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

Yuzhou Feng, Tianpei Yang, John Zhu, Mabel Li, Maria Doyle, Volkan Ozcoban, Greg T. Bass, Angela Pizzolla, Lachlan Cain, Sirui Weng, Anupama Pasam, Nikolce Kocovski, Yu-Kuan Huang, Simon P. Keam, Terence P. Speed, Paul J. Neeson, Richard B. Pearson, Shahneen Sandhu, David L. Goode and Anna S. Trigos ()
Additional contact information
Yuzhou Feng: Peter MacCallum Cancer Centre
Tianpei Yang: Peter MacCallum Cancer Centre
John Zhu: Peter MacCallum Cancer Centre
Mabel Li: Peter MacCallum Cancer Centre
Maria Doyle: Peter MacCallum Cancer Centre
Volkan Ozcoban: Peter MacCallum Cancer Centre
Greg T. Bass: CSL Innovation
Angela Pizzolla: Peter MacCallum Cancer Centre
Lachlan Cain: Peter MacCallum Cancer Centre
Sirui Weng: Peter MacCallum Cancer Centre
Anupama Pasam: Peter MacCallum Cancer Centre
Nikolce Kocovski: Peter MacCallum Cancer Centre
Yu-Kuan Huang: Peter MacCallum Cancer Centre
Simon P. Keam: Peter MacCallum Cancer Centre
Terence P. Speed: The Walter and Eliza Hall Institute of Medical Research
Paul J. Neeson: Peter MacCallum Cancer Centre
Richard B. Pearson: Peter MacCallum Cancer Centre
Shahneen Sandhu: Peter MacCallum Cancer Centre
David L. Goode: Peter MacCallum Cancer Centre
Anna S. Trigos: Peter MacCallum Cancer Centre

Nature Communications, 2023, vol. 14, issue 1, 1-20

Abstract: Abstract Spatial proteomics technologies have revealed an underappreciated link between the location of cells in tissue microenvironments and the underlying biology and clinical features, but there is significant lag in the development of downstream analysis methods and benchmarking tools. Here we present SPIAT (spatial image analysis of tissues), a spatial-platform agnostic toolkit with a suite of spatial analysis algorithms, and spaSim (spatial simulator), a simulator of tissue spatial data. SPIAT includes multiple colocalization, neighborhood and spatial heterogeneity metrics to characterize the spatial patterns of cells. Ten spatial metrics of SPIAT are benchmarked using simulated data generated with spaSim. We show how SPIAT can uncover cancer immune subtypes correlated with prognosis in cancer and characterize cell dysfunction in diabetes. Our results suggest SPIAT and spaSim as useful tools for quantifying spatial patterns, identifying and validating correlates of clinical outcomes and supporting method development.

Date: 2023
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DOI: 10.1038/s41467-023-37822-0

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