Abstract
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.
Highlights
Cells respond in complex ways to their environment, making it challenging to predict a direct relationship between the two
We used nanopit topographies consisting of 120 nm diameter, 100 nm depth and with a 300 nm center-to-center distance in a square array (SQ)[6,7], hexagonal array (HEX)[23,24], and arranged with center-to-center distance offset from 300 nm by 50 nm in both x and y directions (NSQ array)[6,7]
Quantitative polymerase chain reaction (QPCR) was used to assess changes in lineage marker expression induced by nanotopography by day 7 (Fig. 1b)
Summary
Cells respond in complex ways to their environment, making it challenging to predict a direct relationship between the two. We present a platform to relate the effects of cell morphology to gene expression induced by nanotopography. 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. In contrast to active biomolecules, the mechanotransductive effects of topography on cell response do not intuitively relate to topography length scale, isotropy, geometry, and polarity This limits the discovery of functional topography to the screening of libraries for hits using a single, representative cell type[20,21,22]. Using the morphome as predictors and without prior knowledge about nanotopography or cell type, a Bayesian linear regression model robustly predicts quantitative gene expression levels induced by nanotopography. Here we present a quantified and predictive relationship between morphology, gene expression, and topography at the singlecell level
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