REAVER: A program for improved analysis of high-resolution vascular network images

Patient-specific vascularized tumor model: Blocking monocyte recruitment with multispecific antibodies targeting CCR2 and CSF-1R

Tumor-associated inflammation drives cancer progression and therapy resistance, often linked to the infiltration of monocyte-derived tumor-associated macrophages (TAMs), which are associated with poor prognosis in various cancers. To advance immunotherapies, testing on immunocompetent pre-clinical models of human tissue is crucial. We have developed an in vitro model of microvascular networks with tumor spheroids or patient tissues to assess monocyte trafficking into tumors and evaluate immunotherapies targeting the human tumor microenvironment. Our findings demonstrate that macrophages in vascularized breast and lung tumor models can enhance monocyte recruitment via CCL7 and CCL2, mediated by CSF-1R. Additionally, a multispecific antibody targeting CSF-1R, CCR2, and neutralizing TGF-β (CSF1R/CCR2/TGF-β Ab) repolarizes TAMs towards an anti-tumoral M1-like phenotype, reduces monocyte chemoattractant protein secretion, and blocks monocyte migration. This antibody also inhibits monocyte recruitment in patient-specific vascularized tumor models. In summary, this vascularized tumor model recapitulates the monocyte recruitment cascade, enabling functional testing of innovative therapeutic antibodies targeting TAMs in the tumor microenvironment.

Neuronal and glial cell alterations involved in the retinal degeneration of the familial dysautonomia optic neuropathy

Familial dysautonomia (FD) is a rare genetic neurodevelopmental and neurodegenerative disorder. In addition to the autonomic and peripheral sensory neuropathies that challenge patient survival, one of the most debilitating symptoms affecting patients' quality of life is progressive blindness resulting from the steady loss of retinal ganglion cells (RGCs). Within the FD community, there is a concerted effort to develop treatments to prevent the loss of RGCs. However, the mechanisms underlying the death of RGCs are not well understood. To study the mechanisms underlying RGC death, Pax6-cre;Elp1loxp/loxp male and female mice and postmortem retinal tissue from an FD patient were used to explore the neuronal and non-neuronal cellular pathology associated with the FD optic neuropathy. Neurons, astrocytes, microglia, Müller glia, and endothelial cells were investigated using a combination of histological analyses. We identified a novel disruption of cellular homeostasis and gliosis in the FD retina. Beginning shortly after birth and progressing with age, the FD retina is marked by astrogliosis and perturbations in microglia, which coincide with vascular remodeling. These changes begin before the onset of RGC death, suggesting alterations in the retinal neurovascular unit may contribute to and exacerbate RGC death. We reveal for the first time that the FD retina pathology includes reactive gliosis, increased microglial recruitment to the ganglion cell layer (GCL), disruptions in the deep and superficial vascular plexuses, and alterations in signaling pathways. These studies implicate the neurovascular unit as a disease-modifying target for therapeutic interventions in FD.

A single-pixel and non-redundant branching-based algorithm for nailfold capillary skeleton line extraction

Background

Static nailfold capillary parameters are important parameters that reflect the health of the human body. Disease onset or progression is often accompanied by changes in the physiological parameters of the nailfold. Hence, the physiological parameters of the nailfold are closely related to the study of disease, with their automated and high-precision measurements playing a crucial role in these studies. Currently, manually measured values of the nailfold's parameters are the gold standard; however, they are time consuming and labor intensive, making the development of automated measurement methods essential. Most automated measurement methods use skeleton lines; however, current skeleton-thinning algorithms have non-single pixels and redundant branches that lead to reduced measurement accuracy. This study proposes a single-pixel and non-redundant branching-based skeleton line extraction algorithm for nailfold capillaries, which is then applied to nailfold static parameter calculations to improve accuracy.

Methods

The algorithm includes deletion and restoration templates combined with the depth-first search method to obtain single-pixel skeleton lines without redundant branches. These lines are applied to the static nailfold capillary parameter measurement method based on digital image processing to calculate the blood vessel diameter.

Results

The results show that the proposed method can obtain the single-pixel skeleton line without the redundant branches that are required for the parameter calculations and improve the accuracy of the nailfold capillary diameter measurement. Experiments showed that the root mean square errors (RMSEs) of the labeled apical diameter, arterial limb diameter, and venous limb diameter were 0.794, 0.756, and 0.830 µm, respectively, when the calculated results were compared with those of the manual calculations. According to the accuracy formula, the accuracy of the method in this study is 90%. We calculated the P values of the algorithmic and manual measurements to P<0.001 and found that the difference in the measurements of the proposed algorithm is statistically significant. Therefore, the method in this study has high sensitivity and specificity for the measurement of normal nailfold capillaries.

Conclusions

The proposed algorithm could obtain single-pixel skeleton lines without redundant branches, thereby improving the nailfold static parameter measurement accuracy.

Angiogenesis-Enabled Human Ovarian Tumor Microenvironment-Chip Evaluates Pathophysiology of Platelets in Microcirculation

The tumor microenvironment (TME) promotes angiogenesis for its growth through the recruitment of multiple cells and signaling mechanisms. For example, TME actively recruits and activates platelets from the microcirculation to facilitate metastasis, but platelets may simultaneously also support tumor angiogenesis. Here, to model this complex pathophysiology within the TME that involves a signaling triad of cancer cells, sprouting endothelial cells, and platelets, an angiogenesis-enabled tumor microenvironment chip (aTME-Chip) is presented. This platform recapitulates the convergence of physiology of angiogenesis and platelet function within the ovarian TME and describes the contribution of platelets in promoting angiogenesis within an ovarian TME. By including three distinct human ovarian cancer cell-types, the aTME-Chip quantitatively reveals the following outcomes-first, introduction of platelets significantly increases angiogenesis; second, the temporal dynamics of angiogenic signaling is dependent on cancer cell type; and finally, tumor-educated platelets either activated exogenously by cancer cells or derived clinically from a cancer patient accelerate tumor angiogenesis. Further, analysis of effluents available from aTME-Chip validate functional outcomes by revealing changes in cytokine expression and several angiogenic and metastatic signaling pathways due to platelets. Collectively, this tumor microphysiological system may be deployed to derive antiangiogenic targets combined with antiplatelet treatments to arrest cancer metastasis.

AngioMT: A MATLAB based 2D image-to-physics tool to predict oxygen transport in vascularized microphysiological systems

Microphysiological models (MPS) are increasingly getting recognized as in vitro preclinical systems of pathophysiology and drug discovery. However, there is also a growing need to adapt and advance MPS to include the physiological contributions of the capillary vascular dynamics, because they undergo angiogenesis or vasculogenesis to deliver soluble oxygen and nutrients to its organs. Currently, the process of formation of microvessels in MPS is measured arbitrarily, and vascularized MPS do not include oxygen measurements in their analysis. Sensing and measuring tissue oxygen delivery is extremely difficult because it requires access to opaque and deep tissue, and/or requires extensive integration of biosensors that makes such systems impractical to use in the real world. Here, a finite element method-based oxygen transport program, called AngioMT, is built in MATLAB. AngioMT processes the routinely acquired 2D confocal images of microvascular networks in vitro and solves physical equations of diffusion-reaction dominated oxygen transport phenomena. This user-friendly image-to-physics transition in AngioMT is an enabling tool of MPS analysis because unlike the averaged morphological measures of vessels, it provides information of the spatial transport of oxygen both within the microvessels and the surrounding tissue regions. Further, it solves the more complex higher order reaction mechanisms which also improve the physiological relevance of this tool when compared directly against in vivo measurements. Finally, the program is applied in a multicellular vascularized MPS by including the ability to define additional organ/tissue subtypes in complex co-cultured systems. Therefore, AngioMT serves as an analytical tool to enhance the predictive power and performance of MPS that incorporate microcirculation.

Enhanced capillary delivery with nanobubble-mediated blood-brain barrier opening and advanced high resolution vascular segmentation

Overcoming the blood-brain barrier (BBB) is essential to enhance brain therapy. Here, we utilized nanobubbles with focused ultrasound for targeted and improved BBB opening in mice. A microscopy technique method assessed BBB opening at a single blood vessel resolution employing a dual-dye labeling technique using green fluorescent molecules to label blood vessels and Evans blue brain-impermeable dye for quantifying BBB extravasation. A deep learning architecture enabled blood vessels segmentation, delivering comparable accuracy to manual segmentation with a significant time reduction. Segmentation outcomes were applied to the Evans blue channel to quantify extravasation of each blood vessel. Results were compared to microbubble-mediated BBB opening, where reduced extravasation was observed in capillaries with a diameter of 2-6 μm. In comparison, nanobubbles yield an improved opening in these capillaries, and equivalent efficacy to that of microbubbles in larger vessels. These results indicate the potential of nanobubbles to serve as enhanced agents for BBB opening, amplifying bioeffects in capillaries while preserving comparable opening in larger vessels.

SARS-CoV-2 infection of endothelial cells, dependent on flow-induced ACE2 expression, drives hypercytokinemia in a vascularized microphysiological system

Background

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for COVID-19, has caused nearly 7 million deaths worldwide. Severe cases are marked by an aggressive inflammatory response known as hypercytokinemia, contributing to endothelial damage. Although vaccination has reduced hospitalizations, hypercytokinemia persists in breakthrough infections, emphasizing the need for disease models mimicking this response. Using a 3D microphysiological system (MPS), we explored the vascular role in SARS-CoV-2-induced hypercytokinemia.

Methods

The vascularized micro-organ (VMO) MPS, consisting of human-derived primary endothelial cells (ECs) and stromal cells within an extracellular matrix, was used to model SARS-CoV-2 infection. A non-replicative pseudotyped virus fused to GFP was employed, allowing visualization of viral entry into human ECs under physiologic flow conditions. Expression of ACE2, TMPRSS2, and AGTR1 was analyzed, and the impact of viral infection on ACE2 expression, vascular inflammation, and vascular morphology was assessed.

Results

The VMO platform facilitated the study of COVID-19 vasculature infection, revealing that ACE2 expression increased significantly in direct response to shear stress, thereby enhancing susceptibility to infection by pseudotyped SARS-CoV-2. Infected ECs secreted pro-inflammatory cytokines, including IL-6 along with coagulation factors. Cytokines released by infected cells were able to activate downstream, non-infected EC, providing an amplification mechanism for inflammation and coagulopathy.

Discussion

Our findings highlight the crucial role of vasculature in COVID-19 pathogenesis, emphasizing the significance of flow-induced ACE2 expression and subsequent inflammatory responses. The VMO provides a valuable tool for studying SARS-CoV-2 infection dynamics and evaluating potential therapeutics.

Towards standardising retinal OCT angiography image analysis with open-source toolbox OCTAVA

Quantitative assessment of retinal microvasculature in optical coherence tomography angiography (OCTA) images is important for studying, diagnosing, monitoring, and guiding the treatment of ocular and systemic diseases. However, the OCTA user community lacks universal and transparent image analysis tools that can be applied to images from a range of OCTA instruments and provide reliable and consistent microvascular metrics from diverse datasets. We present a retinal extension to the OCTA Vascular Analyser (OCTAVA) that addresses the challenges of providing robust, easy-to-use, and transparent analysis of retinal OCTA images. OCTAVA is a user-friendly, open-source toolbox that can analyse retinal OCTA images from various instruments. The toolbox delivers seven microvascular metrics for the whole image or subregions and six metrics characterising the foveal avascular zone. We validate OCTAVA using images collected by four commercial OCTA instruments demonstrating robust performance across datasets from different instruments acquired at different sites from different study cohorts. We show that OCTAVA delivers values for retinal microvascular metrics comparable to the literature and reduces their variation between studies compared to their commercial equivalents. By making OCTAVA publicly available, we aim to expand standardised research and thereby improve the reproducibility of quantitative analysis of retinal microvascular imaging. Such improvements will help to better identify more reliable and sensitive biomarkers of ocular and systemic diseases.

Application of an artificial intelligence for quantitative analysis of endothelial capillary beds in vitro

BACKGROUND

The use of endothelial cell cultures has become fundamental to study angiogenesis. Recent advances in artificial intelligences (AI) offer opportunities to develop automated assessment methods in medical research, analyzing larger datasets.

OBJECTIVE

The aim of this study was to compare the application of AI with a manual method to morphometrically quantify in vitro angiogenesis.

METHODS

Co-cultures of human microvascular endothelial cells and fibroblasts were incubated mimicking endothelial capillary-beds. An AI-software was trained for segmentation of endothelial capillaries on anti-CD31-labeled light microscope crops. Number of capillaries and branches and average capillary diameter were measured by the AI and manually on 115 crops.

RESULTS

The crops were analyzed faster by the AI than manually (3 minutes vs 1 hour per crop). Using the AI, systematically more capillaries (mean 48/mm2 vs 27/mm2) and branches (mean 23/mm2 vs 11/mm2) were counted than manually. Both methods had a strong linear relationship in counting capillaries and branches (r-capillaries = 0.88, r-branches = 0.89). No correlation was found for measurements of the diameter (r-diameter = 0.15).

CONCLUSIONS

The present AI reduces the time required for quantitative analysis of angiogenesis on large datasets, and correlates well with manual analysis.

A Comprehensive Look at In Vitro Angiogenesis Image Analysis Software

One of the complex challenges faced presently by tissue engineering (TE) is the development of vascularized constructs that accurately mimic the extracellular matrix (ECM) of native tissue in which they are inserted to promote vessel growth and, consequently, wound healing and tissue regeneration. TE technique is characterized by several stages, starting from the choice of cell culture and the more appropriate scaffold material that can adequately support and supply them with the necessary biological cues for microvessel development. The next step is to analyze the attained microvasculature, which is reliant on the available labeling and microscopy techniques to visualize the network, as well as metrics employed to characterize it. These are usually attained with the use of software, which has been cited in several works, although no clear standard procedure has been observed to promote the reproduction of the cell response analysis. The present review analyzes not only the various steps previously described in terms of the current standards for evaluation, but also surveys some of the available metrics and software used to quantify networks, along with the detection of analysis limitations and future improvements that could lead to considerable progress for angiogenesis evaluation and application in TE research.

Machine learning chained neural network analysis of oxygen transport amplifies the physiological relevance of vascularized microphysiological systems

Since every biological system requires capillaries to support its oxygenation, design of engineered preclinical models of such systems, for example, vascularized microphysiological systems (vMPS) have gained attention enhancing the physiological relevance of human biology and therapies. But the physiology and function of formed vessels in the vMPS is currently assessed by non-standardized, user-dependent, and simple morphological metrics that poorly relate to the fundamental function of oxygenation of organs. Here, a chained neural network is engineered and trained using morphological metrics derived from a diverse set of vMPS representing random combinations of factors that influence the vascular network architecture of a tissue. This machine-learned algorithm outputs a singular measure, termed as vascular network quality index (VNQI). Cross-correlation of morphological metrics and VNQI against measured oxygen levels within vMPS revealed that VNQI correlated the most with oxygen measurements. VNQI is sensitive to the determinants of vascular networks and it consistently correlates better to the measured oxygen than morphological metrics alone. Finally, the VNQI is positively associated with the functional outcomes of cell transplantation therapies, shown in the vascularized islet-chip challenged with hypoxia. Therefore, adoption of this tool will amplify the predictions and enable standardization of organ-chips, transplant models, and other cell biosystems.

Accelerating the in vitro emulation of Alzheimer's disease-associated phenotypes using a novel 3D blood-brain barrier neurosphere co-culture model

High failure rates in clinical trials for neurodegenerative disorders such as Alzheimer's disease have been linked to an insufficient predictive validity of current animal-based disease models. This has created an increasing demand for alternative, human-based models capable of emulating key pathological phenotypes in vitro. Here, a three-dimensional Alzheimer's disease model was developed using a compartmentalized microfluidic device that combines a self-assembled microvascular network of the human blood-brain barrier with neurospheres derived from Alzheimer's disease-specific neural progenitor cells. To shorten microfluidic co-culture times, neurospheres were pre-differentiated for 21 days to express Alzheimer's disease-specific pathological phenotypes prior to the introduction into the microfluidic device. In agreement with post-mortem studies and Alzheimer's disease in vivo models, after 7 days of co-culture with pre-differentiated Alzheimer's disease-specific neurospheres, the three-dimensional blood-brain barrier network exhibited significant changes in barrier permeability and morphology. Furthermore, vascular networks in co-culture with Alzheimer's disease-specific microtissues displayed localized β-amyloid deposition. Thus, by interconnecting a microvascular network of the blood-brain barrier with pre-differentiated neurospheres the presented model holds immense potential for replicating key neurovascular phenotypes of neurodegenerative disorders in vitro.

Establishing a Physiologic Human Vascularized Micro-Tumor Model for Cancer Research

A lack of validated cancer models that recapitulate the tumor microenvironment of solid cancers in vitro remains a significant bottleneck for preclinical cancer research and therapeutic development. To overcome this problem, we have developed the vascularized microtumor (VMT), or tumor chip, a microphysiological system that realistically models the complex human tumor microenvironment. The VMT forms de novo within a microfluidic platform by co-culture of multiple human cell types under dynamic, physiological flow conditions. This tissue-engineered micro-tumor construct incorporates a living perfused vascular network that supports the growing tumor mass just as newly formed vessels do in vivo. Importantly, drugs and immune cells must cross the endothelial layer to reach the tumor, modeling in vivo physiological barriers to therapeutic delivery and efficacy. Since the VMT platform is optically transparent, high-resolution imaging of dynamic processes such as immune cell extravasation and metastasis can be achieved with direct visualization of fluorescently labeled cells within the tissue. Further, the VMT retains in vivo tumor heterogeneity, gene expression signatures, and drug responses. Virtually any tumor type can be adapted to the platform, and primary cells from fresh surgical tissues grow and respond to drug treatment in the VMT, paving the way toward truly personalized medicine. Here, the methods for establishing the VMT and utilizing it for oncology research are outlined. This innovative approach opens new possibilities for studying tumors and drug responses, providing researchers with a powerful tool to advance cancer research.

A three-dimensional method for morphological analysis and flow velocity estimation in microvasculature on-a-chip

Three-dimensional (3D) imaging techniques (e.g., confocal microscopy) are commonly used to visualize in vitro models, especially microvasculature on-a-chip. Conversely, 3D analysis is not the standard method to extract quantitative information from those models. We developed the μVES algorithm to analyze vascularized in vitro models leveraging 3D data. It computes morphological parameters (geometry, diameter, length, tortuosity, eccentricity) and intravascular flow velocity. μVES application to microfluidic vascularized in vitro models shows that they successfully replicate functional features of the microvasculature in vivo in terms of intravascular fluid flow velocity. However, wall shear stress is lower compared to in vivo references. The morphological analysis also highlights the model's physiological similarities (vessel length and tortuosity) and shortcomings (vessel radius and surface-over-volume ratio). The addition of the third dimension in our analysis produced significant differences in the metrics assessed compared to 2D estimations. It enabled the computation of new indices, such as vessel eccentricity. These μVES capabilities can find application in analyses of different in vitro vascular models, as well as in vivo and ex vivo microvasculature.

Nailfold Capillaroscopy: A Promising, Noninvasive Approach to Predict Retinopathy of Prematurity

OBJECTIVE

To test the hypothesis that nailfold capillaroscopy can noninvasively detect dysregulated retinal angiogenesis and predict retinopathy of prematurity (ROP) in infants born premature before its development.

METHODS

In a cohort of 32 infants born <33 weeks of gestation, 1386 nailfold capillary network images of the 3 middle fingers of each hand were taken during the first month of life. From these, 25 infants had paired data taken 2 weeks apart during the first month of life. Images were analyzed for metrics of peripheral microvascular density using a machine learning-based segmentation approach and a previously validated microvascular quantification platform (REAVER vascular analysis). Results were correlated with subsequent development of ROP based on a published consensus ROP severity scale.

RESULTS

In total, 18 of 32 (56%) (entire cohort) and 13 of 25 (52%) (2-time point subgroup) developed ROP. Peripheral vascular density decreased significantly during the first month of life. In the paired time point analysis, vessel length density, a key metric of peripheral vascular density, was significantly greater at both time points among infants who later developed ROP (15 563 and 11 996 μm/mm2, respectively) compared with infants who did not (12 252 and 8845 μm/mm2, respectively) (P < .001, both time points). A vessel length density cutoff of >15 100 at T1 or at T2 correctly detected 3 of 3 infants requiring ROP therapy. In a mixed-effects linear regression model, peripheral vascular density metrics were significantly correlated with ROP severity.

CONCLUSIONS

Nailfold microvascular density assessed during the first month of life is a promising, noninvasive biomarker to identify premature infants at highest risk for ROP before detection on eye exam.

Evaluation of the Morphological and Biological Functions of Vascularized Microphysiological Systems with Supervised Machine Learning

Vascularized microphysiological systems and organoids are contemporary preclinical experimental platforms representing human tissue or organ function in health and disease. While vascularization is emerging as a necessary physiological organ-level feature required in most such systems, there is no standard tool or morphological metric to measure the performance or biological function of vascularized networks within these models. Further, the commonly reported morphological metrics may not correlate to the network's biological function-oxygen transport. Here, a large library of vascular network images was analyzed by the measure of each sample's morphology and oxygen transport potential. The oxygen transport quantification is computationally expensive and user-dependent, so machine learning techniques were examined to generate regression models relating morphology to function. Principal component and factor analyses were applied to reduce dimensionality of the multivariate dataset, followed by multiple linear regression and tree-based regression analyses. These examinations reveal that while several morphological data relate poorly to the biological function, some machine learning models possess a relatively improved, but still moderate predictive potential. Overall, random forest regression model correlates to the biological function of vascular networks with relatively higher accuracy than other regression models.

Sex-specific actions of estradiol and testosterone on human fibroblast and endothelial cell proliferation, bioenergetics, and vasculogenesis

Progress toward the development of sex-specific tissue engineered systems has been hampered by the lack of research efforts to define the effects of sex-specific hormone concentrations on relevant human cell types. Here, we investigated the effects of defined concentrations of estradiol (E2) and dihydrotestosterone (DHT) on primary human dermal and lung fibroblasts (HDF and HLF), and human umbilical vein endothelial cells (HUVEC) from female (XX) and male (XY) donors in both 2D expansion cultures and 3D stromal vascular tissues. Sex-matched E2 and DHT stimulation in 2D expansion cultures significantly increased the proliferation index, mitochondrial membrane potential, and the expression of genes associated with bioenergetics (Na+/K+ ATPase, somatic cytochrome C) and beneficial stress responses (chaperonin) in all cell types tested. Notably, cross sex hormone stimulation, i.e., DHT treatment of XX cells in the absence of E2 and E2 stimulation of XY cells in the absence of DHT, decreased bioenergetic capacity and inhibited cell proliferation. We used a microengineered 3D vasculogenesis assay to assess hormone effects on tissue scale morphogenesis. E2 increased metrics of vascular network complexity compared to vehicle in XX tissues. Conversely, and in line with results from 2D expansion cultures, E2 potently inhibited vasculogenesis compared to vehicle in XY tissues. DHT did not significantly alter vasculogenesis in XX or XY tissues but increased the number of non-participating endothelial cells in both sexes. This study establishes a scientific rationale and adaptable methods for using sex hormone stimulation to develop sex-specific culture systems.

Q-VAT: Quantitative Vascular Analysis Tool

As our imaging capability increase, so does our need for appropriate image quantification tools. Quantitative Vascular Analysis Tool (Q-VAT) is an open-source software, written for Fiji (ImageJ), that perform automated analysis and quantification on large two-dimensional images of whole tissue sections. Importantly, it allows separation of the vessel measurement based on diameter, allowing the macro- and microvasculature to be quantified separately. To enable analysis of entire tissue sections on regular laboratory computers, the vascular network of large samples is analyzed in a tile-wise manner, significantly reducing labor and bypassing several limitations related to manual quantification. Double or triple-stained slides can be analyzed, with a quantification of the percentage of vessels where the staining's overlap. To demonstrate the versatility, we applied Q-VAT to obtain morphological read-outs of the vasculature network in microscopy images of whole-mount immuno-stained sections of various mouse tissues.

Digital labeling for 3D histology: segmenting blood vessels without a vascular contrast agent using deep learning

Recent advances in optical tissue clearing and three-dimensional (3D) fluorescence microscopy have enabled high resolution in situ imaging of intact tissues. Using simply prepared samples, we demonstrate here "digital labeling," a method to segment blood vessels in 3D volumes solely based on the autofluorescence signal and a nuclei stain (DAPI). We trained a deep-learning neural network based on the U-net architecture using a regression loss instead of a commonly used segmentation loss to achieve better detection of small vessels. We achieved high vessel detection accuracy and obtained accurate vascular morphometrics such as vessel length density and orientation. In the future, such digital labeling approach could easily be transferred to other biological structures.

A systematic review and comparison of automated tools for quantification of fibrous networks

Fibrous networks are essential structural components of biological and engineered materials. Accordingly, many approaches have been developed to quantify their structural properties, which define their material properties. However, a comprehensive overview and comparison of methods is lacking. Therefore, we systematically searched for automated tools quantifying network characteristics in confocal, stimulated emission depletion (STED) or scanning electron microscopy (SEM) images and compared these tools by applying them to fibrin, a prototypical fibrous network in thrombi. Structural properties of fibrin such as fiber diameter and alignment are clinically relevant, since they influence the risk of thrombosis. Based on a systematic comparison of the automated tools with each other, manual measurements, and simulated networks, we provide guidance to choose appropriate tools for fibrous network quantification depending on imaging modality and structural parameter. These tools are often able to reliably measure relative changes in network characteristics, but absolute numbers should be interpreted with care. STATEMENT OF SIGNIFICANCE: Structural properties of fibrous networks define material properties of many biological and engineered materials. Many methods exist to automatically quantify structural properties, but an overview and comparison is lacking. In this work, we systematically searched for all publicly available automated analysis tools that can quantify structural properties of fibrous networks. Next, we compared them by applying them to microscopy images of fibrin networks. We also benchmarked the automated tools against manual measurements or synthetic images. As a result, we give advice on which automated analysis tools to use for specific structural properties. We anticipate that researchers from a large variety of fields, ranging from thrombosis and hemostasis to cancer research, and materials science, can benefit from our work.

Evaluation of the morphological and biological functions of vascularized microphysiological systems with supervised machine learning

Vascularized microphysiological systems and organoids are contemporary preclinical experimental platforms representing human tissue or organ function in health and disease. While vascularization is emerging as a necessary physiological organ-level feature required in most such systems, there is no standard tool or morphological metric to measure the performance or biological function of vascularized networks within these models. Further, the commonly reported morphological metrics may not correlate to the network's biological function - oxygen transport. Here, a large library of vascular network images was analyzed by the measure of each sample's morphology and oxygen transport potential. The oxygen transport quantification is computationally expensive and user-dependent, so machine learning techniques were examined to generate regression models relating morphology to function. Principal component and factor analyses were applied to reduce dimensionality of the multivariate dataset, followed by multiple linear regression and tree-based regression analyses. These examinations reveal that while several morphological data relate poorly to the biological function, some machine learning models possess a relatively improved, but still moderate predictive potential. Overall, random forest regression model correlates to the biological function of vascular networks with relatively higher accuracy than other regression models.

AngioMT: An in silico platform for digital sensing of oxygen transport through heterogenous microvascular networks

Measuring the capacity of microvascular networks in delivering soluble oxygen and nutrients to its organs is essential in health, disease, and surgical interventions. Here, a finite element method-based oxygen transport program, AngioMT, is designed and validated to predict spatial oxygen distribution and other physiologically relevant transport metrics within both the vascular network and the surrounding tissue. The software processes acquired images of microvascular networks and produces a digital mesh which is used to predict vessel and tissue oxygenation. The image-to-physics translation by AngioMT correlated with results from commercial software, however only AngioMT could provide predictions within the solid tissue in addition to vessel oxygenation. AngioMT predictions were sensitive and positively correlated to spatial heterogeneity and extent of vascularization of 500 different vascular networks formed with variable vasculogenic conditions. The predictions of AngioMT cross-correlate with experimentally-measured oxygen distributions in vivo. The computational power of the software is increased by including calculations of higher order reaction mechanisms, and the program includes defining additional organ and tissue structures for a more physiologically relevant analysis of tissue oxygenation in complex co-cultured systems, or in vivo. AngioMT may serve as a digital performance measuring tool of vascular networks in microcirculation, experimental models of vascularized tissues and organs, and in clinical applications, such as organ transplants.

Extracellular Vesicles from Human Umbilical Cord-Derived MSCs Affect Vessel Formation In Vitro and Promote VEGFR2-Mediated Cell Survival

Mesenchymal stromal cell (MSC)-derived extracellular vesicles (EVs) have emerged as novel tools in regenerative medicine. Angiogenesis modulation is widely studied for the treatment of ischaemic diseases, wound healing, and tissue regeneration. Here, we have shown that EVs from human umbilical cord-derived MSCs can affect VEGFR2 signalling, a master regulator of angiogenesis homeostasis, via altering the phosphorylation of AKT. This translates into an inhibition of apoptosis, promoting exclusively cell survival, but not proliferation, in human microvascular endothelial cells. Interestingly, when comparing EVs from normoxic cells to those obtained from hypoxia (1% O₂) preconditioned cells, hypoxia-derived EVs appear to have a slightly enhanced effect. Furthermore, when studied in a longer term endothelial-fibroblast co-culture angiogenesis model in vitro, both EV populations demonstrated a positive effect on vessel formation, evidenced by increased vessel networks with tubes of significantly larger diameters. Our data reveals that EVs selectively target components of the angiogenic pathway, promoting VEGFR2-mediated cell survival via enhancement of AKT activation. Our data show that EVs are able to enhance specific components of the VEGF signalling pathway and may have therapeutic potential to support endothelial cell survival.

Visualizing the spatiotemporal pattern of yolk sac membrane vascular network by enhanced local fractal analysis

OBJECTIVE

To establish methods for providing a comprehensive and detailed description of the spatial distribution of the vascular networks, and to reveal the spatiotemporal pattern of the yolk sac membrane vascular network during the angiogenic procedure.

METHODS

Addressing the limitations in the conventional local fractal analysis, an improved approach, named scanning average local fractal dimension, was proposed. This method was conducted on 6 high-resolution vascular images of the yolk sac membrane for 3 eggs at two stages (E3 and E4) to characterize the spatial distribution of the complexity of the vascular network.

RESULTS

With the proposed method, the spatial distribution of the complexity of the yolk sac membrane vascular network was visualized. From E3 to E4, the local fractal dimension increased in 3 eggs, 1.80 ± 0.02 vs. 1.85 ± 0.02, 1.72 ± 0.03 vs. 1.83 ± 0.02, and 1.77 ± 0.03 vs. 1.82 ± 0.02, respectively. The mean local fractal dimension in the most distal area from the embryo proper was the lowest at E3 while the highest at E4. At E3, the most peaks of the local fractal dimension were located in the vein territories and shifted to artery territories at E4.

CONCLUSIONS

The spatial distribution of the complexity of the yolk sac membrane vascular network exhibited diverse patterns at different stages. In addition from E3 to E4, the increment of complexity at the intersection areas between arteries and sinus terminalis was with the most advance. This is consistent with the physiologic evidence. The present work provides a potential approach for investigating the spatiotemporal pattern of the angiogenic process.

Dysfunctional cGMP Signaling Leads to Age-Related Retinal Vascular Alterations and Astrocyte Remodeling in Mice

The nitric oxide-guanylyl cyclase-1-cyclic guanylate monophosphate (NO-GC-1-cGMP) pathway is integral to the control of vascular tone and morphology. Mice lacking the alpha catalytic domain of guanylate cyclase (GC1-/-) develop retinal ganglion cell (RGC) degeneration with age, with only modest fluctuations in intraocular pressure (IOP). Increasing the bioavailability of cGMP in GC1-/- mice prevents neurodegeneration independently of IOP, suggesting alternative mechanisms of retinal neurodegeneration. In continuation to these studies, we explored the hypothesis that dysfunctional cGMP signaling leads to changes in the neurovascular unit that may contribute to RGC degeneration. We assessed retinal vasculature and astrocyte morphology in young and aged GC1-/- and wild type mice. GC1-/- mice exhibit increased peripheral retinal vessel dilation and shorter retinal vessel branching with increasing age compared to Wt mice. Astrocyte cell morphology is aberrant, and glial fibrillary acidic protein (GFAP) density is increased in young and aged GC1-/- mice, with areas of dense astrocyte matting around blood vessels. Our results suggest that proper cGMP signaling is essential to retinal vessel morphology with increasing age. Vascular changed are preceded by alterations in astrocyte morphology which may together contribute to retinal neurodegeneration and loss of visual acuity observed in GC1-/- mice.

Pericyte Loss Leads to Capillary Stalling Through Increased Leukocyte-Endothelial Cell Interaction in the Brain

The neurovascular unit is a functional unit composed of neurons, glial cells, pericytes, and endothelial cells which sustain brain activity. While pericyte is a key component of the neurovascular unit, its role in cerebral blood flow regulation remains elusive. Recently, capillary stalling, which means the transient interruption of microcirculation in capillaries, has been shown to have an outsized impact on microcirculatory changes in several neurological diseases. In this study, we investigated capillary stalling and its possible causes, such as the cerebral endothelial glycocalyx and leukocyte adhesion molecules after depleting pericytes postnatally in mice. Moreover, we investigated hypoxia and gliosis as consequences of capillary stalling. Although there were no differences in the capillary structure and RBC flow, longitudinal optical coherence tomography angiography showed an increased number of stalled segments in capillaries after pericyte loss. Furthermore, the extent of the cerebral endothelial glycocalyx was decreased with increased expression of leukocyte adhesion molecules, suggesting enhanced interaction between leukocytes and endothelial cells. Finally, pericyte loss induced cerebral hypoxia and gliosis. Cumulatively, the results suggest that pericyte loss induces capillary stalling through increased interaction between leukocytes and endothelial cells in the brain.

OCTAVA: An open-source toolbox for quantitative analysis of optical coherence tomography angiography images

Optical coherence tomography angiography (OCTA) performs non-invasive visualization and characterization of microvasculature in research and clinical applications mainly in ophthalmology and dermatology. A wide variety of instruments, imaging protocols, processing methods and metrics have been used to describe the microvasculature, such that comparing different study outcomes is currently not feasible. With the goal of contributing to standardization of OCTA data analysis, we report a user-friendly, open-source toolbox, OCTAVA (OCTA Vascular Analyzer), to automate the pre-processing, segmentation, and quantitative analysis of en face OCTA maximum intensity projection images in a standardized workflow. We present each analysis step, including optimization of filtering and choice of segmentation algorithm, and definition of metrics. We perform quantitative analysis of OCTA images from different commercial and non-commercial instruments and samples and show OCTAVA can accurately and reproducibly determine metrics for characterization of microvasculature. Wide adoption could enable studies and aggregation of data on a scale sufficient to develop reliable microvascular biomarkers for early detection, and to guide treatment, of microvascular disease.

Comparative Analysis of Blood-Derived Endothelial Cells for Designing Next-Generation Personalized Organ-on-Chips

Background

Organ‐on‐chip technology has accelerated in vitro preclinical research of the vascular system, and a key strength of this platform is its promise to impact personalized medicine by providing a primary human cell–culture environment where endothelial cells are directly biopsied from individual tissue or differentiated through stem cell biotechniques. However, these methods are difficult to adopt in laboratories, and often result in impurity and heterogeneity of cells. This limits the power of organ‐chips in making accurate physiological predictions. In this study, we report the use of blood‐derived endothelial cells as alternatives to primary and induced pluripotent stem cell–derived endothelial cells.

Methods and Results

Here, the genotype, phenotype, and organ‐chip functional characteristics of blood‐derived outgrowth endothelial cells were compared against commercially available and most used primary endothelial cells and induced pluripotent stem cell–derived endothelial cells. The methods include RNA‐sequencing, as well as criterion standard assays of cell marker expression, growth kinetics, migration potential, and vasculogenesis. Finally, thromboinflammatory responses under shear using vessel‐chips engineered with blood‐derived endothelial cells were assessed. Blood‐derived endothelial cells exhibit the criterion standard hallmarks of typical endothelial cells. There are differences in gene expression profiles between different sources of endothelial cells, but blood‐derived cells are relatively closer to primary cells than induced pluripotent stem cell–derived. Furthermore, blood‐derived endothelial cells are much easier to obtain from individuals and yet, they serve as an equally effective cell source for functional studies and organ‐chips compared with primary cells or induced pluripotent stem cell–derived cells.

Conclusions

Blood‐derived endothelial cells may be used in preclinical research for developing more robust and personalized next‐generation disease models using organ‐on‐chips.

Heparin Microislands in Microporous Annealed Particle Scaffolds for Accelerated Diabetic Wound Healing

Mimicking growth factor-ECM interactions for promoting cell migration is a powerful technique to improve tissue integration with biomaterial scaffolds for the regeneration of damaged tissues. This has been attempted by scaffold-mediated controlled delivery of exogenous growth factors; however, the predetermined nature of this delivery can limit the scaffold's ability to meet each wound's unique spatiotemporal regenerative needs and presents translational hurdles. To address this limitation, we present a new approach to growth factor presentation by incorporating heparin microislands, which are spatially isolated heparin-containing microparticles that can reorganize and protect endogenous local growth factors via heterogeneous sequestration at the microscale in vitro and result in functional improvements in wound healing. More specifically, we incorporated our heparin microislands within microporous annealed particle (MAP) scaffolds, which allows facile tuning of microenvironment heterogeneity through ratiometric mixing of microparticle sub-populations. In this manuscript, we demonstrate the ability of heparin microislands to heterogeneously sequester applied growth factor and control downstream cell migration in vitro. Further, we present their ability to significantly improve wound healing outcomes (epidermal regeneration and re-vascularization) in a diabetic wound model relative to two clinically relevant controls.

Quantifying Vascular Density in Tissue Engineered Constructs Using Machine Learning

Given the considerable research efforts in understanding and manipulating the vasculature in tissue health and function, making effective measurements of vascular density is critical for a variety of biomedical applications. However, because the vasculature is a heterogeneous collection of vessel segments, arranged in a complex three-dimensional architecture, which is dynamic in form and function, it is difficult to effectively measure. Here, we developed a semi-automated method that leverages machine learning to identify and quantify vascular metrics in an angiogenesis model imaged with different modalities. This software, BioSegment, is designed to make high throughput vascular density measurements of fluorescent or phase contrast images. Furthermore, the rapidity of assessments makes it an ideal tool for incorporation in tissue manufacturing workflows, where engineered tissue constructs may require frequent monitoring, to ensure that vascular growth benchmarks are met.

Vascular Patterning as Integrative Readout of Complex Molecular and Physiological Signaling by VESsel GENeration Analysis

The molecular signaling cascades that regulate angiogenesis and microvascular remodeling are fundamental to normal development, healthy physiology, and pathologies such as inflammation and cancer. Yet quantifying such complex, fractally branching vascular patterns remains difficult. We review application of NASA's globally available, freely downloadable VESsel GENeration (VESGEN) Analysis software to numerous examples of 2D vascular trees, networks, and tree-network composites. Upon input of a binary vascular image, automated output includes informative vascular maps and quantification of parameters such as tortuosity, fractal dimension, vessel diameter, area, length, number, and branch point. Previous research has demonstrated that cytokines and therapeutics such as vascular endothelial growth factor, basic fibroblast growth factor (fibroblast growth factor-2), transforming growth factor-beta-1, and steroid triamcinolone acetonide specify unique "fingerprint" or "biomarker" vascular patterns that integrate dominant signaling with physiological response. In vivo experimental examples described here include vascular response to keratinocyte growth factor, a novel vessel tortuosity factor; angiogenic inhibition in humanized tumor xenografts by the anti-angiogenesis drug leronlimab; intestinal vascular inflammation with probiotic protection by Saccharomyces boulardii, and a workflow programming of vascular architecture for 3D bioprinting of regenerative tissues from 2D images. Microvascular remodeling in the human retina is described for astronaut risks in microgravity, vessel tortuosity in diabetic retinopathy, and venous occlusive disease.

REAVER: A program for improved analysis of high-resolution vascular network images

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).