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Tissue characterization at an enhanced resolution across spatial omics platforms with deep generative model

Author

Listed:
  • Bohan Li

    (Beihang University)

  • Feng Bao

    (Beijing National Research Center for Information Science and Technology (BNRist)
    Tsinghua University (THUIBCS)
    Tsinghua University)

  • Yimin Hou

    (Beihang University)

  • Fengji Li

    (Beihang University)

  • Hongjue Li

    (Beihang University)

  • Yue Deng

    (Beihang University)

  • Qionghai Dai

    (Beijing National Research Center for Information Science and Technology (BNRist)
    Tsinghua University (THUIBCS)
    Tsinghua University)

Abstract

Recent advances in spatial omics have expanded the spectrum of profiled molecular categories beyond transcriptomics. However, many of these technologies are constrained by limited spatial resolution, hindering our ability to deeply characterize intricate tissue architectures. Existing computational methods primarily focus on the resolution enhancement of transcriptomics data, lacking the adaptability to address the emerging spatial omics technologies that profile various omics types. Here, we introduce soScope, a unified generative framework designed to enhance data quality and spatial resolution for molecular profiles obtained from diverse spatial technologies. soScope aggregates multimodal tissue information from omics, spatial relations and images, and jointly infers omics profiles at enhanced resolutions with omics-specific modeling through distribution priors. With comprehensive evaluations on diverse spatial omics platforms, including Visium, Xenium, spatial-CUT&Tag, and slide-DNA/RNA-seq, soScope improves performances in identifying biologically meaningful intestine and kidney architectures, revealing embryonic heart structure that cannot be resolved at the original resolution and correcting sample and technical biases arising from sequencing and sample processing. Furthermore, soScope extends to spatial multiomics technology spatial-CITE-seq and spatial ATAC-RNA-seq, leveraging cross-omics reference for simultaneous multiomics enhancement. soScope provides a versatile tool to improve the utilization of continually expanding spatial omics technologies and resources.

Suggested Citation

  • Bohan Li & Feng Bao & Yimin Hou & Fengji Li & Hongjue Li & Yue Deng & Qionghai Dai, 2024. "Tissue characterization at an enhanced resolution across spatial omics platforms with deep generative model," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50837-5
    DOI: 10.1038/s41467-024-50837-5
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    1. Tongtong Zhao & Zachary D. Chiang & Julia W. Morriss & Lindsay M. LaFave & Evan M. Murray & Isabella Del Priore & Kevin Meli & Caleb A. Lareau & Naeem M. Nadaf & Jilong Li & Andrew S. Earl & Evan Z. M, 2022. "Spatial genomics enables multi-modal study of clonal heterogeneity in tissues," Nature, Nature, vol. 601(7891), pages 85-91, January.
    2. Xinyi Zhang & Xiao Wang & G. V. Shivashankar & Caroline Uhler, 2022. "Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer’s disease," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    3. 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.
    4. Chee-Huat Linus Eng & Michael Lawson & Qian Zhu & Ruben Dries & Noushin Koulena & Yodai Takei & Jina Yun & Christopher Cronin & Christoph Karp & Guo-Cheng Yuan & Long Cai, 2019. "Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+," Nature, Nature, vol. 568(7751), pages 235-239, April.
    5. Emelie Berglund & Jonas Maaskola & Niklas Schultz & Stefanie Friedrich & Maja Marklund & Joseph Bergenstråhle & Firas Tarish & Anna Tanoglidi & Sanja Vickovic & Ludvig Larsson & Fredrik Salmén & Chri, 2018. "Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity," Nature Communications, Nature, vol. 9(1), pages 1-13, December.
    6. Wenguang Shao & Tiannan Guo & Nora C. Toussaint & Peng Xue & Ulrich Wagner & Li Li & Konstantina Charmpi & Yi Zhu & Jianmin Wu & Marija Buljan & Rui Sun & Dorothea Rutishauser & Thomas Hermanns & Chri, 2019. "Comparative analysis of mRNA and protein degradation in prostate tissues indicates high stability of proteins," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
    7. Yodai Takei & Jina Yun & Shiwei Zheng & Noah Ollikainen & Nico Pierson & Jonathan White & Sheel Shah & Julian Thomassie & Shengbao Suo & Chee-Huat Linus Eng & Mitchell Guttman & Guo-Cheng Yuan & Long , 2021. "Integrated spatial genomics reveals global architecture of single nuclei," Nature, Nature, vol. 590(7845), pages 344-350, February.
    8. S. Vickovic & B. Lötstedt & J. Klughammer & S. Mages & Å Segerstolpe & O. Rozenblatt-Rosen & A. Regev, 2022. "SM-Omics is an automated platform for high-throughput spatial multi-omics," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
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