Spatial transition tensor of single cells

Peijie Zhou, Federico Bocci, Tiejun Li, Qing Nie

Abstract:

Spatial transcriptomics and messenger RNA splicing encode extensive
spatiotemporal information for cell states and transitions. The current
lineage-inference methods either lack spatial dynamics for state
transition or cannot capture different dynamics associated with multiple
cell states and transition paths. Here we present spatial transition
tensor (STT), a method that uses messenger RNA splicing and spatial
transcriptomes through a multiscale dynamical model to characterize
multistability in space. By learning a four-dimensional transition tensor
and spatial-constrained random walk, STT reconstructs cell-state-specific
dynamics and spatial state transitions via both short-time local tensor
streamlines between cells and long-time transition paths among attractors.
Benchmarking and applications of STT on several transcriptome datasets
via multiple technologies on epithelial–mesenchymal transitions,
blood development, spatially resolved mouse brain and chicken heart
development, indicate STT’s capability in recovering cell-state-specific
dynamics and their associated genes not seen using existing methods.
Overall, STT provides a consistent multiscale description of single-cell
transcriptome data across multiple spatiotemporal scales.