• João P. Garcia
  • Alessia Longoni
  • Debby Gawlitta
  • Antoine J.W.P. Rosenberg
  • Mark W. Grinstaff
  • Juha Töyräs
  • Harrie Weinans
  • Laura B. Creemers
  • Behdad Pouran
Tissue engineering and regenerative medicine are two therapeutic strategies to treat, and to potentially cure, diseases affecting cartilaginous tissues, such as osteoarthritis and cartilage defects. Insights into the processes occurring during regeneration are essential to steer and inform development of the envisaged regenerative strategy, however tools are needed for longitudinal and quantitative monitoring of cartilage matrix components. In this study, we introduce a contrast-enhanced computed tomography (CECT)-based method using a cationic iodinated contrast agent (CA4+) for longitudinal quantification of glycosaminoglycans (GAG) in cartilage-engineered constructs. CA4+ concentration and scanning protocols were first optimized to ensure no cytotoxicity and a facile procedure with minimal radiation dose. Chondrocyte and mesenchymal stem cell pellets, containing different GAG content were generated and exposed to CA4+. The CA4+ content in the pellets, as determined by micro computed tomography, was plotted against GAG content, as measured by 1,9-dimethylmethylene blue analysis, and showed a high linear correlation. The established equation was used for longitudinal measurements of GAG content over 28 days of pellet culture. Importantly, this method did not adversely affect cell viability or chondrogenesis. Additionally, the CA4+ distribution accurately matched safranin-O staining on histological sections. Hence, we show proof-of-concept for the application of CECT, utilizing a positively charged contrast agent, for longitudinal and quantitative imaging of GAG distribution in cartilage tissue-engineered constructs.
Original languageEnglish
Pages (from-to)202-212
JournalActa Biomaterialia
Volume100
DOIs
Publication statusPublished - 2019

    Research areas

  • Tissue Engineering, Cartilage Regeneration, Glycosaminoglycans, 3D Imaging, Computed Tomography

ID: 57399172