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SANTO: a coarse-to-fine alignment and stitching method for spatial omics

Author

Listed:
  • Haoyang Li

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

  • Yingxin Lin

    (Yale University)

  • Wenjia He

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

  • Wenkai Han

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

  • Xiaopeng Xu

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

  • Chencheng Xu

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

  • Elva Gao

    (King Abdullah University of Science and Technology (KAUST))

  • Hongyu Zhao

    (Yale University)

  • Xin Gao

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

Abstract

With the flourishing of spatial omics technologies, alignment and stitching of slices becomes indispensable to decipher a holistic view of 3D molecular profile. However, existing alignment and stitching methods are unpractical to process large-scale and image-based spatial omics dataset due to extreme time consumption and unsatisfactory accuracy. Here we propose SANTO, a coarse-to-fine method targeting alignment and stitching tasks for spatial omics. SANTO firstly rapidly supplies reasonable spatial positions of two slices and identifies the overlap region. Then, SANTO refines the positions of two slices by considering spatial and omics patterns. Comprehensive experiments demonstrate the superior performance of SANTO over existing methods. Specifically, SANTO stitches cross-platform slices for breast cancer samples, enabling integration of complementary features to synergistically explore tumor microenvironment. SANTO is then applied to 3D-to-3D spatiotemporal alignment to study development of mouse embryo. Furthermore, SANTO enables cross-modality alignment of spatial transcriptomic and epigenomic data to understand complementary interactions.

Suggested Citation

  • Haoyang Li & Yingxin Lin & Wenjia He & Wenkai Han & Xiaopeng Xu & Chencheng Xu & Elva Gao & Hongyu Zhao & Xin Gao, 2024. "SANTO: a coarse-to-fine alignment and stitching method for spatial omics," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50308-x
    DOI: 10.1038/s41467-024-50308-x
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    References listed on IDEAS

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    1. Haoyang Li & Juexiao Zhou & Zhongxiao Li & Siyuan Chen & Xingyu Liao & Bin Zhang & Ruochi Zhang & Yu Wang & Shiwei Sun & Xin Gao, 2023. "A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    2. Kalen Clifton & Manjari Anant & Gohta Aihara & Lyla Atta & Osagie K. Aimiuwu & Justus M. Kebschull & Michael I. Miller & Daniel Tward & Jean Fan, 2023. "STalign: Alignment of spatial transcriptomics data using diffeomorphic metric mapping," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    3. Benjamin L. Walker & Qing Nie, 2023. "NeST: nested hierarchical structure identification in spatial transcriptomic data," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    4. Amanda Janesick & Robert Shelansky & Andrew D. Gottscho & Florian Wagner & Stephen R. Williams & Morgane Rouault & Ghezal Beliakoff & Carolyn A. Morrison & Michelli F. Oliveira & Jordan T. Sicherman &, 2023. "High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    5. Suoqin Jin & Christian F. Guerrero-Juarez & Lihua Zhang & Ivan Chang & Raul Ramos & Chen-Hsiang Kuan & Peggy Myung & Maksim V. Plikus & Qing Nie, 2021. "Inference and analysis of cell-cell communication using CellChat," Nature Communications, Nature, vol. 12(1), pages 1-20, December.
    6. Chen-Rui Xia & Zhi-Jie Cao & Xin-Ming Tu & Ge Gao, 2023. "Spatial-linked alignment tool (SLAT) for aligning heterogenous slices," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    7. Hao Xu & Shuyan Wang & Minghao Fang & Songwen Luo & Chunpeng Chen & Siyuan Wan & Rirui Wang & Meifang Tang & Tian Xue & Bin Li & Jun Lin & Kun Qu, 2023. "SPACEL: deep learning-based characterization of spatial transcriptome architectures," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    8. 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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