Department of Biomaterials, Max Planck Institute of Colloids and Interfaces, 14476, Potsdam, Germany.
Berlin-Brandenburg School of Regenerative Therapies (BSRT), Föhrer Str. 15, 13353, Berlin, Germany.
Biomech Model Mechanobiol. 2020 Jun;19(3):823-840. doi: 10.1007/s10237-019-01250-1. Epub 2019 Nov 28.
A popular hypothesis explains the mechanosensitivity of bone due to osteocytes sensing the load-induced flow of interstitial fluid squeezed through the lacunocanalicular network (LCN). However, the way in which the intricate structure of the LCN influences fluid flow through the network is largely unexplored. We therefore aimed to quantify fluid flow through real LCNs from human osteons using a combination of experimental and computational techniques. Bone samples were stained with rhodamine to image the LCN with 3D confocal microscopy. Image analysis was then performed to convert image stacks into mathematical network structures, in order to estimate the intrinsic permeability of the osteons as well as the load-induced fluid flow using hydraulic circuit theory. Fluid flow was studied in both ordinary osteons with a rather homogeneous LCN as well as a frequent subtype of osteons-so-called osteon-in-osteons-which are characterized by a ring-like zone of low network connectivity between the inner and the outer parts of these osteons. We analyzed 8 ordinary osteons and 9 osteon-in-osteons from the femur midshaft of a 57-year-old woman without any known disease. While the intrinsic permeability was 2.7 times smaller in osteon-in-osteons compared to ordinary osteons, the load-induced fluid velocity was 2.3 times higher. This increased fluid velocity in osteon-in-osteons can be explained by the longer path length, needed to cross the osteon from the cement line to the Haversian canal, including more fluid-filled lacunae and canaliculi. This explanation was corroborated by the observation that a purely structural parameter-the mean path length to the Haversian canal-is an excellent predictor for the average fluid flow velocity. We conclude that osteon-in-osteons may be particularly significant contributors to the mechanosensitivity of cortical bone, due to the higher fluid flow in this type of osteons.
一种流行的假说解释了骨的机械敏感性,即骨细胞感知通过骨陷窝-小管网络(LCN)挤压的负载诱导的间质液流动。然而,LCN 的复杂结构影响网络中流体流动的方式在很大程度上仍未得到探索。因此,我们旨在使用实验和计算技术的组合来定量测量来自人骨单位的真实 LCN 中的流体流动。用罗丹明对骨样本进行染色,用 3D 共聚焦显微镜对 LCN 进行成像。然后进行图像分析,将图像堆叠转换为数学网络结构,以便使用液压电路理论估计骨单位的固有渗透性以及负载诱导的流体流动。在具有相当均匀的 LCN 的普通骨单位以及所谓的骨单位中的骨单位-这些骨单位的内、外部分之间具有低网络连通性的环状区域为特征-这两种骨单位中研究了流体流动。我们分析了来自一名 57 岁女性股骨中段的 8 个普通骨单位和 9 个骨单位中的骨单位,该女性没有任何已知疾病。尽管骨单位中的固有渗透率比普通骨单位低 2.7 倍,但负载诱导的流体速度却高 2.3 倍。骨单位中的这种增加的流体速度可以用穿过从骨单位的水泥线到哈夫氏管的较长路径长度来解释,包括更多充满液体的骨陷窝和小管。这种解释得到了以下观察结果的证实:纯粹的结构参数-通向哈夫氏管的平均路径长度-是平均流体流速的极好预测指标。我们得出的结论是,由于这种骨单位中的流体流动较高,骨单位中的骨单位可能是皮质骨机械敏感性的特别重要的贡献者。