Rational Reprogramming of Cellular States by Combinatorial Perturbation

Ectopic expression of transcription factors (TFs) can reprogram cell state. However, because of the large combinatorial space of possible TF cocktails, it remains difficult to identify TFs that reprogram specific cell types. Here, we develop Reprogram-Seq to experimentally screen thousands of TF cocktails for reprogramming performance. Reprogram-Seq leverages organ-specific cell-atlas data with single-cell perturbation and computational analysis to predict, evaluate, and optimize TF combinations that reprogram a cell type of interest. Focusing on the cardiac system, we perform Reprogram-Seq on MEFs using an undirected library of 48 cardiac factors and, separately, a directed library of 10 epicardial-related TFs. We identify a combination of three TFs, which efficiently reprogram MEFs to epicardial-like cells that are transcriptionally, molecularly, morphologically, and functionally similar to primary epicardial cells. Reprogram-Seq holds promise to accelerate the generation of specific cell types for regenerative medicine. Direct reprogramming of a cellular state holds promise for regenerative medicine. Duan et al. present Reprogram-Seq to identify, evaluate, and optimize transcription factor cocktails that drive direct reprogramming of a cell state. They apply Reprogram-Seq to generate epicardial-like cells and show how the approach can be leveraged for rational cellular reprogramming.