Nondestructive Assessment of Glycosaminoglycans in Engineered Cartilages Using Hexabrix-Enhanced Micro-Computed Tomography View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-04-25

AUTHORS

Long Hao Jin, Byung Hyune Choi, Young Jick Kim, Hyun Ju Oh, Byoung Ju Kim, Xiang Yun Yin, Byoung-Hyun Min

ABSTRACT

It is very useful to evaluate the content and 3D distribution of extracellular matrix non-destructively in tissue engineering. This study evaluated the feasibility of using micro-computed tomography (µCT) with Hexabrix to measure quantitatively sulfated glycosaminoglycans (GAGs) of engineered cartilage. Rabbit chondrocytes at passage 2 were used to produce artificial cartilages in polyglycolic acid scaffolds in vitro. Engineered cartilages were incubated with Hexabrix 320 for 20 min and analyzed via µCT scanning. The number of voxels in the 2D and 3D scanning images were counted to estimate the amount of sulfated GAGs. The optimal threshold value for quantification was determined by regression analysis. The 2D µCT images of an engineered cartilage showed positive correlation with the histological image of Safranin-O staining. Quantitative data obtained with the 3D µCT images of 14 engineered cartilages showed strong correlation with sulfated GAGs contents obtained by biochemical analysis (R2 = 0.883, p < 0.001). Repeated exposure of engineered cartilages to Hexabrix 320 and µCT scanning did not significantly affect cell viability, total DNA content, or the total content of sulfated GAGs. We conclude that µCT imaging using Hexabrix 320 provides high spatial resolution and sensitivity to assess the content and 3D distribution of sulfated GAGs in engineered cartilages. It is expected to be a valuable tool to evaluate the quality of engineered cartilage for commercial development in the future. More... »

PAGES

311-319

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s13770-018-0117-y

DOI

http://dx.doi.org/10.1007/s13770-018-0117-y

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https://app.dimensions.ai/details/publication/pub.1103638315

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30603556


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