Reconstruction of developmental landscapes by optimal-transport analysis of single-cell gene expression sheds light on cellular reprogramming.
Geoffrey R. SchiebingeriD, Jian ShuiD, Marcin TabakaiD, Brian ClearyiD, Vidya Subramanian, Aryeh SolomoniD, Siyan LiuiD, Stacie L. LiniD, Peter BerubeiD, Lia Lee, Jenny CheniD, Justin BrumbaughiD, Philippe RigolletiD, Konrad HochedlingeriD, Rudolf JaenischiD, Aviv RegeviD, Eric S. LanderiD
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.
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Abstract summary
Claude Opus 5.5 · xhigh effort · 18 Oct 2026 · from the abstract
Optimal transport converts a 65,781-cell reprogramming time course into probabilistic fates and trajectories, exposing alternative cell fates and a gene, Obox6, that improves reprogramming efficiency.
Understanding the molecular programs behind cellular differentiation is framed as a major goal of modern biology. WADDINGTON-OT recasts that problem as optimal transport, inferring developmental landscapes, probabilistic cell fates and dynamic trajectories from time-course scRNA-seq, and is offered as a general framework for studying differentiation.
Key results
SuggestedApplied to fibroblast-to-iPSC reprogramming, the method yields a fine-grained map that recovers known features and points to alternative fates, including neural- and placental-like cells.
Built from 65,781 scRNA-seq profiles taken at 10 time points over 16 days; the abstract reports the scale of the time course but no accuracy, resolution or benchmark numbers for the inferred map. · Abstract
SuggestedObox6, nominated from the inferred trajectories, raises reprogramming efficiency when tested experimentally.
Authors state they experimentally show the effect, but the abstract gives no efficiency values, controls or effect size. · Abstract
ClaimedA senescent-like cell population is proposed to assist reprogramming via paracrine signalling, and particular trajectories are linked to candidate regulatory models.
Hedged by the authors ("may support"); the abstract names no signalling molecules, no regulator sets and no supporting measurement. · Abstract
Methods, data and reproducibility
WADDINGTON-OT applies optimal transport to large-scale single-cell RNA sequencing collected along a time course, inferring developmental landscapes, probabilistic cellular fates and dynamic trajectories; a candidate gene from the map was then tested experimentally for its effect on reprogramming efficiency.
- scRNA-seq time course of fibroblast reprogramming to iPSCs, 10 time points spanning 16 days · n = 65,781 scRNA-seq profiles
- Methods & tools
- WADDINGTON-OT
- Reproducibility gaps
- No repository, accession or software availability statement appears in the abstract.
Limitations (2)
- The approach is demonstrated on one biological system, a single fibroblast-to-iPSC reprogramming time course, so transfer to other differentiation settings is asserted rather than tested here. · noted in this summary
- Abstract only: no comparison against other trajectory-inference methods and no quantitative accuracy measure for the inferred fates or couplings is reported, and the organism is never stated. · noted in this summary
Who should read:Read this if you infer lineage relationships from time-course scRNA-seq and want an optimal-transport formulation that returns probabilistic fates rather than a single tree.
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