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Klein, D. ; Palla, G. ; Lange, M. ; Klein, M.* ; Piran, Z.* ; Gander, M. ; Meng-Papaxanthos, L.* ; Sterr, M. ; Saber, L. ; Jing, C. ; Bastidas-Ponce, A. ; Cota, P. ; Tarquis Medina, M. ; Parikh, S. ; Gold, I. ; Lickert, H. ; Bakhti, M. ; Nitzan, M.* ; Cuturi, M.C.* ; Theis, F.J.

Mapping cells through time and space with moscot.

Nature 638, 1065–1075 (2025)
Verlagsversion DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
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 We also resolve endocrine-lineage relationships of delta and epsilon cells in a previously unpublished mouse, time-resolved pancreas development dataset using paired measurements of gene expression and chromatin accessibility.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.
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Genome-wide Expression; Rna-seq; Mouse Embryos; Differentiation; Apoptosis; Reconstruction; Identification; Organogenesis; Heterogeneity; Trajectories
ISSN (print) / ISBN 0028-0836
e-ISSN 1476-4687
Zeitschrift Nature
Quellenangaben Band: 638, Heft: , Seiten: 1065–1075 Artikelnummer: , Supplement: ,
Verlag Nature Publishing Group
Verlagsort London
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Institute of Diabetes and Regeneration Research (IDR)
Förderungen Joachim Herz Foundation
EMBO Postdoctoral Fellowship
Israeli Council for Higher Education
Azrieli Foundation Early Career Faculty Fellowship
Center for Interdisciplinary Data Science Research at the Hebrew University of Jerusalem
Israel Science Foundation
European Union (ERC)
Wellcome Leap
Helmholtz Association under the joint research school Munich School for Data Science
BMBF-funded de.NBI Cloud within the German Network for Bioinformatics Infrastructure (de.NBI)
EU