Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming

By G. Schiebinger, Jian Shu, M. Tabaka, B. Cleary, V. Subramanian, A. Solomon, Joshua Gould, Siyan Liu, Stacie Lin, Peter Berube and 8 more. Cell, 2019

992 citations recorded by the source corpus.

Summary Understanding the molecular programs that guide differentiation during development is a major challenge. Here, we introduce Waddington-OT, an approach for studying developmental time courses to infer ancestor-descendant fates and model the regulatory programs that underlie them. We apply the method to reconstruct the landscape of reprogramming from 315,000 scRNA-seq profiles, collected at half-day intervals across 18 days. The results reveal a wider range of developmental programs than previously characterized. Cells gradually adopt either a terminal stromal state or a mesenchymal-to-epithelial transition state. The latter gives rise to populations related to pluripotent, extra-embryonic, and neural cells, with each harboring multiple finer subpopulations. The analysis predicts transcription factors and paracrine signals that affect fates, and experiments validate that the TF Obox6 and the cytokine GDF9 enhance reprogramming efficiency. Our approach sheds light on the process and outcome of reprogramming and provides a framework applicable to diverse temporal processes in biology.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5f0a897b41b86390044f1494e0724e7c8ded5792

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Citations in the corpus

Learning single-cell perturbation responses using neural optimal transport (2021, positive): “Optimal transport has previously been applied in the domain of single-cell biology to uncover trajectories of single-cell reprogramming and to link rich, non-spatially-resolved with sparse, spatially resolved measurements (Schiebinger et al., 2019; Cang and Nie, 2020; Demetci et al., 2020; Huizing e”
CellRank for directed single-cell fate mapping (2020, positive): “Note that, while we use RNA velocity here to approximate the direction of cellular dynamics, CellRank generalizes to accommodate any vector field that provides a directional measure, such as metabolic labeling 4–6 or real time information 17,18 .”
Lineage tracing on transcriptional landscapes links state to fate during differentiation (2018, positive): “We tested three recent approaches, Population Balance Analysis (PBA) 4 , WaddingtonOT (WOT) 5 , and FateID 7 for their ability to predict the fate of a cell choosing between neutrophil and monocyte fates.”
Plugin estimation of smooth optimal transport maps (2021, background): “, 2021), and in a wide range of statistical applications to the sciences (Read, 1999; Wang et al., 2011; Schiebinger et al., 2019; Komiske et al., 2020).”
TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics (2020, positive): “Matching Populations from Multiple Time Points Recent methods get around some of these challenges using multiple timepoints (Hashimoto et al., 2016; Schiebinger et al., 2019; Yang & Uhler, 2019).”
Spatial components of molecular tissue biology (2022, positive): “From a causal modeling perspective, branching events might be explained by the spatial context; in particular, it may be possible to infer the paracrine signals affecting developmental processes 69,70 , readily moving from a cell-intrinsic view of differentiation to a more comprehensive tissue view.”
Screening cell–cell communication in spatial transcriptomics via collective optimal transport (2023, positive): “Optimal transport has recently been used for transcriptomic data analysis, including batch effect correction 29 , developmental trajectory reconstruction 30 and spatial annotation of scRNA-seq data 31,32 .”
Participatory provenance as representational auditing for AI-mediated public consultation (2026, unknown): “…has been applied to evaluate generative models [Arjovsky et al., 2017], to quantify distributional shift in machine learning [Courty et al., 2017], and to compare biological distributions [Schiebinger et al., 2019], but has not been directed at the problem of auditing AI-mediated policy summaries.”