Retinal vascular development proceeds in a highly orchestrated, spatiotemporal sequence requiring the coordination of many signaling gradients and cell types that are highly sensitive to environmental stimuli.1 Quantifying the development and health of the retinal vascular network using a variety of imaging techniques is an important tool both clinically and experimentally. Clinical uses vary from quantifying the degree of retinopathy of prematurity to the progression of age-related macular degeneration.23.4.–5 Experimentally, rodents are an ideal model for studying the vascular abnormalities associated with premature birth in humans because rodent retinal vasculature develops entirely postnatally.1,5 Rodent vascular plexus development is also highly reproducible, making structural changes easily observable in real time.5 While there are differences in timing between mouse and rat retinal development, both have been utilized to great effect to study retinal vascular development.67.8.–9 In this study, we utilized a modified rat model with severe disruption of angiogenesis due to prolonged hyperoxia (HO) exposure.10
Rat retinal vascular growth is preceded by an astrocyte network in the superficial primary plexus, reaching the retinal periphery at approximately postnatal day 8 (P8). Between postnatal day 7 (P7) and postnatal day 12 (P12), vessels from the primary plexus sprout downward to form the deeper secondary plexus. The intermediate vascular plexus is formed by sprouting from the secondary plexus, reaching full formation between P12 and postnatal day 15 (P15).1 Many experimental studies of disrupted retinal angiogenesis solely focus on revascularization or vitreous neovascularization in the planar structure of the primary plexus. The imaging depth of these experiments is commonly limited by the physical sample preparation, chosen imaging modality, and optical tissue properties.1,1011.12.13.14.15.–16 Across species, the retina varies from to in thickness, with minimal optical absorption and optical scattering coefficients ranging from 0.12 to at 488 nm.1,1718.–19 Therefore, deeper three-dimensional (3-D) imaging to quantify vertical sprouts and the deeper secondary plexus requires optical sectioning microscopy, commonly achieved through point scanning techniques that limit experimental throughput.2021.22.23.24.25.26.–27 An alternative method is the computational reconstruction of many serial cross-section preparations.28
Beyond the complexities of sample preparation and imaging, quantifying the information contained in imaging data to extract meaningful metrics on the 3-D development of retinal vasculature presents a separate, yet equally important, problem. For data generated by fluorescence imaging, many groups have utilized a simple skeletonization and feature extraction scheme.11,16 This analysis is inherently two-dimensional (2-D) and does not provide any information on deeper vasculature or the spatial patterning of proteins and key cell types. Alternatively, Milde et al. demonstrated that calculating the Minkowsky functionals of a segmented 3-D image of retinal flat mount vasculature provides an alternative quantification method to skeletonization and feature extraction. They further demonstrated that this method can map the developmental pathway of retinal vasculature in 3-D through iterative model fitting. While this technique provides new information about the timing of development and distribution of retinal vasculature, it relies on retinal flat mounts and two-photon point scanning imaging.21
In this study, we present a methodology based on optical tissue clearing and light-sheet microscopy. We demonstrate rapid quantification of vertical sprouting and secondary plexus complexity within intact retinas that do not require physical alteration beyond removal from the eye. Motivated by the ongoing developments in single-cell phenotyping and network mapping within intact tissue,29,30 we quantified the vasculature of rat retinas using passive CLARITY and a home-built digital scanned light-sheet microscope (DSLM).3132.–33 Because our DSLM does not require physical translation of the sample, we were able to image the 3-D network of the retinal vasculature with minimal sample disturbance. After 3-D imaging, we filtered the images,34 performed semiautomated network identification,3536.–37 and calculated a simple metric based on Sholl analysis38,39 that captured the complexity of the entire retinal vascular network.
We then applied this microscopy pipeline to vascular development at P7 and P14 of control rat retinas and retinas from animals exposed to continuous HO. We quantified the primary plexus of retinal flat mounts with epifluorescence imaging and the primary and secondary plexus of retinal flat mounts using deconvolution epifluorescence imaging. We then optically cleared intact retinas and quantified the 3-D vascular network using DSLM. The primary plexus of experimental P14 HO retinas developed vascular density and complexity resembling control P14 retinas. This provides evidence that the first two postnatal weeks are significant in recovery from HO-induced vascular abnormalities. However, 3-D imaging revealed that vertical sprouting and secondary plexus development were severely altered in the continuous HO model. Quantification of this impairment was lost using standard epifluorescence imaging of retinal flat mounts, highlighting the importance of robust 3-D quantification methods.
Materials and Methods
The Institutional Animal Care and Use Committee at the University of Colorado Anschutz Medical Campus approved all procedures and protocols. Pregnant Sprague-Dawley rats were purchased from Charles River Laboratories (Wilmington, Massachusetts). Animals were fed ad libitum and exposed to day–night cycles alternatively every 12 h. Litters were delivered in room air (RA) and randomly assigned to HO chambers () or RA () at day 1 of life (). HO chambers were maintained at Denver’s altitude (1600 m; barometric pressure, 630 mm Hg; inspired oxygen tension, 122 mm Hg). HO exposure was continuous with brief interruptions for animal care (). Oxygen concentration was regulated using a ProOx device (Reming Bioinstruments). Eye tissue from pups in HO and normoxia conditions was harvested at P7 and P14. Half of the eyes were used for retinal flat mounts, and the other half were used for 3-D quantification methods. Animals were killed with intraperitoneal injections of pentobarbital sodium ( body wt., Fort Dodge Animal Health, Fort Dodge, Indiana).
Retina and Entire Eye Isolation from Sprague-Dawley Rats
Eyes harvested at P7 and P14 were enucleated using curved forceps and immediately immersed in 2% (weight/volume) paraformaldehyde for fixation (90 min). Eyes were transferred to phosphate-buffered saline (PBS), where the cornea and lens were removed under a dissecting microscope. Using fine-tipped forceps, sclera and choroid vasculature were separated from retinas and discarded [Fig. 1(a)]. Retinas were examined for tears or folding and included in the study based on overall integrity, measured by the absence of folding, tearing, and other structural abnormalities. Following dissection, intact retinas were transferred to 2-ml tubes (2 to ) of PBS with Triton X-100 (PBS-T, 0.1%) for staining.
Two-Dimensional Retinal Immunofluorescence Sample Preparation
Tubes containing retinas in PBS-T were placed on an orbital rocker at room temperature for 1 to 4 h. PBS-T was drained and replaced with a PBS-T primary label [Biotinylated Griffonia lectin (IB4) Vector #B-1205] solution at 1:100. Tubes were kept on a rocker for 48 to 72 h at room temperature and then washed four times for 1 h with PBS-T. Retinas were immersed in PBS-T secondary label (Alexa-488 conjugated streptavidin, Jackson Immunoresearch #016-540-084) and kept wrapped in foil on a rocker for 24 h. Tubes were drained and washed with PBS-T four times for 1 h.
Two-Dimensional Retinal Flat Mount Preparation
Precleaned microscope slides were prepared by adhering two secure seal spacers to each slide face, allowing two flat mounts per slide. A sample spoon was used to transfer intact stained retinas from 2-ml tubes to microscope slides under a dissecting microscope. To prepare flat mounts, retinas were oriented photoreceptor side up in a drop of PBS on the slides. Using fine-tipped scissors, four even interval incisions were made from the peripheral retina toward the optic nerve. A fine-tipped pipette was used to remove PBS from the retina, flattening the retina and revealing a clover-like structure [Fig. 1(b)]. No. 1 cover slips were placed on the secure seal spacers and adhered with Hydromount mounting media (National Diagnostics). Slides were stored in light-tight box at 4°C until imaging.
Passive Optical Clearing of Isolated Retina and Intact Eyes
To render tissue optically transparent, retinas harvested from pups were incubated in 4% acrylamide and 0.25% photoinitiator, 2,2′-azobis[2-(2-imidazolin-2-yl) propane] dihydrochloride (VA-044, Wako Chemicals) monomer solution overnight at 4°C. Infused samples were then degassed in nitrogen for 5 min and incubated at 37°C for 2 h to establish hydrogel polymerization. In addition, the polymerized acrylamide hydrogel was cross-linked to endogenous proteins, such as those expressed on the surface of vascular endothelial cells.29,30 Retinas were removed from hydrogel solution and washed in PBS. Retinas were then incubated in 4% sodium dodecyl sulfate (SDS) solution for 5 days at 37°C with gentle shaking. Retinas were then washed 5 to 8 times over the course of 8 h in PBS to remove excess SDS from sample tissue. Biotinylated GS I4B lectin (Vector Labs Catalog #B-1205) was used to label endothelial cells in retinal vasculature. Retinas were then washed in PBS to remove excess GS I4B lectin and incubated in streptavidin for 2 days. The secondary labeling solution contained the same amount of serum and Triton-X 100 with a 1:250 dilution of Alexa-488 conjugated streptavidin (Jackson ImmunoResearch Labs, catalog #016-540-084). Eyes were then washed in PBS to remove excess secondary label and incubated in refractive index matching solution (RIMS) index matching media until cleared. RIMS imaging media contained 40 g of Histodenz (Sigma D2158) in 30 ml of 0.02 M phosphate buffer with 0.1% Triton-X 100.
Passive Optical Clearing-Treated Sample Mounting for Minimal Deformation
Eyes were rendered optically transparent through a 24-h incubation in RIMS at room temperature. Each eye was then stitched with a small suture at the optic nerve. This stitch was adhered to a pipette tip using adhesive (Loctite). Once eyes were mounted to pipette tips, they were resubmerged in fresh RIMS.
Two-Dimensional Fluorescent Imaging of Retinal Flat Mount Samples
Entire retinal flat mounts were imaged by epifluorescence microscopy (Nikon Eclipse Ti-E) with an excitation wavelength of 488 nm. To obtain pictures of complete retinal flat mounts, the focal plane containing the primary plexus was imaged in nine segments using the air objective and Cy3 filter cube with an in-plane pixel size . Segments were assembled into a complete picture with a 10% overlap using NIS-Elements Ar image stitching software (Nikon). Additionally, images of each retina (in-plane pixel size ) were taken at the midline between the retinal periphery and the optic nerve for quantification using Angiotool. One image was taken per flat mount petal, per retina.
Deconvolution epifluorescence imaging of retinal flat mounts was performed on a home-built deconvolution epifluorescence microscope. The microscope consists of a four-color LED excitation unit (Lumencor Spectra-X), microscope base (Olympus IX71), quad-band excitation and emission filter set (Semrock LED-DA/FI/TR-Cy5-4X-A), numerical aperture (NA) 1.3 oil microscope objective (Olympus UPLFLN ), automated XY stage (Mad City Labs Microdrive), automated objective piezo (Mad City Labs F-200), and a sCMOS camera (Hamamatsu C11440-22CU). The entire retinal flat mount was imaged using multiple areas with 30% overlap. Each image stack had an in-plane pixel size of and an axial step size of over . Raw images were deconvolved on-the-fly using custom GPU software.40 After deconvolution, individual image tiles were reassembled using FIJI.41,42
Light-Sheet Imaging of Passive Optical Clearing-Treated Retinas
Stitched retinas were mounted on a home-built cleared tissue-specific DSLM (Fig. 2).43 The excitation arm consisted of a multicolor laser source (Coherent OBIS 488 nm, 532-nm diode laser, and Coherent OBIS 640; Thorlabs RGB46HA), fiber collimator (Thorlabs TC06FC-633), pair of galvanometer mirrors (Thorlabs GVSM002), scan lens (Thorlabs CLS-SL), tube lens (Nikon MXA22018), electrotunable lens (ETL-1 – Optotune EL-10-30), and NA 0.14 33.5-mm WD excitation objective (Edmund Optics 59-876). The excitation arm was used to create the sheet and displace the beam along the optical axis of the detection arm. The galvanometer mirrors and camera triggering were controlled by an Arduino Uno unit (Sparkfun DEV-11021) with three PowerShield DAC boards (Visgence, Inc.) while the laser intensity was controlled through analog input to the Coherent OBIS controller by an Arduino Uno unit (Sparkfun DEV-11021) with three PowerShield DAC boards (Visgence, Inc.).
A 3-D printed imaging chamber, with glued #1 quartz coverslips (SPI Supplies 01019T-AB), was mounted to a manual rotation stage (Thorlabs PR01) and centered at the focal length of both the excitation and detection objectives (DOs), so the full range of excitation beam scanning was available (). An automated micrometer-driven stage controlled using OpenStage hardware and software was utilized for sample positioning and tiling.44 This stage positioned a sample rotation stage/sample mount consisting of an Arduino Uno controlled stepper motor (Sparkfun DEV-11021, Sparkfun ROB-09238) and rigid mounted pipette tip within the imaging chamber.
The detection arm accommodated a NA 0.13 17.2-mm WD (Nikon MRH00041) or NA 0.28 33.5-mm WD (Edmund Optics 59-877) DO using a nonrotating translatable objective mount (Thorlabs SM1NR1). A tube lens (Nikon MXA22018), adjustable periscope mirror (Thorlabs BB1-E02), and electrotunable lens (ETL-2 – Optotune EL-16-40-TC) were placed at the first focal point of a system (Thorlabs AC254-300-A). An adjustable periscope mirror (Thorlabs BB1-E02), automated filter wheel (Thorlabs FW102C), and sCMOS camera (Hamamatsu C11440-22CU) completed the detection optics. ETL-2 was placed in a configuration to allow the focus to be swept without changing telecentricity. To generate images the scanning mirror corresponding to the focal plane (fast-axis) was swept once per acquisition. The second scanning mirror positioned the light sheet along the detection axis. ETL-2 positioned the focal plane of camera at the axial location of the light sheet. ETL-1 optimized the alignment of each laser source, enabling fine tuning of the excitation arm focus within the sample. These values were user determined at multiple image planes throughout the sample. The full table of axial mirror, ETL-1, and ETL-2 values were then built using linear interpolation. This technique allowed the sample to stay immobile during volumetric imaging.
For all images in this work, fluorescence was collected with either the NA 0.13 or NA 0.28 DO. This yielded in-plane pixel sizes of 1.625 or and axial step sizes of 3.5 or , respectively. A manually adjustable pinhole was utilized to limit the NA of the excitation objective to adjust the Rayleigh length to best match the imaging area. This yielded a Gaussian width of for the DO and for the DO. Volumetric images of varying total sizes at orientations parallel or perpendicular to the optic nerve were acquired for each retina. The orientation that best visualized the vasculature was chosen for image analysis.
Two-Dimensional Fluorescent Image Quantification
All 20× images were imported into Angiotool for automated network tracing.16 Analysis parameters were visually modified for accurate skeletonization. Controls were set to a low threshold of 15, high threshold of 255, and vessel diameter of 15 and to fill holes at 2000. Output metrics included vessel percentage area, total number of junctions, junction density, total vessel length, average vessel length, total number of endpoints, and mean lacunarity. Statistical analysis was performed in Graphpad PRISM using an ANOVA test.
Additionally, the binary image produced by Angiotool was analyzed using 2-D Sholl analysis in FIJI.39,41 The number of intersections as a function of radius was normalized by the area of the circle. The resulting Sholl curves were then integrated using trapezoidal integration with unit spacing in MATLAB®. Statistical analysis was performed in MATLAB® using a Kruskal–Wallis test.
Two-Dimensional Fluorescent Optically Sectioned Image Quantification
Optically sectioned images of retina flat mounts were analyzed using FIJI and Vaa3D.32,36 Images were filtered using a multiscale adaptive enhancement filter.30 Semiautomated network tracing was carried out on filtered images using APP-2.0.31 The resulting network trace was used to mask the retinal vasculature into a binary image. The binary image was analyzed using 2-D Sholl analysis for each image plane in FIJI.39,41 The number of intersections as a function of radius was normalized by the area of the circle for each focal plane. The resulting Sholl curves were then integrated using trapezoidal integration with unit spacing in MATLAB®. Statistical analysis was performed in MATLAB® using a Kruskal–Wallis test.
Three-Dimensional Fluorescent Image Network Quantification Pipeline
Volumetric images of intact retinas were analyzed in Vaa3D.36 Raw images were filtered using a multiscale adaptive enhancement filter.34 Semiautomated network tracing was carried out on filtered images using APP-2.0.35 The resulting network trace was used to mask the retinal vasculature into a binary image. The binary image was analyzed using 3-D Sholl analysis in FIJI.39,41 The number of intersections as a function of radius was normalized by the surface area of the sphere. The resulting Sholl curves were then integrated using trapezoidal integration with unit spacing in MATLAB®. Statistical analysis was performed in MATLAB® using a Kruskal–Wallis test.
Superficial Vascular Plexus was Disturbed at P7 in Continuous Hyperoxia Exposure Model
P7 RA control retinas displayed dense and consistent vascular distributions with narrow avascular zones at the retinal periphery [Fig. 3(a)]. In contrast, P7 HO flat mounts showed simplified vessel branching and decreased vessel concentration throughout the primary plexus, with large avascular zones at the retinal periphery [Fig. 3(b)].
To better visualize the 3-D structure of the developing rat retinal vasculature, we imaged intact retinas using DSLM (Fig. 4, Videos 1 and 2). This imaging method offered two advantages over traditional flat mount preparations. The native structure of the eye was better preserved because the retina was not physically altered, and volumetric imaging proceeded rapidly, at to 500 ms per image plane. For Figs. 4(a) and 4(b), the image acquisition required roughly 5 min. For the multiple overlapping images that comprise Figs. 4(c) and 4(d), image acquisition required roughly 30 min. In comparison, acquisition of a similar volume in a retinal flat mount using a wide-field deconvolution microscope or a confocal microscope required nearly 10 h.
Individual image planes contained within volumetric images showed the presence of the choroid layer of vasculature, as well as the primary plexus and the secondary plexus found at the deepest portion of the retina. Connections were observed between the two plexuses in control retinas, indicating the presence of vertical sprouts [Fig. 5(a)]. In the HO retinas, the strongest fluorescent signal was found at the site of the primary plexus. This appeared as dense fluorescence, suggesting an abnormal vascular distribution [Fig. 5(b)]. Additionally, the choroids of the HO retinas were consistently deformed compared to those of the control retinas [Fig. 5(b)].
Secondary Vascular Plexus Remained Disorganized in P14 Retinas
Vascular development at P14 was more robust compared with P7 in both control and HO retinas. Relative to control P14 retinas [Fig. 6(a)], P14 HO retinas demonstrated reduced vascular complexity surrounding the optic nerve and larger avascular zones at the retinal periphery [Fig. 6(b)]. P14 DSLM images also showed more robust vasculature compared with P7 images. Control P14 retinas demonstrated more vertical sprouts and more organized primary and secondary plexuses [Fig. 7(a)]. HO P14 retinas demonstrated a strong fluorescent signal in the primary plexus, but they appeared disorganized compared to control retinas. [Fig. 7(b)].
To verify these findings, we performed optical sectioning of retinal flat mounts utilizing a high NA objective, epifluorescence, and computational deconvolution. Figure 8 shows an axial montage of the vascular network isolated from a large-scale epifluorescence image ( image areas per retina) of control and HO retinas at P7. Vertical sprouts and secondary plexuses were missing from HO retinas [red arrows note sprouts in Fig. 8(a)].
Quantification of Vascular Complexity Using Traditional Network Tracing
We used Angiotool software to quantify the differences in the primary plexus between control and continuous HO exposed retinas at the two time points used in this study. Angiotool measures morphometric and spatial parameters of vascularity through network tracing [Fig. 9(a)]. We utilized the total vessel length, junction density, and mean lacunarity tools for assessment of the images (Figs. 2 and 5).16 We observed a significant decrease in total vessel length and junction density between control and HO P7 retinas [Figs. 9(b) and 9(c)]. This lack of vascular complexity appeared to resolve by P14, with no statistical difference between the two study groups at that time point. Inversely, we saw an increase in avascular space measured by mean lacunarity at P7 in HO relative to controls [Fig. 9(d)]. Again, there was no statistical difference between mean lacunarity in the two groups by P14. Angiotool was designed to measure morphometric and spatial parameters of vascularity in two dimensions through skeletonization and feature extraction. Because the algorithm does not consider axial network connections and requires high signal-to-noise for feature identification, it was not applied to 3-D and volumetric images.
Quantification of Vascular Complexity Using Sholl Analysis
We conducted Sholl analysis to quantify the variations in vascular distribution between control and HO retinas. Previously used to quantify characteristics of dendritic processes branching off neuronal cell bodies, Sholl analysis examines the number of branches that intersect concentric circles (retinal flat mounts) or spheres (intact retinas) of increasing radii around the cell body or central focal point of the image area (Fig. 10).38,39
Using this method, we observed an initial decrease in vascular branching in HO P7 retinas compared with those of the control [Figs. 11(a) and 11(c)]. This was consistent with our observations using DSLM and intact retinas in which there was a reduction or complete absence of the secondary plexus in HO retinas. Three-dimensional Sholl analysis applied to intact P14 retinas captured this observation while 2-D Sholl analysis applied to P14 retinal flat mounts failed to account for the lack of secondary plexus [Figs. 11(b) and 11(d)]. We utilized the “integrated” Sholl metric by taking the integral of the area under the Sholl analysis curve to calculate an approximate value of the total number of vascular network intersections (Fig. 12).4 This simple metric captures changes in 3-D vascular structure of intact retinas, providing a methodology to rapidly quantify the full vascular development of the retina.
In this study, we used a continuous HO exposure model to produce significant vascular abnormality in rat retinas. We quantified these abnormalities by comparing control retinas to HO retinas using multiple imaging modalities. These data were collected using two distinct techniques in microscopy and image analysis; epifluorescence imaging with Angiotool and Sholl analysis, as well as DSLM imaging with Sholl analysis. The two methods were compared based on retinal distortion due to sample preparation, imaging depth, and vascular network quantification.
At P7, both imaging techniques showed that HO exposure caused stunted vessel growth in the central retina and the cessation of normal radial vessel growth in the primary plexus. DSLM images also displayed an absent secondary plexus in the HO retinas. Previous studies have shown that HO during postnatal retinal development causes downregulation of hypoxia inducible factor, preventing the release of vascular endothelial growth factor and insulin-like growth factor and the formation of a normal retinal vascular plexus.45,46 Because sprouting from the primary plexus toward the secondary plexus appeared delayed, we speculate that oxygen exposure may have negatively impacted other cell types driving angiogenesis. The exact molecular mechanisms that control this sprouting into the secondary plexus are not well understood, making this a potential area of future study using these techniques.47,48
At P14, we observed recovery of the primary vascular plexus in HO retinas with both imaging techniques but saw that the secondary plexus was not always restored in the DSLM images. This suggests that compensatory signaling, possibly initiated by an increasingly avascular retina or hyperoxia suppresses development of the deeper vasculature.49
Traditionally, retinal vascular networks are quantified based on features detectable in fluorescent images of the primary plexus. These quantifications involve morphometric measurements, such as the number of junctions, branch points, vessel length, and overall vessel density. These images come from flat mounts, which require significant sample manipulation and therefore may compromise biological data. Traditional network tracing programs such as Angiotool are designed for 2-D image analysis, limiting their capacity to measure vascular development below the primary plexus.16
For comparison, we employed DSLM to visualize the locations of the primary and secondary plexus, separated by an intermediary layer (Fig. 6). This allowed for visualization of all layers of the retinal vasculature in the native confirmations. Not only did DSLM provide more accurate and robust data, but we gained additional spatial information regarding pathological vascularization. In control retinas, vertical sprouts were easily visualized in the intermediary layer at P7 and P14. In HO retinas, we observed an absence of intermediate sprouting at P7 and subsequent recovery by P14.
The use of air-immersion microscope objectives in our DSLM led to a spherical aberration when imaging optically cleared tissue immersed in a refractive index matching solution.33,50 While this aberration minimally disrupts imaging of the vascular system, further investigations of molecular mechanisms or single-cell identification will be complicated by aberrations. Future work to integrate refractive index corrected optics (narrow field of view) or computational fusion/deconvolution methods (multiple image orientations and intensive computation) will increase imaging time but reduce optical aberrations.33,51,52
We show that Sholl analysis captures the complexity of the vascular network in two- and three-dimensions. Integrating the resulting Sholl curves provides a simplified metric that can be utilized to rapidly compare retinal vasculature without morphometric measurements. Adapting Sholl analysis to 3-D confocal or two-photon imaging of retinal flat mounts will require careful consideration of the correct normalization procedure.
As treatments to promote physiologic retinal vascular development are evaluated, 3-D imaging techniques that are compatible with cellular and protein-specific staining are highly sought after. DSLM imaging techniques provide a promising approach to studying the molecular mechanisms involved in the regulation of angiogenesis. Most studies of retinal vascular development focus on the superficial primary vascular plexus, whereas the intermediate and deep secondary plexuses have been less explored. Mapping the entire 3-D, interconnected network can comprehensively capture the complex nature of vascular development in a way that current methods cannot. When combined with optical tissue clearing, this imaging methodology allows for quantification of protein signaling and individual cell type. This will enable future studies to map the 3-D distribution of signaling fronts and vascular structure within intact retinas. Reducing the complexity of high-dimensional datasets into meaningful metrics and statistical measures to test hypotheses also remains an ongoing challenge. Here, we have shown that Sholl analysis can be readily adapted to generate an integrated normalized Sholl metric that provides an easy way to compare the measure of vascular complexity in both retinal flat mounts and intact retinas.
We thank Dr. Julie Siegenthaler for helpful discussions regarding rodent models of retinal development. J.N.S. and D.P.S. acknowledge the CU Denver College of Liberal Arts and Sciences start-up funding and National Institutes of Health (NIH) National Institute on Aging AG053690. T.M.N., G.J.S., and S.H.A. acknowledge funding from NIH National Heart, Lung, and Blood Institute HL68702.
Douglas P. Shepherd is an assistant professor at the University of Colorado Denver. He received his BS degree in physics from the University of California, Santa Barbara, in 2003 and his PhD in physics from Colorado State University in 2013. His current research interests include single-molecule biophysics and light-sheet microscopy of optically cleared tissue.