A Single-cell Spatiotemporal Manifold of Tissue Morphology and Dynamics.

ABSTRACT: Complex tissues are now characterizable at single-cell resolution, but the spatial logic underlying tissue organization remains challenging to access without effective single-cell spatial descriptors. We present a learned single-cell manifold of cell positions over time, with which one can measure tissue morphology and trace dynamics. Learning co-processes pairs of cell point clouds sampled along time using a Transformer encoder with inter-sample attention, a strategy that promotes efficient joint spatiotemporal learning. The manifold shows desirable properties of a general descriptor, e.g. interpretable cell type clusters, preserved local distances, and a pseudo-time axis, and enables common but challenging spatial reasoning tasks such as annotation of anatomical landmarks at cellular res

SUBMITTER: Haus E 

PROVIDER: S-EPMC12633369 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature