Skip to main content

Advertisement

Log in

Diffusion tensor imaging predicting neurological repair of spinal cord injury with transplanting collagen/chitosan scaffold binding bFGF

  • Tissue Engineering Constructs and Cell Substrates
  • Original Research
  • Published:
Journal of Materials Science: Materials in Medicine Aims and scope Submit manuscript

Abstract

Prognosis and treatment evaluation of spinal cord injury (SCI) are still in the long-term research stage. Prognostic factors for SCI treatment need effective biomarker to assess therapeutic effect. Quantitative diffusion tensor imaging (DTI) may become a potential indicators for assessing SCI repair. However, its correlation with the results of locomotor function recovery and tissue repair has not been carefully studied. The aim of this study was to use quantitative DTI to predict neurological repair of SCI with transplanting collagen/chitosan scaffold binding basic fibroblast growth factor (bFGF). To achieve our research goals, T10 complete transection SCI model was established. Then collagen/chitosan mixture adsorbed with bFGF (CCS/bFGF) were implanted into rats with SCI. At 8 weeks after modeling, implanting CCS/bFGF demonstrated more significant improvements in locomotor function according to Basso-Beattie-Bresnahan (BBB) score, inclined-grid climbing test, and electrophysiological examinations. DTI was carried out to evaluate the repair of axons by diffusion tensor tractgraphy (DTT), fractional anisotropy (FA) and apparent diffusion coefficient (ADC), a numerical measure of relative white matter from the rostral to the caudal. Parallel to locomotor function recovery, the CCS/bFGF group could significantly promote the regeneration of nerve fibers tracts according to DTT, magnetic resonance imaging (MRI), Bielschowsky’s silver staining and immunofluorescence staining. Positive correlations between imaging and locomotor function or histology were found at all locations from the rostral to the caudal (P < 0.0001). These results demonstrated that DTI might be used as an effective predictor for evaluating neurological repair after SCI in experimental trails and clinical cases.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Saxena T, Loomis KH, Pai BS, Karumbaiah L, Gaupp E, Patil K, et al. Nanocarrier mediated inhibition of macrophage migration inhibitory factor attenuates secondary injury after spinal cord injury. ACS Nano. 2015;9:1492–505.

    Article  CAS  Google Scholar 

  2. Devivo MJ, Krause JS, Lammertse DP. Recent trends in mortality and causes of death among persons with spinal cord injury. Arch Phys Med Rehabil. 1999;80:1411–9.

    Article  CAS  Google Scholar 

  3. Oliveira AL, Sousa EC, Silva NA, Sousa N, Salgado AJ, Reis RL. Peripheral mineralization of a 3D biodegradable tubular construct as a way to enhance guidance stabilization in spinal cord injury regeneration. J Mater Sci Mater Med. 2012;23:2821–30.

    Article  CAS  Google Scholar 

  4. Li G, Che MT, Zhang K, Qin LN, Zhang YT, Chen RQ, et al. Graft of the NT-3 persistent delivery gelatin sponge scaffold promotes axon regeneration, attenuates inflammation, and induces cell migration in rat and canine with spinal cord injury. Biomaterials. 2016;83:233–48.

    Article  CAS  Google Scholar 

  5. Hejčl A, Růžička J, Proks V, Macková H, Kubinová Š, Tukmachev D, et al. Dynamics of tissue ingrowth in SIKVAV-modified highly superporous PHEMA scaffolds with oriented pores after bridging a spinal cord transection. J Mater Sci Mater Med. 2018;29:89.

    Article  CAS  Google Scholar 

  6. Nd HE, Tuszynski MH. Neurotrophins: potential therapeutic tools for the treatment of spinal cord injury. Neurotherapeutics. 2011;8:694–703.

    Article  CAS  Google Scholar 

  7. Mccall J, Weidner N, Blesch A. Neurotrophic factors in combinatorial approaches for spinal cord regeneration. Cell Tissue Res. 2012;349:27–37.

    Article  CAS  Google Scholar 

  8. Shi Q, Gao W, Han X, Zhu X, Sun J, Xie F, et al. Collagen scaffolds modified with collagen-binding bFGF promotes the neural regeneration in a rat hemisected spinal cord injury model. Sci China Life Sci. 2014;57:232–40.

    Article  CAS  Google Scholar 

  9. Oliveri RS, Segun B, Fin BS. Mesenchymal stem cells improve locomotor recovery in traumatic spinal cord injury: systematic review with meta-analyses of rat models. Neurobiol Dis. 2014;62:338–53.

    Article  CAS  Google Scholar 

  10. Raspa A, Pugliese R, Maleki M, Gelain F. Recent therapeutic approaches for spinal cord injury. Biotechnol Bioeng. 2016;113:253–9.

    Article  CAS  Google Scholar 

  11. Fakhoury M. Spinal cord injury: overview of experimental approaches used to restore locomotor activity. Rev Neurosci. 2015;26:397–405.

    Article  Google Scholar 

  12. Elizei SS, Kwon BK. The translational importance of establishing biomarkers of human spinal cord injury. Neural Regen Res. 2017;12:385–8.

    Article  Google Scholar 

  13. Yung A, Mattucci S, Bohnet B, Liu J, Fournier C, Tetzlaff W, et al. Diffusion tensor imaging shows mechanism-specific differences in injury pattern and progression in rat models of acute spinal cord injury. Neuroimage. 2018;186:43–55.

    Article  Google Scholar 

  14. Wang-Leandro A, Hobert MK, Kramer S, Rohn K, Stein VM, Tipold A. The role of diffusion tensor imaging as an objective tool for the assessment of motor function recovery after paraplegia in a naturally-occurring large animal model of spinal cord injury. J Transl Med. 2018;16:258.

    Article  Google Scholar 

  15. Kim J, Song SK, Burke DA, Magnuson DSK. Comprehensive locomotor outcomes correlate to hyperacute diffusion tensor measures after spinal cord injury in the adult rat. Exp Neurol. 2012;235:188–96.

    Article  Google Scholar 

  16. Wang X, Duffy P, McGee AW, Hasan O, Gould G, Tu N, et al. Recovery from chronic spinal cord contusion after Nogo receptor intervention. Ann Neurol. 2011;70:805–21.

    Article  CAS  Google Scholar 

  17. Shanmuganathan K, Gullapalli RP, Zhuo J, Mirvis SE. Diffusion tensor MR imaging in cervical spine trauma. AJNR Am J Neuroradiol. 2008;29:655–9.

    Article  CAS  Google Scholar 

  18. Ellingson BM, Ulmer JL, Kurpad SN, Schmit BD. Diffusion tensor MR imaging in chronic spinal cord injury. AJNR Am J Neuroradiol. 2008;29:1976.

    Article  CAS  Google Scholar 

  19. Loy DN, Kim JH, Xie M, Schmidt RE, Trinkaus K, Song SK. Diffusion tensor imaging predicts hyperacute spinal cord injury severity. J Neurotrauma. 2007;24:979–90.

    Article  Google Scholar 

  20. Zhao C, Rao JS, Pei XJ, Lei JF, Wang ZJ, Yang ZY, et al. Longitudinal study on diffusion tensor imaging and diffusion tensor tractography following spinal cord contusion injury in rats. Neuroradiology. 2016;58:607–14.

    Article  Google Scholar 

  21. Ellingson BM, Salamon N, Holly LT. Imaging techniques in spinal cord injury. World Neurosurg. 2014;82:1351–8.

    Article  Google Scholar 

  22. Fujiyoshi K, Konomi T, Yamada M, Hikishima K, Tsuji O, Komaki Y, et al. Diffusion tensor imaging and tractography of the spinal cord: from experimental studies to clinical application. Exp Neurol. 2013;242:74–82.

    Article  Google Scholar 

  23. Xu J, Shimony JS, Klawiter EC, Snyder AZ, Trinkaus K, Naismith RT, et al. Improved in vivo diffusion tensor imaging of human cervical spinal cord. Neuroimage. 2013;67:64–76.

    Article  Google Scholar 

  24. Poretti A, Meoded A, Bunge M, Fatemi A, Barrette P, Huisman TA, et al. Novel diffusion tensor imaging findings in Krabbe disease. Eur J Paediatr Neurol. 2014;18:150–6.

    Article  Google Scholar 

  25. Koskinen EA, Hakulinen U, Brander AE, Luoto TM, Ylinen A, Ohman JE. Clinical correlates of cerebral diffusion tensor imaging findings in chronic traumatic spinal cord injury. Spinal Cord. 2014;52:202–8.

    Article  CAS  Google Scholar 

  26. Bodley R. Imaging in chronic spinal cord injury–indications and benefits. Eur J Radiol. 2002;42:135–53.

    Article  Google Scholar 

  27. Schwartz ED, Yezierski RP, Pattany PM, Quencer RM, Weaver RG. Diffusion-weighted MR imaging in a rat model of syringomyelia after excitotoxic spinal cord injury. AJNR Am J Neuroradiol. 1999;20:1422–8.

    CAS  Google Scholar 

  28. Blomster LV, Cowin GJ, Kurniawan ND, Ruitenberg MJ. Detection of endogenous iron deposits in the injured mouse spinal cord through high-resolution ex vivo and in vivo MRI. NMR Biomed. 2013;26:141–50.

    Article  CAS  Google Scholar 

  29. Zhao C, Rao JS, Pei XJ, Lei JF, Wang ZJ, Zhao W, et al. Diffusion tensor imaging of spinal cord parenchyma lesion in rat with chronic spinal cord injury. Magn Reson Imaging. 2018;47:25–32.

    Article  Google Scholar 

  30. Rajasekaran S, Kanna RM, Shetty AP, Ilayaraja V. Efficacy of diffusion tensor anisotropy indices and tractography in assessing the extent of severity of spinal cord injury: an in vitro analytical study in calf spinal cords. Spine J. 2012;12:1147–53.

    Article  Google Scholar 

  31. Wang F, Huang SL, He XJ, Li XH. Determination of the ideal rat model for spinal cord injury by diffusion tensor imaging. Neuroreport. 2014;25:1386–92.

    Article  Google Scholar 

  32. Jirjis MB, Kurpad SN, Schmit BD. Ex vivo diffusion tensor imaging of spinal cord injury in rats of varying degrees of severity. J Neurotrauma. 2013;30:1577–86.

    Article  Google Scholar 

  33. Kelley BJ, Harel NY, Kim CY, Papademetris X, Coman D, Wang X, et al. Diffusion tensor imaging as a predictor of locomotor function after experimental spinal cord injury and recovery. J Neurotrauma. 2014;31:1362–73.

    Article  Google Scholar 

  34. Li XH, Li JB, He XJ, Wang F, Huang SL, Bai ZL. Timing of diffusion tensor imaging in the acute spinal cord injury of rats. Sci Rep. 2015;5:12639.

    Article  CAS  Google Scholar 

  35. Basser PJ, Pierpaoli C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson. 2011;213:560–70.

    Article  CAS  Google Scholar 

  36. Yoo WK, Kim TH, Hai DM, Sundaram S, Yang YM, Park MS, et al. Correlation of magnetic resonance diffusion tensor imaging and clinical findings of cervical myelopathy. Spine J. 2013;13:867–76.

    Article  Google Scholar 

  37. Sun Y, Yang C, Zhu X, Wang JJ, Liu XY, Yang XP, et al. 3D printing collagen/chitosan scaffold ameliorated axon regeneration and neurological recovery after spinal cord injury. J Biomed Mater Res A. 2019;107(9):1898–908.

  38. Fu F, Zhu X, Qin Z, Wang JJ, Xu C, Wang LN, et al. Differential degradation rate and underlying mechanism of a collagen/chitosan complex in subcutis, spinal cord and brain tissues of rat. J Mater Sci Mater Med. 2018;29:35.

    Article  CAS  Google Scholar 

  39. Harris J, Lee H, Tu CT, Cribbs D, Cotman C, Jeon NL. Preparing e18 cortical rat neurons for compartmentalization in a microfluidic device. J Visualized Exp. 2007;8:305.

    Google Scholar 

  40. Chen C, Zhao M, Zhang R, Lu G, Zhao C, Fu F, et al. Collagen/heparin sulfate scaffolds fabricated by a 3D bioprinter improved mechanical properties and neurological function after spinal cord injury in rats. J Biomed Mater Res Part A. 2017;105:1324–32.

    Article  CAS  Google Scholar 

  41. Basso DM, Beattie MS, Bresnahan JC. A sensitive and reliable locomotor rating scale for open field testing in rats. J Neurotrauma. 1995;12:1.

    Article  CAS  Google Scholar 

  42. RamãN-Cueto A, Cordero MI, Santos-Benito FF, Avila J. Functional recovery of paraplegic rats and motor axon regeneration in their spinal cords by olfactory ensheathing glia. Neuron. 2000;25:425–35.

    Article  Google Scholar 

  43. Rivlin AS, Tator CH. Objective clinical assessment of motor function after experimental spinal cord injury in the rat. J Neurosurg. 1977;47:577–81.

    Article  CAS  Google Scholar 

  44. Bazley FA, Hu C, Maybhate A, Pourmorteza A, Pashai N, Thakor NV, et al. Electrophysiological evaluation of sensory and motor pathways after incomplete unilateral spinal cord contusion. J Neurosurg Spine. 2012;16:414–23.

    Article  Google Scholar 

  45. Xu X, Li N, Zhu L, Zhou Y, Cheng H. Beneficial effects of local profound hypothermia and the possible mechanism after experimental spinal cord injury in rats. J Am Paraplegia Soc. 2015;39:220–8.

    Google Scholar 

  46. Sedý J, Urdzíková L, Jendelová P, Syková E. Methods for behavioral testing of spinal cord injured rats. Neurosci Biobehav Rev. 2008;32:550–80.

    Article  Google Scholar 

  47. Li XF, Dai LY. Three-dimensional finite element model of the cervical spinal cord: preliminary results of injury mechanism analysis. Spine. 2009;34:1140–7.

    Article  Google Scholar 

  48. Huang SL, Liu YX, Yuan GL, Zhang J, Yan HW. Characteristics of lumbar disc herniation with exacerbation of presentation due to spinal manipulative therapy. Medicine. 2015;94:e661.

    Article  Google Scholar 

  49. Huang SL, He XJ, Xiang L, Yuan GL, Ning N, Lan BS. CT and MRI features of patients with diastematomyelia. Spinal Cord. 2014;52:689–92.

    Article  CAS  Google Scholar 

  50. Bosma R, Stroman PW. Diffusion tensor imaging in the human spinal cord: development, limitations, and clinical applications. Crit Rev Biomed Eng. 2012;40:1–20.

    Article  CAS  Google Scholar 

  51. Li W, Long Y, Liu Y, Long K, Liu S, Wang Z, et al. Fabrication and characterization of chitosan-collagen crosslinked membranes for corneal tissue engineering. J Biomater Sci Polym Ed. 2014;25:1962–72.

    Article  CAS  Google Scholar 

  52. Takagi T, Kimura Y, Shibata S, Saito H, Ishii K, Okano HJ, et al. Sustained bFGF-release tubes for peripheral nerve regeneration: comparison with autograft. Plast Reconstructive Surg. 2012;130:866–76.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Nature Scientific Fund of China (81771352, 81971782, 81671222, 81771350, 81772018, 81801240) and the Nature Scientific Fund of Tianjin (18JCJQJC48500, 15ZXLCSY00040, 16ZXHLSY00120).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Dong Ming or Xiao-Hong Li.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, XY., Liang, J., Wang, Y. et al. Diffusion tensor imaging predicting neurological repair of spinal cord injury with transplanting collagen/chitosan scaffold binding bFGF. J Mater Sci: Mater Med 30, 123 (2019). https://doi.org/10.1007/s10856-019-6322-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10856-019-6322-y

Navigation