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Updated: Aug 24, 2026
Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
Published on: February 9, 2017
Plant Spatio-Temporal Integration Network (PSTN): A framework for dynamic single-cell and spatial transcriptomics in
Yuheng Zhu1, Hao Zhang1, Xianyu Zhang2
1College of Computer Science and Technology, Jilin University, 2699 Qianjin street, 130012, Jilin, China.
Motivation:
Rice blast caused by Magnaporthe oryzae (M. oryzae) is a dynamic cross-kingdom infection process involving rapid transcriptional reprogramming and spatial tissue remodeling across successive stages. Although single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) provide complementary information, most integration methods analyze stages independently and ignore temporal continuity.
Results:
We developed the Plant Spatio-Temporal Integration Network (PSTN), which jointly learns stage-specific cell-to-space mappings through expression reconstruction and bidirectional maximum-similarity temporal regularization. Applied to matched rice scRNA-seq and ST data at 0, 12, and 24 h, PSTN outperformed Tangram, SpaGE, cell2location, and DestVI across three gene-set sizes. At 4,000 highly variable genes (HVGs), mean squared error (MSE) remained below 0.09 at all stages, with high gene-wise and spot-wise correlations. Mapping-structure and marker-based analyses recovered tissue-associated spatial and temporal patterns. Gene-permutation controls markedly reduced performance, demonstrating reliance on correct cross-modal gene correspondence. An independent mouse-cortex benchmark supported the static reconstruction component across systems. PSTN enables temporally coupled single-cell and spatial integration in dynamic host-pathogen systems.
Availability:
PSTN is available at https://github.com/zhuyuheng111/PSTN. The Zenodo DOI for the repository is 10.5281/zenodo.21365720.
Supplementary Information:
Supplementary data are available at Journal Name online.
