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

Computer model of non-Newtonian canalicular fluid flow in lacunar-canalicular system of bone tissue

Brittle bone diseases are a global healthcare problem for orthopaedic clinicians, that reduces bone strength and promotes bone fracture risk. In vivo studies reported that loading-induced fluid flow through the lacunar-canalicular channel (LCS) of bone tissue inhibit such bone loss and encourages osteogenesis i.e. new bone formation. Canalicular fluid flow converts mechanical signals into biological signals and regulates bone reconstruction by releasing signalling molecules responsible for mechanotransduction. In-silico model mostly considers canalicular fluid is Newtonian, however, physiological canalicular fluid may be non-Newtonian in nature as it contains nutrients and supplements. Accordingly, this study attempts to develop a two-dimensional in-silico model to compute loading-induced non-Newtonian canalicular fluid flow in a complex LCS of bone tissue. Moreover, canalicular fluid is considered as a Jeffery fluid, that can easily be reduced to Newtonian fluid as a special case. The results show that physiological loading modulates the canalicular fluid flow, wall shear stress (WSS) and streamline in bone LCS. Fluid velocity and WSS increases with increase in non-dimensional frequency and non-Newtonian parameter (Jeffery fluid parameters) and reduce with change in permeability. The outcomes of this study may provide new insights in the role of mechanical loading-induced non-Newtonian canalicular fluid flow dynamics in bone LCS. The key findings of this study can be used to improve the understanding of osteocyte mechanobiology involved inside the bone tissue.

Peripheral canalicular branching is decreased in streptozotocin-induced diabetes and correlates with decreased whole-bone ultimate load and perilacunar elastic work

Osteocytes, the most abundant cell type in bone, play a crucial role in mechanosensation and signaling for bone formation and resorption. These cells reside within a complex lacuno-canalicular network (OLCN). Osteocyte signaling is reduced under diabetic conditions, and both type 1 and type 2 diabetes lead to reduced bone turnover, perturbed bone composition, and increased fracture risk. We hypothesized that this reduced bone turnover, and altered bone composition with diabetes is associated with reduced OLCN architecture and connectivity. This study aimed to elucidate: (1) the sequence of OLCN changes with diabetes related to bone turnover and (2) whether changes to the OLCN are associated with tissue composition and mechanical properties. Twelve- to fourteen-week-old male C57BL/6 mice were administered streptozotocin at 50 mg/kg for 5 consecutive days to induce hyperglycemia, sacrificed at baseline (BL), or after being diabetic for 3 (D3) and 7 (D7) wk with age-matched (C3, C7) controls (n = 10-12 per group). Mineralized femoral sections were infiltrated with rhodamine, imaged with confocal microscopy, then the OLCN morphology and topology were characterized and correlated against bone histomorphometry, as well as local and whole-bone mechanics and composition. D7 mice exhibited a lower number of peripheral branches relative to C7. The total number of canalicular intersections (nodes) was lower in D3 and D7 relative to BL (P < 0.05 for all), and a reduced bone formation rate (BFR) was observed at D7 vs C7. The number of nodes explained only 15% of BFR, but 45% of Ct.BV/TV, and 31% of ultimate load. The number of branches explained 30% and 22% of the elastic work at the perilacunar and intracortical region, respectively. Collectively, the reduction in OLCN architecture and association of OLCN measures with bone turnover, mechanics, and composition highlights the relevance of the osteocyte and the OLCN and a potential therapeutic target for treating diabetic skeletal fragility.

Gabapentin Disrupts Binding of Perlecan to the α2δ1 Voltage Sensitive Calcium Channel Subunit and Impairs Skeletal Mechanosensation

Our understanding of how osteocytes, the principal mechanosensors within bone, sense and perceive force remains unclear. Previous work identified "tethering elements" (TEs) spanning the pericellular space of osteocytes and transmitting mechanical information into biochemical signals. While we identified the heparan sulfate proteoglycan perlecan (PLN) as a component of these TEs, PLN must attach to the cell surface to induce biochemical responses. As voltage-sensitive calcium channels (VSCCs) are critical for bone mechanotransduction, we hypothesized that PLN binds the extracellular α₂δ₁ subunit of VSCCs to couple the bone matrix to the osteocyte membrane. Here, we showed co-localization of PLN and α₂δ₁ along osteocyte dendritic processes. Additionally, we quantified the molecular interactions between α₂δ₁ and PLN domains and demonstrated for the first time that α₂δ₁ strongly associates with PLN via its domain III. Furthermore, α₂δ₁ is the binding site for the commonly used pain drug, gabapentin (GBP), which is associated with adverse skeletal effects when used chronically. We found that GBP disrupts PLN::α₂δ₁ binding in vitro, and GBP treatment in vivo results in impaired bone mechanosensation. Our work identified a novel mechanosensory complex within osteocytes composed of PLN and α₂δ₁, necessary for bone force transmission and sensitive to the drug GBP.

A morphometric analysis of the osteocyte canaliculus using applied automatic semantic segmentation by machine learning

INTRODUCTION

Osteocytes play a role as mechanosensory cells by sensing flow-induced mechanical stimuli applied on their cell processes. High-resolution imaging of osteocyte processes and the canalicular wall are necessary for the analysis of this mechanosensing mechanism. Focused ion beam-scanning electron microscopy (FIB-SEM) enabled the visualization of the structure at the nanometer scale with thousands of serial-section SEM images. We applied machine learning for the automatic semantic segmentation of osteocyte processes and canalicular wall and performed a morphometric analysis using three-dimensionally reconstructed images.

MATERIALS AND METHODS

Six-week-old-mice femur were used. Osteocyte processes and canaliculi were observed at a resolution of 2 nm/voxel in a 4 × 4 μm region with 2000 serial-section SEM images. Machine learning was used for automatic semantic segmentation of the osteocyte processes and canaliculi from serial-section SEM images. The results of semantic segmentation were evaluated using the dice similarity coefficient (DSC). The segmented data were reconstructed to create three-dimensional images and a morphological analysis was performed.

RESULTS

The DSC was > 83%. Using the segmented data, a three-dimensional image of approximately 3.5 μm in length was reconstructed. The morphometric analysis revealed that the median osteocyte process diameter was 73.8 ± 18.0 nm, and the median pericellular fluid space around the osteocyte process was 40.0 ± 17.5 nm.

CONCLUSION

We used machine learning for the semantic segmentation of osteocyte processes and canalicular wall for the first time, and performed a morphological analysis using three-dimensionally reconstructed images.

Bone permeability and mechanotransduction: Some current insights into the function of the lacunar-canalicular network

Lacunar-canalicular (LC) permeability involves the passage of fluids, nutrients, oxygen, ions, and signalling molecules through bone tissue, facilitating the maintenance of bone vitality and function and responses to various physiological conditions and diseases. LC permeability and fluid flow-shear stress/drag force play important roles in mechanotransduction in bone tissue by inducing mechanical stimuli in osteocytes, modulating cellular functions, and determining bone adaptation. Alterations in LC structure may therefore influence the fluid flow pattern through the LC network, thereby affecting the ability of osteocytes to sense and translate mechanical signals and possibly contributing to bone remodelling. Several bone-health conditions are associated with changes in LC structure and function and may affect mechanotransduction and responses, although the mechanisms underlying these associations are still not fully understood. In this review, recent studies of LC networks, their formation and transfer mechanical stimuli, and changes in structure, functional permeability, and mechanotransduction that result from age, pathology, and mechanical loading are discussed. Additionally, applications of vibration and low-intensity pulsed ultrasound in bone healthcare and regeneration fields are also presented.

Computational framework for analyzing flow-induced strain on osteocyte as modulated by microenvironment

Osteocytes buried in bone matrix are major mechanosensory cells that regulate bone remodeling in response to interstitial fluid flow in a lacuno-canalicular porosity. To gain an understanding of the mechanism of osteocyte mechanosensing, it is important to be able to evaluate the local strain on the osteocyte process membrane induced by the interstitial fluid flow. The microenvironment of the osteocytes, including the pericellular matrix (PCM) and canalicular ultrastructure, is a key modulator of the flow-induced strain on the osteocyte process membrane because it produces heterogeneous flow patterns in the pericellular space. To investigate the effect of changes in the microenvironment of osteocytes on the flow-induced strain, we developed a novel computational framework for analyzing the fluid-structure interaction. Computer simulations based on the proposed framework enabled evaluation of the spatial distribution of flow-induced strain on the osteocyte process membrane according to changes in the PCM density and canalicular curvature. The simulation results reveal that a decrease in PCM density and an increase in canalicular curvature, each of which is associated with aging and bone disease, have the notable effect of enhancing local flow-induced strain on the osteocyte process membrane. We believe that the proposed computational framework is a promising framework for investigating cell-specific mechanical stimuli and that it has the potential to accelerate the mechanobiological study of osteocytes by providing a deeper understanding of their mechanical environment in living bone tissue.