despite the many advantages of this model system, efficient and reliable quantification of HCs from cultured utricles has been a persistent challenge with this model system. Utricular HCs are commonly quantified by counting immunolabeled HCs in regions of interest (ROIs) placed over an image of the utricle. Our data indicate that the accuracy of HC counts obtained using this method can be impacted by variability in HC density across different regions of the utricle. In addition, the commonly used HC marker myosin 7a results in a diffuse cytoplasmic stain that is not conducive to automated quantification and must be quantified manually, a labor-intensive task. Furthermore, myosin 7a immunoreactivity is retained in dead HCs, resulting in inaccurate quantification of live HCs using this marker. Here we have developed a method for semi-automated quantification of surviving HCs that combines immunoreactivity for the HC-specific transcription factor Pou4f3 with labeling of activated caspase 3/7 (AC3/7) to detect apoptotic HCs. The discrete nuclear Pou4f3 signal allowed us to utilize the binary or threshold function within ImageJ to automate HC quantification. To further streamline this process, we created an ImageJ macro that automates the process from raw image loading to a final quantified image that can be immediately evaluated for accuracy. Within this quantified image, the user can manually correct the quantification via an image overlay indicating the counted HC nuclei. Pou4f3-positive HCs that also express AC3/7 are subtracted to yield accurate counts of surviving HCs. Overall, we present a semi-automated method that is faster than manual HC quantification and identifies surviving HCs with high accuracy. [ABSTRACT FROM AUTHOR]