Pandian Navaneeth Krishna Rajeeva, Farell Alanna, Davis Emily, Sundaram Subramanian, van Steen Abraham Christoffel Ignatius, Chang Teo Jessica Li, Eyckmans Jeroen, Chen Christopher S
Harvard Wyss Institute for Biologically Inspired Engineering, Boston, MA 02115, USA.
Biological Design Center, Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.
bioRxiv. 2025 Feb 15:2024.02.28.582152. doi: 10.1101/2024.02.28.582152.
The health and function of microvascular beds are dramatically impacted by the mechanical forces that they experience due to fluid flow. These fluid flow-generated forces are challenging to measure directly and are typically calculated from experimental flow data. However, current computational fluid dynamics (CFD) models either employ truncated 2D models or overlook the presence of extraluminal flows within the interstitial space between vessels that result from the permeability of the endothelium lining the vessels, which are crucial components affecting flow dynamics. To address this, we present a bottom-up modeling approach that assesses fluid flow in 3D-engineered vessel networks featuring an endothelial lining and interstitial space. Using image processing algorithms to segment 3D confocal image stacks from engineered capillary networks, we reconstructed a 3D computational model of the networks. We incorporated vascular permeability and matrix porosity values to model the contributions of the endothelial lining and interstitial spaces to the flow dynamics in the networks. Simulations suggest that including the endothelial monolayer and the interstitium significantly affects the predicted flow magnitude in the vessels and flow profiles in the interstitium. To demonstrate the importance of these factors, we showed experimentally and computationally that while cytokine (IL-1β) treatment did not affect the network architecture, it significantly increased vessel permeability and resulted in a dramatic decrease in wall shear stresses and flow velocities intraluminally within the networks. In conclusion, this framework offers a robust methodology for studying flow dynamics in 3D in vitro vessel networks, enhancing our understanding of vascular physiology and pathology.
微血管床的健康和功能会受到因流体流动而产生的机械力的显著影响。这些由流体流动产生的力很难直接测量，通常是根据实验流动数据计算得出的。然而，当前的计算流体动力学（CFD）模型要么采用截断的二维模型，要么忽略了血管之间间质空间内由于血管内皮通透性而产生的管腔外流，而这些是影响流动动力学的关键因素。为了解决这个问题，我们提出了一种自下而上的建模方法，用于评估具有内皮衬里和间质空间的三维工程血管网络中的流体流动。使用图像处理算法对工程化毛细血管网络的三维共聚焦图像堆栈进行分割，我们重建了网络的三维计算模型。我们纳入了血管通透性和基质孔隙率值，以模拟内皮衬里和间质空间对网络流动动力学的贡献。模拟结果表明，纳入内皮单层和间质会显著影响预测的血管内流量大小和间质内的流动剖面。为了证明这些因素的重要性，我们通过实验和计算表明，虽然细胞因子（IL-1β）处理不影响网络结构，但它会显著增加血管通透性，并导致网络内管腔内的壁面剪应力和流速急剧下降。总之，这个框架为研究三维体外血管网络中的流动动力学提供了一种强大的方法，增强了我们对血管生理学和病理学的理解。
