June 2015
Volume 56, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2015
New method for semi-automated identification of changes in DARC-labelled cells in the same eye at different time points in vivo
Author Affiliations & Notes
  • Ma-Arej Ghaffar
    Visual Neurosciences, UCL institute of Ophthalmology, London, United Kingdom
    ICORG, Western Eye Hospital, London, United Kingdom
  • Ben Davis
    Visual Neurosciences, UCL institute of Ophthalmology, London, United Kingdom
  • Lisa Turner
    Visual Neurosciences, UCL institute of Ophthalmology, London, United Kingdom
  • Eduardo M Normando
    Visual Neurosciences, UCL institute of Ophthalmology, London, United Kingdom
    ICORG, Western Eye Hospital, London, United Kingdom
  • M Francesca Cordeiro
    Visual Neurosciences, UCL institute of Ophthalmology, London, United Kingdom
    ICORG, Western Eye Hospital, London, United Kingdom
  • Footnotes
    Commercial Relationships Ma-Arej Ghaffar, None; Ben Davis, None; Lisa Turner, None; Eduardo Normando, None; M Francesca Cordeiro, DARC technology (P)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2015, Vol.56, 4520. doi:
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      Ma-Arej Ghaffar, Ben Davis, Lisa Turner, Eduardo M Normando, M Francesca Cordeiro; New method for semi-automated identification of changes in DARC-labelled cells in the same eye at different time points in vivo. Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):4520.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose: Retinal ganglion cell (RGC) loss is one of the earliest and most important cellular changes in glaucoma. DARC (Detection of Apoptosing Retinal Cells) technology enables in vivo real-time non-invasive imaging of single fluorescent-labelled retinal cells in animal models of glaucoma and neurodegeneration. We recently described a semi-automated counting method of cells identified through DARC. Here, we investigate a new method which assesses the change in cellular labelling at different time points.

Methods: Images of experimental glaucoma rat and transgenic Alzheimer mouse eyes were acquired in vivo using DARC. Pairs of images from the same eye at different timepoints were aligned and overlaid according to blood vessel structure. All images underwent a pre-processing step involving local-luminance and local-contrast "gain control", a method to exclude non-cell structures using specific combined 'size' and 'aspect' ratio criteria. Maximal pixel density was then used to identify RGC-fluorescent cells. Differences between the DARC cells in both images were computed by subtracting one image from another to establish the number of new apoptosing retinal ganglion cells. 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.

Results: The method was able to detect differences in labelled DARC cells by virtue of their position spatially. Newly apoptosing cells could thus be easily identified. Comparison between automated cell counts and the mean manual cell counts showed significant correlation (R2=0.82) between the two methods with Bland-Altman analysis.

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. Subtraction of fluorescence on one retinal image compared to another is a new method of spatially identifying newly apoptosing RGCs, enabling an important assessment of the change in cellular labelling at different time points.. This may support a valuable new method of quantifying apoptosing retinal cells by location, 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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