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

Abstract

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.

Modeling of the Human Bone Environment: Mechanical Stimuli Guide Mesenchymal Stem Cell–Extracellular Matrix Interactions

“…Scaffolds’ ultra-topography, particularly their trabeculae structure and pore size, has been shown to have a major influence on hMSCs proliferation and osteogenic commitment [ 44 , 45 ]. Interestingly, Smith et al [ 42 ] concluded that decellularized scaffolds derived from elderly bone donors with Tb.Th values comparable with the ones obtained in the present study (average of 120.7 µm) showed improved osteoinductive capacity with higher osteogenic gene expression and ALP activity [ 46 ].…”

High-resolution image-based simulation reveals membrane strain concentration on osteocyte processes caused by tethering elements

“…An additional limitation of the present analysis is the restricted size of the analyzed region in relation to the canalicular network scale, which originates from the limitation posed by the electron transmissibility of UHVEM. In a bone tissue, numerous osteocytes form a complex network via their branching processes, which can change the flow velocity inside each canaliculus (van Tol et al 2020a ; b ). Therefore, the strain concentration regions in the osteocyte network can be clarified with an analysis considering the network structure.…”

Cancellous Bone May Have a Greater Adaptive Strain Threshold Than Cortical Bone

“…Other models that incorporate fluid flow might be able to shed further light on the relationship between peak strains and the bone anabolic response that we describe here. (45,46) The expression of genes related to bone formation and turnover in response to loading is time-and load-thresholddependent and is different between cortico-cancellous and cortical tissues. Sclerostin (Sost) is a negative regulator of Wnt signaling and subsequently bone formation.…”