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

Abstract

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 semiautomated 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. Methods: 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. Results: 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.

A novel retinal ganglion cell quantification tool based on deep learning

“…This is in stark contrast to a nuclear RGC staining, which has the advantage of resulting in a more consistent labelling in terms of size as well as circularity and brightness, rendering it more amenable for automatic quantification. Indeed, the majority of research articles studying RBPMS-immunopositive (RBPMS+) RGCs are reporting manual counts 11 , 14 – 16 , still resulting in a lack of a fully automated pipeline that does not require entering user-defined variables, although attempts are being made with classic image analysis techniques 8 , 17 . Fully automated RBPMS labelling requires to go beyond conventional automatic counting methods and look towards artificial intelligence.…”

Uptake and Distribution of Administered Bone Marrow Mesenchymal Stem Cell Extracellular Vesicles in Retina

“…In retinal flat mounts ( Figure 1 , Figure 2 and Figure 3 ), we examined colocalization of the intravitreally administered fluorescent EVs in RGCs with anti-brain-specific homeobox/POU domain protein 3A (Brn3a) to stain nuclei [ 49 ], and anti-β-tubulinIII (BT3) for cytoplasm. Microglial cells were stained with anti-ionized calcium-binding adaptor molecule (IBA-1) [ 50 ], and astrocytes and Muller cells with anti-vimentin [ 51 ].…”

AxonDeep: Automated Optic Nerve Axon Segmentation in Mice with Deep Learning

“…59 Quantifications of axon number is a gold-standard for measuring disease severity, 17,[60][61][62][63] but the labor-intensive nature of manual axon counting often results in studies instead using qualitative grading scales. 19,64,65 As with all of the tools that the field has put forward to count RGC somas 26,27,39,[66][67][68][69][70] or axons 26,27,34,[71][72][73] in various animal models, the automated counts performed by AxonDeep greatly reduce the labor of manual counts and eliminate the possibility of user-to-user, lab-to-lab, or model-to-model variability inherent to subjective grading scales. An advantage of AxonDeep is that it performs axon segmentations as well as counts.…”