ArticleTranslational vision science & technology2020
Software for Quantifying and Batch Processing Images of Brn3a and RBPMS Immunolabelled Retinal Ganglion Cells in Retinal Wholemounts.
Article in Translational vision science & technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- AxoMetric platform facilitates rapid and unbiased automated quantification of axon regeneration in tissue sections.Cell reports methods · 2026Article
- Optimizing retinal ganglion cell nuclear staining for automated cell counting.Experimental eye research · 2024Article
- Pan-retinal ganglion cell markers in mice, rats, and rhesus macaques.Zoological research · 2023Article
- Automatic counting of retinal ganglion cells in the entire mouse retina based on improved YOLOv5.Zoological research · 2022Article
- Effect of Strain on Rodent Glaucoma Models: Magnetic Bead Injection Versus Hydrogel Injection Versus Circumlimbal Suture.Translational vision science & technology · 2022Article
- Article
- AxonDeep: Automated Optic Nerve Axon Segmentation in Mice With Deep Learning.Translational vision science & technology · 2021Article
- Quantification and image-derived phenotyping of retinal ganglion cell nuclei in the nee mouse model of congenital glaucoma.Experimental eye research · 2021Article
- Uptake and Distribution of Administered Bone Marrow Mesenchymal Stem Cell Extracellular Vesicles in Retina.Cells · 2021Article
- A novel retinal ganglion cell quantification tool based on deep learning.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: 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. 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.
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