Microvasculature in Health and Disease

Beare, Jason E.; Curtis-Whitchurch, L.; LeBlanc, Amanda J.; Hoying, James B.

doi:10.1016/b978-0-12-809657-4.99820-x

Cited by 5 publications

(4 citation statements)

References 162 publications

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“…The architecture of a microvascular network is complex and characterized by constantly bifurcating and merging vessels. The topology of networks can vary from organ to organ and differ under healthy and disease conditions 1 – 7 . Moreover, these vessels are not necessarily straight, and often highly winding.…”

Section: Introductionmentioning

confidence: 99%

A computational study of red blood cell deformability effect on hemodynamic alteration in capillary vessel networks

Ebrahimi

Bagchi

2022

Sci Rep

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Capillary blood vessels, the smallest vessels in the body, form an intricate network with constantly bifurcating, merging and winding vessels. Red blood cells (RBCs) must navigate through such complex microvascular networks in order to maintain tissue perfusion and oxygenation. Normal, healthy RBCs are extremely deformable and able to easily flow through narrow vessels. However, RBC deformability is reduced in many pathological conditions and during blood storage. The influence of reduced cell deformability on microvascular hemodynamics is not well established. Here we use a high-fidelity, 3D computational model of blood flow that retains exact geometric details of physiologically realistic microvascular networks, and deformation of every one of nearly a thousand RBCs flowing through the networks. We predict that reduced RBC deformability alters RBC trafficking with significant and heterogeneous changes in hematocrit. We quantify such changes along with RBC partitioning and lingering at vascular bifurcations, perfusion and vascular resistance, and wall shear stress. We elucidate the cellular-scale mechanisms that cause such changes. We show that such changes arise primarily due to the altered RBC dynamics at vascular bifurcations, as well as cross-stream migration. Less deformable cells tend to linger less at majority of bifurcations increasing the fraction of RBCs entering the higher flow branches. Changes in vascular resistance also seen to be heterogeneous and correlate with hematocrit changes. Furthermore, alteration in RBC dynamics is shown to cause localized changes in wall shear stress within vessels and near vascular bifurcations. Such heterogeneous and focal changes in hemodynamics may be the cause of morphological abnormalities in capillary vessel networks as observed in several diseases.

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Section: Introductionmentioning

confidence: 99%

A computational study of red blood cell deformability effect on hemodynamic alteration in capillary vessel networks

Ebrahimi

Bagchi

2022

Sci Rep

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“…Historically, vascular research predominantly focused on larger blood vessels, mainly due to ease of imaging and physical accessibility. In contrast, microvessels received less attention, despite their critical functions in regulating nutrient and gas exchange 1 , facilitating immune cell activity 2 , and their significant implications in various diseases 3 . The recent introduction of microfluidic models - referred to as microvessel-on-chip - has allowed for comprehensive studies into human-derived microvessels for the first time 4 .…”

Section: Introductionmentioning

confidence: 99%

Label-free phenotyping of human microvessel networks

Rappez,

Akinbote,

Cherubini

et al. 2024

Preprint

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Understanding the spatial heterogeneity in blood vessel formation and development is crucial for various biomedical applications. Traditional methods forin-vitromicrovessel segmentation rely on fluorescent labeling, which either interferes with the sample homeostasis, limits the study to a restricted set of precursor cells, or requires sample fixation, thus preventing live measurements. Moreover, these methods often focus on small, cropped images, neglecting global spatial heterogeneity of microvasculature, leading to biased data interpretation. To overcome these limitations, we present VascuMap, a deep-learning-based tool for label-free vessel segmentation and spatial analysis. VascuMap enables a comprehensive examination of entire vessel networks, capturing both morphological and topological features across the full vascular bed. Our method achieves high segmentation accuracy, comparable to the state-of-the-art fluorescence-based models. VascuMap’s capabilities extend to characterizing vasculature generated from label-free patient-derived samples, a vital step towards personalized medicine. Its compatibility with widefield label-free microscopy also accelerates sample acquisition, making it ideal for high-throughput systems crucial for drug toxicity and safety screens.

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“…Capillary vessels, the smallest blood vessels in the body, are responsible for delivering oxygen and other metabolites to tissues. Together with vascular bifurcations and mergers, they form a complex network of vessels referred to as the microvascular network ( 1–3 ). The distribution of blood flow and red blood cells (RBCs) in the network is critical to the healthy function of the body as it dictates the oxygen and nutrient delivery and waste removal ( 4 , 5 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting capillary vessel network hemodynamics in silico by machine learning

Ebrahimi,

Bagchi

2024

PNAS Nexus

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Blood velocity and red blood cell (RBC) distribution profiles in a capillary vessel cross-section in the microcirculation are generally complex and do not follow Poiseuille’s parabolic or uniform pattern. Existing imaging techniques used to map large microvascular networks in vivo do not allow a direct measurement of full 3D velocity and RBC concentration profiles, although such information is needed for accurate evaluation of the physiological variables, such as the wall shear stress (WSS) and near-wall cell-free layer (CFL), that play critical roles in blood flow regulation, disease progression, angiogenesis, and hemostasis. Theoretical network flow models, often used for hemodynamic predictions in experimentally acquired images of the microvascular network, cannot provide the full 3D profiles either. In contrast, such information can be readily obtained from high-fidelity computational models that treat blood as a suspension of deformable RBCs. These models, however, are computationally expensive and not feasible for extension to the microvascular network at large spatial scales up to an organ level. To overcome such limitations, here we present machine learning (ML) models that bypass such expensive computations but provide highly accurate and full 3D profiles of the blood velocity, RBC concentration, WSS, and CFL in every vessel in the microvascular network. The ML models, which are based on artificial neural network and convolution-based U-net models, predict hemodynamic quantities that compare very well against the true data but reduce the prediction time by several orders. This study therefore paves the way for ML to make detailed and accurate hemodynamic predictions in spatially large microvascular networks at an organ-scale.

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Microvasculature in Health and Disease

Cited by 5 publications

References 162 publications

A computational study of red blood cell deformability effect on hemodynamic alteration in capillary vessel networks

A computational study of red blood cell deformability effect on hemodynamic alteration in capillary vessel networks

Label-free phenotyping of human microvessel networks

Predicting capillary vessel network hemodynamics in silico by machine learning

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