Predicting gene expression using morphological cell responses to nanotopography

Cells respond in complex ways to topographies, making it challenging to identify a direct relationship between surface topography and cell response. A key problem is the lack of informative representations of topographical parameters that translate directly into biological properties. Here, we present a platform to relate the effects of nanotopography on morphology to function. This platform utilizes the morphome, a multivariate dataset containing single cell measures of focal adhesions, the cytoskeleton, and chromatin. We demonstrate that nanotopography-induced changes in cell phenotype are uniquely encoded by the morphome. The morphome was used to create a Bayesian linear regression model that robustly predicted changes in bone, cartilage, muscle and fibrous tissue gene expression induced by nanotopography. Furthermore, the morphome effectively predicted nanotopography-induced phenotype within a complex co-culture microenvironment. Thus, the morphome enables the cell function-oriented exploration of new topographies, with potential applications in the development of novel surface-patterned biomaterials for tissue implants.