Evidence map›Paper›PMID 32821525›Full record

ArticleTranslational vision science & technology2020

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

Chelsea Guymer, Lloyd Damp, Glyn Chidlow, John Wood, Yi Fan Tang, Robert Casson

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Chelsea GuymerOphthalmic Research Laboratories, Discipline of Ophthalmology and Visual Sciences, University of Adelaide, Adelaide Health and Medical Sciences Building, North Terrace, Adelaide, Australia.
Lloyd DampSouthern Launch, Adelaide, Australia.
Glyn ChidlowOphthalmic Research Laboratories, Discipline of Ophthalmology and Visual Sciences, University of Adelaide, Adelaide Health and Medical Sciences Building, North Terrace, Adelaide, Australia.
John WoodOphthalmic Research Laboratories, Discipline of Ophthalmology and Visual Sciences, University of Adelaide, Adelaide Health and Medical Sciences Building, North Terrace, Adelaide, Australia.
Yi Fan TangDepartment of Ophthalmology, Royal Adelaide Hospital, Adelaide, Australia.
Robert CassonOphthalmic Research Laboratories, Discipline of Ophthalmology and Visual Sciences, University of Adelaide, Adelaide Health and Medical Sciences Building, North Terrace, Adelaide, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

GlaucomaRetinal Ganglion CellsAnimalsRatsRetinaRNA-Binding ProteinsSoftwareRNA-Binding Proteinsbatch processingquantificationretinal ganglion cellretinal wholemountsoftware

Identifiers

PMID32821525
PMCPMC7409150

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.