Friederike A. Schulte

In vivo micro-computed tomography allows direct three-dimensional quantification of both bone formation and bone resorption parameters using time-lapsed imaging

Bone is a living tissue able to adapt its structure to external influences such as altered mechanical loading. This adaptation process is governed by two distinct cell types: bone-forming cells called osteoblasts and bone-resorbing cells called osteoclasts. It is therefore of particular interest to have quantitative access to the outcomes of bone formation and resorption separately. This article presents a non-invasive three-dimensional technique to directly extract bone formation and resorption parameters from time-lapsed in vivo micro-computed tomography scans. This includes parameters such as Mineralizing Surface (MS), Mineral Apposition Rate (MAR), and Bone Formation Rate (BFR), which were defined in accordance to the current nomenclature of dynamic histomorphometry. Due to the time-lapsed and non-destructive nature of in vivo micro-computed tomography, not only formation but also resorption can now be assessed quantitatively and time-dependent parameters Eroded Surface (ES) as well as newly defined indices Mineral Resorption Rate (MRR) and Bone Resorption Rate (BRR) are introduced. For validation purposes, dynamic formation parameters were compared to the traditional quantitative measures of dynamic histomorphometry, where MAR correlated with R = 0.68 and MS with R = 0.78 (p < 0.05). Reproducibility was assessed in 8 samples that were scanned 5 times and errors ranged from 0.9% (MRR) to 6.6% (BRR). Furthermore, the new parameters were applied to a murine in vivo loading model. A comparison of directly extracted parameters between formation and resorption within each animal revealed that in the control group, i.e., during normal remodeling, MAR was significantly lower than MRR (p < 0.01), whereas MS compared to ES was significantly higher (p < 0.0001). This implies that normal remodeling seems to take place by many small formation packets and few but large resorption volumes. After 4 weeks of mechanical loading, newly extracted trabecular BFR and MS were significantly higher (p < 0.01) in the loading compared to the control group. At the same time, ES was significantly decreased (p < 0.01). This indicates that modeling induced by mechanical loading takes place primarily by increased area, not width of formation packets. With these results, we conclude that the non-invasive direct technique is well suited to extract dynamic bone morphometry parameters and eventually gain more insight into the processes of bone adaptation not only for formation but also resorption.

Local Mechanical Stimuli Regulate Bone Formation and Resorption in Mice at the Tissue Level

Bone is able to react to changing mechanical demands by adapting its internal microstructure through bone forming and resorbing cells. This process is called bone modeling and remodeling. It is evident that changes in mechanical demands at the organ level must be interpreted at the tissue level where bone (re)modeling takes place. Although assumed for a long time, the relationship between the locations of bone formation and resorption and the local mechanical environment is still under debate. The lack of suitable imaging modalities for measuring bone formation and resorption in vivo has made it difficult to assess the mechanoregulation of bone three-dimensionally by experiment. Using in vivo micro-computed tomography and high resolution finite element analysis in living mice, we show that bone formation most likely occurs at sites of high local mechanical strain (p<0.0001) and resorption at sites of low local mechanical strain (p<0.0001). Furthermore, the probability of bone resorption decreases exponentially with increasing mechanical stimulus (R2 = 0.99) whereas the probability of bone formation follows an exponential growth function to a maximum value (R2 = 0.99). Moreover, resorption is more strictly controlled than formation in loaded animals, and ovariectomy increases the amount of non-targeted resorption. Our experimental assessment of mechanoregulation at the tissue level does not show any evidence of a lazy zone and suggests that around 80% of all (re)modeling can be linked to the mechanical micro-environment. These findings disclose how mechanical stimuli at the tissue level contribute to the regulation of bone adaptation at the organ level.

Mouse tail vertebrae adapt to cyclic mechanical loading by increasing bone formation rate and decreasing bone resorption rate as shown by time-lapsed in vivo imaging of dynamic bone morphometry

It is known that mechanical loading leads to an increase in bone mass through a positive shift in the balance between bone formation and bone resorption. How the remodeling sites change over time as an effect of loading remains, however, to be clarified. The purpose of this paper was to investigate how bone formation and resorption sites are modulated by mechanical loading over time by using a new imaging technique that extracts three dimensional formation and resorption parameters from time-lapsed in vivo micro-computed tomography images. To induce load adaptation, the sixth caudal vertebra of C57BL/6 mice was cyclically loaded through pins in the adjacent vertebrae at either 8 N or 0 N (control) three times a week for 5 min (3000 cycles) over a total of 4 weeks. The results showed that mechanical loading significantly increased trabecular bone volume fraction by 20% (p<0.001) and cortical area fraction by 6% (p<0.001). The bone formation rate was on average 23% greater (p<0.001) and the bone resorption rate was on average 25% smaller (p<0.001) for the 8 N group than for the 0 N group. The increase in bone formation rate for the 8 N group was mostly an effect of a significantly increased surface of bone formation sites (on average 16%, p<0.001), while the thickness of bone formation packages was less affected (on average 5% greater, p<0.05). At the same time the surface of bone resorption sites was significantly reduced (on average 15%, p<0.001), while the depth of resorption pits remained the same. For the 8 N group, the strength of the whole bone increased significantly by 24% (p<0.001) over the loading period, while the strain energy density in the trabecular bone decreased significantly by 24% (p<0.001). In conclusion, mouse tail vertebrae adapt to mechanical loading by increasing the surface of formation sites and decreasing the surface of resorption sites, leading to an overall increase in bone strength. This new imaging technique will provide opportunities to investigate in vivo bone remodeling in the context of disease and treatment options, with the added value that both bone formation and bone resorption parameters can be nondestructively calculated over time.

Strain-adaptive in silico modeling of bone adaptation — A computer simulation validated by in vivo micro-computed tomography data

Computational models are an invaluable tool to test different mechanobiological theories and, if validated properly, for predicting changes in individuals over time. Concise validation of in silico models, however, has been a bottleneck in the past due to a lack of appropriate reference data. Here, we present a strain-adaptive in silico algorithm which is validated by means of experimental in vivo loading data as well as by an in vivo ovariectomy experiment in the mouse. The maximum prediction error following four weeks of loading resulted in 2.4% in bone volume fraction (BV/TV) and 8.4% in other bone structural parameters. Bone formation and resorption rate did not differ significantly between experiment and simulation. The spatial distribution of formation and resorption sites matched in 55.4% of the surface voxels. Bone loss was simulated with a maximum prediction error of 12.1% in BV/TV and other bone morphometric indices, including a saturation level after a few weeks. Dynamic rates were more difficult to be accurately predicted, showing evidence for significant differences between simulation and experiment (p<0.05). The spatial agreement still amounted to 47.6%. In conclusion, we propose a computational model which was validated by means of experimental in vivo data. The predictive value of an in silico model may become of major importance if the computational model should be applied in clinical settings to predict bone changes due to disease and test the efficacy of potential pharmacological interventions.

Trabecular bone adapts to long-term cyclic loading by increasing stiffness and normalization of dynamic morphometric rates.

Bone has the ability to adapt to external loading conditions. Especially the beneficial effect of short-term cyclic loading has been investigated in a number of in vivo animal studies. The aim of this study was to assess the long-term effect (>10 weeks) of cyclic mechanical loading on the bone microstructure, bone stiffness, and bone remodeling rates. Mice were subjected to cyclic mechanical loading at the sixth caudal vertebra with 8N or 0N (control) three times per week for a total period of 14 weeks. Structural bone parameters were determined from in vivo micro-computed tomography (micro-CT) scans performed at week 0, 4, 6, 8, 10, 12, and 14. Mechanical parameters were derived from micro-finite element analysis. Dynamic bone morphometry was calculated using registration of serial micro-CT scans. Bone volume fraction and trabecular thickness increased significantly more for the loaded group than for the control group (p = 0.006 and p = 0.002 respectively). The trabecular bone microstructure adapted to the load of 8N in approximately ten weeks, indicated by the trabecular bone volume fraction, which increased from 16.7% at 0 weeks to 21.6% at week 10 and only showed little change afterwards (bone volume fraction of 21.5% at 14 weeks). Similarly bone stiffness - (at the start of the experiment 649N/mm) - reached 846N/mm at 10 weeks in the loaded group and was maintained to the end of the experiment (850N/mm). At 4 weeks the bone formation rate was 32% greater and the bone resorption rate 22% less for 8N compared to 0N. This difference was significantly reduced as the bone adapted to 8N, with 8N remodeling rates returning to the values of the 0N group at approximately 10 weeks. Together these data suggest that once bone has adapted to a new loading state, the remodeling rates reduce gradually while maintaining bone volume fraction and stiffness.

Mineralization kinetics in murine trabecular bone quantified by time-lapsed in vivo micro-computed tomography

Trabecular bone is a highly dynamic tissue due to bone remodeling, mineralization and demineralization. The mineral content and its spatial heterogeneity are main contributors to bone quality. Using time-lapsed in vivo micro-computed tomography (micro-CT), it is now possible to resolve in three dimensions where bone gets resorbed and deposited over several weeks. In addition, the gray values in the micro-CT images contain quantitative information about the local tissue mineral density (TMD). The aim of this study was to measure how TMD increases with time after new bone formation and how this mineralization kinetics is influenced by mechanical stimulation. Our analysis of changes in TMD was based on an already reported experiment on 15-week-old female mice (C57BL/6), where in one group the sixth caudal vertebra was mechanically loaded with 8N, while in the control group no loading was applied. Comparison of two consecutive images allows the categorization of bone into newly formed, resorbed, and quiescent bone for different time points. Gray values of bone in these categories were compared layer-wise to minimize the effects of beam hardening artifacts. Quiescent bone in the control group was found to mineralize with a rate of 8 ± 1 mgHA/cm(3) per week, which is about half as fast as observed for newly formed bone. Mechanical loading increased the rate of mineral incorporation by 63% in quiescent bone. The week before bone resorption, demineralization could be observed with a drop of TMD by 36 ± 4 mgHA/cm(3) in the control and 34 ± 3 mgHA/cm(3) in the loaded group. In conclusion, this study shows how time-lapsed in vivo micro-CT can be used to assess changes in TMD of bone with high spatial and temporal resolution. This will allow a quantification of how bone diseases and pharmaceutical interventions influence not only microarchitecture of trabecular bone, but also its material quality.

The Clinical Biomechanics Award 2012 — Presented by the European Society of Biomechanics: Large scale simulations of trabecular bone adaptation to loading and treatment

BACKGROUND Microstructural simulations of bone remodeling are particularly relevant in the clinical management of osteoporosis. Before a model can be applied in the clinics, a validation against controlled in vivo data is crucial. Here we present a strain-adaptive feedback algorithm for the simulation of trabecular bone remodeling in response to loading and pharmaceutical treatment and report on the results of the large-scale validation against in vivo data. METHODS The algorithm follows the mechanostat principle and incorporates mechanical feedback, based on the local strain-energy density. For the validation, simulations of bone remodeling and adaptation in 180 osteopenic mice were performed. Permutations of the conditions for early (20th week) and late (26th week) loading of 8N or 0N, and treatments with bisphosphonates, or parathyroid hormone were simulated. Static and dynamic morphometry and local remodeling sites from in vivo and in silico studies were compared. FINDINGS For each study an individual set of model parameters was selected. Trabecular bone volume fraction was chosen as an indicator of the accuracy of the simulations. Overall errors for this parameter were 0.1-4.5%. Other morphometric indices were simulated with errors of less than 19%. Dynamic morphometry was more difficult to predict, which resulted in significant differences from the experimental data. INTERPRETATION We validated a new algorithm for the simulation of bone remodeling in trabecular bone. The results indicate that the simulations accurately reflect the effects of treatment and loading seen in respective experimental data, and, following adaptation to human data, could be transferred into clinics.

In vivo validation of a computational bone adaptation model using open-loop control and time-lapsed micro-computed tomography

Cyclic mechanical loading augments trabecular bone mass, mainly by increasing trabecular thickness. For this reason, we hypothesized that an in silico thickening algorithm using open-loop control would be sufficient to reliably predict the response of trabecular bone to cyclic mechanical loading. This would also mean that trabecular bone adaptation could be modeled as a system responding to an input signal at the onset of the process in a predefined manner, without feedback from the outputs. Here, time-lapsed in vivo micro-computed tomography scans of mice cyclically loaded at the sixth caudal vertebra were used to validate the in silico model. When comparing in silico and in vivo data sets after a period of four weeks, a maximum prediction error of 2.4% in bone volume fraction and 5.4% in other bone morphometric indices was calculated. Superimposition of sequentially acquired experimental images and simulated structures revealed that in silico simulations deposited thin and homogeneous layers of bone whilst the experiment was characterized by local areas of strong thickening, as well as considerable volumes of bone resorption. From the results, we concluded that the proposed computational algorithm predicted changes in bone volume fraction and global parameters of bone structure very well over a period of four weeks while it was unable to reproduce accurate spatial patterns of local bone formation and resorption. This study demonstrates the importance of validation of computational models through the use of experimental in vivo data, including the local comparison of simulated and experimental remodeling sites. It is assumed that the ability to accurately predict changes in bone micro-architecture will facilitate a deeper understanding of the cellular mechanisms underlying bone remodeling and adaptation due to mechanical loading.

Strain energy density gradients in bone marrow predict osteoblast and osteoclast activity: a finite element study.

Huiskes et al. hypothesized that mechanical strains sensed by osteocytes residing in trabecular bone dictate the magnitude of load-induced bone formation. More recently, the mechanical environment in bone marrow has also been implicated in bone׳s response to mechanical stimulation. In this study, we hypothesize that trabecular load-induced bone formation can be predicted by mechanical signals derived from an integrative µFE model, incorporating a description of both the bone and marrow phase. Using the mouse tail loading model in combination with in vivo micro-computed tomography (µCT) we tracked load induced changes in the sixth caudal vertebrae of C57BL/6 mice to quantify the amount of newly mineralized and eroded bone volumes. To identify the mechanical signals responsible for adaptation, local morphometric changes were compared to micro-finite element (µFE) models of vertebrae prior to loading. The mechanical parameters calculated were strain energy density (SED) on trabeculae at bone forming and resorbing surfaces, SED in the marrow at the boundary between bone forming and resorbing surfaces, along with SED in the trabecular bone and marrow volumes. The gradients of each parameter were also calculated. Simple regression analysis showed mean SED gradients in the trabecular bone matrix to significantly correlate with newly mineralized and eroded bone volumes R(2)=0.57 and 0.41, respectively, p<0.001). Nevertheless, SED gradients in the marrow were shown to be the best predictor of osteoblastic and osteoclastic activity (R(2)=0.83 and 0.60, respectively, p<0.001). These data suggest that the mechanical environment of the bone marrow plays a significant role in determining osteoblast and osteoclast activity.

Longitudinal in vivo imaging of bone formation and resorption using fluorescence molecular tomography

Bone research often focuses on anatomical imaging of the bone microstructure, but in order to gain better understanding in how bone remodeling is modulated through interventions also bone formation and resorption processes should be investigated. With this in mind, the purpose of this study was to establish a longitudinal in vivo imaging approach of bone formation and resorption using fluorescence molecular tomography (FMT). In this study the reproducibility, accuracy and sensitivity of FMT for bone imaging were assessed by performing longitudinal measurements with FMT and comparing it to in vivo micro-computed tomography on a set of control mice, and mice in which load-adaptation was induced in the sixth caudal vertebra. The precision error for FMT measurements, expressed as coefficient of variation, was smaller than 16%, indicating acceptable reproducibility. A correlation was found between bone resorption measured with FMT and bone resorption rate measured with in vivo micro-computed tomography only over the first 14days (R=0.81, p<0.01), but not between bone formation measured with FMT and bone formation rate measured with in vivo micro-CT. Bone formation measured by FMT was 89-109% greater (p<0.05) for mice subjected to mechanical loading than control mice. Bone resorption was 5-8% lower, but did not reach a significant difference between groups, indicating moderate sensitivity for FMT. In conclusion, in vivo FMT in mouse tail bones is feasible but needs to be optimized for monitoring load adaptation in living mice.