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Related Experiment Video

Updated: Jun 4, 2025

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

Rafael Dos Santos Peixoto1,2, Brendan F Miller1,2, Maigan A Brusko3

  • 1Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA.

Nature Communications
|January 3, 2025
PubMed
Summary

CRAWDAD is a new R package that analyzes cell types in tissues using spatially resolved omics (SRO) data. It quantifies multi-scale spatial relationships, aiding in tissue organization and function understanding.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Spatial Omics

Background:

  • Spatially resolved omics (SRO) technologies reveal cell types and their tissue organization.
  • Understanding cell-type spatial relationships is crucial for tissue function.
  • Existing methods lack multi-scale analysis capabilities for SRO data.

Purpose of the Study:

  • Introduce CRAWDAD, an open-source R package for quantifying multi-scale cell-type spatial relationships.
  • Demonstrate CRAWDAD's utility in analyzing diverse SRO datasets.
  • Provide a tool for comparing spatial relationships across samples and tissues.

Main Methods:

  • Developed CRAWDAD, an R package for Cell-type Relationship Analysis Workflow Done Across Distances.
  • Applied CRAWDAD to simulated and real SRO datasets from various tissues.
  • Evaluated CRAWDAD against existing spatial analysis methods.
  • Utilized CRAWDAD for comparative analysis across multiple samples.

Main Results:

  • CRAWDAD successfully quantifies multi-scale cell-type spatial relationships.
  • The package recapitulates expected spatial patterns and performs robustly against other methods.
  • Demonstrated ability to compare spatial relationships across different samples and tissues.
  • Identified consistent and sample-specific cell-type associations in human spleen SRO data.

Conclusions:

  • CRAWDAD provides essential quantitative metrics for analyzing SRO data.
  • Facilitates identification, characterization, and comparison of cell-type spatial relationships.
  • Enables deeper insights into tissue organization and function across multiple scales.