Software for Quantifying and Batch Processing Images of Brn3a and RBPMS Immunolabelled Retinal Ganglion Cells in Retinal Wholemounts

This automated cell counting software dramatically accelerates data acquisition while maintaining accurate RGC counts across different immunolabels, methods of injury, and spatial heterogeneity of RGC loss. This software likely has potential for wider application. Translational relevance: This study provides a valuable tool for preclinical RGC neuroprotection studies that facilitates the translation of neuroprotection to the clinic.

Retinal wholemounts from control rat eyes, and eyes subjected to either chronic ocular hypertension or N-methyl-D-aspartate (NMDA)-induced excitotoxicity, were labeled by immunohistochemistry for two different RGC-specific markers, Brn3a and RNA-binding protein with multiple splicing (RBPMS). For feasibility of manual counting, images were sampled from predefined retinal sectors, totaling 160 images for Brn3a and 144 images for RBPMS. The automated program was initially calibrated for each antibody prior to batch analysis to ensure adequate cell capture. Blinded manual RGC counts were performed by three independent observers.

The ability to accurately quantify immunohistochemically labeled retinal ganglion cells (RGCs) on wholemounts is an important histopathological determinant in experimental retinal research. Traditionally, this has been performed by manual or semi-automated counting of RGCs. Here, we describe an automated software that accurately and efficiently counts immunolabeled RGCs with the ability to batch process images and perform whole-retinal analysis to permit isodensity map generation.

The automated counts of RGCs labeled for Brn3a and RBPMS closely matched manual counts. The automated script accurately quantified both physiological and damaged retinas. Efficiency in counting labeled RGC wholemount images is accelerated 40-fold with the automated software. Whole-retinal analysis was demonstrated with integrated retinal isodensity map generation. Conclusions: This automated cell counting software dramatically accelerates data acquisition while maintaining accurate RGC counts across different immunolabels, methods of injury, and spatial heterogeneity of RGC loss. This software likely has potential for wider application. Translational relevance: This study provides a valuable tool for preclinical RGC neuroprotection studies that facilitates the translation of neuroprotection to the clinic.