Mapping cells through time and space with moscot

Dominik Klein, Giovanni Palla, Marius Lange, Michal Klein, Zoe Piran, Manuel Gander, Laetitia Meng-Papaxanthos, Michael Sterr, Lama Saber, Changying Jing, Aimée Bastidas-Ponce, Perla Cota, Marta Tarquis-Medina, Shrey Parikh, Ilan Gold, Heiko Lickert (), Mostafa Bakhti, Mor Nitzan, Marco Cuturi and Fabian J. Theis ()
Additional contact information
Dominik Klein: Helmholtz Center
Giovanni Palla: Helmholtz Center
Marius Lange: Helmholtz Center
Michal Klein: Apple
Zoe Piran: The Hebrew University of Jerusalem
Manuel Gander: Helmholtz Center
Laetitia Meng-Papaxanthos: Google DeepMind
Michael Sterr: Helmholtz Center
Lama Saber: Helmholtz Center
Changying Jing: Helmholtz Center
Aimée Bastidas-Ponce: Helmholtz Center
Perla Cota: Helmholtz Center
Marta Tarquis-Medina: Helmholtz Center
Shrey Parikh: Helmholtz Center
Ilan Gold: Helmholtz Center
Heiko Lickert: Helmholtz Center
Mostafa Bakhti: Helmholtz Center
Mor Nitzan: The Hebrew University of Jerusalem
Marco Cuturi: Apple
Fabian J. Theis: Helmholtz Center

Nature, 2025, vol. 638, issue 8052, 1065-1075

Abstract: Abstract Single-cell genomic technologies enable the multimodal profiling of millions of cells across temporal and spatial dimensions. However, experimental limitations hinder the comprehensive measurement of cells under native temporal dynamics and in their native spatial tissue niche. Optimal transport has emerged as a powerful tool to address these constraints and has facilitated the recovery of the original cellular context1–4. Yet, most optimal transport applications are unable to incorporate multimodal information or scale to single-cell atlases. Here we introduce multi-omics single-cell optimal transport (moscot), a scalable framework for optimal transport in single-cell genomics that supports multimodality across all applications. We demonstrate the capability of moscot to efficiently reconstruct developmental trajectories of 1.7 million cells from mouse embryos across 20 time points. To illustrate the capability of moscot in space, we enrich spatial transcriptomic datasets by mapping multimodal information from single-cell profiles in a mouse liver sample and align multiple coronal sections of the mouse brain. We present moscot.spatiotemporal, an approach that leverages gene-expression data across both spatial and temporal dimensions to uncover the spatiotemporal dynamics of mouse embryogenesis. We also resolve endocrine-lineage relationships of delta and epsilon cells in a previously unpublished mouse, time-resolved pancreas development dataset using paired measurements of gene expression and chromatin accessibility. Our findings are confirmed through experimental validation of NEUROD2 as a regulator of epsilon progenitor cells in a model of human induced pluripotent stem cell islet cell differentiation. Moscot is available as open-source software, accompanied by extensive documentation.

Date: 2025
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DOI: 10.1038/s41586-024-08453-2

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