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Estimation of cell lineages in tumors from spatial transcriptomics data

Author

Listed:
  • Beibei Ru

    (National Institutes of Health)

  • Jinlin Huang

    (The University of Hong Kong
    Sun Yat-sen University Cancer Center, Guangzhou)

  • Yu Zhang

    (National Institutes of Health
    The University of Hong Kong
    Collaborative Innovation Center for Cancer Medicine)

  • Kenneth Aldape

    (National Institutes of Health)

  • Peng Jiang

    (National Institutes of Health)

Abstract

Spatial transcriptomics (ST) technology through in situ capturing has enabled topographical gene expression profiling of tumor tissues. However, each capturing spot may contain diverse immune and malignant cells, with different cell densities across tissue regions. Cell type deconvolution in tumor ST data remains challenging for existing methods designed to decompose general ST or bulk tumor data. We develop the Spatial Cellular Estimator for Tumors (SpaCET) to infer cell identities from tumor ST data. SpaCET first estimates cancer cell abundance by integrating a gene pattern dictionary of copy number alterations and expression changes in common malignancies. A constrained regression model then calibrates local cell densities and determines immune and stromal cell lineage fractions. SpaCET provides higher accuracy than existing methods based on simulation and real ST data with matched double-blind histopathology annotations as ground truth. Further, coupling cell fractions with ligand-receptor coexpression analysis, SpaCET reveals how intercellular interactions at the tumor-immune interface promote cancer progression.

Suggested Citation

  • Beibei Ru & Jinlin Huang & Yu Zhang & Kenneth Aldape & Peng Jiang, 2023. "Estimation of cell lineages in tumors from spatial transcriptomics data," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36062-6
    DOI: 10.1038/s41467-023-36062-6
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