Identification of topographical architectures supporting the phenotype of rat tenocytes

Abstract

Tenocytes, the main cell type of the tendon, require mechanical stimuli for their proper function. When the tenocyte environment changes due to tissue damage or by transferring tenocytes from their native environment into cell culture, the signals from the tenocyte niche are lost, leading towards a decline of phenotypic markers. It is known that micro-topographies can influence cell fate by the physical cues they provide. To identify the optimal topography-induced biomechanical niche in vitro, we seeded tenocytes on the TopoChip, a micro-topographical screening platform, and measured expression of the tendon transcription factor Scleraxis. Through machine learning algorithms, we associated elevated Scleraxis levels with topological design parameters. Fabricating micro-topographies with optimal surface characteristics on larger surfaces allowed finding an improved expression of multiple tenogenic markers. However, long-term confluent culture conditions coincided with osteogenic marker expression and the loss of morphological characteristics. In contrast, passaging tenocytes which migrated from the tendon directly on the topography resulted in prolonged elongated morphology and elevated Scleraxis levels. This research provides new insights into how micro-topographies influence tenocyte cell fate, and supports the notion that micro-topographical design can be implemented in a new generation of tissue culture platforms for supporting the phenotype of tenocytes. STATEMENT OF SIGNIFICANCE: The challenge in controlling in vitro cell behavior lies in controlling the complex culture environment. Here, we present for the first time the use of micro-topographies as a biomechanical niche to support the phenotype of tenocytes. For this, we applied the TopoChip platform, a screening tool with 2176 unique micro-topographies for identifying feature characteristics associated with elevated Scleraxis expression, a tendon related marker. Large area fabrication of micro-topographies with favorable characteristics allowed us to find a beneficial influence on other tenogenic markers as well. Furthermore, passaging cells is more beneficial for Scleraxis marker expression and tenocyte morphology compared to confluent conditions. This study presents important insights for the understanding of tenocyte behavior in vitro, a necessary step towards tendon engineering.

Keywords: Cell morphology; Machine learning; Micro-topography; Phenotypic maintenance; Tenocytes.