Vascular Network Topology Research Articles

A mathematical model describing the tip-stalk regulation in angiogenesis

Angiogenesis is the process of new blood vessel growth from existing vessels, involving extensive cell signaling. Under normal conditions, new vessels are robust and organized, with a balance among angiogenesis factors. In abnormal conditions, such as tumor development, vessels are stunted and tangled due to an imbalance of these factors. Pathological angiogenesis stimulates rapid vessel growth to feed the oxygen and nutriente starved tumor. Inhibiting angiogenesis can cause side effects like hypertension, thrombosis, and fatigue. To better understand this process, significant effort has gone into studying signaling pathways, contributing to drug development for diseases like cancer. This study presents a mathematical model describing angiogenesis on a microscopic scale, comparing its results with experimental data on vascular network topology. The model, implemented in MatLab®, uses ordinary differential equations to represent cell behavior. Results show that altering VEGF (Vascular Endothelial Growth Factor) disrupts system balance, impacting angiogenesis and possibly explaining differences in network topology seen experimentally.

Extracellular Vesicles from Adipose Tissue-Derived Stromal Cells Stimulate Angiogenesis in a Scaffold-Dependent Fashion

Background:The extracellular vesicles (EVs) secreted by adipose tissue-derived stromal cells (ASC) are microenvironment modulators in tissue regeneration by releasing their molecular cargo, including miRNAs. However, the influence of ASC-derived extracellular vesicles (ASC-EVs) on endothelial cells (ECs) and vascularisation is poorly understood. The present study aimed to determine the pro-angiogenic effects of ASC-EVs and explore their miRNA profile.Methods:EVs were isolated from normoxic and hypoxic cultured ASC conditioned culture medium. The miRNA expression profile was determined by miRseq, and EV markers were determined by Western blot and immunofluorescence staining. The uptake dynamics of fluorescently labelled EVs were monitored for 24 h. ASC-EVs' pro-angiogenic effect was assessed by sprouting ex vivo rat aorta rings in left ventricular-decellularized extracellular matrix (LV dECM) hydrogel or basement membrane hydrogel (Geltrex®).Results:ASC-EVs augmented vascular network formation by aorta rings. The vascular network topology and stability were influenced in a hydrogel scaffold-dependent fashion. The ASC-EVs were enriched for several miRNA families/clusters, including Let-7 and miR-23/27/24. The miRNA-1290 was the highest enriched non-clustered miRNA, accounting for almost 20% of all reads in hypoxia EVs.Conclusion:Our study revealed that ASC-EVs augment in vitro and ex vivo vascularisation, likely due to the enriched pro-angiogenic miRNAs in EVs, particularly miR-1290. Our results show promise for regenerative and revascularisation therapies based on ASC-EV-loaded ECM hydrogels.

Segmentation of Low-Light Optical Coherence Tomography Angiography Images under the Constraints of Vascular Network Topology.

Optical coherence tomography angiography (OCTA) offers critical insights into the retinal vascular system, yet its full potential is hindered by challenges in precise image segmentation. Current methodologies struggle with imaging artifacts and clarity issues, particularly under low-light conditions and when using various high-speed CMOS sensors. These challenges are particularly pronounced when diagnosing and classifying diseases such as branch vein occlusion (BVO). To address these issues, we have developed a novel network based on topological structure generation, which transitions from superficial to deep retinal layers to enhance OCTA segmentation accuracy. Our approach not only demonstrates improved performance through qualitative visual comparisons and quantitative metric analyses but also effectively mitigates artifacts caused by low-light OCTA, resulting in reduced noise and enhanced clarity of the images. Furthermore, our system introduces a structured methodology for classifying BVO diseases, bridging a critical gap in this field. The primary aim of these advancements is to elevate the quality of OCTA images and bolster the reliability of their segmentation. Initial evaluations suggest that our method holds promise for establishing robust, fine-grained standards in OCTA vascular segmentation and analysis.

A global sensitivity analysis approach applied to a multiscale model of microvascular flow

A global sensitivity analysis of a multiscale computational model of microvascular flow is presented. A total of 140 simulations have been completed and analyzed varying 6 input parameters and considering their effects on 7 output variables. Interestingly, the vascular network topology has been found as a determinant factor for both vasculature-related and interstitium-related quantities. Regarding the firsts, the vascular network topology has obtained a score of 5.5/6 and 6/6 for average and spatial distribution respectively (where 6 is the maximum and 1 is the minimum). On the other hand, considering interstitium-related quantities, the score is 4/6 and 5/6 for average and spatial distribution respectively. These results suggest that the network topology has a significant influence on the outcome of the computational analysis.

Mean-Subtraction Method for De-Shadowing of Tail Artifacts in Cerebral OCTA Images: A Proof of Concept

When imaging brain vasculature with optical coherence tomography angiography (OCTA), volumetric analysis of cortical vascular networks in OCTA datasets is frequently challenging due to the presence of artifacts, which appear as multiple-scattering tails beneath superficial large vessels in OCTA images. These tails shadow underlying small vessels, making the assessment of vascular morphology in the deep cortex difficult. In this work, we introduce an image processing technique based on mean subtraction of the depth profile that can effectively reduce these tails to better reveal small hidden vessels compared to the current tail removal approach. With the improved vascular image quality, we demonstrate that this simple method can provide better visualization of three-dimensional vascular network topology for quantitative cerebrovascular studies.

Complexity, Economic Inequality and Income Distribution: What Can the Vascular Network Topology and Empirical Analysis of Readily Available Data Tell Us About the End of Poverty?

Complexity, Economic Inequality and Income Distribution: What Can the Vascular Network Topology and Empirical Analysis of Readily Available Data Tell Us About the End of Poverty?

Retinal Vascular Network Topology Reconstruction and Artery/Vein Classification via Dominant Set Clustering.

The estimation of vascular network topology in complex networks is important in understanding the relationship between vascular changes and a wide spectrum of diseases. Automatic classification of the retinal vascular trees into arteries and veins is of direct assistance to the ophthalmologist in terms of diagnosis and treatment of eye disease. However, it is challenging due to their projective ambiguity and subtle changes in appearance, contrast, and geometry in the imaging process. In this paper, we propose a novel method that is capable of making the artery/vein (A/V) distinction in retinal color fundus images based on vascular network topological properties. To this end, we adapt the concept of dominant set clustering and formalize the retinal blood vessel topology estimation and the A/V classification as a pairwise clustering problem. The graph is constructed through image segmentation, skeletonization, and identification of significant nodes. The edge weight is defined as the inverse Euclidean distance between its two end points in the feature space of intensity, orientation, curvature, diameter, and entropy. The reconstructed vascular network is classified into arteries and veins based on their intensity and morphology. The proposed approach has been applied to five public databases, namely INSPIRE, IOSTAR, VICAVR, DRIVE, and WIDE, and achieved high accuracies of 95.1%, 94.2%, 93.8%, 91.1%, and 91.0%, respectively. Furthermore, we have made manual annotations of the blood vessel topologies for INSPIRE, IOSTAR, VICAVR, and DRIVE datasets, and these annotations are released for public access so as to facilitate researchers in the community.

Positive Feedback Defines the Timing, Magnitude, and Robustness of Angiogenesis

SummaryAngiogenesis is driven by the coordinated collective branching of specialized leading “tip” and trailing “stalk” endothelial cells (ECs). While Notch-regulated negative feedback suppresses excessive tip selection, roles for positive feedback in EC identity decisions remain unexplored. Here, by integrating computational modeling with in vivo experimentation, we reveal that positive feedback critically modulates the magnitude, timing, and robustness of angiogenic responses. In silico modeling predicts that positive-feedback-mediated amplification of VEGF signaling generates an ultrasensitive bistable switch that underpins quick and robust tip-stalk decisions. In agreement, we define a positive-feedback loop exhibiting these properties in vivo, whereby Vegf-induced expression of the atypical tetraspanin, tm4sf18, amplifies Vegf signaling to dictate the speed and robustness of EC selection for angiogenesis. Consequently, tm4sf18 mutant zebrafish select fewer motile ECs and exhibit stunted hypocellular vessels with unstable tip identity that is severely perturbed by even subtle Vegfr attenuation. Hence, positive feedback spatiotemporally shapes the angiogenic switch to ultimately modulate vascular network topology.

Retinal vascular segmentation using superpixel-based line operator and its application to vascular topology estimation.

Automatic methods of analyzing of retinal vascular networks, such as retinal blood vessel detection, vascular network topology estimation, and arteries/veins classification are of great assistance to the ophthalmologist in terms of diagnosis and treatment of a wide spectrum of diseases. We propose a new framework for precisely segmenting retinal vasculatures, constructing retinal vascular network topology, and separating the arteries and veins. A nonlocal total variation inspired Retinex model is employed to remove the image intensity inhomogeneities and relatively poor contrast. For better generalizability and segmentation performance, a superpixel-based line operator is proposed as to distinguish between lines and the edges, thus allowing more tolerance in the position of the respective contours. The concept of dominant sets clustering is adopted to estimate retinal vessel topology and classify the vessel network into arteries and veins. The proposed segmentation method yields competitive results on three public data sets (STARE, DRIVE, and IOSTAR), and it has superior performance when compared with unsupervised segmentation methods, with accuracy of 0.954, 0.957, and 0.964, respectively. The topology estimation approach has been applied to five public databases (DRIVE,STARE, INSPIRE, IOSTAR, and VICAVR) and achieved high accuracy of 0.830, 0.910, 0.915, 0.928, and 0.889, respectively. The accuracies of arteries/veins classification based on the estimated vascular topology on three public databases (INSPIRE, DRIVE and VICAVR) are 0.90.9, 0.910, and 0.907, respectively. The experimental results show that the proposed framework has effectively addressed crossover problem, a bottleneck issue in segmentation and vascular topology reconstruction. The vascular topology information significantly improves the accuracy on arteries/veins classification.

Large-scale subject-specific cerebral arterial tree modeling using automated parametric mesh generation for blood flow simulation

In this paper, we present a novel technique for automatic parametric mesh generation of subject-specific cerebral arterial trees. This technique generates high-quality and anatomically accurate computational meshes for fast blood flow simulations extending the scope of 3D vascular modeling to a large portion of cerebral arterial trees. For this purpose, a parametric meshing procedure was developed to automatically decompose the vascular skeleton, extract geometric features and generate hexahedral meshes using a body-fitted coordinate system that optimally follows the vascular network topology. To validate the anatomical accuracy of the reconstructed vasculature, we performed statistical analysis to quantify the alignment between parametric meshes and raw vascular images using receiver operating characteristic curve. Geometric accuracy evaluation showed an agreement with area under the curves value of 0.87 between the constructed mesh and raw MRA data sets. Parametric meshing yielded on-average, 36.6% and 21.7% orthogonal and equiangular skew quality improvement over the unstructured tetrahedral meshes. The parametric meshing and processing pipeline constitutes an automated technique to reconstruct and simulate blood flow throughout a large portion of the cerebral arterial tree down to the level of pial vessels. This study is the first step towards fast large-scale subject-specific hemodynamic analysis for clinical applications.

Fundamental principles of vascular network topology.

The vascular system is arguably the most important biological system in many organisms. Although the general principles of its architecture are simple, the growth of blood vessels occurs under extreme physical conditions. Optimization is an important aspect of the development of computational models of the vascular branching structures. This review surveys the approaches used to optimize the topology and estimate different geometrical parameters of the vascular system. The review is focused on optimizations using complex cost functions based on the minimum total energy principle and the relationship between the laws of growth and precise vascular network topology. Experimental studies of vascular networks in different species are also discussed.

Time to Decide? Dynamical Analysis Predicts Partial Tip/Stalk Patterning States Arise during Angiogenesis.

Angiogenesis is a highly dynamic morphogenesis process; however, surprisingly little is known about the timing of the different molecular processes involved. Although the role of the VEGF-notch-DLL4 signaling pathway has been established as essential for tip/stalk cell competition during sprouting, the speed and dynamic properties of the underlying process at the individual cell level has not been fully elucidated. In this study, using mathematical modeling we investigate how specific, biologically meaningful, local conditions around and within an individual cell can influence their unique tip/stalk phenotype switching kinetics. To this end we constructed an ordinary differential equation model of VEGF-notch-DLL4 signaling in a system of two, coupled endothelial cells (EC). Our studies reveal that at any given point in an angiogenic vessel the time it takes a cell to decide to take on a tip or stalk phenotype may be drastically different, and this asynchrony of tip/stalk cell decisions along vessels itself acts to speed up later competitions. We unexpectedly uncover intermediate “partial” yet stable states lying between the tip and stalk cell fates, and identify that internal cellular factors, such as NAD-dependent deacetylase sirtuin-1 (Sirt1) and Lunatic fringe 1 (Lfng1), can specifically determine the length of time a cell spends in these newly identified partial tip/stalk states. Importantly, the model predicts that these partial EC states can arise during normal angiogenesis, in particular during cell rearrangement in sprouts, providing a novel two-stage mechanism for rapid adaptive behavior to the cells highly dynamic environment. Overall, this study demonstrates that different factors (both internal and external to EC) can be used to modulate the speed of tip/stalk decisions, opening up new opportunities and challenges for future biological experiments and therapeutic targeting to manipulate vascular network topology, and our basic understanding of developmental/pathological angiogenesis.

Temporal modulation of collective cell behavior controls vascular network topology.

Vascular network density determines the amount of oxygen and nutrients delivered to host tissues, but how the vast diversity of densities is generated is unknown. Reiterations of endothelial-tip-cell selection, sprout extension and anastomosis are the basis for vascular network generation, a process governed by the VEGF/Notch feedback loop. Here, we find that temporal regulation of this feedback loop, a previously unexplored dimension, is the key mechanism to determine vascular density. Iterating between computational modeling and in vivo live imaging, we demonstrate that the rate of tip-cell selection determines the length of linear sprout extension at the expense of branching, dictating network density. We provide the first example of a host tissue-derived signal (Semaphorin3E-Plexin-D1) that accelerates tip cell selection rate, yielding a dense network. We propose that temporal regulation of this critical, iterative aspect of network formation could be a general mechanism, and additional temporal regulators may exist to sculpt vascular topology.

Imaging and graphing of cortical vasculature using dynamically focused optical coherence microscopy angiography.

Recently, optical coherence tomography (OCT) angiography has enabled label-free imaging of vasculature based on dynamic scattering in vessels. However, quantitative volumetric analysis of the vascular networks depicted in OCT angiography data has remained challenging. Multiple-scattering tails (artifacts specific to the imaging geometry) make automated assessment of vascular morphology problematic. We demonstrate that dynamically focused optical coherence microscopy (OCM) angiography with a high numerical aperture, chosen so the scattering length greatly exceeds the depth-of-field, significantly reduces the deleterious effect of multiple-scattering tails in synthesized angiograms. Capitalizing on the improved vascular image quality, we devised and tailored a self-correcting automated graphing approach that achieves a reconstruction of cortical microvasculature from OCM angiography data sets with accuracy approaching that attained by trained operators. The automated techniques described here will facilitate more widespread study of vascular network topology in health and disease.

A filter-flow perspective of haematogenous metastasis offers a non-genetic paradigm for personalised cancer therapy

Research into mechanisms of haematogenous metastasis has largely become genetic in focus, attempting to understand the molecular basis of ‘seed–soil’ relationships. Preceding this biological mechanism is the physical process of dissemination of circulating tumour cells (CTCs) in the circulation. Patterns of metastatic spread have been previously quantified using the metastatic efficiency index, a measure quantifying metastatic incidence for a given primary-target organ pair and the relative blood flow between them. We extend this concept to take into account the reduction in CTCs which occurs in organ capillary beds connected by a realistic vascular network topology. Application to a dataset of metastatic incidence reveals that metastatic patterns depend strongly on assumptions about the existence and location of micrometastatic disease which governs CTC dynamics on the network, something which has heretofore not been considered – an oversight which precludes our ability to predict metastatic patterns in individual patients.

Microvascular network architecture in a mammary carcinoma.

The distribution of blood flow in a given tumor regulates the exchange and uptake of relevant molecules in chemo-, immuno-, and radiation therapy, thereby determining the efficacy of present day cancer treatments. Because it plays a significant role in heat transfer, the distribution of blood flow is also important in both thermographic detection and hyperthermia treatment [1, 2]. But what determines the distribution of blood flow in a microvascular network? In both normal and pathologic tissues, blood flow through a vascular bed is determined by several factors including the vascular network topology and the dimensions of the blood vessels. Previous workers have made qualitative comparisons of host and tumor vasculature including both animal tumors (for review see [3]) as well as human tumor xenotransplants [4]. To date, however, there exists no quantitative data describing the microvascular network architecture of any tumor type. If such data can be obtained, then a network model capable of predicting the blood pressure and flow distribution in a given tumor can be constructed, thus allowing insight into growth rate variability among tumors and the mechanisms governing current cancer therapies. Below is a brief presentation of the results of such a quantitative analysis of the vascular network topology in a mammary adenocarcinoma from Less et al. [5].KeywordsTumor VasculatureTumor Blood FlowVessel LengthBranch AngleVessel DimensionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.