Cell2Spatial is a computational framework that maps single cells to spatial transcriptomic spots to reconstruct...

Single-cell (SC) sequencing enables detailed characterization of transcriptional heterogeneity but lacks spatial context, while spatial transcriptomics (ST) preserves tissue organization yet is limited by resolution and incomplete gene capture. To bridge these gaps, we developed Cell2Spatial, a computational framework that segments spatial spots at single-cell resolution, even when SC and ST datasets are not fully matched in cell types. The method integrates information-theoretic gene selection, spatially weighted likelihood modeling, and spatial hotspot detection to improve signal fidelity. A corrected saturation model calibrates library size against gene complexity, ensuring accurate cell count estimation in low-resolution ST. To enhance scalability and spatial coherence, Cell2Spatial incorporates neural-network-guided clustering and a cost-minimizing assignment algorithm that balances transcriptional similarity with spatial proximity. Evaluations on synthetic data demonstrated that Cell2Spatial consistently outperforms existing tools in reconstructing tissue architectures and cellular compositions, with particular strength in handling unmatched datasets. Applications to 10× Visium data across mouse brain, human thymus, mouse kidney, and human dorsolateral prefrontal cortex revealed detailed anatomical structures and developmental trajectories. Moreover, for high-resolution platforms including Xenium In Situ, Visium HD, and Slide-seqV2, Cell2Spatial remained robust despite reduced transcript capture, effectively delineating fine-scale spatial patterns in complex tissues. Collectively, these results highlight Cell2Spatial as a versatile framework that expands the analytical scope of ST and provides a powerful tool for uncovering the spatial organization of cellular function and tissue architecture.