Reconstruction of developmental landscapes by optimal-transport analysis of single-cell gene expression sheds light on cellular reprogramming

Abstract

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.

The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution

“…When inferring cell state transitions from scRNA-seq snapshots separated by large gaps in time, intermediate progenitor states may be overlooked. Overall these results support a growing body of single-cell bioinformatics methods that seek to infer developmental cell trajectories on the basis of continuity (10, 11) and distance minimization (12) in gene expression space, but also illuminate where this principle can be misleading.…”

“…Error bars represent time interval of scRNA-seq experiment. ( B and C ) scRNA-seq reveals an early endothelial / hemangioblast progenitor that appears at stage 18 (red lineage), as compared to stage 26 for hemangioblasts and stage 31 for endothelial cells [XAO; (12, 13)], with recognizable activation of the endothelial/hemangioblast gene expression program (C).…”

Modelling acute myeloid leukaemia in a continuum of differentiation states

“…In order to describe the entire modeling process, in this section we briefly describe methods for reducing the dimension of high dimensional scRNA-data, before reviewing pseudotime reconstruction techniques, and conclude this section by examining a technique from Schiebinger et al (2017) for construction of a directed graph that represents hematopoietic differentiation space. While the focus of this paper is not dimension reduction techniques or pseudotime reconstruction, we summarize some of these techniques that are most relevant to our modeling approach, without advocating for one over another.…”

“…This manuscript is structured as follows: first, we review the state of the art of dimension reduction methods that are used to construct and define hematopoietic differentiation spaces that can be represented as graphs, including a review of Schienbinger et al.’s method for modeling transport on a graph from reduced dimension gene expression data (Schiebinger et al 2017). Then we introduce our partial differential equation (PDE) model of flow and transport on a graph, and illustrate the model on simple “Y” shaped graph with symmetric and asymmetric differentiation.…”

Beyond pseudotime: Following T-cell maturation in single-cell RNAseq time series

“…Steadystate problems were previously addressed in the context of cell cycle transitions 7,8 based on discretized population structures. The problem of developmental trajectory estimation from time series data is not a steadystate problem which was recently addressed via an optimal transport framework for discrete transitions 9 ( Fig. 1b).…”