April 2014
Volume 55, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2014
A novel classification system for rat RGCs in retinal degeneration
Author Affiliations & Notes
  • James R Tribble
    School of Optometry and Vision Science, Cardiff University, Cardiff, United Kingdom
  • Paulina Samsel
    School of Optometry and Vision Science, Cardiff University, Cardiff, United Kingdom
  • Stephen D Cross
    School of Optometry and Vision Science, Cardiff University, Cardiff, United Kingdom
  • Frank Sengpiel
    Cardiff School of Biosciences, Cardiff University, Cardiff, United Kingdom
  • James E Morgan
    School of Optometry and Vision Science, Cardiff University, Cardiff, United Kingdom
    University Hospital of Wales, Cardiff Eye Unit, Cardiff, United Kingdom
  • Footnotes
    Commercial Relationships James Tribble, None; Paulina Samsel, None; Stephen Cross, None; Frank Sengpiel, None; James Morgan, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 2389. doi:https://doi.org/
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    • Get Citation

      James R Tribble, Paulina Samsel, Stephen D Cross, Frank Sengpiel, James E Morgan; A novel classification system for rat RGCs in retinal degeneration. Invest. Ophthalmol. Vis. Sci. 2014;55(13):2389. doi: https://doi.org/.

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

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Abstract

Purpose: Retinal ganglion cells (RGC) types are typically classified based on the morphology of their dendritic arbour. This can be problematic when quantifying the dendritic remodeling that occurs in rodent models of retinal degenerative diseases where labeling bias among RGC types could be an important potential confounder. To control for this effect we developed a novel quantitative RGC classification based on proximal dendritic features that are usually resistant to early degeneration.

Methods: Retinas from 20 adult Brown Norway Rats were flat mounted and labelled ballistically (Biorad Helios gene gun) by delivery of DiO and DiI coated tungsten coated particles. RGC dendritic arbours were imaged with a Zeiss LSM 510 confocal microscope. RGCs were classified according to Sun et al. (2002) using soma diameter, dendritic field diameter and stratification depth. Primary and secondary dendrite features were quantified and assessed via principle component analysis (PCA) to generate novel condensed variables or components. Discriminant analysis was then used to assess their value in the classification of RGC types. A hold out sample (n=16, taken from RGC traces in Sun et al. 2002) provided validation of the model.

Results: 140 RGCs were imaged; according to standard classification (Sun et al. 2002) 26% (n=37) were RGCA, 29% (n=40) RGCB, 31% (n=43) RGCC and 14% (n=20) RGCD.PCA gave a 3 component solution, separating RGCs based on descriptors of cell soma and dendritic field size, dendritic tree asymmetry and branching density. RGCA and RGCC were separated from the smaller RGCB and RGCD based on descriptors of cell soma and dendritic tree size. RGCA and RGCC were separated based on branching density, while RGCB and RGCD were separated by a combination of all 3 factors. Discriminant analysis showed that the new variables correctly classified 64.3% (n=90) of RGCs and 62.5% (n=10) of the hold-out sample indicating an effective model.

Conclusions: Primary and secondary dendrite characteristics provide quantitative data for a classification system that is relatively resistant to early degeneration. By measuring only the proximal dendritic field the atrophy of distal dendrites and reduction in overall dendritic field size does not confound the classification. This quantitative classification system can be used to control for sample bias in the assessment of RGC degeneration in experimental retinal disease.

Keywords: 531 ganglion cells • 695 retinal degenerations: cell biology  
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