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A semi-automated technique for labeling and counting of apoptosing retinal cells

BACKGROUND: Retinal ganglion cell (RGC) loss is one of the earliest and most important cellular changes in glaucoma. The DARC (Detection of Apoptosing Retinal Cells) technology enables in vivo real-time non-invasive imaging of single apoptosing retinal cells in animal models of glaucoma and Alzheime...

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Autores principales: Bizrah, Mukhtar, Dakin, Steve C, Guo, Li, Rahman, Farzana, Parnell, Miles, Normando, Eduardo, Nizari, Shereen, Davis, Benjamin, Younis, Ahmed, Cordeiro, M Francesca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4063694/
https://www.ncbi.nlm.nih.gov/pubmed/24902592
http://dx.doi.org/10.1186/1471-2105-15-169
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author Bizrah, Mukhtar
Dakin, Steve C
Guo, Li
Rahman, Farzana
Parnell, Miles
Normando, Eduardo
Nizari, Shereen
Davis, Benjamin
Younis, Ahmed
Cordeiro, M Francesca
author_facet Bizrah, Mukhtar
Dakin, Steve C
Guo, Li
Rahman, Farzana
Parnell, Miles
Normando, Eduardo
Nizari, Shereen
Davis, Benjamin
Younis, Ahmed
Cordeiro, M Francesca
author_sort Bizrah, Mukhtar
collection PubMed
description BACKGROUND: Retinal ganglion cell (RGC) loss is one of the earliest and most important cellular changes in glaucoma. The DARC (Detection of Apoptosing Retinal Cells) technology enables in vivo real-time non-invasive imaging of single apoptosing retinal cells in animal models of glaucoma and Alzheimer’s disease. To date, apoptosing RGCs imaged using DARC have been counted manually. This is time-consuming, labour-intensive, vulnerable to bias, and has considerable inter- and intra-operator variability. RESULTS: A semi-automated algorithm was developed which enabled automated identification of apoptosing RGCs labeled with fluorescent Annexin-5 on DARC images. Automated analysis included a pre-processing stage involving local-luminance and local-contrast “gain control”, a “blob analysis” step to differentiate between cells, vessels and noise, and a method to exclude non-cell structures using specific combined ‘size’ and ‘aspect’ ratio criteria. Apoptosing retinal cells were counted by 3 masked operators, generating ‘Gold-standard’ mean manual cell counts, and were also counted using the newly developed automated algorithm. Comparison between automated cell counts and the mean manual cell counts on 66 DARC images showed significant correlation between the two methods (Pearson’s correlation coefficient 0.978 (p < 0.001), R Squared = 0.956. The Intraclass correlation coefficient was 0.986 (95% CI 0.977-0.991, p < 0.001), and Cronbach’s alpha measure of consistency = 0.986, confirming excellent correlation and consistency. No significant difference (p = 0.922, 95% CI: −5.53 to 6.10) was detected between the cell counts of the two methods. CONCLUSIONS: The novel automated algorithm enabled accurate quantification of apoptosing RGCs that is highly comparable to manual counting, and appears to minimise operator-bias, whilst being both fast and reproducible. This may prove to be a valuable method of quantifying apoptosing retinal cells, with particular relevance to translation in the clinic, where a Phase I clinical trial of DARC in glaucoma patients is due to start shortly.
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spelling pubmed-40636942014-06-30 A semi-automated technique for labeling and counting of apoptosing retinal cells Bizrah, Mukhtar Dakin, Steve C Guo, Li Rahman, Farzana Parnell, Miles Normando, Eduardo Nizari, Shereen Davis, Benjamin Younis, Ahmed Cordeiro, M Francesca BMC Bioinformatics Research Article BACKGROUND: Retinal ganglion cell (RGC) loss is one of the earliest and most important cellular changes in glaucoma. The DARC (Detection of Apoptosing Retinal Cells) technology enables in vivo real-time non-invasive imaging of single apoptosing retinal cells in animal models of glaucoma and Alzheimer’s disease. To date, apoptosing RGCs imaged using DARC have been counted manually. This is time-consuming, labour-intensive, vulnerable to bias, and has considerable inter- and intra-operator variability. RESULTS: A semi-automated algorithm was developed which enabled automated identification of apoptosing RGCs labeled with fluorescent Annexin-5 on DARC images. Automated analysis included a pre-processing stage involving local-luminance and local-contrast “gain control”, a “blob analysis” step to differentiate between cells, vessels and noise, and a method to exclude non-cell structures using specific combined ‘size’ and ‘aspect’ ratio criteria. Apoptosing retinal cells were counted by 3 masked operators, generating ‘Gold-standard’ mean manual cell counts, and were also counted using the newly developed automated algorithm. Comparison between automated cell counts and the mean manual cell counts on 66 DARC images showed significant correlation between the two methods (Pearson’s correlation coefficient 0.978 (p < 0.001), R Squared = 0.956. The Intraclass correlation coefficient was 0.986 (95% CI 0.977-0.991, p < 0.001), and Cronbach’s alpha measure of consistency = 0.986, confirming excellent correlation and consistency. No significant difference (p = 0.922, 95% CI: −5.53 to 6.10) was detected between the cell counts of the two methods. CONCLUSIONS: The novel automated algorithm enabled accurate quantification of apoptosing RGCs that is highly comparable to manual counting, and appears to minimise operator-bias, whilst being both fast and reproducible. This may prove to be a valuable method of quantifying apoptosing retinal cells, with particular relevance to translation in the clinic, where a Phase I clinical trial of DARC in glaucoma patients is due to start shortly. BioMed Central 2014-06-05 /pmc/articles/PMC4063694/ /pubmed/24902592 http://dx.doi.org/10.1186/1471-2105-15-169 Text en Copyright © 2014 Bizrah et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Bizrah, Mukhtar
Dakin, Steve C
Guo, Li
Rahman, Farzana
Parnell, Miles
Normando, Eduardo
Nizari, Shereen
Davis, Benjamin
Younis, Ahmed
Cordeiro, M Francesca
A semi-automated technique for labeling and counting of apoptosing retinal cells
title A semi-automated technique for labeling and counting of apoptosing retinal cells
title_full A semi-automated technique for labeling and counting of apoptosing retinal cells
title_fullStr A semi-automated technique for labeling and counting of apoptosing retinal cells
title_full_unstemmed A semi-automated technique for labeling and counting of apoptosing retinal cells
title_short A semi-automated technique for labeling and counting of apoptosing retinal cells
title_sort semi-automated technique for labeling and counting of apoptosing retinal cells
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4063694/
https://www.ncbi.nlm.nih.gov/pubmed/24902592
http://dx.doi.org/10.1186/1471-2105-15-169
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