Microvascular networks play key roles in oxygen transport and nutrient delivery to meet the varied and dynamic metabolic needs of different tissues throughout the body, and their spatial architectures of interconnected blood vessel segments are highly complex. Moreover, functional adaptations of the microcirculation enabled by structural adaptations in microvascular network architecture are required for development, wound healing, and often invoked in disease conditions, including the top eight causes of death in the Unites States. Effective characterization of microvascular network architectures is not only limited by the available techniques to visualize microvessels but also reliant on the available quantitative metrics that accurately delineate between spatial patterns in altered networks. In this review, we survey models used for studying the microvasculature, methods to label and image microvessels, and the metrics and software packages used to quantify microvascular networks. These programs have provided researchers with invaluable tools, yet we estimate that they have collectively attained low adoption rates, possibly due to limitations with basic validation, segmentation performance, and nonstandard sets of quantification metrics. To address these existing constraints, we discuss opportunities to improve effectiveness, rigor, and reproducibility of microvascular network quantification to better serve the current and future needs of microvascular research.

Diabetic retinopathy is a potentially blinding eye disease that threatens the vision of one-ninth of patients with diabetes. Progression of the disease has long been attributed to an initial dropout of pericytes that enwrap the retinal microvasculature. Revealed through retinal vascular digests, a subsequent increase in basement membrane bridges was also observed. Using cell-specific markers, we demonstrate that pericytes rather than endothelial cells colocalize with these bridges. We show that the density of bridges transiently increases with elevation of Ang-2, PDGF-BB, and blood glucose; is rapidly reversed on a timescale of days; and is often associated with a pericyte cell body located off vessel. Cell-specific knockout of KLF4 in pericytes fully replicates this phenotype. In vivo imaging of limbal vessels demonstrates pericyte migration off vessel, with rapid pericyte filopodial-like process formation between adjacent vessels. Accounting for off-vessel and on-vessel pericytes, we observed no pericyte loss relative to nondiabetic control retina. These findings reveal the possibility that pericyte perturbations in location and process formation may play a role in the development of pathological vascular remodeling in diabetic retinopathy.

Alterations in vascular networks, including angiogenesis and capillary regression, play key roles in disease, wound healing, and development. The spatial structures of blood vessels can be captured through imaging, but effective characterization of network architecture requires both metrics for quantification and software to carry out the analysis in a high‐throughput and unbiased fashion. We present Rapid Editable Analysis of Vessel Elements Routine (REAVER), an open‐source tool that researchers can use to analyze high‐resolution 2D fluorescent images of blood vessel networks, and assess its performance compared to alternative image analysis programs. Using a dataset of manually analyzed images from a variety of murine tissues as a ground‐truth, REAVER exhibited high accuracy and precision for all vessel architecture metrics quantified, including vessel length density, vessel area fraction, mean vessel diameter, and branchpoint count, along with the highest pixel‐by‐pixel accuracy for the segmentation of the blood vessel network. In instances where REAVER's automated segmentation is inaccurate, we show that combining manual curation with automated analysis improves the accuracy of vessel architecture metrics. REAVER can be used to quantify differences in blood vessel architectures, making it useful in experiments designed to evaluate the effects of different external perturbations (eg, drugs or disease states).

PURPOSE. To establish Myh11 as a marker of a subset of corneal endothelial cells (CECs), and to demonstrate the feasibility of restoring the corneal endothelium with Myh11-lineage (Myh11-Lin[þ]) adipose-derived stromal cells (ASCs). METHODS. Intraperitoneal administration of tamoxifen and (Z)-4-hydroxytamoxifen eyedrops were used to trace the lineage of Myh11-expressing cells with the Myh11-Cre-ER T2-flox-tdTomato mouse model. Immunostaining and Western blot characterized marker expression and spatial distribution of Myh11-Lin(þ) cells in the cornea, and administration of 5-ethynyl-2 0-deoxyuridine labeled proliferating cells. ASCs were isolated from epididymal adipose Myh11þ mural cells and treated with cornea differentiation media to evaluate corneal endothelial differentiation potential. Differentiated ASCs were injected into the anterior chamber to test for incorporation into corneal endothelium following scratch injury. RESULTS. A subset of CECs express Myh11, a marker previously thought restricted to only mural cells. Myh11-Lin(þ) CECs marked a stable subpopulation of cells in the cornea endothelium. Myh11-Lin(þ) ASCs undergo CEC differentiation in vitro and incorporate into injured corneal endothelium. CONCLUSIONS. Dystrophy and dysfunction of the corneal endothelium accounts for almost half of all corneal transplants, the maintenance of the cornea endothelium is poorly understood, and there are a lack of mouse models to study specific CEC populations. We establish a mouse model that can trace the cell fate of a subpopulation of CECs based on Myh11 expression. A subset of ASCs that share this Myh11 transcriptional lineage are capable of differentiating into CECs that can incorporate into injured corneal endothelium, revealing a potential cell source for creating engineered transplant material.

Cover Artwork: The cover image is based on the Invited Review Methods to Label, Image, and Analyze the Complex Structural Architectures of Microvascular Networks by Bruce Corliss et al., https://doi.org/10.1111/micc.12520.