Dos Santos Peixoto Rafael, Miller Brendan F, Brusko Maigan A, Aihara Gohta, Atta Lyla, Anant Manjari, Atkinson Mark A, Brusko Todd M, Wasserfall Clive H, Fan Jean
Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Nat Commun. 2025 Jan 3;16(1):350. doi: 10.1038/s41467-024-55700-1.
Spatially resolved omics (SRO) technologies enable the identification of cell types while preserving their organization within tissues. Application of such technologies offers the opportunity to delineate cell-type spatial relationships, particularly across different length scales, and enhance our understanding of tissue organization and function. To quantify such multi-scale cell-type spatial relationships, we present CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, as an open-source R package. To demonstrate the utility of such multi-scale characterization, recapitulate expected cell-type spatial relationships, and evaluate against other cell-type spatial analyses, we apply CRAWDAD to various simulated and real SRO datasets of diverse tissues assayed by diverse SRO technologies. We further demonstrate how such multi-scale characterization enabled by CRAWDAD can be used to compare cell-type spatial relationships across multiple samples. Finally, we apply CRAWDAD to SRO datasets of the human spleen to identify consistent as well as patient and sample-specific cell-type spatial relationships. In general, we anticipate such multi-scale analysis of SRO data enabled by CRAWDAD will provide useful quantitative metrics to facilitate the identification, characterization, and comparison of cell-type spatial relationships across axes of interest.
空间分辨组学（SRO）技术能够在保留细胞在组织内组织结构的同时识别细胞类型。应用此类技术提供了描绘细胞类型空间关系的机会，尤其是在不同长度尺度上，并增进我们对组织结构和功能的理解。为了量化这种多尺度细胞类型空间关系，我们提出了CRAWDAD（跨距离完成的细胞类型关系分析工作流程），作为一个开源的R包。为了证明这种多尺度表征的实用性，重现预期的细胞类型空间关系，并与其他细胞类型空间分析进行比较，我们将CRAWDAD应用于通过各种SRO技术检测的不同组织的各种模拟和真实SRO数据集。我们进一步展示了由CRAWDAD实现的这种多尺度表征如何用于比较多个样本之间的细胞类型空间关系。最后，我们将CRAWDAD应用于人类脾脏的SRO数据集，以识别一致的以及患者和样本特异性的细胞类型空间关系。总体而言，我们预计由CRAWDAD实现的这种SRO数据的多尺度分析将提供有用的定量指标，以促进跨感兴趣轴的细胞类型空间关系的识别、表征和比较。
