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BIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data

Author

Listed:
  • Xiaohang Fu

    (The University of Sydney
    The University of Sydney
    University of Sydney
    The University of Sydney)

  • Yingxin Lin

    (The University of Sydney
    University of Sydney
    The University of Sydney
    Science Park)

  • David M. Lin

    (Cornell University)

  • Daniel Mechtersheimer

    (The University of Sydney
    University of Sydney
    The University of Sydney)

  • Chuhan Wang

    (The University of Sydney
    University of Sydney
    Science Park)

  • Farhan Ameen

    (The University of Sydney
    University of Sydney
    The University of Sydney)

  • Shila Ghazanfar

    (The University of Sydney
    University of Sydney
    The University of Sydney)

  • Ellis Patrick

    (The University of Sydney
    University of Sydney
    The University of Sydney
    Science Park)

  • Jinman Kim

    (The University of Sydney
    University of Sydney
    Science Park)

  • Jean Y. H. Yang

    (The University of Sydney
    University of Sydney
    The University of Sydney
    Science Park)

Abstract

Recent advances in subcellular imaging transcriptomics platforms have enabled high-resolution spatial mapping of gene expression, while also introducing significant analytical challenges in accurately identifying cells and assigning transcripts. Existing methods grapple with cell segmentation, frequently leading to fragmented cells or oversized cells that capture contaminated expression. To this end, we present BIDCell, a self-supervised deep learning-based framework with biologically-informed loss functions that learn relationships between spatially resolved gene expression and cell morphology. BIDCell incorporates cell-type data, including single-cell transcriptomics data from public repositories, with cell morphology information. Using a comprehensive evaluation framework consisting of metrics in five complementary categories for cell segmentation performance, we demonstrate that BIDCell outperforms other state-of-the-art methods according to many metrics across a variety of tissue types and technology platforms. Our findings underscore the potential of BIDCell to significantly enhance single-cell spatial expression analyses, enabling great potential in biological discovery.

Suggested Citation

  • Xiaohang Fu & Yingxin Lin & David M. Lin & Daniel Mechtersheimer & Chuhan Wang & Farhan Ameen & Shila Ghazanfar & Ellis Patrick & Jinman Kim & Jean Y. H. Yang, 2024. "BIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-023-44560-w
    DOI: 10.1038/s41467-023-44560-w
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    1. 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.
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    Cited by:

    1. Quentin Blampey & Kevin Mulder & Margaux Gardet & Stergios Christodoulidis & Charles-Antoine Dutertre & Fabrice André & Florent Ginhoux & Paul-Henry Cournède, 2024. "Sopa: a technology-invariant pipeline for analyses of image-based spatial omics," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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