Spatial relationship between bone formation and mechanical stimulus within cortical bone: Combining 3D fluorochrome mapping and poroelastic finite element modelling

Carriero, Alessandra; Pereira, André F.; Wilson, Amanda J.; Castagno, Simone; Javaheri, Behzâd; Pitsillides, A A; Marenzana, Massimo; Shefelbine, Sandra J.

doi:10.1016/j.bonr.2018.02.003

Cited by 64 publications

(61 citation statements)

References 44 publications

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“…This is similar to the results obtained from 26-week-old mice that underwent mechanical loading, which had higher bone remodelling on the periosteal surface at the proximal end, and on the endosteal surface at the diaphysis (Birkhold et al 2017). In contrast, 3D fluorochrome mapping results obtained in a non-loaded contralateral control leg at week 22 showed that bone formation was located mainly on the endosteal surfaces (Carriero et al 2018). The predicted overlap using subjectspecific parameters from weeks 14-16 and 20-22 were similar, demonstrating the robustness of the model.…”

Section: Figsupporting

confidence: 87%

“…Although the regions of remodelling generally matched with the SED distribution ( Supplementary Fig. S2), it is slightly different from the bone remodelling observed in the murine tibia loading model, where bone formation has been reported to occur on the medial surface and the interosseous crest (Carriero et al 2018;Pereira et al 2015). Both SED and ε maxprinc as the stimulus were able to predict bone apposition on both the endosteal and periosteal surfaces (Fig.…”

Section: Figmentioning

confidence: 65%

“…Both SED and ε maxprinc as the stimulus were able to predict bone apposition on both the endosteal and periosteal surfaces (Fig. 7), even though Carriero et al (2018) reported that SED was able to capture external load-driven bone formation only on the periosteal surface. However, the spatial match for apposition was higher on the periosteal than the endosteal surface, for both mechanical stimuli (Fig.…”

Section: Figmentioning

confidence: 99%

“…The former introduces a rotational error, while the latter bias the alignment longitudinally by a fixed distance. Bone growth and formation are highest near the growth plates and decrease distally (Carriero et al 2018), and an elastic registration should be considered as part of future work to map the locations of the bone with time. Moreover, the optimisation code uses the whole length of the tibia, in 10 approximately equal sections, for the calculation for the volumetric second moment.…”

Section: Figmentioning

confidence: 99%

“…Micro-FEA in the mouse tibia has mainly been limited to predicting the strain and stiffness of the mouse tibia, and correlating the results with time-lapsed testing and digital volume correlation (Birkhold et al 2017;Giorgi and Dall'Ara 2018;Oliviero et al 2018;Patel et al 2014). Bone remodelling in whole murine tibia has only been modelled using tetrahedral elements with an inhomogeneous mesh size, and by comparing the predicted shape and density of the loaded leg to the non-loaded contralateral leg (Carriero et al 2018;Pereira et al 2015), under the strong assumption that the control leg did not undergo adaptive changes since the start of the experiment. The spatial match of remodelled regions in the caudal vertebra and tibia found by previous studies was accurate in approximately 50% of the surface (Pereira et al 2015;Schulte et al 2013b), but the remodelling parameters were determined for a loaded model, which may be different under physiological loading.…”

mentioning

confidence: 99%

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A novel algorithm to predict bone changes in the mouse tibia properties under physiological conditions

Cheong

Marin

Lacroix

et al. 2019

Biomech Model Mechanobiol

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Understanding how bone adapts to mechanical stimuli is fundamental for optimising treatments against musculoskeletal diseases in preclinical studies, but the contribution of physiological loading to bone adaptation in mouse tibia has not been quantified so far. In this study, a novel mechanistic model to predict bone adaptation based on physiological loading was developed and its outputs were compared with longitudinal scans of the mouse tibia. Bone remodelling was driven by the mechanical stimuli estimated from micro-FEA models constructed from micro-CT scans of C57BL/6 female mice (N = 5) from weeks 14 and 20 of age, to predict bone changes in week 16 or 22. Parametric analysis was conducted to evaluate the sensitivity of the models to subject-specific or averaged parameters, parameters from week 14 or week 20, and to strain energy density (SED) or maximum principal strain (ε maxprinc ). The results at week 20 showed no significant difference in bone densitometric properties between experimental and predicted images across the tibia for both stimuli, and 59% and 47% of the predicted voxels matched with the experimental sites in apposition and resorption, respectively. The model was able to reproduce regions of bone apposition in both periosteal and endosteal surfaces (70% and 40% for SED and ε maxprinc , respectively), but it under-predicted the experimental sites of resorption by over 85%. This study shows for the first time the potential of a subject-specific mechanoregulation algorithm to predict bone changes in a mouse model under physiological loading. Nevertheless, the weak predictions of resorption suggest that a combined stimulus or biological stimuli should be accounted for in the model.

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Section: Figsupporting

confidence: 87%

Section: Figmentioning

confidence: 65%

Section: Figmentioning

confidence: 99%

Section: Figmentioning

confidence: 99%

mentioning

confidence: 99%

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A novel algorithm to predict bone changes in the mouse tibia properties under physiological conditions

Cheong

Marin

Lacroix

et al. 2019

Biomech Model Mechanobiol

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Age and Sex Differences in Load‐Induced Tibial Cortical Bone Surface Strain Maps

et al. 2021

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Bone adapts its architecture to the applied load; however, it is still unclear how bone mechano-adaptation is coordinated and why potential for adaptation adjusts during the life course. Previous animal models have suggested strain as the mechanical stimulus for bone adaptation, but yet it is unknown how mouse cortical bone load-related strains vary with age and sex. In this study, full-field strain maps (at 1 N increments up to 12 N) on the bone surface were measured in young, adult, and old (aged 10, 22 weeks, and 20 months, respectively), male and female C57BL/6J mice with load applied using a noninvasive murine tibial model. Strain maps indicate a nonuniform strain field across the tibial surface, with axial compressive loads resulting in tension on the medial side of the tibia because of its curved shape. The load-induced surface strain patterns and magnitudes show sexually dimorphic changes with aging. A comparison of the average and peak tensile strains indicates that the magnitude of strain at a given load generally increases during maturation, with tibias in female mice having higher strains than in males. The data further reveal that postmaturation aging is linked to sexually dimorphic changes in average and maximum strains. The strain maps reported here allow for loading male and female C57BL/6J mouse legs in vivo at the observed ages to create similar increases in bone surface average or peak strain to more accurately explore bone mechano-adaptation differences with age and sex.

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A Neuroskeletal Atlas: Spatial Mapping and Contextualization of Axon Subtypes Innervating the Long Bones of C3H and B6 Mice

Lorenz

Brazill

Beeve

et al. 2020

Journal of Bone and Mineral Research

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Nerves in bone play well‐established roles in pain and vasoregulation and have been associated with progression of skeletal disorders, including osteoporosis, fracture, arthritis, and tumor metastasis. However, isolation of the region‐specific mechanisms underlying these relationships is limited by our lack of quantitative methods for neuroskeletal analysis and precise maps of skeletal innervation. To overcome these limitations, we developed an optimized workflow for imaging and quantitative analysis of axons in and around the bone, including validation of Baf53b‐Cre in concert with R26R‐tdTomato (Ai9) as a robust pan‐neuronal reporter system for use in musculoskeletal tissues. In addition, we created comprehensive maps of sympathetic adrenergic and sensory peptidergic axons within and around the full length of the femur and tibia in two strains of mice (B6 and C3H). In the periosteum, these maps were related to the surrounding musculature, including entheses and myotendinous attachments to bone. Three distinct patterns of periosteal innervation (termed type I, II, III) were defined at sites that are important for bone pain, bone repair, and skeletal homeostasis. For the first time, our results establish a gradient of bone marrow axon density that increases from proximal to distal along the length of the tibia and define key regions of interest for neuroskeletal studies. Lastly, this information was related to major nerve branches and local maps of specialized mechanoreceptors. This detailed mapping and contextualization of the axonal subtypes innervating the skeleton is intended to serve as a guide during the design, implementation, and interpretation of future neuroskeletal studies and was compiled as a resource for the field as part of the NIH SPARC consortium. © 2021 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR)..

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Spatial relationship between bone formation and mechanical stimulus within cortical bone: Combining 3D fluorochrome mapping and poroelastic finite element modelling

Cited by 64 publications

References 44 publications

A novel algorithm to predict bone changes in the mouse tibia properties under physiological conditions

A novel algorithm to predict bone changes in the mouse tibia properties under physiological conditions

Age and Sex Differences in Load‐Induced Tibial Cortical Bone Surface Strain Maps

A Neuroskeletal Atlas: Spatial Mapping and Contextualization of Axon Subtypes Innervating the Long Bones of C3H and B6 Mice

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