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

Author

Listed:
  • 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
    The University of Melbourne)

  • Lachlan Cain

    (Peter MacCallum Cancer Centre)

  • Sirui Weng

    (Peter MacCallum Cancer Centre
    The University of Melbourne)

  • Anupama Pasam

    (Peter MacCallum Cancer Centre)

  • Nikolce Kocovski

    (Peter MacCallum Cancer Centre)

  • Yu-Kuan Huang

    (Peter MacCallum Cancer Centre
    The University of Melbourne)

  • Simon P. Keam

    (Peter MacCallum Cancer Centre
    The University of Melbourne)

  • Terence P. Speed

    (The Walter and Eliza Hall Institute of Medical Research)

  • Paul J. Neeson

    (Peter MacCallum Cancer Centre
    The University of Melbourne)

  • Richard B. Pearson

    (Peter MacCallum Cancer Centre
    The University of Melbourne)

  • Shahneen Sandhu

    (Peter MacCallum Cancer Centre
    The University of Melbourne)

  • David L. Goode

    (Peter MacCallum Cancer Centre
    The University of Melbourne)

  • Anna S. Trigos

    (Peter MacCallum Cancer Centre
    The University of Melbourne)

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.

Suggested Citation

  • 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 , 2023. "Spatial analysis with SPIAT and spaSim to characterize and simulate tissue microenvironments," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37822-0
    DOI: 10.1038/s41467-023-37822-0
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    Cited by:

    1. Jingyang Qian & Hudong Bao & Xin Shao & Yin Fang & Jie Liao & Zhuo Chen & Chengyu Li & Wenbo Guo & Yining Hu & Anyao Li & Yue Yao & Xiaohui Fan & Yiyu Cheng, 2024. "Simulating multiple variability in spatially resolved transcriptomics with scCube," Nature Communications, Nature, vol. 15(1), pages 1-21, December.

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