Monitoring of random microvessel network formation by in-line sensing of flow rates: A numerical and in vitro investigation
Monitoring of random microvessel network formation by in-line sensing of flow rates: A numerical and in vitro investigation
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DOI：
10.1016/j.sna.2021.112970
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发表时间：
2021-11
影响因子：
4.6
通讯作者：
V. Pozdin;Patrick D. Erb;McKenna L. Downey;Kristina R. Rivera;M. Daniele
中科院分区：
文献类型：
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作者：
V. Pozdin;Patrick D. Erb;McKenna L. Downey;Kristina R. Rivera;M. Daniele
在工程化组织构建体中定向或从头形成微血管对于准确复制生理功能至关重要。依赖于自发微血管形成的系统的限制因素是不能精确地量化微血管网络的发展和控制流体移动通过形成的血管。在此，我们报告了一种监测微尺度流体网络动态形成的策略，该策略可以转化为对工程组织构建体中微血管发育的监测。流体网络的非侵入性、非破坏性监测和表征通过流体流速的在线感测来实现，并将该测量与流体网络的流体动力学阻力相关联，以模拟微血管形成和连通性的进展。计算流体动力学，等效电路和实验模型进行了比较，模拟多代分支或分裂微血管网络。该网络模拟了具有不同横截面积的血管，多达16个分支血管，微血管网络体积范围为20 - 30 mm 3。在所有模型中，网络复杂性和体积的增加程度对应于测量的跨接管流速的降低;然而，血管横截面也影响测量的跨接管流速，即在低血管高度（<200 μm）下，响应主要由增加的网络体积决定，而在较高血管高度（>200 μm）下，响应主要由窄通道的阻力决定。模型之间显示出约2%的误差，这归因于制造模型的几何形状变化，并说明了精确和非破坏性监测微血管网络发育和体积变化的潜力。
The directed or de novo formation of microvasculature in engineered tissue constructs is essential for accurately replicating physiological function. A limiting factor of a system relying on spontaneous microvessel formation is the inability to precisely quantify the development of the microvascular network and control fluid moving through formed vessels. Herein, we report a strategy to monitor the dynamic formation of microscale fluid networks, which can be translated to the monitoring of microvasculature development in engineered tissue constructs. The non-invasive, non-destructive monitoring and characterization of the fluid network is achievedviain-line sensing of fluid flow rate and correlating this measurement to the hydrodynamic resistance of the fluid network to model the progression of microvessel formation and connectivity. Computational fluid dynamics, equivalent circuit, and experimental models were compared, which simulated multi-generational branching or splitting microvessel networks. The networks simulated vessels with varying cross-sectional area, up to 16 branching vessels, and microvessel network volume ranging from ˜20−30 mm3. In all models, the increasing degree of network complexity and volume corresponded to a decrease in jumper flow-rate measured; however, vessel cross-section also impacted the measured jumper flow rate,i.e. at low vessel height (<200 μm) response was dominated by increased network volume and at higher vessel height (>200 μm) the response was dominated by resistance of narrow channels. An approximately 2% error was exhibited between the models, which was attributed to variation in the geometry of the fabricated models and illustrates the potential to precisely and non-destructively monitor microvessel network development and volumetric changes.