Systems-level modeling of cell migration using spatially-resolved single-cell data

The ability to capture cellular dynamics at high spatial and molecular resolution is crucial for understanding complex biological processes, including development, tissue remodeling, and disease progression. Single-cell RNA sequencing (scRNA-seq) provides detailed transcriptional profiles of individual cells, while spatial transcriptomics (ST) preserves their spatial organization within tissues. However, integrating these complementary modalities across multiple time points to reconstruct temporal changes in spatial cell-type organization and cell-state composition remains a major challenge. Here, we present a computational framework that integrates time-series scRNA-seq and spatial transcriptomics data to reconstruct spatiotemporal cell-type redistribution and inferred cell-state dynamics. The framework combines reference-based spatial deconvolution with single-cell spatial mapping to generate temporally resolved representations of cellular organization. Temporal changes in spatial cell-type distributions are quantified using complementary abundance-weighted spatial descriptors, whereas inferred cell-state dynamics are characterized through changes in dominant cell-type assignments across consecutive time points. We further evaluated the spatial abundance component of the framework using simulation-based quantitative benchmarking against alternative spatial deconvolution and mapping approaches, using known simulated cell-type compositions as ground truth. We demonstrate the utility and generalizability of the framework using two biologically distinct systems: human glioblastoma organoids (GBOs) and embryonic chicken heart development. Across both datasets, the framework captures temporal patterns of spatial cell-type organization and inferred cell-state composition that are not accessible through static analyses alone. This work provides a broadly applicable computational framework for investigating temporal tissue organization through the integration of single-cell and spatial transcriptomic data, thereby enabling systematic characterization of dynamic cellular behaviors across developmental and disease-related processes. Quantitative benchmarking supports the ability of the framework to recover simulated spatial cell-type abundance patterns, while the inferred redistribution and cell-state dynamics should be interpreted as descriptive computational measures rather than direct evidence of physical cell migration or lineage-validated state transitions.