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Updated: Jun 26, 2025

Concentric Gel System to Study the Biophysical Role of Matrix Microenvironment on 3D Cell Migration

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Spatial transition tensor of single cells.

Peijie Zhou^1,2,3,4, Federico Bocci¹, Tiejun Li⁵

¹Department of Mathematics, University of California, Irvine, Irvine, CA, USA.

|May 16, 2024

Summary

This summary is machine-generated.

Spatial transcriptomics and messenger RNA splicing reveal cell state dynamics. The new spatial transition tensor (STT) method reconstructs cell-state-specific transitions and dynamics across spatiotemporal scales.

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Area of Science:

Computational Biology
Genomics
Developmental Biology

Background:

Spatial transcriptomics and mRNA splicing provide spatiotemporal information on cell states and transitions.
Existing lineage-inference methods often lack spatial dynamics or cannot capture multiple cell state transition paths.

Purpose of the Study:

To develop a novel method, spatial transition tensor (STT), for characterizing multistability in space using spatial transcriptomes and mRNA splicing.
To reconstruct cell-state-specific dynamics and spatial state transitions across multiple spatiotemporal scales.

Main Methods:

Utilized a multiscale dynamical model incorporating mRNA splicing and spatial transcriptomes.
Employed a four-dimensional transition tensor and spatial-constrained random walk for analysis.
Reconstructed cell-state-specific dynamics and spatial transitions through short-time local tensor streamlines and long-time transition paths.

Main Results:

Benchmarking and applications on diverse transcriptome datasets demonstrated STT's capability.
Successfully recovered cell-state-specific dynamics and associated genes missed by existing methods.
Validated STT on epithelial-mesenchymal transitions, blood development, mouse brain, and chicken heart development.

Conclusions:

STT offers a robust method for analyzing spatiotemporal dynamics in single-cell transcriptomics.
Provides a consistent multiscale description of single-cell transcriptome data.
Enhances understanding of cell state transitions and their underlying gene regulation across biological systems.