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Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry

Author

Listed:
  • Qihuang Zhang

    (McGill University)

  • Shunzhou Jiang

    (University of Pennsylvania)

  • Amelia Schroeder

    (University of Pennsylvania)

  • Jian Hu

    (Emory University)

  • Kejie Li

    (Biogen, Inc.)

  • Baohong Zhang

    (Biogen, Inc.)

  • David Dai

    (University of Pennsylvania)

  • Edward B. Lee

    (University of Pennsylvania)

  • Rui Xiao

    (University of Pennsylvania)

  • Mingyao Li

    (University of Pennsylvania)

Abstract

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity in health and disease. However, the lack of physical relationships among dissociated cells has limited its applications. To address this issue, we present CeLEry (Cell Location recovEry), a supervised deep learning algorithm that leverages gene expression and spatial location relationships learned from spatial transcriptomics to recover the spatial origins of cells in scRNA-seq. CeLEry has an optional data augmentation procedure via a variational autoencoder, which improves the method’s robustness and allows it to overcome noise in scRNA-seq data. We show that CeLEry can infer the spatial origins of cells in scRNA-seq at multiple levels, including 2D location and spatial domain of a cell, while also providing uncertainty estimates for the recovered locations. Our comprehensive benchmarking evaluations on multiple datasets generated from brain and cancer tissues using Visium, MERSCOPE, MERFISH, and Xenium demonstrate that CeLEry can reliably recover the spatial location information for cells using scRNA-seq data.

Suggested Citation

  • Qihuang Zhang & Shunzhou Jiang & Amelia Schroeder & Jian Hu & Kejie Li & Baohong Zhang & David Dai & Edward B. Lee & Rui Xiao & Mingyao Li, 2023. "Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39895-3
    DOI: 10.1038/s41467-023-39895-3
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    References listed on IDEAS

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    1. Orit Rozenblatt-Rosen & Michael J. T. Stubbington & Aviv Regev & Sarah A. Teichmann, 2017. "The Human Cell Atlas: from vision to reality," Nature, Nature, vol. 550(7677), pages 451-453, October.
    2. Xiangjie Li & Kui Wang & Yafei Lyu & Huize Pan & Jingxiao Zhang & Dwight Stambolian & Katalin Susztak & Muredach P. Reilly & Gang Hu & Mingyao Li, 2020. "Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    3. Mor Nitzan & Nikos Karaiskos & Nir Friedman & Nikolaus Rajewsky, 2019. "Gene expression cartography," Nature, Nature, vol. 576(7785), pages 132-137, December.
    4. 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.
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