stTrace: detecting spatial-temporal domains from spatial transcriptome to trace developmental path

Development shapes the growth and organization of organisms, enabling the emergence of complex biological structures and functions. Investigating the development process is vital for uncovering the formation of complex biological systems. However, current approaches to studying development from gene expression rely primarily on single-cell gene expression data to infer developmental trajectories, neglecting the spatial distribution of cells within tissues and their interactions. Although spatial transcriptomics provides spatial context for gene expression, existing algorithms focus mainly on identifying spatial regions without further exploring their developmental connections. In this study, we propose an algorithm for detecting spatial-temporal domains in tissue to trace developmental path (stTrace) using spatial transcriptomics. stTrace integrates the degree of cell development, gene expression, and spatial location to identify "spatial-temporal domains," regions where cells share similar functions and developmental stages within the tissue. Moreover, hierarchical relationships exist among these domains, reflecting developmental connections between cells in the tissue. Applied to mouse embryo and human breast cancer datasets, stTrace achieved higher resolution and developmental consistency than traditional spatial domain identification algorithms. In the mouse dataset, spatial-temporal domains identified by stTrace in brain and eye areas have significant gene expression differences, while in human cancer data, it enabled reconstruction of a developmental tree that inferred cancer cell spread directions consistent with marker gene expression patterns.