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ST-GEARS: Advancing 3D downstream research through accurate spatial information recovery

Author

Listed:
  • Tianyi Xia

    (BGI Research
    BGI Research)

  • Luni Hu

    (BGI Research
    BGI Research)

  • Lulu Zuo

    (BGI)

  • Lei Cao

    (BGI Research
    BGI Research)

  • Yunjia Zhang

    (BGI Research
    BGI Research)

  • Mengyang Xu

    (BGI Research
    BGI Research)

  • Qin Lu

    (BGI Research)

  • Lei Zhang

    (BGI Research
    BGI Research)

  • Taotao Pan

    (BGI Research
    BGI Research)

  • Bohan Zhang

    (BGI Research
    BGI Research)

  • Bowen Ma

    (BGI Research
    BGI Research)

  • Chuan Chen

    (BGI Research
    BGI Research)

  • Junfu Guo

    (BGI)

  • Chang Shi

    (BGI)

  • Mei Li

    (BGI Research)

  • Chao Liu

    (BGI Research
    BGI Research)

  • Yuxiang Li

    (BGI Research
    BGI Research
    BGI research)

  • Yong Zhang

    (BGI Research
    BGI Research
    BGI research)

  • Shuangsang Fang

    (BGI Research
    BGI Research)

Abstract

Three-dimensional Spatial Transcriptomics has revolutionized our understanding of tissue regionalization, organogenesis, and development. However, existing approaches overlook either spatial information or experiment-induced distortions, leading to significant discrepancies between reconstruction results and in vivo cell locations, causing unreliable downstream analysis. To address these challenges, we propose ST-GEARS (Spatial Transcriptomics GEospatial profile recovery system through AnchoRS). By employing innovative Distributive Constraints into the Optimization scheme, ST-GEARS retrieves anchors with exceeding precision that connect closest spots across sections in vivo. Guided by the anchors, it first rigidly aligns sections, next solves and denoises Elastic Fields to counteract distortions. Through mathematically proved Bi-sectional Fields Application, it eventually recovers the original spatial profile. Studying ST-GEARS across number of sections, sectional distances and sequencing platforms, we observed its outstanding performance on tissue, cell, and gene levels. ST-GEARS provides precise and well-explainable ‘gears’ between in vivo situations and in vitro analysis, powerfully fueling potential of biological discoveries.

Suggested Citation

  • Tianyi Xia & Luni Hu & Lulu Zuo & Lei Cao & Yunjia Zhang & Mengyang Xu & Qin Lu & Lei Zhang & Taotao Pan & Bohan Zhang & Bowen Ma & Chuan Chen & Junfu Guo & Chang Shi & Mei Li & Chao Liu & Yuxiang Li , 2024. "ST-GEARS: Advancing 3D downstream research through accurate spatial information recovery," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51935-0
    DOI: 10.1038/s41467-024-51935-0
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    References listed on IDEAS

    as
    1. Lyla Atta & Jean Fan, 2021. "Computational challenges and opportunities in spatially resolved transcriptomic data analysis," Nature Communications, Nature, vol. 12(1), pages 1-5, December.
    2. Anjali Rao & Dalia Barkley & Gustavo S. França & Itai Yanai, 2021. "Exploring tissue architecture using spatial transcriptomics," Nature, Nature, vol. 596(7871), pages 211-220, August.
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