Reconstruction of developmental landscapes by optimal transport analysis of single cell gene expression sheds light on cellular reprogramming
Understanding the molecular programs that guide cellular differentiation during development is a major goal of modern biology. Here, we introduce an approach, WADDINGTON OT, based on the mathematics of optimal transport, for inferring developmental landscapes, probabilistic cellular fates and dynamic trajectories from large scale single cell RNA seq (scRNA seq) data collected along a time course. We demonstrate the power of WADDINGTON OT by applying the approach to study 65,781 scRNA seq profiles collected at 10 time points over 16 days during reprogramming of fibroblasts to iPSCs. We construct a high resolution map of reprogramming that rediscovers known features; uncovers new alternative cell fates including neuraland placental like cells; predicts the origin and fate of any cell class; highlights senescent like cells that may support reprogramming through paracrine signaling; and implicates regulatory models in particular trajectories. Of these findings, we highlight Obox6, which we experimentally show enhances reprogramming efficiency. Our approach provides a general framework for investigating cellular differentiation.