A systems mechanobiology model to predict cardiac reprogramming outcomes on different biomaterials.

ABSTRACT: During normal development, the extracellular matrix (ECM) regulates cell fate mechanically and biochemically. However, the ECM's influence on lineage reprogramming, a process by which a cell's developmental cycle is reversed to attain a progenitor-like cell state followed by subsequent differentiation into a desired cell phenotype, is unknown. Using a material mimetic of the ECM, here we show that ligand identity, ligand density, and substrate modulus modulate indirect cardiac reprogramming efficiency, but were not individually correlated with phenotypic outcomes in a predictive manner. Alternatively, we developed a data-driven model using partial least squares regression to relate short-term cell states, defined by quantitative mechanosensitive responses to different material environments, with long-term changes in phenotype. This model was validated by accurately predicting the reprogramming outcomes on a different material platform. Collectively, these findings suggest a means to rapidly screen candidate biomaterials that support reprogramming with high efficiency, without subjecting cells to the entire reprogramming process.

SUBMITTER: Kong YP 

PROVIDER: S-EPMC6119647 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature