July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
Characterization of Pericyte Loss in Cadaveric Human Retinas in Diabetes Mellitus
Author Affiliations & Notes
  • Timothy Nguyen
    Virginia Tech Carilion School of Medicine, Roanoke, Virginia, United States
  • Harsh Patolia
    Virginia Tech Carilion School of Medicine, Roanoke, Virginia, United States
  • Michaela Rikard
    Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States
  • Joseph Walpole
    Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States
  • Shayn Peirce-Cottler
    Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States
  • John Chappell
    Virginia Tech Carilion Research Institute, Roanoke, Virginia, United States
  • Footnotes
    Commercial Relationships   Timothy Nguyen, None; Harsh Patolia, None; Michaela Rikard, None; Joseph Walpole, None; Shayn Peirce-Cottler, None; John Chappell, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 3571. doi:
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      Timothy Nguyen, Harsh Patolia, Michaela Rikard, Joseph Walpole, Shayn Peirce-Cottler, John Chappell; Characterization of Pericyte Loss in Cadaveric Human Retinas in Diabetes Mellitus. Invest. Ophthalmol. Vis. Sci. 2018;59(9):3571.

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

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Abstract

Purpose : Although diabetic retinopathy (DR) strongly correlates with pericyte (PC) loss, the chronology of PC loss during diabetes mellitus (DM) remains poorly defined. Here, we analyzed PCs within retinal vessels of cadaveric tissue from DM and non-DM subjects to determine if significant retinal PC loss occurs prior to DR onset.

Methods : Retinal tissue from donors with DM history (DM, N=4) and without DM (non-DM, N=8) were immunostained, imaged, and analyzed. PCs were labeled with antibodies to neural/glial antigen-2 (NG2), and endothelial cells (ECs) with platelet-endothelial cell adhesion molecule-1 (PECAM-1) antibodies. Retinal vessels were visualized by confocal microscopy. ImageJ facilitated analysis of vessel length from a region-of-interest (ROI), as well as quantifying PCs along vessels (identified by NG2+ staining and morphology) within the same ROI. PC count per vessel length was calculated and averaged for each sample. A two sample t-test was used for statistical analysis. The Virginia Tech IRB confirmed compliance in using cadaveric tissue.

Results : Non-DM retinas contained an average of 3.84 PCs/mm of vessel length [standard deviation (SD)=1.30]. DM vessels trended toward fewer PCs per vessel length (avg=2.33, SD=1.24), though the difference between non-DM vs. DM retinas did not reach statistical significance (p=0.959). To gain insight into whether or not subtle changes in PCs per vessel length impact capillary density and/or vessel sprouting, we also developed an agent-based computational model of retinal vessels that predicts PC and EC behaivors based on key signaling pathways (e.g. VEGF-A, PDGF-BB, etc.) that may be perturbed in DR.

Conclusions : Our data show that, in the cohort of retinas analyzed, there was no statistically significant difference between DM vs. non-DM retinas when comparing average PC count per unit vessel length. Our provisional conclusion is that PCs in diabetic retinas, prior to the development of overt DR, are reduced only modestly when compared to non-diabetic retinas. Therefore, the metabolic changes seen in DM prior to the onset of DR might disrupt PC function but may not cause drastic PC loss from human retinal microvasculature. These data are critical for guiding treatment of DR, as well as for ongoing efforts to build a computational model that accurately predicts vascular defects at different stages of DR based on PC coverage.

This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.

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