Niroobakhsh Mohammad, Xie Yixia, Dallas Sarah L, Moore David, Johnson Mark L, Ganesh Thiagarajan
School of Science and Engineering, University of Missouri-Kansas City; Department of Oral and Craniofacial Sciences, School of Dentistry, University of Missouri-Kansas City.
Department of Oral and Craniofacial Sciences, School of Dentistry, University of Missouri-Kansas City.
J Vis Exp. 2024 Nov 29(213). doi: 10.3791/64699.
Osteocytes are the bone cells that are thought to respond to mechanical strains and fluid flow shear stress (FFSS) by activating various biological pathways in a process known as mechanotransduction. Confocal image-derived models of osteocyte networks are a valuable tool for conducting Computational Fluid Dynamics (CFD) analysis to evaluate shear stresses on the osteocyte membrane, which cannot be determined by direct measurement. Computational modeling using these high-resolution images of the microstructural architecture of bone was used to numerically simulate the mechanical loading exerted on bone and understand the load-induced stimulation of osteocytes. This study elaborates on the methods to develop 3D single osteocyte models using confocal microscope images of the Lacunar-Canalicular Network (LCN) to perform CFD analysis utilizing various computational modeling software. Prior to confocal microscopy, the mouse bones are sectioned and stained with Fluorescein isothiocyanate (FITC) dye to label the LCN. At 100x resolution, Z-stack images are collected using a confocal microscope and imported into MIMICS software (3D image-based processing software) to construct a surface model of the LCN and osteocyte-dendritic processes. These surfaces are then subtracted using a Boolean operation in 3-Matic software (3D data optimization software) to model the lacunar fluidic space around the osteocyte cell body and canalicular space around the dendrites containing lacunocanalicular fluid. 3D volumetric fluid geometry is imported into ANSYS software (simulation software) for CFD analysis. ANSYS CFX (CFD software) is used to apply physiological loading on the bone as fluid pressure, and the wall shear stresses on the osteocytes and dendritic processes are determined. The morphology of the LCN affects the shear stress values sensed by the osteocyte cell membrane and cell processes. Therefore, the details of how confocal image-based models are developed can be valuable in understanding osteocyte mechanosensation and can lay the groundwork for future studies in this area.
骨细胞是一种骨细胞,人们认为它们通过激活各种生物途径来响应机械应变和流体流动剪切应力(FFSS),这一过程称为机械转导。源自共聚焦图像的骨细胞网络模型是进行计算流体动力学(CFD)分析以评估骨细胞膜上剪切应力的宝贵工具,而这种应力无法通过直接测量来确定。利用这些高分辨率的骨微观结构图像进行计算建模,以数值模拟施加在骨上的机械负荷,并了解负荷诱导的骨细胞刺激。本研究详细阐述了使用腔隙-小管网络(LCN)的共聚焦显微镜图像开发3D单个骨细胞模型的方法,以便利用各种计算建模软件进行CFD分析。在进行共聚焦显微镜检查之前,将小鼠骨骼切片并用异硫氰酸荧光素(FITC)染料染色,以标记LCN。在100倍分辨率下,使用共聚焦显微镜收集Z-stack图像,并导入MIMICS软件(基于3D图像的处理软件)以构建LCN和骨细胞树突状突起的表面模型。然后在3-Matic软件(3D数据优化软件)中使用布尔运算减去这些表面,以模拟骨细胞胞体周围的腔隙流体空间以及包含腔隙小管液的树突周围的小管空间。将3D体积流体几何形状导入ANSYS软件(模拟软件)进行CFD分析。使用ANSYS CFX(CFD软件)将生理负荷作为流体压力施加在骨上,并确定骨细胞和树突状突起上的壁面剪切应力。LCN的形态会影响骨细胞膜和细胞突起所感知的剪切应力值。因此,基于共聚焦图像的模型开发细节对于理解骨细胞机械感受具有重要价值,并可为该领域的未来研究奠定基础。