Characterizing cell-type spatial relationships across length scales in spatially resolved omics data

Rafael dos Santos Peixoto, Brendan F. Miller, Maigan A. Brusko, Gohta Aihara, Lyla Atta, Manjari Anant, Mark A. Atkinson, Todd M. Brusko, Clive H. Wasserfall and Jean Fan ()
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Rafael dos Santos Peixoto: Johns Hopkins University
Brendan F. Miller: Johns Hopkins University
Maigan A. Brusko: Immunology, and Laboratory Medicine, University of Florida
Gohta Aihara: Johns Hopkins University
Lyla Atta: Johns Hopkins University
Manjari Anant: Johns Hopkins University
Mark A. Atkinson: Immunology, and Laboratory Medicine, University of Florida
Todd M. Brusko: Immunology, and Laboratory Medicine, University of Florida
Clive H. Wasserfall: Immunology, and Laboratory Medicine, University of Florida
Jean Fan: Johns Hopkins University

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-024-55700-1

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