Estimation of cell lineages in tumors from spatial transcriptomics data

Beibei Ru, Jinlin Huang, Yu Zhang, Kenneth Aldape and Peng Jiang ()
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Beibei Ru: National Institutes of Health
Jinlin Huang: The University of Hong Kong
Yu Zhang: National Institutes of Health
Kenneth Aldape: National Institutes of Health
Peng Jiang: National Institutes of Health

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-36062-6

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