Institute for Biomechanics, ETH Zurich, Zurich, Switzerland.
Bone. 2010 Nov;47(5):848-58. doi: 10.1016/j.bone.2010.07.026. Epub 2010 Aug 3.
Osteocytes are the most abundant cells in bone and the only cells embedded in the bone mineral matrix. They form an extended, three-dimensional (3D) network, whose processes interconnecting the cell bodies reside in thin canals, the canaliculi. Together with the osteocyte lacunae, the canaliculi form the lacuno-canalicular network (LCN). As the negative imprint of the cellular network within bone tissue, the LCN morphology is considered to play a central role for bone mechanosensation and mechanotransduction. However, the LCN has neither been visualized nor quantified in an adequate way up to now. On this account, this article summarizes the current state of knowledge of the LCN morphology and then reviews different imaging methods regarding the quantitative 3D assessment of bone tissue in general and of the LCN in particular. These imaging methods will provide new insights in the field of bone mechanosensation and mechanotransduction and thus, into processes of strain sensation and transduction, which are tightly associated with osteocyte viability and bone quality.
成骨细胞是骨组织中含量最丰富的细胞，也是唯一嵌入骨矿物质基质的细胞。它们形成一个延伸的三维（3D）网络，其细胞体连接的过程位于细管中，即骨小管。骨小管与骨陷窝一起构成骨陷窝小管网络（LCN）。作为骨组织内细胞网络的负印，LCN 形态被认为在骨机械感知和机械转导中起核心作用。然而，到目前为止，LCN 既没有被可视化，也没有被充分量化。有鉴于此，本文总结了 LCN 形态的现有知识，然后回顾了不同的成像方法，这些方法涉及对骨组织的定量 3D 评估，特别是对 LCN 的评估。这些成像方法将为骨机械感知和机械转导领域提供新的见解，从而深入了解与成骨细胞活力和骨质量密切相关的应变感知和转导过程。
