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Abstract:
Bone is a living material, which adapts its structure in response to the mechanical environment. For structural adaptation bone need to sense the mechanical loading. However, bone is so stiff that the local strains are too small to be directly sensed by bone cells. Osteocytes are bone cells that form a cell network located within the mineralized matrix. Their cell bodies are housed in lacunae and their cell processes in narrow canals, the canaliculi. According to the fluid flow hypothesis, load induced fluid flow through this lacunocanalicular network (LCN) provides an amplification mechanism which allows osteocytes to sense dynamic loading of the bone. We hypothesize that the network architecture of the LCN plays an essential role in bone’s mechanosensitivity, as it influences the fluid flow. We aimed to test these hypotheses by using real LCN architectures in a model of load induced fluid flow, and compare the resulting flow with the mechanoresponse of bone. We imaged the LCN using confocal laser scanning microscopy (CLSM). Image processing was then used to describe the LCN as a mathematical network consisting of edges and nodes, representing the canaliculi and their connections respectively. We then employed circuit theory, based on Kirchhoff’s laws, to model the velocities of the fluid in all the imaged canaliculi. Based on these velocities, the mechanoresponse of bone was predicted. Mice were used in my study, as this allowed a controlled in vivo loading and a measurement of the mechanoresponse in terms of formed/resorbed bone using in vivo µCT. Fluid flow patterns through the LCN of mice correlated with the measured mechanoresponse, i.e., bone formation was observed near surfaces of higher flow, while resorption was observed near surfaces with low flow. The prediction of the mechanoresponse considering the architecture of the LCN was quantitatively better than a prediction based on strains only. Qualitatively, we identified that vascular canals in the cortex act as local sinks of fluid flow and, therefore, reduce the flow at the nearby bone surface. In contrast, flow velocities increased in convergent network structures, where the flow is channeled into fewer canaliculi nearby the surface. In a second project we focused on healthy human osteonal bone. Osteons are cylindrical structures around vascular canals, which are practically sealed off from the surrounding bone. We analyzed 8 ordinary osteons with a rather homogeneous LCN, and 9 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. A substantially higher load-induced fluid flow was observed in canaliculi that bridge the two parts of the osteon-in-osteons. This resulted in an average flow, which was 2.3 times higher compared to ordinary osteons. It is therefore likely that osteon-in-osteons particularly contribute to the mechanosensitivity of cortical bone. Based on both studies in this PhD thesis we conclude that LCN architecture should be considered as a key determinant of bone adaptation besides mechanical loading.
