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Identifying potential patient-specific predictors for anterior cruciate ligament reconstruction outcome: a diagnostic in vitro tissue remodeling platform

Vijven van M, Groningen van B, Kimenai J, Steen van der MC, Doeselaar van M, Janssen RPA, Ito K, Foolen J
J Exp Orthop. 2020;7(1):48-2

Abstract

Purpose: Upon anterior cruciate ligament (ACL) rupture, reconstruction is often required, with the hamstring tendon autograft as the most widely used treatment. Post-operative autograft remodeling enhances the risk of graft rupture, which occurs in up to 10% of the patient population, increasing up to 30% of patients aged under 20 years. Therefore, the aim of this research was to identify potential biological predictors for graft rupture, derived from patient-specific tissue remodeling-related cell properties.

Methods: Hamstring tendon-derived cells were obtained from remnant autograft tissue after ACL reconstructions, and seeded in collagen I gels on a micro-tissue platform. Micro-tissue compaction over time – induced by altering the boundary constraints – was monitored. Pro-collagen I expression was assessed using ELISA, and protein expression of tenomodulin and α-smooth muscle actin were measured using Western blot. Expression and activity of matrix metalloproteinase 2 were determined using gelatin zymography. 

Results: Cells were obtained from 36 patients (aged 12-55 years). Only micro-tissues corresponding to younger patients occasionally released themselves from the constraining posts. Pro-collagen I expression was significantly higher in younger patients. Differences in α-smooth muscle actin and tenomodulin expression between patients were found, but these were age-independent. Active matrix metalloproteinase 2 expression was slightly more abundant in younger patients. 

Conclusions: The presented micro-tissue platform exposed patient-specific remodeling-related differences between tendon-derived cells, with the micro-tissues that released from constraining posts and pro-collagen I expression best reflecting the clinical age-dependency of graft rupture. This platform could become a predictive tool to identify individual patients at risk for graft rupture.

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