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The mechanoresponse of bone is closely related to the osteocyte lacunocanalicular network architecture

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Van Tol,  Alexander
Richard Weinkamer, Biomaterialien, Max Planck Institute of Colloids and Interfaces, Max Planck Society;

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Schemenz,  Victoria
Richard Weinkamer, Biomaterialien, Max Planck Institute of Colloids and Interfaces, Max Planck Society;

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Wagermaier,  Wolfgang
Wolfgang Wagermaier, Biomaterialien, Max Planck Institute of Colloids and Interfaces, Max Planck Society;

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Roschger,  Andreas
Wolfgang Wagermaier, Biomaterialien, Max Planck Institute of Colloids and Interfaces, Max Planck Society;

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Razi,  Hajar
Biomaterialien, Max Planck Institute of Colloids and Interfaces, Max Planck Society;

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Fratzl,  Peter
Peter Fratzl, Biomaterialien, Max Planck Institute of Colloids and Interfaces, Max Planck Society;

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Weinkamer,  Richard
Richard Weinkamer, Biomaterialien, Max Planck Institute of Colloids and Interfaces, Max Planck Society;

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Citation

Van Tol, A., Schemenz, V., Wagermaier, W., Roschger, A., Razi, H., Vitienes, I., et al. (2020). The mechanoresponse of bone is closely related to the osteocyte lacunocanalicular network architecture. Proceedings of the National Academy of Sciences of the United States of America, 117(51), 32251-32259. doi:10.1073/pnas.2011504117.


Cite as: http://hdl.handle.net/21.11116/0000-0007-8A11-A
Abstract
The explanation of how bone senses and adapts to mechanical stimulation still relies on hypotheses. The fluid flow hypothesis claims that a load-induced fluid flow through the lacunocanalicular network can be sensed by osteocytes, which reside within the network structure. We show that considering the network architecture results in a better prediction of bone remodeling than mechanical strain alone. This was done by calculating the fluid flow through the lacunocanalicular network in bone volumes covering the complete cross-sections of mouse tibiae, which underwent controlled in vivo loading. The established relationship between mechanosensitivity and network architecture in individual animals implies possibilities for patient-specific therapies. A new connectomics approach to analyze lacunocanalicular network properties is necessary to understand skeletal mechanobiology.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.Raw microscopy data have been deposited in the Open Access Data Repository of the Max Planck Society (https://edmond.mpdl.mpg.de/imeji/collection/0fK7DWn6fkD13hs). All study data are included in the article and SI Appendix.