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Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues

Author

Listed:
  • Duy Pham

    (The University of Queensland)

  • Xiao Tan

    (The University of Queensland)

  • Brad Balderson

    (The University of Queensland
    The University of Queensland)

  • Jun Xu

    (The University of Queensland)

  • Laura F. Grice

    (The University of Queensland
    The University of Queensland)

  • Sohye Yoon

    (The University of Queensland)

  • Emily F. Willis

    (The University of Queensland)

  • Minh Tran

    (The University of Queensland)

  • Pui Yeng Lam

    (The University of Queensland)

  • Arti Raghubar

    (The University of Queensland)

  • Priyakshi Kalita-de Croft

    (The University of Queensland)

  • Sunil Lakhani

    (The University of Queensland)

  • Jana Vukovic

    (The University of Queensland
    The University of Queensland)

  • Marc J. Ruitenberg

    (The University of Queensland)

  • Quan H. Nguyen

    (The University of Queensland
    QIMR Berghofer Medical Research Institute)

Abstract

Spatial transcriptomics (ST) technologies generate multiple data types from biological samples, namely gene expression, physical distance between data points, and/or tissue morphology. Here we developed three computational-statistical algorithms that integrate all three data types to advance understanding of cellular processes. First, we present a spatial graph-based method, pseudo-time-space (PSTS), to model and uncover relationships between transcriptional states of cells across tissues undergoing dynamic change (e.g. neurodevelopment, brain injury and/or microglia activation, and cancer progression). We further developed a spatially-constrained two-level permutation (SCTP) test to study cell-cell interaction, finding highly interactive tissue regions across thousands of ligand-receptor pairs with markedly reduced false discovery rates. Finally, we present a spatial graph-based imputation method with neural network (stSME), to correct for technical noise/dropout and increase ST data coverage. Together, the algorithms that we developed, implemented in the comprehensive and fast stLearn software, allow for robust interrogation of biological processes within healthy and diseased tissues.

Suggested Citation

  • Duy Pham & Xiao Tan & Brad Balderson & Jun Xu & Laura F. Grice & Sohye Yoon & Emily F. Willis & Minh Tran & Pui Yeng Lam & Arti Raghubar & Priyakshi Kalita-de Croft & Sunil Lakhani & Jana Vukovic & Ma, 2023. "Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues," Nature Communications, Nature, vol. 14(1), pages 1-25, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43120-6
    DOI: 10.1038/s41467-023-43120-6
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    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.
    2. Shijia Zhu & Naoto Kubota & Shidan Wang & Tao Wang & Guanghua Xiao & Yujin Hoshida, 2024. "STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics," Nature Communications, Nature, vol. 15(1), pages 1-18, December.

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