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In vivo Visualisation and Quantification of Bone Resorption and Bone Formation from Time-Lapse Imaging

  • Osteocytes (T Bellido and J Klein-Nulend, Section Editors)
  • Published:
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Abstract

Purpose of Review

Mechanoregulation of bone cells was proposed over a century ago, but only now can we visualise and quantify bone resorption and bone formation and its mechanoregulation. In this review, we show how the newest advances in imaging and computational methods paved the way for this breakthrough.

Recent Findings

Non-invasive in vivo assessment of bone resorption and bone formation was demonstrated by time-lapse micro-computed tomography in animals, and by high-resolution peripheral quantitative computed tomography in humans. Coupled with micro-finite element analysis, the relationships between sites of bone resorption and bone formation and low and high tissue loading, respectively, were shown.

Summary

Time-lapse in vivo imaging and computational methods enabled visualising and quantifying bone resorption and bone formation as well as its mechanoregulation. Future research includes visualising and quantifying mechanoregulation of bone resorption and bone formation from molecular to organ scales, and translating the findings into medicine using personalised bone health prognosis.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Roux W. Der Kampf der Theile im Organismus: Ein Beitrag zur vervollständigung der mechanischen Zweckmässigkeitslehre. Leipzig: W. Engelmann; 1881.

    Google Scholar 

  2. Shen V, Liang XG, Birchman R, Wu DD, Healy D, Lindsay R, et al. Short term immobilization-induced cancellous bone loss is limited to regions undergoing high turnover and/or modeling in mature rats. Bone. 1997;21:71–8.

    Article  CAS  Google Scholar 

  3. Jämsä T, Koivukangas A, Ryhänen J, Jalovaara P, Tuukkanen J. Femoral neck is a sensitive indicator of bone loss in immobilized hind limb of mouse. Journal of Bone and Mineral Research. John Wiley and Sons and The American Society for Bone and Mineral Research (ASBMR);. 1999;14:1708–13.

    Article  Google Scholar 

  4. Armbrecht G, Belavý DL, Backström M, Beller G, Alexandre C, Rizzoli R, et al. Trabecular and cortical bone density and architecture in women after 60 days of bed rest using high-resolution pQCT: WISE 2005. Journal of Bone and Mineral Research. Wiley Subscription Services, Inc., A Wiley Company. 2011;26:2399–410.

    Article  Google Scholar 

  5. Vico L, Collet P, Guignandon A, Lafage-Proust M-H, Thomas T, Rehailia M, et al. Effects of long-term microgravity exposure on cancellous and cortical weight-bearing bones of cosmonauts. The Lancet. 2000;355:1607–11.

    Article  CAS  Google Scholar 

  6. Lang T, LeBlanc A, Evans H, Lu Y, Genant H, Yu A. Cortical and trabecular bone mineral loss from the spine and hip in long-duration spaceflight. Journal of Bone and Mineral Research. John Wiley and Sons and The American Society for Bone and Mineral Research (ASBMR). 2004;19:1006–12.

    Article  Google Scholar 

  7. Smith SM, Wastney ME, O’Brien KO, Morukov BV, Larina IM, Abrams SA, et al. Bone markers, calcium metabolism, and calcium kinetics during extended-duration space flight on the Mir space station. Journal of Bone and Mineral Research. John Wiley and Sons and The American Society for Bone and Mineral Research (ASBMR). 2004;20:208–18.

    Article  Google Scholar 

  8. Rubin CT, Lanyon LE. Osteoregulatory nature of mechanical stimuli: Function as a determinant for adaptive remodeling in bone. Journal of Orthopaedic Research. Wiley Subscription Services, Inc., A Wiley Company. 1987;5:300–10.

    Article  CAS  Google Scholar 

  9. Lee K, Maxwell A, Lanyon LE. Validation of a technique for studying functional adaptation of the mouse ulna in response to mechanical loading. Bone. 2002;31:407–12.

    Article  CAS  Google Scholar 

  10. Sugiyama T, Price JS, Lanyon LE. Functional adaptation to mechanical loading in both cortical and cancellous bone is controlled locally and is confined to the loaded bones. Bone. 2010;46:314–21.

    Article  Google Scholar 

  11. Lambers FM, Schulte FA, Kuhn G, Webster DJ, Müller R. 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. Bone. 2011;49:1340–50.

    Article  Google Scholar 

  12. Ducher G, Daly RM, Bass SL. Effects of repetitive loading on bone mass and geometry in young male tennis players: A quantitative study using MRI. Journal of Bone and Mineral Research. John Wiley and Sons and The American Society for Bone and Mineral Research (ASBMR). 2009;24:1686–92.

    Article  Google Scholar 

  13. Ducher G, Tournaire N, Meddahi-Pelle A, Benhamou C-L, Courteix D. Short-term and long-term site-specific effects of tennis playing on trabecular and cortical bone at the distal radius. Journal of Bone and Mineral Metabolism. 2006;24:484–90.

    Article  Google Scholar 

  14. Warden SJ, Roosa SMM, Kersh ME, Hurd AL, Fleisig GS, Pandy MG, et al. Physical activity when young provides lifelong benefits to cortical bone size and strength in men. Proceedings of the National Academy of Sciences of the United States of America. 2014;111:5337–42.

    Article  CAS  Google Scholar 

  15. Pontzer H. Trabecular bone in the bird knee responds with high sensitivity to changes in load orientation. Journal of Experimental Biology. 2006;209:57–65.

    Article  CAS  Google Scholar 

  16. Barak MM, Lieberman DE, Hublin J-J. A Wolff in sheep’s clothing: Trabecular bone adaptation in response to changes in joint loading orientation. Bone. 2011;49:1141–51.

    Article  Google Scholar 

  17. Klein-Nulend J, van Oers RFM, Bakker AD, Bacabac RG. Nitric oxide signaling in mechanical adaptation of bone. Osteoporosis International. Springer London. 2014;25:1427–37.

    CAS  PubMed  Google Scholar 

  18. Vatsa A, Mizuno D, Smit TH, Schmidt CF, MacKintosh FC, Klein-Nulend J. Bio imaging of intracellular NO production in single bone cells after mechanical stimulation. Journal of Bone and Mineral Research. 2006;21:1722–8.

    Article  CAS  Google Scholar 

  19. van Oers RFM, Wang H, Bacabac RG. Osteocyte shape and mechanical loading. Current Osteoporosis Reports. Springer US. 2015;13:61–6.

    Article  Google Scholar 

  20. •• Christen P, Ito K, Ellouz R, Boutroy S, Sornay-Rendu E, Chapurlat RD, et al. Bone remodelling in humans is load-driven but not lazy. Nature Communications. Nature Publishing Group; 2014;5. This study demonstrates, for the first time, local mechanoregulation of bone resorption and bone formation in humans employing time-lapse in vivo HR-pQCT and micro-FE analysis.

  21. •• Schulte FA, Ruffoni D, Lambers FM, Christen D, Webster DJ, Kuhn G, et al. Local Mechanical Stimuli Regulate Bone Formation and Resorption in Mice at the Tissue Level. Kupczik K, editor. Plos One. Public Library of Science; 2013;8. This study demonstrates, for the first time, local mechanoregulation of bone resorption and bone formation in animals employing time-lapse in vivo micro-CT and micro-FE analysis.

  22. Boyd SK, Davison P, Müller R, Gasser JA. Monitoring individual morphological changes over time in ovariectomized rats by in vivo micro-computed tomography. Bone. 2006;39:854–62.

    Article  Google Scholar 

  23. Lambers FM, Koch K, Kuhn G, Ruffoni D, Weigt C, Schulte FA, et al. Trabecular bone adapts to long-term cyclic loading by increasing stiffness and normalization of dynamic morphometric rates. Bone. 2013;55:325–34.

    Article  Google Scholar 

  24. Schulte FA, Lambers FM, Kuhn G, Müller R. In vivo micro-computed tomography allows direct three-dimensional quantification of both bone formation and bone resorption parameters using time-lapsed imaging. Bone. 2011;48:433–42.

    Article  Google Scholar 

  25. Birkhold AI, Razi H, Duda GN, Weinkamer R, Checa S, Willie BM. The influence of age on adaptive bone formation and bone resorption. Biomaterials. 2014;35:9290–301.

    Article  CAS  Google Scholar 

  26. • Birkhold AI, Razi H, Weinkamer R, Duda GN, Checa S, Willie BM. Monitoring in vivo (re)modeling: A computational approach using 4D microCT data to quantify bone surface movements. Bone. 2015;75:210–21. This study proposes a novel method to visualise and quantify bone resorption and bone formation based on time-lapse in vivo micro-CT that allows to track sites of bone resorption and bone formation over time.

    Article  Google Scholar 

  27. Ellouz R, Chapurlat R, van Rietbergen B, Christen P, Pialat J-B, Boutroy S. Challenges in longitudinal measurements with HR-pQCT: Evaluation of a 3D registration method to improve bone microarchitecture and strength measurement reproducibility. Bone. 2014;63:147–57.

    Article  Google Scholar 

  28. Chapurlat R In vivo evaluation of bone microstructure in humans: Clinically useful? BoneKEy Reports. 2016;5.

  29. Nishiyama KK, Shane E. Clinical imaging of bone microarchitecture with HR-pQCT. Current Osteoporosis Reports. Current Science Inc. 2013;11:147–55.

    Article  Google Scholar 

  30. Geusens P, Chapurlat R, Schett G, Ghasem-Zadeh A, Seeman E, de Jong J, et al. High-resolution in vivo imaging of bone and joints: a window to microarchitecture. Nature Reviews Rheumatology. Nature Research. 2014;10:304–13.

    Article  Google Scholar 

  31. Christen P, Lee WY-W, van Rietbergen B, Chen JC-Y, Müller R. Time-lapse in vivo image analysis to determine local disease and treatment effects on bone remodelling in patients. IBMS BoneKEy. 2015;13:116–7.

    Google Scholar 

  32. Nishiyama KK, Pauchard Y, Nikkel LE, Iyer S, Zhang C, McMahon DJ, et al. Longitudinal HR-pQCT and image registration detects endocortical bone loss in kidney transplantation patients. Journal of Bone and Mineral Research. 2015;30:456–63.

    Article  CAS  Google Scholar 

  33. Lu Y, Boudiffa M, Dall’Ara E, Bellantuono I, Viceconti M. Development of a protocol to quantify local bone adaptation over space and time: Quantification of reproducibility. Journal of Biomechanics. 2016;49:2095–9.

    Article  Google Scholar 

  34. Altman AR, Tseng W-J, de Bakker CMJ, Chandra A, Lan S, Huh BK, et al. Quantification of skeletal growth, modeling, and remodeling by in vivo micro computed tomography. Bone. 2015;81:370–9.

    Article  Google Scholar 

  35. de Jong JJA, Willems PC, Arts JJ, Bours SGP, Brink PRG, van Geel TACM, et al. Assessment of the healing process in distal radius fractures by high-resolution peripheral quantitative computed tomography. Bone. 2014;64:65–74.

    Article  Google Scholar 

  36. de Jong JJA, Christen P, Chapurlat RD, Geusens PP, van den Bergh JPW, Müller R, et al. Feasibility of rigid 3D image registration of images of healing distal radius fractures. Plos One Public Library of Science (accepted). 2017.

  37. van Rietbergen B, Weinans H, Huiskes R, Odgaard A. A new method to determine trabecular bone elastic properties and loading using micromechanical finite-element models. Journal of Biomechanics. 1995;28:69–81.

    Article  CAS  Google Scholar 

  38. Pahr DH, Zysset PK. Finite element-based mechanical assessment of bone quality on the basis of in vivo images. Current Osteoporosis Reports. Springer US. 2016;14:374–85.

    Article  Google Scholar 

  39. Agarwal S, Rosete F, Zhang C, McMahon DJ, Guo XE, Shane E, et al. In vivo assessment of bone structure and estimated bone strength by first- and second-generation HR-pQCT. Osteoporosis International. Springer London. 2016;27:2955–66.

    Article  CAS  Google Scholar 

  40. Cresswell EN, Goff MG, Nguyen TM, Lee WX, Hernandez CJ. Spatial relationships between bone formation and mechanical stress within cancellous bone. Journal of Biomechanics. 2016;49:222–8.

    Article  CAS  Google Scholar 

  41. An improved inverse dynamics formulation for estimation of external and internal loads during human sagittal plane movements. Computer Methods in Biomechanics and Biomedical Engineering. 2015; 18:362–75.

  42. Wehner T, Wolfram U, Henzler T, Niemeyer F, Claes L, Simon U. Internal forces and moments in the femur of the rat during gait. Journal of Biomechanics. 2010;43:2473–9.

    Article  Google Scholar 

  43. Christen P, van Rietbergen B, Lambers FM, Müller R, Ito K. Bone morphology allows estimation of loading history in a murine model of bone adaptation. Biomechanics and Modeling in Mechanobiology. Springer-Verlag. 2012;11:483–92.

    Article  Google Scholar 

  44. Christen P, Ito K, Galis F, van Rietbergen B. Determination of hip-joint loading patterns of living and extinct mammals using an inverse Wolff’s law approach. Biomechanics and Modeling in Mechanobiology. Springer Berlin Heidelberg. 2015;14:427–32.

    Article  Google Scholar 

  45. Christen P, Ito K, Knippels I, Müller R, van Lenthe GH, van Rietbergen B. Subject-specific bone loading estimation in the human distal radius. Journal of Biomechanics. 2013;46:759–66.

    Article  Google Scholar 

  46. Christen P, Schulte FA, Zwahlen A, van Rietbergen B, Boutroy S, Melton LJI, et al. Voxel size dependency, reproducibility and sensitivity of an in vivo bone loading estimation algorithm. Journal of the Royal Society Interface. The Royal Society; 2016;13.

    Article  Google Scholar 

  47. Christen P, Ito K, Santos dos AA, Müller R, van Rietbergen B. Validation of a bone loading estimation algorithm for patient-specific bone remodelling simulations. J Biomech. 2013;46:941–8.

    Article  Google Scholar 

  48. Trüssel A, Müller R, Webster D. Toward mechanical systems biology in bone. Annals of Biomedical Engineering. Springer US. 2012;40:2475–87.

    Article  Google Scholar 

  49. Taylor C, Scheuren A, Trüssel A, Müller R 3D Local in vivo Environment (LivE) imaging for single cell protein analysis of bone tissue. Current Directions in Biomedical Engineering. 2016;2.

  50. Nioi P, Taylor S, Hu R, Pacheco E, He YD, Hamadeh H, et al. Transcriptional profiling of laser capture microdissected subpopulations of the osteoblast lineage provides insight into the early response to sclerostin antibody in rats. Journal of Bone and Mineral Research. 2015;30:1457–67.

    Article  CAS  Google Scholar 

  51. Trüssel AJ Spatial mapping and high throughput microfluidic gene expression analysis of osteocytes in mechanically controlled bone remodeling [Internet]. Department of Health Sciences and Technology, Diss Nr. 22716, ETH Zurich; 2015. doi:10.3929/ethz-a-010465264.

  52. Ohs N, Keller F, Blank O, Lee WY-W, Chen JC-Y, Arbenz P, et al. Towards in silico prognosis using big data. Current Directions in Biomedical Engineering. 2016;2:57.

    Article  Google Scholar 

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Acknowledgments

This work has been supported by the Holcim Stiftung for the Advancement of Scientific Research.

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  1. ETH Zurich, Institute for Biomechanics, Leopold-Ruzicka-Weg 4, 8093, Zurich, Switzerland

    Patrik Christen & Ralph Müller

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  1. Patrik Christen
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  2. Ralph Müller
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Correspondence to Ralph Müller.

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Patrik Christen and Ralph Müller declare no conflicts of interest.

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This article does not contain any studies with human or animal subjects performed by any of the authors.

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This article is part of the Topical Collection on Osteocytes

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Christen, P., Müller, R. In vivo Visualisation and Quantification of Bone Resorption and Bone Formation from Time-Lapse Imaging. Curr Osteoporos Rep 15, 311–317 (2017). https://doi.org/10.1007/s11914-017-0372-1

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  • DOI: https://doi.org/10.1007/s11914-017-0372-1

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