Ontogeny informs regeneration: explant models to investigate the role of the extracellular matrix in cartilage tissue assembly and development

ABSTRACT

Osteoarthritis (OA) is typically managed in late stages by replacement of the articular cartilage surface with a prosthesis as an effective, though undesirable outcome. As an alternative, hydrogel implants or growth factor treatments are currently of great interest in the tissue engineering community, and scaffold materials are often designed to emulate the mechanical and chemical composition of mature extracellular matrix (ECM) tissue. However, scaffolds frequently fail to capture the structure and organization of cartilage. Additionally, many current scaffold designs do not mimic processes by which structurally sound cartilage is formed during musculoskeletal development. The objective of this review is to highlight methods that investigate cartilage ontogenesis with native and model systems in the context of regenerative medicine. Specific emphasis is placed on the use of cartilage explant cultures that provide a physiologically relevant microenvironment to study tissue assembly and development. Ex vivo cartilage has proven to be a cost-effective and accessible model system that allows researchers to control the culture conditions and stimuli and perform proteomics and imaging studies that are not easily possible using in vivo experiments, while preserving native cell–matrix interactions. We anticipate our review will promote a developmental biology approach using explanted tissues to guide cartilage tissue engineering and inform new treatment methods for OA and joint damage.

KEYWORDS:

• Musculoskeletal explant models
• osteoarthritis and degeneration
• atomic force microscopy
• biomechanics
• mechanobiology

Acknowledgments

The authors acknowledge Adrienne Scott and Jeanne Barthold for helpful comments and edits.

Author contributions

All authors contributed to the conception and design of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by grants from the NIH [grants R01 AR063712 and AR071359]; NSF CAREER [grant 1349735]; and the Interdisciplinary Quantitative Biology (IQ Biology) Program at the BioFrontiers Institute, University of Colorado Boulder [NSF IGERT 1144807].