Spatial Patterning Analysis of Cellular Ensembles (SPACE) finds complex spatial organization at the cell and tissue...

Area of Science:

• Computational Biology
• Bioinformatics
• Spatial Transcriptomics

Background:

• Tissue organization and spatial patterns of cells are crucial for physiological and pathological processes.
• Analyzing complex spatial arrangements of multiple biological elements in tissues is challenging with existing methods.

Purpose of the Study:

• To introduce Spatial Patterning Analysis of Cellular Ensembles (SPACE), an R package designed for the comprehensive analysis of high-plex spatial data.
• To enable the detection of diverse spatial patterns, including co-occurrence, context-dependent associations, gradients, and orientations.

Main Methods:

• SPACE utilizes an information-theoretic framework to explore all possible ensembles of tissue elements (single, pairs, triplets, etc.).
• It ranks ensembles based on their deviation from random assortment for individual images or across sample groups.
• The package is compatible with various data modalities recording values at fixed spatial coordinates (pixels or voxels).

Main Results:

• SPACE successfully identifies and characterizes complex spatial patterns in biological tissues.
• Validation using murine lymph node images with defined ground truth demonstrated SPACE's advantages.
• New spatial patterns were detected in diverse datasets, including tumors and tuberculosis granulomas.

Conclusions:

• SPACE provides a robust and versatile tool for uncovering intricate spatial organization in high-plex biological data.
• This R package facilitates a deeper understanding of how tissue architecture influences biological functions and diseases.
• SPACE enhances the analysis of spatial omics data, opening new avenues for discovery in various research areas.