September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
Measuring Primary and Secondary Degeneration of Retinal Ganglion Cells in the Rodent Partial Optic Nerve Transection Model
Author Affiliations & Notes
  • Ben Davis
    UCL, London, England, United Kingdom
  • Li Guo
    UCL, London, England, United Kingdom
  • Jonathan Brenton
    UCL, London, England, United Kingdom
  • Lawrence Langley
    UCL, London, England, United Kingdom
  • Eduardo Maria Normando
    Imperial College, London, United Kingdom
  • M Francesca Cordeiro
    UCL, London, England, United Kingdom
  • Footnotes
    Commercial Relationships   Ben Davis, None; Li Guo, None; Jonathan Brenton, None; Lawrence Langley, None; Eduardo Normando, None; M Francesca Cordeiro, None
  • Footnotes
    Support  UCLB
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 880. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to Subscribers Only
      Sign In or Create an Account ×
    • Get Citation

      Ben Davis, Li Guo, Jonathan Brenton, Lawrence Langley, Eduardo Maria Normando, M Francesca Cordeiro; Measuring Primary and Secondary Degeneration of Retinal Ganglion Cells in the Rodent Partial Optic Nerve Transection Model. Invest. Ophthalmol. Vis. Sci. 2016;57(12):880.

      Download citation file:


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

      ×
  • Supplements
Abstract

Purpose : Partial optic nerve transection (pONT) in the rodent is a useful model to assess RGC secondary degeneration which is thought to play a role in diseases such as glaucoma. Previous studies segregated primary and secondary degeneration in this model spatially, with primary degeneration suggested to occur exclusively in the superior retina and secondary degeneration in the inferior retina. The present study describes a novel algorithm for the automatic spatial segmentation of Brn3a labelled RGCs into retinal segments to enable the percentage contribution of primary and secondary degeneration.

Methods : A previously described Brn3a automatic counting algorithm (Cell Death & Disease 2014 e1460) was adapted to automatically divide RGC counts into pre-defined sectors from which RGC density (RGC/mm2) were determined. This algorithm was applied to 42 Dark Agouti rat retinal whole mounts at 0 (control), 3, 7, 21 and 56 days post pONT induction. The rate of RGC loss in each sector was determined by fitting RGC density to exponential decay equations from which the percentage of primary and secondary degeneration in each non-overlapping segment could be determined.

Results : Mean Brn3a-positive RGC count (82,592 ± 681) and RGC density (1,695 ± 23.3 RGC/mm2) in control eyes were comparable with previous studies (PLoS ONE, 2012 e49830). A significant reduction in RGC density was observed from 3 days of pONT model induction (one-way ANOVA, p <0.001), declining by an average of 70% by day 56. On further spatial segmentation, significantly greater loss in RGC density was observed in the superior versus inferior retinal quadrants at 21 and 56 days post pONT induction (paired two tailed T-tests, p < 0.001). While the majority of primary degeneration was observed in the superior and central retinal sectors, secondary degeneration was also observed in these sectors.

Conclusions : A novel algorithm to automatically segment Brn3a retinal wholemounts into non-overlapping segments is described, which has been succesfully used to delineate primary and secondary degeneration in pONT models. The interesting finding of heterogeneity in the spatial distribution of retinal degeneration in this model highlights the importance of accurate assessment of these processes. We recommend that similar analyses of RGC loss is applied to any model where it is being used as an endpoint.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×