Tissue-Level Regeneration and Remodeling Dynamics are Driven by Mechanical Stimuli in the Microenvironment in a Post-Bridging Loaded Femur Defect Healing Model in Mice

Abstract

Bone healing and remodeling are mechanically driven processes. While the generalized response to mechanical stimulation in bone is well-understood, much less is known about the mechanobiology-regulating tissue-scale bone formation and resorption during the reparative and remodeling phases of fracture healing. In this study, we combined computational approaches in the form of finite element analysis and experimental approaches by using a loaded femoral defect model in mice to investigate the role of mechanical stimulation in the microenvironment of bone. Specifically, we used longitudinal micro-computed tomography to observe temporal changes in bone at different densities and micro-finite element analysis to map the mechanics of the microenvironment to tissue-scale formation, quiescence (no change in bone presence between time points), and resorption dynamics in the late reparative and remodeling phases (post bridging). Increasing levels of effective strain led to increasing conditional probability of bone formation, while decreasing levels of effective strain led to increasing probability of bone resorption. In addition, the analysis of mineralization dynamics showed both a temporal and effective strain level-dependent behavior. A logarithmic-like response was displayed, where the conditional probability of bone formation or resorption increased rapidly and plateaued or fell rapidly and plateaued as mechanical strain increased.

Spatial Transcriptomics in Bone Mechanomics: Exploring the Mechanoregulation of Fracture Healing in the Era of Spatial Omics

“…ParOSol - a linear micro-FE solver - was used to solve each finite element simulation and compute the mechanical environment ( 80 ). Effective strain, which combines both volumetric and deviatoric strains (and drives fluid movement and direct strain, respectively), represented the mechanical environment ( 9 , 77 ). The results of the simulation were scaled as follows: where ε simulation is the effective strain result based on the simulation of uniaxial loading, F resulation is the sum of the reaction forces of all the nodes of the uppermost surface, F applied is the cyclic loading force applied and ε actual is the strain induced under the applied force ( 77 ).…”

“…Micro-FE analyses based upon the registered in vivo micro-CT images were used to simulate axial compression and generate tissue-scale 3D maps of the mechanical environment ( 12 , 77 ). In brief, the micro-CT images were cropped to dimensions of 300 x 300 x 186 voxels.…”

“…where 𝜀 𝑠𝑖𝑚𝑢𝑙𝑎𝑡𝑖𝑜𝑛 is the effective strain result based on the simulation of uniaxial loading, 𝐹 𝑟𝑒𝑠𝑢𝑙𝑡𝑎𝑛𝑡 is the sum of the reaction forces of all the nodes of the uppermost surface, 𝐹 𝑎𝑝𝑝𝑙𝑖𝑒𝑑 is the cyclic loading force applied and 𝜀 𝑎𝑐𝑡𝑢𝑎𝑙 is the strain induced under the applied force (77).…”

An in silico micro-multiphysics agent-based approach for simulating bone regeneration in a mouse femur defect model

“…Both mechanical signals were Gaussian-filtered (sigma = 1.0, support = 0.8) to mitigate partial volume effects following the standard approach when evaluating micro-CT images ( Bouxsein et al, 2010 ; Ohs et al, 2020 ). This signal regularisation was also previously applied to micro-FE analysis on in vivo micro-CT images ( Schulte et al, 2013b ; Tourolle né Betts et al, 2020 ; Paul et al, 2022 ). The presence of these two mechanical signals allowed tissue-independent standardisation of the various strain-related thresholds concerning the cell behaviours ( Kendall et al, 2022a ).…”

“…To measure the degree of mineralisation and its progression, a higher mineral threshold of 645 mg HA/cm 3 was applied and the ratio of lowly mineralised to highly mineralised tissue (BV645/BV395) was determined ( Tourolle né Betts et al, 2020 ). Volumes of formation, quiescence, and resorption (FQR) were calculated by the difference between two thresholded images of consecutive time points to establish the respective bone formation rate (BFR) and bone resorption rate (BRR) ( Paul et al, 2022 ). Importantly, the postprocessing analysis pipeline was equal for both in vivo and in silico samples.…”