Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/130336
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Type: | Journal article |
Title: | Software for quantifying and batch processing images of brn3a and rbpms immunolabelled retinal ganglion cells in retinal wholemounts |
Author: | Guymer, C. Damp, L. Chidlow, G. Wood, J. Tang, Y.F. Casson, R. |
Citation: | Translational Vision Science and Technology, 2020; 9(6):28-1-28-13 |
Publisher: | Association for Research in Vision and Ophthalmology |
Issue Date: | 2020 |
ISSN: | 2164-2591 2164-2591 |
Statement of Responsibility: | Chelsea Guymer, LloydDamp, Glyn Chidlow, JohnWood, Yi Fan Tang, and Robert Casson |
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 bymanual or semiautomated counting of RGCs. Here, we describe an automated software that accurately andefficientlycounts immunolabeledRGCswith theability tobatchprocess 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 proteinwith multiple splicing (RBPMS). For feasibility of manual counting, imageswere sampled frompredefined 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 RGCwholemount 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. |
Keywords: | Retinal Ganglion Cells Retina Animals Rats Glaucoma RNA-Binding Proteins Software |
Description: | Published: May 27, 2020 |
Rights: | Copyright 2020 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. |
DOI: | 10.1167/TVST.9.6.28 |
Grant ID: | http://purl.org/au-research/grants/nhmrc/1102568 |
Published version: | http://dx.doi.org/10.1167/tvst.9.6.28 |
Appears in Collections: | Aurora harvest 4 Opthalmology & Visual Sciences publications |
Files in This Item:
File | Description | Size | Format | |
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hdl_130336.pdf | Published version | 4.19 MB | Adobe PDF | View/Open |
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