Predicting gene expression using morphological cell responses to nanotopography

Cells respond in complex ways to their environment, making it challenging to predict a direct relationship between the two. A key problem is the lack of informative representations of parameters that translate directly into biological function. Here we present a platform to relate the effects of cell morphology to gene expression induced by nanotopography. This platform utilizes the ‘morphome’, a multivariate dataset of cell morphology parameters. We create a Bayesian linear regression model that uses the morphome to robustly predict changes in bone, cartilage, muscle and fibrous gene expression induced by nanotopography. Furthermore, through this model we effectively predict nanotopography-induced gene expression from a complex co-culture microenvironment. The information from the morphome uncovers previously unknown effects of nanotopography on altering cell–cell interaction and osteogenic gene expression at the single cell level. The predictive relationship between morphology and gene expression arising from cell-material interaction shows promise for exploration of new topographies. The surface nanotopography of biomaterials direct cell behavior, but screening for desired effects is inefficient. Here, the authors introduce a platform that enables prediction of nanotopography-induced gene expression changes from changes in cell morphology, including in co-culture environments.