The need to derive and culture diverse cell or tissue types in vitro has prompted investigations on how changes in culture conditions affect cell states. However, the identification of the optimal conditions (e.g., signaling molecules and growth factors) required to maintain cell types or convert between cell types remains a time-consuming task. Here, we developed EpiMogrify, an approach that leverages data from ∼100 human cell/tissue types available from ENCODE and Roadmap Epigenomics consortia to predict signaling molecules and factors that can either maintain cell identity or enhance directed differentiation (or cell conversion). EpiMogrify integrates protein-protein interaction network information with a model of the cell’s epigenetic landscape based on H3K4me3 histone modifications. Using EpiMogrify-predicted factors for maintenance conditions, we were able to better potentiate the maintenance of astrocytes and cardiomyocytes in vitro. We report a significant increase in the efficiency of astrocyte and cardiomyocyte differentiation using EpiMogrify-predicted factors for conversion conditions.

需要在体外衍生和培养多种细胞或组织类型促使研究培养条件的变化如何影响细胞状态。然而，确定维持细胞类型或在细胞类型之间转换所需的最佳条件（例如，信号分子和生长因子）仍然是一项耗时的任务。在这里，我们开发了 EpiMogrify，这是一种方法，它利用来自 ENCODE 和 Roadmap Epigenomics 联盟的大约 100 种人类细胞/组织类型的数据来预测信号分子和因子，这些分子和因子可以保持细胞身份或增强定向分化（或细胞转化）。EpiMogrify 将蛋白质-蛋白质相互作用网络信息与基于 H3K4me3 组蛋白修饰的细胞表观遗传模型相结合。使用 EpiMogrify 预测的维护条件因子，体外。我们报告了使用 EpiMogrify 预测的转换条件因子显着提高星形胶质细胞和心肌细胞分化的效率。