Topology of connective tissues: a key parameter in cellular heterogeneity, beyond composition and stiffness
C. Chen, Z. Ibrahim, M. F. Marchand, T. Piolot, S. Kamboj, F. Carreiras, A. Yamada, M. C. Schanne Klein, Y. Chen, A. Lambert, C. Aime
Abstract
Cellular plasticity is essential in physiological contexts, including pathological ones. It is the basis of morphogenesis and organogenesis, as well as tumorigenesis and metastasis. The extracellular matrix (ECM) is a key player in the generation of cellular heterogeneity. Advances in our understanding of cell plasticity rely on our ability to provide relevant in vitro models. This requires to catch the characteristics of the tissues that are essential for controlling cell fate. To do this, we must consider the diversity of tissues, the diversity of physiological contexts, and the constant remodeling of ECM along these processes. To this aim, we have fabricated a library of ECM models for reproducing the scaffold of connective tissues and basement membrane with different biofabrication routes based on the electrospining and drop casting of biopolymers. Using a combination of multiphoton imaging and nanoindentation, we show that we can vary independently protein composition, topology of connective tissues and stiffness of ECM models. Reproducing the features of a tissue and physiological context in turns allows to generate the complexity of the phenotypic landscape associated with the epithelial-to-mesenchymal transition (EMT) in human ovarian cancer. We show that EMT shift cannot be directly correlated with a unique ECM feature, which reflects the multidimensionality of living environments. Very importantly, our combinatorial approach allows us to provide in vitro models, where the impact of the topological cues on cellular phenotypes can be revealed, beyond protein composition and stiffness of the ECM matrix. On this line, this work is a further step towards the development of ECM models recapitulating the constantly remodeled scaffolding environment that cells face and provides new insights for the development of cell-free matrices.
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