This article introduces Cellular Mapping of Attributes with Position (CMAP), an innovative computational method designed to enhance the resolution of spatial transcriptomic analysis by accurately mapping individual cells to their precise locations within tissue microenvironments. Traditional methods, while providing valuable data, often compromise on single-cell resolution or gene recovery, particularly for intricate tissues like the cancer immune microenvironment. CMAP utilizes a three-level "divide-and-conquer" strategy—Domain Division, Optimal Spot assignment, and Precise Location calculation—to integrate single-cell and spatial data, even when discrepancies exist between the datasets. Extensive benchmarking on simulated, high-resolution, and real-world datasets demonstrates that CMAP consistently outperforms existing mapping tools like CellTrek and CytoSPACE in terms of accuracy and the fidelity of reconstructed spatial gene expression patterns. The authors showcase CMAP's utility in dissecting complex biological systems, such as identifying organ-specific endothelial cell heterogeneity and revealing the spatial organization and cell-cell interactions within tumors.
References:
