vascular networks
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2022 ◽  
Author(s):  
Gopalan Gnanaguru ◽  
Steven J Tabor ◽  
Kentaro Yuda ◽  
Ryo Mukai ◽  
Jörg Köhl ◽  
...  

Microglia, the resident immune cell of the central nervous system, play a pivotal role in facilitating neurovascular development through mechanisms that are not fully understood. This current work resolves a previously unknown role for microglia in facilitating the developmental pruning of the astrocytic template resulting in a spatially organized retinal vascular bed. Mechanistically, our study identified that local microglial expression of complement (C)3 and C3aR is necessary for the regulation of astrocyte patterning and vascular growth during retinal development. Ablation of retinal microglia, loss of C3 or C3aR reduced developmental pruning and clearance of astrocytic bodies leading to increased astrocyte density leading to altered vascular patterning during retinal vascular development. This data demonstrates that C3/C3aR signaling is an important checkpoint required for the finetuning of vascular density during neuroretinal development.


2022 ◽  
Vol 12 ◽  
Author(s):  
Sara Petrillo ◽  
Tullio Genova ◽  
Giorgia Chinigò ◽  
Ilaria Roato ◽  
Giorgia Scarpellino ◽  
...  

Bone formation involves a complex crosstalk between endothelial cells (EC) and osteodifferentiating stem cells. This functional interplay is greatly mediated by the paracrine and autocrine action of soluble factors released at the vasculature-bone interface. This study elucidates the molecular and functional responses triggered by this intimate interaction. In this study, we showed that human dermal microvascular endothelial cells (HMEC) induced the expression of pro-angiogenic factors in stem cells from human exfoliated deciduous teeth (SHED) and sustain their osteo-differentiation at the same time. In contrast, osteodifferentiating SHED increased EC recruitment and promoted the formation of complex vascular networks. Moreover, HMEC enhanced anaerobic glycolysis in proliferating SHED without compromising their ability to undergo the oxidative metabolic shift required for adequate osteo-differentiation. Taken together, these findings provide novel insights into the molecular mechanism underlying the synergistic cooperation between EC and stem cells during bone tissue renewal.


Author(s):  
Mark Whiting ◽  
Joseph Mettenburg ◽  
Enrico Novelli ◽  
Philip LeDuc ◽  
Jonathan Cagan

Abstract As machine learning is used to make strides in med- ical diagnostics, few methods provide heuristics from which human doctors can learn directly. This work introduces a method for leveraging human observable structures, such as macro scale vascular formations, for producing assessments of medical conditions with rela- tively few training cases, and uncovering patterns that are potential diagnostic aids. The approach draws on shape grammars, a rule-based technique, pioneered in design and architecture, and accelerated through a re- cursive sub-graph mining algorithm. The distribution of rule instances in the data from which they are in- duced is then used as an intermediary representation en- abling common classification and anomaly detection ap- proaches to identify indicative rules with relatively small data sets. The method is applied to 7 Tesla time-of- flight (TOF) angiography MRI (n = 54) of human brain vasculature. The data were segmented and induced to generate representative grammar rules. Ensembles of rules were isolated to implicate vascular conditions reli- ably. This application demonstrates the power of auto- mated structured intermediary representations for as- sessing nuanced biological form relationships, and the strength of shape grammars, in particular for identify- ing indicative patterns in complex vascular networks.


Author(s):  
P. Pushpalatha

Abstract: Optical coherence tomography angiography (OCTA) is an imaging which can applied in ophthalmology to provide detailed visualization of the perfusion of vascular networks in the eye. compared to previous state of the art dye-based imaging, such as fluorescein angiography. OCTA is non-invasive, time efficient, and it allows for the examination of retinal vascular in 3D. These advantage of the technique combined with the good usability in commercial devices led to a quick adoption of the new modality in the clinical routine. However, the interpretation of OCTA data is not without problems commonly observed image artifacts and the quite involved algorithmic details of OCTA signal construction can make the clinical assessment of OCTA exams challenging. In this paper we describe the technical background of OCTA and discuss the data acquisition process, common image visualization techniques, as well as 3D to 2D projection using high pass filtering, relu function and convolution neural network (CNN) for more accuracy and segmentation results.


2021 ◽  
Author(s):  
Sophie Marbach ◽  
Noah Ziethen ◽  
Leonie Bastin ◽  
Felix Baeuerle ◽  
Karen Alim

Vascular networks continuously reorganize their morphology by growing new or shrinking existing veins to optimize function. Flow shear stress on vein walls has been set forth as the local driver for this continuous adaptation. Yet, shear feedback alone cannot account for the observed diversity of network dynamics -- a puzzle made harder by scarce spatio-temporal data. Here, we resolve network-wide vein dynamics and shear during spontaneous reorganization in the prototypical vascular networks of Physarum polycephalum. Our experiments reveal a plethora of vein dynamics (stable, growing, shrinking) that are not directly proportional to local shear. We observe (a) that shear rate sensing on vein walls occurs with a time delay of 1 to 3 min and (b) that network architecture dependent parameters -- such as relative pressure or relative vein resistance -- are key to determine vein fate. We derive a model for vascular adaptation, based on force balance at the vein walls. Together with the time delay, our model reproduces the diversity of experimentally observed vein dynamics, and confirms the role of network architecture. Finally, we observe avalanches of network reorganization events which cause entire clusters of veins to vanish. Such avalanches are consistent with architectural feedback as the vein connections perpetually change with reorganization. As these network architecture dependent parameters are intrinsically connected with the laminar fluid flow in the veins, we expect our findings to play a role across flow-based vascular networks.


2021 ◽  
Author(s):  
Clément Quintard ◽  
Gustav Jonsson ◽  
Camille Laporte ◽  
Caroline Bissardon ◽  
Amandine Pitaval ◽  
...  

The development of vascular networks on-chip is crucial for the long-term culture of three-dimensional cell aggregates such as organoids, spheroids, tumoroids, and tissue explants. Despite the rapid advancement of microvascular network systems and organoid technology, vascularizing organoids-on-chips remains a challenge in tissue engineering. Moreover, most existing microfluidic devices poorly reflect the complexity of in vivo flows and require complex technical settings to operate. Considering these constraints, we developed an innovative platform to establish and monitor the formation of endothelial networks around model spheroids of mesenchymal and endothelial cells as well as blood vessel organoids generated from pluripotent stem cells, cultured for up to 15 days on-chip. Importantly, these networks were functional, demonstrating intravascular perfusion within the spheroids or vascular organoids connected to neighbouring endothelial beds. This microphysiological system thus represents a viable organ-on-chip model to vascularize biological tissues and should allow to establish perfusion into organoids using advanced microfluidics.


2021 ◽  
Author(s):  
Nikhilesh Bappoo ◽  
Lachlan J Kelsey ◽  
Yutthapong Tongpob ◽  
Kirk W Feindel ◽  
Harrison Caddy ◽  
...  

The placenta is a temporary and complex organ critical for fetal development through its subtle but convoluted harmonization of endocrine, vascular, haemodynamic and exchange adaptations. Yet, due to experimental, technological and ethical constraints, this unique organ remains poorly understood. In silico tools are emerging as a powerful means to overcome these challenges and have the potential to actualize novel breakthroughs. Here, we present an interdisciplinary framework combining in vitro experiments used to develop an elegant and scalable in silico model of oxygen diffusion. We then use in utero imaging of placental perfusion and oxygenation in both control and growth-restricted rodent placentas for validation of our in silico model. Our framework revealed the structure-function relationship in the feto-placental vasculature; oxygen diffusion is impaired in growth-restricted placentas, due to the diminished arborization of growth-restricted feto-placental vasculature and the lack of decelerated flow for adequate oxygen diffusion and exchange. We highlight the mechanisms of impairment in a rat model of growth restriction, underpinned by placental vascular impairment. Our framework reports and validates the prediction of blood flow deceleration impairment in growth restricted placentas with the placenta's oxygen transfer capability being significantly impaired, both globally and locally. Key words: Placenta; fetal growth restriction; oxygen diffusion; computational fluid dynamics; MRI


2021 ◽  
Vol 8 (12) ◽  
Author(s):  
L. F. M. Cury ◽  
G. D. Maso Talou ◽  
M. Younes-Ibrahim ◽  
P. J. Blanco

Given the relevance of the inextricable coupling between microcirculation and physiology, and the relation to organ function and disease progression, the construction of synthetic vascular networks for mathematical modelling and computer simulation is becoming an increasingly broad field of research. Building vascular networks that mimic in vivo morphometry is feasible through algorithms such as constrained constructive optimization (CCO) and variations. Nevertheless, these methods are limited by the maximum number of vessels to be generated due to the whole network update required at each vessel addition. In this work, we propose a CCO-based approach endowed with a domain decomposition strategy to concurrently create vascular networks. The performance of this approach is evaluated by analysing the agreement with the sequentially generated networks and studying the scalability when building vascular networks up to 200 000 vascular segments. Finally, we apply our method to vascularize a highly complex geometry corresponding to the cortex of a prototypical human kidney. The technique presented in this work enables the automatic generation of extensive vascular networks, removing the limitation from previous works. Thus, we can extend vascular networks (e.g. obtained from medical images) to pre-arteriolar level, yielding patient-specific whole-organ vascular models with an unprecedented level of detail.


2021 ◽  
Vol 10 (23) ◽  
pp. 2170114
Author(s):  
Xin Liu ◽  
Xinhuan Wang ◽  
Liming Zhang ◽  
Lulu Sun ◽  
Heran Wang ◽  
...  

2021 ◽  
Author(s):  
Emma L Brown ◽  
Thierry L Lefebvre ◽  
Paul W Sweeney ◽  
Bernadette Stolz ◽  
Janek Gröhl ◽  
...  

Mesoscopic photoacoustic imaging (PAI) enables non-invasive visualisation of tumour vasculature and has the potential to assess prognosis and therapeutic response. Currently, evaluating vasculature using mesoscopic PAI involves visual or semi-quantitative 2D measurements, which fail to capture 3D vessel network complexity, and lack robust ground truths for assessment of segmentation accuracy. Here, we developed an in silico, phantom, in vivo, and ex vivo-validated end-to-end framework to quantify 3D vascular networks captured using mesoscopic PAI. We applied our framework to evaluate the capacity of rule-based and machine learning-based segmentation methods, with or without vesselness image filtering, to preserve blood volume and network structure by employing topological data analysis. We first assessed segmentation performance against ground truth data of in silico synthetic vasculatures and a photoacoustic string phantom. Our results indicate that learning-based segmentation best preserves vessel diameter and blood volume at depth, while rule-based segmentation with vesselness image filtering accurately preserved network structure in superficial vessels. Next, we applied our framework to breast cancer patient-derived xenografts (PDXs), with corresponding ex vivo immunohistochemistry. We demonstrated that the above segmentation methods can reliably delineate the vasculature of 2 breast PDX models from mesoscopic PA images. Our results underscore the importance of evaluating the choice of segmentation method when applying mesoscopic PAI as a tool to evaluate vascular networks in vivo.


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