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Retina  |   August 2012
Quantitative Morphometry of Perifoveal Capillary Networks in the Human Retina
Author Affiliations & Notes
  • Geoffrey Chan
    From the Centre for Ophthalmology and Visual Science and
    the Australian Research Council Centre of Excellence in Vision Science, The University of Western Australia, Perth, Australia.
  • Chandrakumar Balaratnasingam
    From the Centre for Ophthalmology and Visual Science and
    the Australian Research Council Centre of Excellence in Vision Science, The University of Western Australia, Perth, Australia.
  • Paula K. Yu
    From the Centre for Ophthalmology and Visual Science and
    the Australian Research Council Centre of Excellence in Vision Science, The University of Western Australia, Perth, Australia.
  • William H. Morgan
    From the Centre for Ophthalmology and Visual Science and
  • Ian L. McAllister
    From the Centre for Ophthalmology and Visual Science and
  • Stephen J. Cringle
    From the Centre for Ophthalmology and Visual Science and
    the Australian Research Council Centre of Excellence in Vision Science, The University of Western Australia, Perth, Australia.
  • Dao-Yi Yu
    From the Centre for Ophthalmology and Visual Science and
    the Australian Research Council Centre of Excellence in Vision Science, The University of Western Australia, Perth, Australia.
  • Corresponding author: Dao-Yi Yu, Centre for Ophthalmology and Visual Science, The University of Western Australia, Nedlands, Western Australia 6009; dyyu@cyllene.uwa.edu.au
Investigative Ophthalmology & Visual Science August 2012, Vol.53, 5502-5514. doi:10.1167/iovs.12-10265
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      Geoffrey Chan, Chandrakumar Balaratnasingam, Paula K. Yu, William H. Morgan, Ian L. McAllister, Stephen J. Cringle, Dao-Yi Yu; Quantitative Morphometry of Perifoveal Capillary Networks in the Human Retina. Invest. Ophthalmol. Vis. Sci. 2012;53(9):5502-5514. doi: 10.1167/iovs.12-10265.

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      © 2016 Association for Research in Vision and Ophthalmology.

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Abstract

Purpose.: To quantify the distribution and morphometric characteristics of capillary networks in the human perifovea. To determine correlations between the location of neuronal subcellular compartments and the morphometric features of regional capillary networks in the layered retina.

Methods.: The perifoveal region, located 2 mm nasal to the fovea, was studied in 17 human donor eyes. Novel micropipette technology was used to cannulate the central retinal artery and label the retinal microcirculation using a phalloidin perfusate. γ-synuclein, Goα, and parvalbumin antibodies were also used to co-localize the nerve fiber layer (NFL), retinal ganglion cell layer (RGCL), inner plexiform layer (IPL), and inner nuclear layer (INL). Confocal scanning laser microscopy was used for capillary imaging. Capillary diameter, capillary density, and capillary loop area measurements were compared between networks.

Results.: Four capillary networks were identified in the following retinal layers: (1) NFL, (2) RGCL and superficial portion of IPL, (3) deep portion of IPL and superficial portion of INL, and (4) deep portion of INL. Laminar configurations were present in NFL and deep INL networks. Remaining networks demonstrated three-dimensional configurations. Capillary density was greatest in the networks serving the IPL. Capillary loop area was smallest in the two innermost networks. There was no difference in capillary diameter between networks.

Conclusions.: Capillary networks in the human perifovea are morphometrically heterogeneous. Morphometric features of regional capillary networks in the layered retina may serve a critical role in supporting neuronal homeostasis. Improved knowledge of these features may be important for understanding pathogenic mechanisms underlying retinal vascular diseases.

Introduction
The human retina has enormous capacity for parallel processing despite an average thickness of only 300 μm. 1 The capacity of the retina to partition light stimuli into a series of complex visual signals, prior to cortical transmission, is largely attributed to the layered organization of neuronal populations. 2 Each neuronal population involved in visual processing has distinct metabolic demands with significant disparity in the rate of oxygen consumption between retinal layers. 3,4 It is expected that the distribution of capillary networks within the retina correlate with the metabolic demands of soma, dendrites, and synapses 5 —neuronal layers with the greatest energy demand may have the greatest capillary supply. Few studies, however, have aimed to validate this hypothesis. 
Unlike other regions in the central nervous system, the retinal circulation must achieve neuronal nutrition without compromising the optical properties of the pathway transmitting light to the photoreceptor layer in the outer retina. Increases in neuronal energy demand cannot therefore be compensated by a simple increase in capillary number. It is likely that retinal capillary networks are morphometrically adapted in order that the balance between cellular nutrition and optical clarity can be achieved. In other organs, microcirculatory adaptive mechanisms that serve to satisfy the unique metabolic demands of distinct cell populations include modifications in capillary diameter, 614 intercapillary distance, 6 capillary density, 7,11,1316 and the area bounded by capillary loops. 17 Similar capillary network adaptations may be present in the human retina. 
Our laboratory has developed novel perfusion-based labeling techniques, utilizing micropipette cannulation of the central retinal artery and targeted antibodies, to accurately label the human retinal circulation. 1820 We have also coupled immunolabeling and microscopy techniques with this perfusion methodology to perform detailed analyses of intercellular relationships in the human macula. 21 The present report employs previously validated state-of-the-art technology to quantify the morphometric features of capillary networks in a specialized region of the macula. The perifovea, located approximately 2 mm from the foveal center, is examined. 22 This region is histologically characterized by approximately four rows of retinal ganglion cell nuclei and five to seven rows of nuclei in the inner and outer nuclear layers. 22 Nerve fiber layer (NFL) thickness in the perifoveal macula–papillary bundle is also greater than most other parts of the macula. 23 The retina is described to have one of the highest oxygen consumption rates of any tissue in the body and as a consequence is highly susceptible to irreversible injury during states of vascular compromise. 24,25 The aim of this study was to improve our understanding of vascular mechanisms that support retinal homeostasis. 
Materials and Methods
This study was approved by the human research ethics committee at The University of Western Australia. All human tissue was handled according to the tenets of the Declaration of Helsinki. 
Human Donor Eyes
A total of 17 human eyes from 14 donors were used for this study. All eyes were obtained from the Lions Eye Bank of Western Australia (Lions Eye Institute, Perth, Western Australia) after removal of corneal buttons for transplantation. Donor eyes used for this research had no documented history of eye disease. The demographic data and medical comorbidities of each donor are presented in Table 1
Table 1. 
 
Donor Demographic Details. Age, Sex, Cause of Death, and Time to Perfusion for Each Eye Donor is Provided
Table 1. 
 
Donor Demographic Details. Age, Sex, Cause of Death, and Time to Perfusion for Each Eye Donor is Provided
Patient ID Sex Age Eye Comorbid Conditions Time to Perfusion (h)
A M 32 L MVA 20
B M 23 L Suicide 22
C M 53 L MVA 14
D* M 66 R+L Colon cancer 15
E M 22 L Suicide 15
F M 27 L MVA 8
G M 59 R+L Melanoma 12
H M 39 L Bacterial endocarditis 20
I M 68 R COPD 11
J M 60 L Prostate cancer 18
K M 65 R Cardiomyopathy 6.5
L F 64 L Short illness 5
M M 72 R Drowning 15
N‡ M 60 R+L Alzheimer disease 2.5
Tissue Preparation
Our previously reported technique of central retinal artery cannulation, microvascular fixation, and targeted endothelial cell labeling was used for this work. 19,21 Briefly, the central retinal artery was cannulated using a glass micropipette, and the retinal circulation was perfused with a mixture of oxygenated Ringer's solution and 1% BSA. After the 20-minute Ringer's wash, the retinal circulation was perfusion fixed using a solution of 4% paraformaldehyde in 0.1 M phosphate buffer. Triton-X-100 (0.1%) in 0.1 M phosphate-buffered solution was then used to aid in the permeabilization of endothelial cell membranes. Detergent was removed from the retinal circulation by perfusion with 0.1 M phosphate buffer solution. Endothelial microfilaments and nuclei were labeled over 2 hours by perfusion with a solution comprising phalloidin conjugated to Alexa Fluor 546 (30 U; Invitrogen, Carlsbad, CA) and bisbenzimide (1.2 μg/mL; Sigma-Aldrich, St. Louis, MO). Residual label was cleared from the vasculature by further perfusion with 0.1 M phosphate buffer. Eye cups were immersion fixed in 4% paraformaldehyde overnight prior to dissection. The posterior globe was dissected at the equator to allow viewing of the posterior retina. Relaxing radial incisions were used to permit retinal flat mounting. 
Immunolabeling
To aid co-localization between capillary networks and retinal layers, further immunolabeling was performed in four postperfused flat-mount sections and two transverse retinal sections (Table 1). In two flat-mount sections, the relationship between capillary networks, NFL, and the retinal ganglion cell layer (RGCL) was explored by incubating the retina with rabbit monoclonal γ-synuclein antibody 26 (1:200; Abcam, Cambridge, MA) followed by incubation with goat anti-rabbit antibody conjugated with Alexa Fluor 488 (1:200; Invitrogen). In two flat-mount sections, the relationship between capillary networks, bipolar cells, and horizontal cells was explored using mouse-Goα antibody 27 (1:200; Millipore, Billerica, MA) and rabbit parvalbumin antibody 28 (1:500; Swant, Marly, Fribourg), respectively. Goat anti-mouse conjugated with Alexa Fluor 488 and anti-rabbit antibodies conjugated with Alexa Fluor 633 were used for secondary labeling. All primary antibodies were incubated for 3 days followed by overnight incubation of secondary antibodies. 21  
Transverse retinal sections used for co-localization studies were of 12-μm thickness and were prepared from regions that were used for flat-mount confocal microscopy studies. Transverse retinal sections were immunolabeled with γ-synuclein antibody 26 (1:200; Abcam), Goα antibody 27 (1:200; Millipore), and parvalbumin antibody 28 (1:500; Swant). Lectin-TRITC 29 (1:40; Sigma-Aldrich) was also used to label blood vessels in transverse sections. 
Microscopy
In all flat-mount retinal specimens, confocal microscopy images were acquired from the perifoveal region—located 2 mm nasal to the center of the fovea (Fig. 1). 22 Wide-field images were captured prior to confocal microscopy with the aid of a ×4 dry lens (Plan NA 0.2; Nikon, Tokyo, Japan) and a fluorescent microscope (Eclipse E800; Nikon). Wide-field images were used to accurately measure distances from the center of the fovea prior to confocal scanning. Confocal microscope images were captured using Nikon C1 Confocal with EZ-C1 (Version 3.20; Nikon) image acquisition software. A ×20 dry objective lens (NA 0.4) was used for all scans. Using a motorized stage, a series of z-stack were captured for each specimen beginning from the vitreal surface, at the level of the inner limiting membrane, to the outer retina. Each z-stack consisted of a depth of optical sections collected at 0.35-μm increments along the z-plane. 
Figure 1. 
 
Human perifovea. (A) Color fundus and (B) corresponding Zeiss Cirrus high-definition ocular coherence tomography image from a healthy subject illustrate the perifoveal region that was used for capillary morphometric studies (fenestrated box). NFL thickness in the perifoveal region (indicated by arrows) is greater than most other parts of the macula. (C) Toluidine blue–stained section from a separate subject demonstrates normal perifoveal histology. This region has approximately four rows of retinal ganglion cell nuclei and five to seven rows of nuclei in the inner and outer nuclear layers. Scale bar in color fundus and ocular coherence tomography image = 1000 μm. Scale bar in toluidine-blue histology = 50 μm. OD, optic disk.
Figure 1. 
 
Human perifovea. (A) Color fundus and (B) corresponding Zeiss Cirrus high-definition ocular coherence tomography image from a healthy subject illustrate the perifoveal region that was used for capillary morphometric studies (fenestrated box). NFL thickness in the perifoveal region (indicated by arrows) is greater than most other parts of the macula. (C) Toluidine blue–stained section from a separate subject demonstrates normal perifoveal histology. This region has approximately four rows of retinal ganglion cell nuclei and five to seven rows of nuclei in the inner and outer nuclear layers. Scale bar in color fundus and ocular coherence tomography image = 1000 μm. Scale bar in toluidine-blue histology = 50 μm. OD, optic disk.
Images of different wavelengths were acquired sequentially. Visualization of sections labeled with Alexa Fluor 408 secondary antibody was achieved by laser excitation at a 408-nm line from an argon laser with emissions detected through a 450/35-nm band-pass filter. Visualization of sections labeled with Alexa Fluor 488 secondary antibody was achieved by laser excitation at a 488-nm line from an argon laser with emissions detected through a 525/50-nm band-pass filter. Visualization of sections labeled with Alexa Fluor 564 secondary antibody was achieved by laser excitation at a 561-nm line from an argon laser with emissions detected through a 595/40-nm band-pass filter. Visualization of sections labeled with Alexa Fluor 637 secondary antibody was achieved by laser excitation at a 637-nm line from an argon laser with emissions detected through a 450/35-nm band-pass filter. 
Image Preparation
ImagePro Plus (Version 7.1; Media Cybernetics, Rockville, MD) and ImageJ (Version 1.43; National Institutes of Health, Bethesda, MD; in the public domain at http://rsb.info.nih.gov/ij) were used to quantify confocal microscope images. All images for the manuscript were prepared using Adobe Photoshop (Version 12.1; Adobe Systems Inc., San Jose, CA) and Adobe Illustrator CS5 (Version 12.1.0; Adobe Systems Inc.). Confocal images in this manuscript were pseudo-colored using Look Up Tables available on ImageJ (in the public domain at http://rsbweb.nih.gov/ij/download.html). 
Three-dimensional (3-D) reconstruction of capillary network morphology was performed using the entire z-stack from a single donor (Table 1) and Imaris software (Version 7.4.2; Bitplane, Zurich, Switzerland). To minimize artifact caused by minor fluctuations in signal intensity, the dataset for visualization was refined by utilizing the Gaussian filter tool and background subtraction prior to 3-D reconstruction. 
Qualitative Differentiation of Capillary Networks
Previous central nervous system studies have demonstrated that capillary networks within the brain, subserving distinct neuronal and glial populations, are characterized by unique cyto-architectural features. 1214,30 Morphometric criteria previously defined by these structure–function histologic studies were used to partition the retinal circulation into different capillary networks. 
A z-stack of confocal images that only contained information derived from the vascular channel of each eye was viewed using an animation sequence on ImageJ, and a capillary network was defined as being different when one of the following morphometric criteria was satisfied: (1) alteration in projected direction and orientation of capillaries, 12,31,32 (2) alteration in capillary branching pattern, 12,31 or (3) reduced presence of capillaries within retinal tissue. 
Qualitative division of the retinal circulation into different capillary networks was performed by four separate observers using the above criteria. Each observer recorded the location of the first and final image slice, within the z-stack, for each capillary network. Interobserver correlation between the number and location of capillary networks was subsequently performed (described below). 
Following the division of z-stacks into separate capillary networks, information acquired from remaining laser channels was merged with vascular channels and used to co-localize individual capillary networks with neuronal structures in the layered retina. Specifically, capillary networks were localized in relation to nuclei, NFL, RGCL, bipolar cells, and horizontal cells. Quadruple-labeled transverse retinal sections were also used to aid in co-localization studies. 
Morphometric Quantification of Capillary Networks
Quantitative data from capillary networks were attained following z-projection of all images between the first and final image slice for each network as determined above. Capillary morphometric measurements were performed using previously defined histologic parameters. 8,14,17 Manual tracing methods were used to attain the following quantitative measurements from each capillary network (Fig. 2): 
Figure 2. 
 
Methodology for quantification of capillary network morphometry. Representative z-projected confocal microscope image of (A) the deep INL network and (B) a corresponding manually traced image illustrate the vascular parameters that were measured. Vessel density (highlighted in red) was expressed as a percentage of total area. Capillary loop area (highlighted in green) expressed in square micrometers and capillary diameter (blue marks) expressed in micrometers were also measured. Each image was divided into nine equal portions (fenestrated lines), and capillary diameter measurements were obtained from each region. Scale bar = 100 μm.
Figure 2. 
 
Methodology for quantification of capillary network morphometry. Representative z-projected confocal microscope image of (A) the deep INL network and (B) a corresponding manually traced image illustrate the vascular parameters that were measured. Vessel density (highlighted in red) was expressed as a percentage of total area. Capillary loop area (highlighted in green) expressed in square micrometers and capillary diameter (blue marks) expressed in micrometers were also measured. Each image was divided into nine equal portions (fenestrated lines), and capillary diameter measurements were obtained from each region. Scale bar = 100 μm.
  •  
    Capillary diameter—defined as the perpendicular distance across the maximum chord axis of each vessel. Each confocal image was partitioned into nine equal regions (Fig. 2B), and measurements were obtained from each region to ensure representative sampling. An average of 45 measurements was obtained from each image. Capillaries were defined as vessels with an absence of smooth muscle cells with diameter less than 10 μm. 12
  •  
    Capillary loop area—defined as the area circumscribed within visually enclosed capillary loops. 17
  •  
    Capillary density—defined as percentage of the sample area occupied by capillary lumens.
Quantification of Nuclei Density
Confocal images derived from the nuclear channel were used to quantify nuclei density. Projected z-stacks, using the same confocal slices as for capillary morphometric measurements, were used for nuclei counting. Nuclei density in each capillary network was calculated by employing previously reported manual thresholding techniques available on ImageJ software. 33 The “Analyze Particles” function on ImageJ software was utilized for nuclei counting, and threshold parameters were based on the results of 100 manual nuclei measurements from random samples. Automated nuclei counts were determined for particles between 10 and 100 μm2 and a circularity index between 0.20 and 1.00. 
Statistical Analysis
All data are expressed in terms of mean and standard error, which were calculated using Sigmastat (Sigmastat, Version 3.1; SPSS, Chicago, IL). Multiple measurements from eyes, with data taken from right and left eyes of the same individual, were analyzed using R (R Foundation for Statistical Computing, Vienna, Austria). 34 ANOVA testing was performed to compare measurements between layers. The model used included “Right” or “Left” nested within “eye donor” as random effects using linear mixed modeling to test measurement differences between retinal layers. 34 The assignment of donor as a random effect was used to account for the effects of intra-“eye” correlation and similarly “Right” and “Left” to account for right and left eye correlation. ANOVA was also used to determine if “postmortem time” (time to perfusion) influenced capillary morphometric measurements in retinal layers. 
Interobserver Correlation and Measurement Reproducibility
To assess interobserver variation in distinguishing capillary networks, four masked observers were asked to identify stack numbers for the beginning and end of each morphometrically distinct capillary network in five eye specimens. Two-way ANOVA was performed, modeling stack number with observer and layer to see if observer accounted for any variation in first or last stack number identified. 
Twelve capillary microvasculature images derived from three different specimens were quantified on three separate occasions, each at least 1-week apart, by the same masked observer who performed all the data analyses. Capillary diameter, capillary loop area, and capillary density were quantified for each image. Two-way ANOVA was used to assess the effect of day of measurement and capillary network layer on each of the capillary morphometric parameters, with eye donor as a random effect. “Right” or “Left” was not considered because only one eye from each eye donor was used. 
Results
Eye Donors
The mean age of donors was 50.71 ± 4.84 years (age range, 22–72 years). We examined 6 right eyes and 11 left eyes from a total of 14 donors (13 male and 1 female). The average postmortem time before eyes were perfused was 13.14 ± 3.51 hours. 
General
All orders of retinal microvasculature were clearly labeled after perfusion labeling via the central retinal artery. Endothelial cells and nuclei were clearly identified following excitation with separate laser channels. Immersion immunolabeling of flat-mount and transverse retinal sections, post perfusion, also allowed identification of capillaries relative to the NFL, RGCL, inner plexiform layer (IPL), and inner nuclear layer (INL). Transverse retinal sections (Fig. 3), from the region of interest, demonstrated well-delineated NFL, RGCL, IPL, and INL. In the perifoveal region, the RGCL demonstrated several strata of cell bodies (Fig. 1C). 
Figure 3. 
 
Co-localization of capillary networks within retinal layers. (A) Triple-labeled and (B) quadruple-labeled transverse retinal sections demonstrate the relationship between capillary networks and NFL, RGCL, IPL, and INL. Endothelial cells are labeled with phalloidin; nuclei are labeled with Hoescht; RGCL and NFL are labeled with γ-synuclein. Goα and parvalbumin were used to label bipolar and horizontal cells, respectively. Four capillary networks (fenestrated red brackets) were identified in the inner retina. The innermost capillary network was identified in the NFL. The second network was located at the level of the RGCL and superficial portion of the IPL. A separate network was identified at the boundary between the deep portion of IPL and superficial portion of INL. The outermost capillary network was identified at the level of the deep INL. Scale bar = 50 μm.
Figure 3. 
 
Co-localization of capillary networks within retinal layers. (A) Triple-labeled and (B) quadruple-labeled transverse retinal sections demonstrate the relationship between capillary networks and NFL, RGCL, IPL, and INL. Endothelial cells are labeled with phalloidin; nuclei are labeled with Hoescht; RGCL and NFL are labeled with γ-synuclein. Goα and parvalbumin were used to label bipolar and horizontal cells, respectively. Four capillary networks (fenestrated red brackets) were identified in the inner retina. The innermost capillary network was identified in the NFL. The second network was located at the level of the RGCL and superficial portion of the IPL. A separate network was identified at the boundary between the deep portion of IPL and superficial portion of INL. The outermost capillary network was identified at the level of the deep INL. Scale bar = 50 μm.
Qualitative Study of Retinal Capillary Networks
Four morphologically varied retinal capillary networks were consistently observed by the four observers. Three-dimensional image reconstructions demonstrated the unique morphometric configuration of each network (Fig. 4). The morphometric features of each capillary network were as follows: 
  •  
    NFL network (Fig. 5)—characterized by long capillary segments that were predominantly oriented parallel to the direction of retinal ganglion cell axons. A small number of shorter capillary segments that interconnected long radial capillaries were also seen in this network. Interconnecting capillaries were oriented either diagonal or orthogonal to long segments.
  •  
    RGCL/superficial IPL (sIPL) network (Fig. 6)—characterized by a dense meshwork of 3-D vessels that were arranged in a lattice pattern with reduced intercapillary spaces. Capillaries in this network demonstrated looping hairpin turns that projected vertically. There was also close approximation between RGCL capillaries and the larger retinal vessels.
  •  
    Deep IPL (dIPL)/superficial (sINL) network (Fig. 7)—characterized by capillaries composed of vertical and oblique segments that resulted in an irregularly shaped loop configuration. Capillaries in this network demonstrated great tortuosity.
  •  
    Deep INL (dINL) network (Fig. 8)—characterized by capillaries arranged in a one-dimensional laminar configuration. Capillaries ran a linear trajectory with little tortuosity.
Figure 4. 
 
Three-dimensional morphometry of human perifoveal capillary networks. Contour surface-rendered images generated using Imaris software demonstrate the complex organization of different capillary networks in the human retina from two different angles of reconstruction (A) and (B). Arrow allows retinal orientation and indicates projection commencing at the vitreal surface and extending to the outer retina. Scale bar = 150 μm.
Figure 4. 
 
Three-dimensional morphometry of human perifoveal capillary networks. Contour surface-rendered images generated using Imaris software demonstrate the complex organization of different capillary networks in the human retina from two different angles of reconstruction (A) and (B). Arrow allows retinal orientation and indicates projection commencing at the vitreal surface and extending to the outer retina. Scale bar = 150 μm.
Figure 5. 
 
NFL capillary network. Endothelial cells (labeled with phalloidin) stain red; nuclei (labeled with Hoescht) stain blue; and NFL axons (labeled with anti-γ-synuclein) stain green. (A) Confocal capillary images captured from a single laser channel demonstrate that the trajectory of a large number of capillaries parallel axons in the NFL. (B) Merged images demonstrate a relative paucity of nuclei within this network compared with other retinal layers examined. Capillary images from a separate donor (C) merged with a stain specific for retinal ganglion cell axons (D) demonstrate co-localization of this network with the NFL of the retina. Scale bar = 100 μm.
Figure 5. 
 
NFL capillary network. Endothelial cells (labeled with phalloidin) stain red; nuclei (labeled with Hoescht) stain blue; and NFL axons (labeled with anti-γ-synuclein) stain green. (A) Confocal capillary images captured from a single laser channel demonstrate that the trajectory of a large number of capillaries parallel axons in the NFL. (B) Merged images demonstrate a relative paucity of nuclei within this network compared with other retinal layers examined. Capillary images from a separate donor (C) merged with a stain specific for retinal ganglion cell axons (D) demonstrate co-localization of this network with the NFL of the retina. Scale bar = 100 μm.
Figure 6. 
 
Ganglion cell layer/sINL network. Endothelial cells (labeled with phalloidin) stain red; nuclei (labeled with Hoescht) stain blue; retinal ganglion cells (labeled with anti-γ-synuclein) stain green. (A) Confocal capillary images captured from a single laser channel demonstrate the complex capillary configuration in this network. The proximity of capillaries to larger order arterioles and veins in this network is also demonstrated. (B) Merged images demonstrate a high concentration of nuclei within this network. Capillary images from a separate donor (C) merged with a stain specific for retinal ganglion cell nuclei (D) demonstrate co-localization of this network with the ganglion cell layer of the retina. Scale bar = 100 μm.
Figure 6. 
 
Ganglion cell layer/sINL network. Endothelial cells (labeled with phalloidin) stain red; nuclei (labeled with Hoescht) stain blue; retinal ganglion cells (labeled with anti-γ-synuclein) stain green. (A) Confocal capillary images captured from a single laser channel demonstrate the complex capillary configuration in this network. The proximity of capillaries to larger order arterioles and veins in this network is also demonstrated. (B) Merged images demonstrate a high concentration of nuclei within this network. Capillary images from a separate donor (C) merged with a stain specific for retinal ganglion cell nuclei (D) demonstrate co-localization of this network with the ganglion cell layer of the retina. Scale bar = 100 μm.
Figure 7. 
 
Deep IPL/sINL network. Endothelial cells (labeled with phalloidin) stain red; nuclei (labeled with Hoescht) stain blue; bipolar cells and their cell processes (labeled with Goα) stain green. (A) Confocal capillary images captured from a single laser channel demonstrate the irregularly shaped loop configuration of capillaries in this network. (B) Merged images demonstrate a high concentration of nuclei within this network. Capillary images from a separate donor (C) merged with bipolar cell markers (D) demonstrate the intimate relationship of capillaries to processes of bipolar cells, which co-localize this network with the IPL of the retina. Scale bar = 100 μm.
Figure 7. 
 
Deep IPL/sINL network. Endothelial cells (labeled with phalloidin) stain red; nuclei (labeled with Hoescht) stain blue; bipolar cells and their cell processes (labeled with Goα) stain green. (A) Confocal capillary images captured from a single laser channel demonstrate the irregularly shaped loop configuration of capillaries in this network. (B) Merged images demonstrate a high concentration of nuclei within this network. Capillary images from a separate donor (C) merged with bipolar cell markers (D) demonstrate the intimate relationship of capillaries to processes of bipolar cells, which co-localize this network with the IPL of the retina. Scale bar = 100 μm.
Figure 8. 
 
Deep INL network. Endothelial cells (labeled with phalloidin) stain red; nuclei (labeled with Hoescht) stain blue; bipolar cells (labeled with Go-alpha) stain green. (A) Confocal capillary images captured from a single laser channel demonstrate the planar configuration of capillaries in this network. (B) Merged images demonstrate a high concentration of nuclei in the region of this network. Capillary images from a separate donor (C) merged with bipolar cell markers (D) demonstrate a similar concentration of nuclei in comparison with the superficial INL network. Scale bar = 100 μm.
Figure 8. 
 
Deep INL network. Endothelial cells (labeled with phalloidin) stain red; nuclei (labeled with Hoescht) stain blue; bipolar cells (labeled with Go-alpha) stain green. (A) Confocal capillary images captured from a single laser channel demonstrate the planar configuration of capillaries in this network. (B) Merged images demonstrate a high concentration of nuclei in the region of this network. Capillary images from a separate donor (C) merged with bipolar cell markers (D) demonstrate a similar concentration of nuclei in comparison with the superficial INL network. Scale bar = 100 μm.
Quantitative Characteristics of the NFL Network
Mean capillary diameter in the NFL network was 8.30 ± 0.07 μm (n = 347). A total of 84 capillary loops were measured with a mean area of 4940.31 ± 529.38 μm2. Capillary density in the NFL network was 17.37% ± 0.99%. NFL capillary measurements from individual donors are presented in Table 2. Mean nuclei density in the NFL network was 1552.89 ± 109.64 cells per mm2
Table 2. 
 
Quantitative Capillary Network Data for Individual Donors. Mean Capillary Diameter, Capillary Loop Area, and Capillary Density Measurements for Each Donor Eye are Provided
Table 2. 
 
Quantitative Capillary Network Data for Individual Donors. Mean Capillary Diameter, Capillary Loop Area, and Capillary Density Measurements for Each Donor Eye are Provided
Specimen Capillary Diameter (μm) Capillary Loop Area (μm2) Capillary Density (%)
NFL RGCL/sIPL dIPL/sINL dINL NFL RGCL/sIPL dIPL/sINL dINL NFL RGCL/sIPL dIPL/sINL dINL
A 8.6 8.1 8.5 7.6 2,462.5 4,823.8 9,397.2 15,593.1 15.0 23.4 16.5 14.7
B 8.1 7.8 6.5 7.4 7,494.5 1,814.7 0.0 4,841.8 14.8 25.3 14.4 18.9
C 8.3 8.8 8.8 9.0 4,684.0 8,754.2 3,436.5 4,169.8 18.6 16.8 20.4 17.6
D 8.4 8.3 8.7 9.2 5,394.9 2,826.6 7,461.2 6,307.3 22.4 26.0 24.6 24.2
E 8.8 8.0 8.3 7.5 6,987.6 2,782.1 2,354.3 4,562.3 20.2 34.6 25.0 21.7
F 8.6 8.8 8.1 8.1 5,959.9 6,000.8 8,412.4 7,158.2 22.0 16.2 14.1 15.2
G 7.9 8.3 8.4 8.9 3,543.7 5,318.5 5,401.9 7,326.5 10.5 21.1 20.7 18.9
H 7.9 8.3 8.2 8.5 5,688.8 0.0 9,063.6 9,813.8 19.2 19.6 19.7 15.2
I 8.7 8.6 8.1 8.4 3,789.6 6,234.2 4,472.2 0.0 17.4 16.8 15.0 12.6
J 7.6 8.3 8.8 8.4 1,645.9 5,622.6 3,219.5 4,371.5 14.8 21.2 23.1 21.3
K 8.2 7.3 8.4 8.0 4,788.9 3,500.3 3,979.8 8,799.2 16.3 24.6 21.7 17.2
Quantitative Characteristics of the RGCL/sIPL Network
Mean capillary diameter in the RGCL/sIPL network was 8.29 ± 0.07 μm (n = 304). A total of 102 capillary loops were measured with a mean area of 3954.04 ± 505.30 μm2. Capillary density in the RGCL/sIPL network was 22.32% ± 0.99%. RGCL/sIPL capillary measurements from individual donors are presented in Table 2. Mean nuclei density in the RGCL/sIPL network was 2201.13 ± 113.70 cells per mm2
Quantitative Characteristics of the dIPL/sINL Network
Mean capillary diameter in the dIPL/sINL network was 8.25 ± 0.07 μm (n = 325). A total of 63 capillary loops were measured with a mean area of 5424.03 ± 602.72 μm2. Capillary density in the dIPL/sINL network was 19.56% ± 0.99%. Table 2 presents the dIPL/sINL network capillary measurements from individual donors. Mean nuclei density in the dIPL/sINL network was 3051.47 ± 168.71 cells per mm2
Quantitative Characteristics of the dINL Network
Mean capillary diameter in the dINL network was 8.26 ± 0.07 μm (n = 372). A total of 66 capillary loops were measured with a mean area of 6866.52 ± 584.58 μm2. Capillary density in the dINL was 17.95% ± 0.99%. Table 2 presents the dINL capillary measurements from individual donors. Mean nuclei density in the dINL network was 3792.59 ± 235.09 cells per mm2
Morphometric Comparisons between Networks
Postmortem time was not associated with capillary diameter, loop area, or density measurements in any of the networks (all; P > 0.142). There was no difference in capillary diameter between networks (P = 0.715). Capillary loop area was smallest in the RGCL/sIPL network and was significantly smaller than in the dIPL/sINL network (P = 0.028) and the dINL network (P < 0.003), but not the NFL network (P > 0.050). There was no difference in capillary loop area between the dIPL/sINL and dINL networks (P = 0.088) or between the NFL and dIPL/sINL (P = 0.632) and dINL networks (P = 0.107). 
Capillary density was greatest in the RGCL/sIPL network and was significantly greater than in the NFL (P = 0.015) and dINL (P = 0.004) networks. There was no difference in capillary density between the RGCL/sIPL network and the dIPL/sINL (P = 0.074), dIPL/sINL and dINL (P = 0.069), NFL and dIPL/sINL (P = 0.179), or NFL and dINL networks (P = 0.689). 
Mean nuclei density increased with progression through the NFL, RGCL/sIPL, dIPL/sINL, and dINL networks when “layer” was treated as a continuous variable (P = 0.000). Nuclei density was lowest in the NFL network and was significantly lower than in the dIPL/sINL (P = 0.013) and dINL (P = 0.009) networks. Nuclei density was greatest in the dINL network and was significantly greater than in the RGCL/sIPL network (P = 0.010). There was no difference in nuclei density between the NFL and RGCL/sIPL network (P = 0.051) and dIPL/sINL and dINL network (P = 0.062). 
Interobserver Correlation and Measurement Reproducibility
The assignment of first and last stack numbers to define capillary networks was not different between the four observers (P = 0.999). Analysis of measurement reproducibility did not reveal a significant difference in capillary diameter measurement between the 3 measurement days (P = 0.836). There was also no significant difference between the 3 measurement days for capillary loop area (P = 0.976) and capillary density measurements (P = 0.549). 
Discussion
The major findings from this study are as follows: 
  1.  
    Capillary networks in the human perifovea demonstrate morphometric variation according to retinal layer.
  2.  
    Capillary density varies between retinal layers and is greatest in the RGCL/sIPL and dIPL/sINL networks.
  3.  
    Capillary loop area is smallest in the two innermost networks.
  4.  
    There is no difference in capillary diameter between the layers of the retina.
The perifovea is a specialized region of the human eye that is histologically and functionally distinct compared with other portions of the retina. 22 Unlike the peripheral retina, which comprises only a single row of ganglion cells, the nasal perifovea is characterized by four to five rows of retinal ganglion cells. 22 Additionally, the NFL in the perifovea is thicker than in other parts of the macula. 23 The macula–papillary bundle traverses the perifovea and is the conduit through which a large quantity of precortically processed retinal information is transmitted to the brain. Consequently, diseases that preferentially affect the macula–papillary bundle result in devastating visual morbidity. 35 Understanding the structure of capillary networks serving the perifovea may provide insights into vascular-mediated mechanisms that satisfy the metabolic demands of this region. 
The present study identified four morphometrically different capillary networks within the human perifovea. This finding was verified by four masked, independent observers. The innermost and outermost networks, situated in the NFL and deep portion of the INL, respectively, demonstrated a laminar, one-dimensional configuration. Capillary networks in the NFL were also observed to project parallel to the trajectory of retinal ganglion cell axons and resembled the microcirculation described in skeletal muscle, 36 where capillaries are oriented parallel to the direction of muscle fibers. Interconnecting, orthogonally oriented anastamoses are also seen in the NFL, similar to skeletal muscle capillary systems. 36 In contrast, the capillary networks located in the RGCL/sIPL and dIPL/sINL demonstrated a tortuous, 3-D architecture that resembled the Voronoi tessellation described in cortical capillary beds. 37 The variation in retinal capillary network morphology identified in the present study demonstrates important parallels to the human cerebral cortex, where the microcirculation is also altered according to neuronal layer. 3840  
Unlike the brain, the retina is readily accessible for investigating the physiological behavior of subcellular components within distinct neuronal layers. Using oxygen-sensitive microelectrode techniques, intraretinal oxygen distribution and oxygen uptake in different cellular layers has been quantified during physiological and nonphysiological states. 4,25,4148 These previous studies have identified three distinct regions of high oxygen uptake: (1) the inner segment of photoreceptors, (2) the inner plexiform layer, and (3) the outer plexiform layer. 3,25,49 The relationship (and proximity) between each region of high oxygen uptake and the local microcirculation, however, is vastly different. Detailed studies have shown that photoreceptors, including their nuclei, the high energy-consuming inner segments, and the photosensitive outer segments, lie within the avascular layers of the retina. 3 Oxygen and nutrient supplies to photoreceptors are completely dependent upon diffusion mechanisms from choroidal and deep retinal vascular beds. 50 Although oxygen tension in the choroid is high, oxygen tension at the level of the inner segments is paradoxically low. 3 In contrast, the inner half of the retina is supported by a sparse distribution of retinal vessels with significant disparities in oxygen levels between inner retinal layers. 3 The present study identified important relationships between neuronal subcompartments and regional capillary network morphometry, which may be important for understanding vascular-mediated mechanisms that account for the heterogeneous oxygen profile across the inner retina. The present study may also identify vascular-mediated mechanisms that permit momentary variations in neuronal metabolic demands to be satisfied. 25,44 Retinal glia are likely to play a critical role in modulating changes in regional blood supply consequent to variations in neuronal demands. 51  
Investigating how retinal capillary network topography is coupled with regional neuronal demands is important for understanding physiological mechanisms that support retinal homeostasis. The present study demonstrated that the IPL is supported by two capillary networks—situated in the inner and outer boundaries of the IPL. Capillary density is greatest in these networks, relative to other retinal layers, suggesting that the energy demands of neuronal arborisations are high. The 3-D organizations of these networks most likely serve to increase oxygen delivery and waste removal within the IPL. 30,52 It was surprising that the central portion of the IPL was relatively devoid of vasculature; however, this region is known to have considerable Muller cell support. 53 There is increasing evidence to suggest that glial cells play a key role in modulating regional blood supply via neurotransmitter-mediated signaling—particularly through the release of glutamate. 54 Glutamate-mediated signaling leads to the release of arachidonic acid derivatives from glial cells and nitric oxide molecules from neurons, with the net effect being an increase or decrease in blood flow, depending on local oxygen concentration. 54 Through these signaling mechanisms, it is possible for Muller cells to control regional oxygen distribution in different capillary networks. Additionally, Muller cells support the metabolic activity of regional neurons through metabolic symbiosis, a neuron-Muller interaction, where pyruvate released by Muller cells is used as a substrate by neighboring neurons to generate energy through Krebs cycle. 53 We speculate that the latter mechanism is an important means by which Muller cells support neuronal metabolic activity in regions with scant capillary supply, such as the mid-portion of the IPL. Further work, however, is required to validate this hypothesis. 
Intercapillary areas bear important relationships to oxygen diffusion properties, and it is postulated that decreasing intercapillary areas result in decreased oxygen diffusion times 17 —the net effect being increased adenosine triphosphate (ATP) production. Capillary loop area was lowest in the NFL and RGCL/sIPL networks, suggesting that oxygen diffusion may be an important mechanism by which neuronal function is supported in these layers. The significant differences in capillary loop area between the two networks that serve the IPL also suggest that the process of oxygen diffusion plays a disparate role in supporting the inner and outer portions of the IPL. Detailed histologic studies have revealed that the vertebrate IPL is a non-uniform, layered structure with significant dissimilarities in the density and complexity of synaptic contacts between IPL strata. 5557 Furthermore, there is significant heterogeneity in the ratio of amacrine to bipolar to ganglion cell synapses between IPL strata. The axon terminals of ON- and OFF-bipolar cells ramify in distinct IPL strata with terminal arborisations of OFF-type and ON-type cells synapsing in sublamina a and b of the IPL, respectively. 58 We speculate that the heterogeneous metabolic demands of distinct IPL strata, and the unique role served by each strata in parallel processing, may account for the differences in capillary network morphometry between superficial and deep IPL capillary networks. Differences in capillary loop areas between networks may be one means by which the distinct metabolic demands of inner and outer IPL strata are satisfied. Mean capillary diameter is also known to influence the rate of capillary oxygen exchange. 59 Unlike studies in intracortical capillary networks, 60 we did not detect significant differences between perifoveal capillary network diameters. 
The purpose of this study was to identify major differences in capillary network morphometry between perifoveal layers. Our experimental model of central artery cannulation and perfusion is best suited for such an investigation as it ensures reliable and complete labeling of the retinal microcirculation. Confocal microscopy and immunohistochemical techniques developed in our laboratory also ensured accurate correlation of capillary–neuronal relationships. Trypsin digestion 61 and vascular casting 62 techniques were previously used to study the retinal microcirculation; however, inadvertent tissue destruction, consequent of these methodologies, limited accurate delineation of such interrelationships. Although our experimental methodology allowed us to precisely control and monitor perfusion pressure in the retinal circulation, the effect of postmortem artifact may still have influenced some of the morphometric measurements in our study. We accounted for the effect of postmortem artifact in our statistical analysis, and we did not find that this variable was associated with capillary density, loop area, or diameter measurements in any of the retinal layers. Our results are consistent with previous studies involving cerebral capillaries, where a significant change in postmortem capillary diameter was not demonstrated in human or cat cortical tissue. 63 However, mean capillary diameters in the present report are larger than in other regions of the central nervous system, 8 and this may be consequent to contractile mechanisms that continue to act on vasculature in the acute period after death. 64 Cerebral artery diameters in humans and monkeys are known to respond to extracellular milieu changes in the first 24 hours post mortem. 64 Similar mechanisms may have continued to act on retinal capillary networks in the immediate postmortem period and thereby influenced capillary diameter measurements reported in the present study. However, the effects of these mechanisms are expected to be equal in all networks, thus permitting useful inter-network comparisons to be performed during this immediate postmortem period. 
The results of this study suggest that capillary network morphometry is coupled with neuronal demands in the human perifovea. It also demonstrated that the correlation between neuronal metabolic activity and capillary network location is not exact. The IPL is supported by capillary networks that are situated on the boundaries of this layer and not within it. The mismatch between capillary network location, morphometry, and neuronal activity in different layers demonstrated a degree of dissimilarity between retinal capillary networks and the microcirculation in other regions of the central nervous system. In the brain, there is a strong correlation between capillary density and the metabolic requirements of layered neuronal structures. 8,40,60 The findings in the present study also improve our understanding of the relationships between capillary organization and intraretinal oxygen distribution and uptake in the human retina. 3 Oxygen tension in the photoreceptor layer, a predominantly avascular region, is exceedingly high. 65 Similarly, subcompartments in the inner retina, which do not have high-density capillary networks, are also capable of maintaining high oxygen levels. These findings implicate a vital role served by nonvascular structures, such as Muller cells and glia, in nourishing neuronal populations and controlling retinal homeostasis. The differences in neuron–capillary relationships between the retina and brain may be due to the following reasons: (1) the organization of vascular structures in the retina are constrained by the optical properties of the eye; (2) the distribution and organization of Muller cells and astrocytes in the retina are different from the brain; and (3) the cellular interrelationships in the retina are arguably more complex than the brain. Improved understanding of neuron–vascular–glial relationships in the human retina will enhance our understanding of pathophysiological processes involved in retinal vascular diseases. 
Capillary network morphometry is altered with disease, 8 and it will therefore be important to perform similar morphometric studies using abnormal human eyes. A small foveal avascular zone is known to persist in preterm infants despite the absence of clinically evident retinopathy of prematurity (ROP). 66 In some patients with oculocutaneous albinism, the central macular area is also crossed by capillaries. 67 The findings from these previous studies suggest that visual acuity changes in patients with ROP and albinism may be partly due to abnormal macular capillary networks. It is also likely that the presence of capillary structures in retinal eccentricities that are normally devoid of vasculature result in altered neuronal homeostasis and optical clarity, which in-turn may adversely affect retinal function. Further studies are required to delineate the pathogenic mechanisms through which altered retinal capillary networks affect visual acuity, particularly in patients with blunted foveal depressions and small foveal avascular zones. 
Acknowledgments
The authors thank staff from the Lions Eye Bank of Western Australia, Lions Eye Institute, for provision of human donor eyes and staff from DonateWest, the Western Australian agency for organ and tissue donation who facilitated the recruitment of donors into the study by referral and completion of consent processes. We thank Michael McHale for provision of color fundus and ocular coherence tomography images. We also thank Dean Darcey for his expert technical assistance. 
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Footnotes
 Supported by grants from the National Health and Medical Research Council of Australia and the Australian Research Council Centre of Excellence in Vision Science.
Footnotes
 Disclosure: G. Chan, None; C. Balaratnasingam, None; P.K. Yu, None; W.H. Morgan, None; I.L. McAllister, None; S.J. Cringle, None; D.-Y. Yu, None
Figure 1. 
 
Human perifovea. (A) Color fundus and (B) corresponding Zeiss Cirrus high-definition ocular coherence tomography image from a healthy subject illustrate the perifoveal region that was used for capillary morphometric studies (fenestrated box). NFL thickness in the perifoveal region (indicated by arrows) is greater than most other parts of the macula. (C) Toluidine blue–stained section from a separate subject demonstrates normal perifoveal histology. This region has approximately four rows of retinal ganglion cell nuclei and five to seven rows of nuclei in the inner and outer nuclear layers. Scale bar in color fundus and ocular coherence tomography image = 1000 μm. Scale bar in toluidine-blue histology = 50 μm. OD, optic disk.
Figure 1. 
 
Human perifovea. (A) Color fundus and (B) corresponding Zeiss Cirrus high-definition ocular coherence tomography image from a healthy subject illustrate the perifoveal region that was used for capillary morphometric studies (fenestrated box). NFL thickness in the perifoveal region (indicated by arrows) is greater than most other parts of the macula. (C) Toluidine blue–stained section from a separate subject demonstrates normal perifoveal histology. This region has approximately four rows of retinal ganglion cell nuclei and five to seven rows of nuclei in the inner and outer nuclear layers. Scale bar in color fundus and ocular coherence tomography image = 1000 μm. Scale bar in toluidine-blue histology = 50 μm. OD, optic disk.
Figure 2. 
 
Methodology for quantification of capillary network morphometry. Representative z-projected confocal microscope image of (A) the deep INL network and (B) a corresponding manually traced image illustrate the vascular parameters that were measured. Vessel density (highlighted in red) was expressed as a percentage of total area. Capillary loop area (highlighted in green) expressed in square micrometers and capillary diameter (blue marks) expressed in micrometers were also measured. Each image was divided into nine equal portions (fenestrated lines), and capillary diameter measurements were obtained from each region. Scale bar = 100 μm.
Figure 2. 
 
Methodology for quantification of capillary network morphometry. Representative z-projected confocal microscope image of (A) the deep INL network and (B) a corresponding manually traced image illustrate the vascular parameters that were measured. Vessel density (highlighted in red) was expressed as a percentage of total area. Capillary loop area (highlighted in green) expressed in square micrometers and capillary diameter (blue marks) expressed in micrometers were also measured. Each image was divided into nine equal portions (fenestrated lines), and capillary diameter measurements were obtained from each region. Scale bar = 100 μm.
Figure 3. 
 
Co-localization of capillary networks within retinal layers. (A) Triple-labeled and (B) quadruple-labeled transverse retinal sections demonstrate the relationship between capillary networks and NFL, RGCL, IPL, and INL. Endothelial cells are labeled with phalloidin; nuclei are labeled with Hoescht; RGCL and NFL are labeled with γ-synuclein. Goα and parvalbumin were used to label bipolar and horizontal cells, respectively. Four capillary networks (fenestrated red brackets) were identified in the inner retina. The innermost capillary network was identified in the NFL. The second network was located at the level of the RGCL and superficial portion of the IPL. A separate network was identified at the boundary between the deep portion of IPL and superficial portion of INL. The outermost capillary network was identified at the level of the deep INL. Scale bar = 50 μm.
Figure 3. 
 
Co-localization of capillary networks within retinal layers. (A) Triple-labeled and (B) quadruple-labeled transverse retinal sections demonstrate the relationship between capillary networks and NFL, RGCL, IPL, and INL. Endothelial cells are labeled with phalloidin; nuclei are labeled with Hoescht; RGCL and NFL are labeled with γ-synuclein. Goα and parvalbumin were used to label bipolar and horizontal cells, respectively. Four capillary networks (fenestrated red brackets) were identified in the inner retina. The innermost capillary network was identified in the NFL. The second network was located at the level of the RGCL and superficial portion of the IPL. A separate network was identified at the boundary between the deep portion of IPL and superficial portion of INL. The outermost capillary network was identified at the level of the deep INL. Scale bar = 50 μm.
Figure 4. 
 
Three-dimensional morphometry of human perifoveal capillary networks. Contour surface-rendered images generated using Imaris software demonstrate the complex organization of different capillary networks in the human retina from two different angles of reconstruction (A) and (B). Arrow allows retinal orientation and indicates projection commencing at the vitreal surface and extending to the outer retina. Scale bar = 150 μm.
Figure 4. 
 
Three-dimensional morphometry of human perifoveal capillary networks. Contour surface-rendered images generated using Imaris software demonstrate the complex organization of different capillary networks in the human retina from two different angles of reconstruction (A) and (B). Arrow allows retinal orientation and indicates projection commencing at the vitreal surface and extending to the outer retina. Scale bar = 150 μm.
Figure 5. 
 
NFL capillary network. Endothelial cells (labeled with phalloidin) stain red; nuclei (labeled with Hoescht) stain blue; and NFL axons (labeled with anti-γ-synuclein) stain green. (A) Confocal capillary images captured from a single laser channel demonstrate that the trajectory of a large number of capillaries parallel axons in the NFL. (B) Merged images demonstrate a relative paucity of nuclei within this network compared with other retinal layers examined. Capillary images from a separate donor (C) merged with a stain specific for retinal ganglion cell axons (D) demonstrate co-localization of this network with the NFL of the retina. Scale bar = 100 μm.
Figure 5. 
 
NFL capillary network. Endothelial cells (labeled with phalloidin) stain red; nuclei (labeled with Hoescht) stain blue; and NFL axons (labeled with anti-γ-synuclein) stain green. (A) Confocal capillary images captured from a single laser channel demonstrate that the trajectory of a large number of capillaries parallel axons in the NFL. (B) Merged images demonstrate a relative paucity of nuclei within this network compared with other retinal layers examined. Capillary images from a separate donor (C) merged with a stain specific for retinal ganglion cell axons (D) demonstrate co-localization of this network with the NFL of the retina. Scale bar = 100 μm.
Figure 6. 
 
Ganglion cell layer/sINL network. Endothelial cells (labeled with phalloidin) stain red; nuclei (labeled with Hoescht) stain blue; retinal ganglion cells (labeled with anti-γ-synuclein) stain green. (A) Confocal capillary images captured from a single laser channel demonstrate the complex capillary configuration in this network. The proximity of capillaries to larger order arterioles and veins in this network is also demonstrated. (B) Merged images demonstrate a high concentration of nuclei within this network. Capillary images from a separate donor (C) merged with a stain specific for retinal ganglion cell nuclei (D) demonstrate co-localization of this network with the ganglion cell layer of the retina. Scale bar = 100 μm.
Figure 6. 
 
Ganglion cell layer/sINL network. Endothelial cells (labeled with phalloidin) stain red; nuclei (labeled with Hoescht) stain blue; retinal ganglion cells (labeled with anti-γ-synuclein) stain green. (A) Confocal capillary images captured from a single laser channel demonstrate the complex capillary configuration in this network. The proximity of capillaries to larger order arterioles and veins in this network is also demonstrated. (B) Merged images demonstrate a high concentration of nuclei within this network. Capillary images from a separate donor (C) merged with a stain specific for retinal ganglion cell nuclei (D) demonstrate co-localization of this network with the ganglion cell layer of the retina. Scale bar = 100 μm.
Figure 7. 
 
Deep IPL/sINL network. Endothelial cells (labeled with phalloidin) stain red; nuclei (labeled with Hoescht) stain blue; bipolar cells and their cell processes (labeled with Goα) stain green. (A) Confocal capillary images captured from a single laser channel demonstrate the irregularly shaped loop configuration of capillaries in this network. (B) Merged images demonstrate a high concentration of nuclei within this network. Capillary images from a separate donor (C) merged with bipolar cell markers (D) demonstrate the intimate relationship of capillaries to processes of bipolar cells, which co-localize this network with the IPL of the retina. Scale bar = 100 μm.
Figure 7. 
 
Deep IPL/sINL network. Endothelial cells (labeled with phalloidin) stain red; nuclei (labeled with Hoescht) stain blue; bipolar cells and their cell processes (labeled with Goα) stain green. (A) Confocal capillary images captured from a single laser channel demonstrate the irregularly shaped loop configuration of capillaries in this network. (B) Merged images demonstrate a high concentration of nuclei within this network. Capillary images from a separate donor (C) merged with bipolar cell markers (D) demonstrate the intimate relationship of capillaries to processes of bipolar cells, which co-localize this network with the IPL of the retina. Scale bar = 100 μm.
Figure 8. 
 
Deep INL network. Endothelial cells (labeled with phalloidin) stain red; nuclei (labeled with Hoescht) stain blue; bipolar cells (labeled with Go-alpha) stain green. (A) Confocal capillary images captured from a single laser channel demonstrate the planar configuration of capillaries in this network. (B) Merged images demonstrate a high concentration of nuclei in the region of this network. Capillary images from a separate donor (C) merged with bipolar cell markers (D) demonstrate a similar concentration of nuclei in comparison with the superficial INL network. Scale bar = 100 μm.
Figure 8. 
 
Deep INL network. Endothelial cells (labeled with phalloidin) stain red; nuclei (labeled with Hoescht) stain blue; bipolar cells (labeled with Go-alpha) stain green. (A) Confocal capillary images captured from a single laser channel demonstrate the planar configuration of capillaries in this network. (B) Merged images demonstrate a high concentration of nuclei in the region of this network. Capillary images from a separate donor (C) merged with bipolar cell markers (D) demonstrate a similar concentration of nuclei in comparison with the superficial INL network. Scale bar = 100 μm.
Table 1. 
 
Donor Demographic Details. Age, Sex, Cause of Death, and Time to Perfusion for Each Eye Donor is Provided
Table 1. 
 
Donor Demographic Details. Age, Sex, Cause of Death, and Time to Perfusion for Each Eye Donor is Provided
Patient ID Sex Age Eye Comorbid Conditions Time to Perfusion (h)
A M 32 L MVA 20
B M 23 L Suicide 22
C M 53 L MVA 14
D* M 66 R+L Colon cancer 15
E M 22 L Suicide 15
F M 27 L MVA 8
G M 59 R+L Melanoma 12
H M 39 L Bacterial endocarditis 20
I M 68 R COPD 11
J M 60 L Prostate cancer 18
K M 65 R Cardiomyopathy 6.5
L F 64 L Short illness 5
M M 72 R Drowning 15
N‡ M 60 R+L Alzheimer disease 2.5
Table 2. 
 
Quantitative Capillary Network Data for Individual Donors. Mean Capillary Diameter, Capillary Loop Area, and Capillary Density Measurements for Each Donor Eye are Provided
Table 2. 
 
Quantitative Capillary Network Data for Individual Donors. Mean Capillary Diameter, Capillary Loop Area, and Capillary Density Measurements for Each Donor Eye are Provided
Specimen Capillary Diameter (μm) Capillary Loop Area (μm2) Capillary Density (%)
NFL RGCL/sIPL dIPL/sINL dINL NFL RGCL/sIPL dIPL/sINL dINL NFL RGCL/sIPL dIPL/sINL dINL
A 8.6 8.1 8.5 7.6 2,462.5 4,823.8 9,397.2 15,593.1 15.0 23.4 16.5 14.7
B 8.1 7.8 6.5 7.4 7,494.5 1,814.7 0.0 4,841.8 14.8 25.3 14.4 18.9
C 8.3 8.8 8.8 9.0 4,684.0 8,754.2 3,436.5 4,169.8 18.6 16.8 20.4 17.6
D 8.4 8.3 8.7 9.2 5,394.9 2,826.6 7,461.2 6,307.3 22.4 26.0 24.6 24.2
E 8.8 8.0 8.3 7.5 6,987.6 2,782.1 2,354.3 4,562.3 20.2 34.6 25.0 21.7
F 8.6 8.8 8.1 8.1 5,959.9 6,000.8 8,412.4 7,158.2 22.0 16.2 14.1 15.2
G 7.9 8.3 8.4 8.9 3,543.7 5,318.5 5,401.9 7,326.5 10.5 21.1 20.7 18.9
H 7.9 8.3 8.2 8.5 5,688.8 0.0 9,063.6 9,813.8 19.2 19.6 19.7 15.2
I 8.7 8.6 8.1 8.4 3,789.6 6,234.2 4,472.2 0.0 17.4 16.8 15.0 12.6
J 7.6 8.3 8.8 8.4 1,645.9 5,622.6 3,219.5 4,371.5 14.8 21.2 23.1 21.3
K 8.2 7.3 8.4 8.0 4,788.9 3,500.3 3,979.8 8,799.2 16.3 24.6 21.7 17.2
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