RT Journal Article
SR Electronic
T1 Reconstruction of developmental landscapes by optimal-transport analysis of single-cell gene expression sheds light on cellular reprogramming
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 191056
DO 10.1101/191056
A1 Schiebinger, Geoffrey
A1 Shu, Jian
A1 Tabaka, Marcin
A1 Cleary, Brian
A1 Subramanian, Vidya
A1 Solomon, Aryeh
A1 Liu, Siyan
A1 Lin, Stacie
A1 Berube, Peter
A1 Lee, Lia
A1 Chen, Jenny
A1 Brumbaugh, Justin
A1 Rigollet, Philippe
A1 Hochedlinger, Konrad
A1 Jaenisch, Rudolf
A1 Regev, Aviv
A1 Lander, Eric S.
YR 2017
UL http://biorxiv.org/content/early/2017/09/27/191056.abstract
AB 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.