Machine-guided cell-fate engineering

The creation of induced pluripotent stem cells (iPSCs) has enabled scientists to explore the derivation of many types of cells. While there are diverse general approaches for cell-fate engineering, one of the fastest and most efficient approaches is transcription factor (TF) over-expression. However, finding the right combination of TFs to over-express to differentiate iPSCs directly into other cell-types is a difficult task. Here were describe an automatable machine-learning (ML) pipeline, called \textit{CellCartographer}, that uses chromatin accessibility and transcriptomics data to design multiplex TF pooled-screening experiments for cell type conversions that then be iteratively refined. We validate this method by differentiating iPSCs into twelve diverse cell types at low efficiency in preliminary screens and then iteratively refine our TF combinations to achieve high efficiency differentiation for six of these cell types in < 6 days. Finally, we functionally characterized engineered iPSC-derived cytotoxic T-cells (iCytoT), regulatory T-cells (iTReg), type II astrocytes (iAstII), and hepatocytes (iHep) to validate functionally accurate differentiation.