Organisms rely on mechanosensing mechanisms to adapt to changes in their mechanical environment. Fluid-filled network structures not only ensure efficient transport but can also be employed for mechanosensation. The lacunocanalicular network (LCN) is a fluid-filled network structure, which pervades our bones and accommodates a cell network of osteocytes. For the mechanism of mechanosensation, it was hypothesized that load-induced fluid flow results in forces that can be sensed by the cells. We use a controlled in vivo loading experiment on murine tibiae to test this hypothesis, whereby the mechanoresponse was quantified experimentally by in vivo micro-computed tomography (µCT) in terms of formed and resorbed bone volume. By imaging the LCN using confocal microscopy in bone volumes covering the entire cross-section of mouse tibiae and by calculating the fluid flow in the three-dimensional (3D) network, we could perform a direct comparison between predictions based on fluid flow velocity and the experimentally measured mechanoresponse. While local strain distributions estimated by finite-element analysis incorrectly predicts preferred bone formation on the periosteal surface, we demonstrate that additional consideration of the LCN architecture not only corrects this erroneous bias in the prediction but also explains observed differences in the mechanosensitivity between the three investigated mice. We also identified the presence of vascular channels as an important mechanism to locally reduce fluid flow. Flow velocities increased for a convergent network structure where all of the flow is channeled into fewer canaliculi. We conclude that, besides mechanical loading, LCN architecture should be considered as a key determinant of bone adaptation.

The osteocyte lacunar-canalicular network (LCN) penetrates bone and houses the osteocytes and their processes. Despite its rather low volume fraction, the LCN represents an outstanding large surface that is possibly used by the osteocytes to interact with the surrounding mineralized bone matrix thereby contributing to mineral homeostasis. The aim of this study was to quantitatively describe such contributions by spatially correlating the local density of the LCN with the mineral content at the same location in micrometer-sized volume elements in human osteons. For this purpose, 65 osteons from the femur midshaft from healthy adults (n=4) and children (n=2) were structurally characterized with two different techniques. The 3D structure of the LCN in the osteons was imaged with confocal laser scanning microscopy after staining the bone samples with rhodamine. Subsequent image analysis provided the canalicular length density, i.e. the total length of the canaliculi per unit volume (µm/µm 3). Quantitative information on the mineral content (wt%Ca) from the identical regions was obtained using quantitative backscattered electron imaging. As the LCN-porosity lowers the mineral content, a negative correlation between Ca content and network density was expected. Calculations predict a reduction of around-0.97 fmol Ca per µm of network. However, the experiment revealed for 62 out of 65 osteons a positive correlation resulting in an average additional Ca loading of +1.15 fmol per µm of canalicular network, i.e. an accumulation of mineral has occurred at dense network regions. We hypothesize that this accumulation happens in the close vicinity of canaliculi forming mineral reservoirs that can be utilized by osteocytes. Significant differences found between individuals indicate that the extent of mineral loading of the reservoir zone reflects an important parameter for mineral homeostasis.

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 osteonin-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.