During in vitro sub-culturing, tenocytes lose their phenotype which ultimately affects their functioning. As spindle-shaped fibroblasts, tenocytes have a unique thin elongated phenotype and they possess more spread-out shape through phenomena named dedifferentiation1. Given the link between cell shape and cell function, in this study, we first aimed to dedifferentiate tenocytes through in vitro sub-culturing in order to have a model system for dedifferentiation. For this, we isolated human flexor tendon cells from healthy female flexor digitorum longus and seeded at 5000 cells/cm² cell density, passaged every two days for six passages. In order to assess cell phenotype, we fixed with 4% paraformaldehyde and stained with phalloidin and DAPI to visualize the actin cytoskeleton and DNA respectively. We noted that in each passage, cells lost their spindle-shaped phenotype and became more pancake-shaped. At passage 1 and 2, the main cell phenotype is spindle-shaped. However, as the cells are further passaged, the phenotype of the cell population becomes more heterogeneous and at passage 5 and 6, they already display a more spread-out shape. Based on these results, we further hypothesized that they can be re-differentiated through matrix-mediated mechano-transduction and regain their morphology and function. For this aim, we generated decellularized tendon from porcine Achilles tendon and setup a mechanical loading system where we can provide mechanical loadings at physiological levels. This system will provide a new approach on in vitro tenocyte culturing.

Our lab uses computer-aided design to build in silico libraries of surface topographies, which we reproduce on polymeric chips and analyse for cellular responses using high content imaging and machine learning. In addition, we use transcriptomics and mass spectrometry to obtain a holistic view of biomaterial-mediated cellular responses and build gene regulatory networks thereof. This approach enables us to parameterize both the biomaterial properties as well as the cell response and to correlate them using computational tools. We think that this approach can be translated to other biomaterial platforms, such as polymer arrays, and foresee large scale crosstalk between them if we can standardize our methodology to describe the materials and to analyse the cells. To this end, we have started cBIT, the compendium for biomaterial-induced transcriptomics, an open-source database in which scientists can deposit and search material-induced transcriptomics data. The meta-analyses that cBIT enables, could lead to the identification of genes, pathways or expression profiles that can inform the design and development of new biomaterials. As such, by generating new information and simultaneously accumulating it in cBIT, we expect it is possible to one day predict cell responses to biomaterials.