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Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk

Author

Listed:
  • Xin Shao

    (Zhejiang University School of Medicine
    Zhejiang University)

  • Chengyu Li

    (Zhejiang University)

  • Haihong Yang

    (Zhejiang University
    Zhejiang University)

  • Xiaoyan Lu

    (Zhejiang University)

  • Jie Liao

    (Zhejiang University)

  • Jingyang Qian

    (Zhejiang University)

  • Kai Wang

    (Zhejiang University School of Medicine)

  • Junyun Cheng

    (Zhejiang University)

  • Penghui Yang

    (Zhejiang University)

  • Huajun Chen

    (Zhejiang University
    Zhejiang University)

  • Xiao Xu

    (Zhejiang University School of Medicine)

  • Xiaohui Fan

    (Zhejiang University School of Medicine
    Zhejiang University
    Zhejiang University)

Abstract

Spatially resolved transcriptomics provides genetic information in space toward elucidation of the spatial architecture in intact organs and the spatially resolved cell-cell communications mediating tissue homeostasis, development, and disease. To facilitate inference of spatially resolved cell-cell communications, we here present SpaTalk, which relies on a graph network and knowledge graph to model and score the ligand-receptor-target signaling network between spatially proximal cells by dissecting cell-type composition through a non-negative linear model and spatial mapping between single-cell transcriptomic and spatially resolved transcriptomic data. The benchmarked performance of SpaTalk on public single-cell spatial transcriptomic datasets is superior to that of existing inference methods. Then we apply SpaTalk to STARmap, Slide-seq, and 10X Visium data, revealing the in-depth communicative mechanisms underlying normal and disease tissues with spatial structure. SpaTalk can uncover spatially resolved cell-cell communications for single-cell and spot-based spatially resolved transcriptomic data universally, providing valuable insights into spatial inter-cellular tissue dynamics.

Suggested Citation

  • Xin Shao & Chengyu Li & Haihong Yang & Xiaoyan Lu & Jie Liao & Jingyang Qian & Kai Wang & Junyun Cheng & Penghui Yang & Huajun Chen & Xiao Xu & Xiaohui Fan, 2022. "Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32111-8
    DOI: 10.1038/s41467-022-32111-8
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    References listed on IDEAS

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    Cited by:

    1. Jingyang Qian & Jie Liao & Ziqi Liu & Ying Chi & Yin Fang & Yanrong Zheng & Xin Shao & Bingqi Liu & Yongjin Cui & Wenbo Guo & Yining Hu & Hudong Bao & Penghui Yang & Qian Chen & Mingxiao Li & Bing Zha, 2023. "Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    2. Jie Liao & Jingyang Qian & Yin Fang & Zhuo Chen & Xiang Zhuang & Ningyu Zhang & Xin Shao & Yining Hu & Penghui Yang & Junyun Cheng & Yang Hu & Lingqi Yu & Haihong Yang & Jinlu Zhang & Xiaoyan Lu & Li , 2022. "De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    3. Hugo Croizer & Rana Mhaidly & Yann Kieffer & Geraldine Gentric & Lounes Djerroudi & Renaud Leclere & Floriane Pelon & Catherine Robley & Mylene Bohec & Arnaud Meng & Didier Meseure & Emanuela Romano &, 2024. "Deciphering the spatial landscape and plasticity of immunosuppressive fibroblasts in breast cancer," Nature Communications, Nature, vol. 15(1), pages 1-28, December.

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