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Cell migration, the process by which cells move from one location to another, is essential for the proper development and viability of organisms throughout their life. When cells are not able to migrate properly to their ordained locations, various disorders may occur. For example, disruption in cell migration causes chronic inflammatory diseases such as arthritis.
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Related Experiment Video

Updated: May 31, 2025

Visualization of Tangential Cell Migration in the Developing Chick Optic Tectum
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Mapping cells through time and space with moscot.

Dominik Klein1,2, Giovanni Palla1,3, Marius Lange1,2,4

  • 1Institute of Computational Biology, Helmholtz Center, Munich, Germany.

Nature
|January 22, 2025
PubMed
Summary
This summary is machine-generated.

We introduce moscot, a scalable framework for multi-omics single-cell optimal transport. Moscot reconstructs cellular context and developmental trajectories, enabling new insights into spatiotemporal dynamics and cell lineage relationships.

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Area of Science:

  • Computational biology
  • Genomics
  • Developmental biology

Background:

  • Single-cell genomic technologies offer multimodal profiling but face limitations in capturing native temporal dynamics and spatial context.
  • Optimal transport methods can recover cellular context but often struggle with multimodality and scalability for large single-cell atlases.

Purpose of the Study:

  • Introduce moscot (multi-omics single-cell optimal transport), a scalable computational framework designed for single-cell genomics.
  • Enable the integration of multimodal data and overcome limitations in reconstructing cellular context across space and time.
  • Facilitate the analysis of complex biological systems, including developmental trajectories and cell lineage relationships.

Main Methods:

  • Developed moscot, a scalable framework for optimal transport supporting multimodality in single-cell genomics.
  • Applied moscot to reconstruct developmental trajectories of 1.7 million cells across 20 time points in mouse embryos.
  • Utilized moscot.spatiotemporal to analyze gene expression across spatial and temporal dimensions for mouse embryogenesis.
  • Integrated gene expression and chromatin accessibility data to resolve cell lineage relationships in pancreas development.

Main Results:

  • Successfully reconstructed developmental trajectories for a large-scale mouse embryo dataset.
  • Enriched spatial transcriptomic data and aligned multiple brain sections using multimodal information.
  • Uncovered spatiotemporal dynamics of mouse embryogenesis using gene expression data.
  • Resolved endocrine-lineage relationships in pancreas development and validated NEUROD2's role in epsilon progenitor cells.

Conclusions:

  • Moscot provides a scalable and versatile framework for multimodal single-cell optimal transport.
  • The framework enables comprehensive reconstruction of cellular context, developmental trajectories, and spatiotemporal dynamics.
  • Moscot facilitates novel discoveries in developmental biology and cell lineage analysis, supported by experimental validation.