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; Anna S Trigos

doi:10.1038/s41467-023-37822-0

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

Nat Commun. 2023 May 15;14(1):2697. doi: 10.1038/s41467-023-37822-0.

Authors

Yuzhou Feng¹, Tianpei Yang¹, John Zhu¹, Mabel Li¹, Maria Doyle¹, Volkan Ozcoban¹, Greg T Bass², Angela Pizzolla^{1

3}, Lachlan Cain¹, Sirui Weng^{1

3}, Anupama Pasam¹, Nikolce Kocovski¹, Yu-Kuan Huang^{1

3}, Simon P Keam^{1

3}, Terence P Speed⁴, Paul J Neeson^{1

3}, Richard B Pearson^{1

3}, Shahneen Sandhu^{1

3}, David L Goode^{1

3}, Anna S Trigos^{5

6}

Affiliations

¹ Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
² Research & Development, CSL Innovation, Parkville, VIC, Australia.
³ The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia.
⁴ Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.
⁵ Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. anna.trigos@petermac.org.
⁶ The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia. anna.trigos@petermac.org.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Humans
Image Processing, Computer-Assisted / methods
Neoplasms*
Proteomics
Tumor Microenvironment