Co-existence atlas of cell types in mouse kidney.

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.

单细胞测序 (Single-cell sequencing, SC) 可实现对转录异质性的精细表征，但缺失空间背景信息；而空间转录组学 (Spatial Transcriptomics, ST) 虽可保留组织的空间组织结构，却受限于分辨率不足与基因捕获不完整的缺陷。为填补这一研究空白，本研究开发了Cell2Spatial这一计算框架，其可将空间斑点 (spatial spots) 分割至单细胞分辨率，即便单细胞与空间转录组数据集在细胞类型上并未完全匹配。该方法整合了基于信息论的基因选择、空间加权似然建模与空间热点检测技术，以提升信号保真度。经校正的饱和模型可根据基因复杂度对文库大小进行校准，确保在低分辨率空间转录组数据中实现准确的细胞计数估算。为提升算法的可扩展性与空间连贯性，Cell2Spatial融入了神经网络引导的聚类方法，以及一种兼顾转录相似性与空间邻近性的代价最小化分配算法。基于合成数据的评估结果显示，Cell2Spatial在重构组织架构与细胞组成方面始终优于现有工具，尤其在处理不匹配数据集时表现出更强的性能。将该框架应用于小鼠大脑、人类胸腺、小鼠肾脏以及人类背外侧前额叶皮层的10× Visium数据后，成功揭示了精细的解剖结构与发育轨迹。此外，针对包括Xenium原位测序 (Xenium In Situ)、Visium HD与Slide-seqV2在内的高分辨率空间转录组平台，即便在转录本捕获率降低的情况下，Cell2Spatial仍保持了良好的鲁棒性，可有效刻画复杂组织中的精细空间模式。综上，上述结果表明Cell2Spatial是一款通用型计算框架，拓展了空间转录组学的分析范围，同时为揭示细胞功能与组织架构的空间组织模式提供了强有力的研究工具。