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Spatial analysis with SPIAT and spaSim to characterize and simulate tissue microenvironments.

Yuzhou Feng¹, Tianpei Yang¹, John Zhu¹

¹Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.

Nature Communications

|May 15, 2023

Summary

New spatial analysis tools, SPIAT (spatial image analysis of tissues) and spaSim (spatial simulator), quantify cell patterns in tissues. These tools help link cell location to disease and improve analysis methods for spatial biology research.

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Area of Science:

Spatial biology
Computational pathology
Bioinformatics

Background:

Spatial proteomics reveals links between cell location and disease.
Current analysis methods and benchmarking tools lag behind technology development.

Purpose of the Study:

To introduce SPIAT (spatial image analysis of tissues), a toolkit for spatial data analysis.
To present spaSim (spatial simulator) for generating simulated tissue spatial data.
To benchmark SPIAT's spatial metrics using spaSim-generated data.

Main Methods:

SPIAT offers algorithms for colocalization, neighborhood, and spatial heterogeneity analysis.
spaSim simulates tissue spatial data for tool development and benchmarking.
Ten spatial metrics within SPIAT were benchmarked using simulated data.

Main Results:

SPIAT successfully uncovered cancer immune subtypes associated with prognosis.
The toolkit characterized cell dysfunction in diabetes.
Benchmarking confirmed the utility of SPIAT's spatial metrics.

Conclusions:

SPIAT and spaSim are valuable for quantifying spatial patterns in tissues.
These tools aid in identifying and validating clinical outcome correlates.
SPIAT and spaSim support the development of new spatial analysis methods.