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Estimation of cell lineages in tumors from spatial transcriptomics data.
Beibei Ru1, Jinlin Huang2,3, Yu Zhang1,2,4
1Cancer Data Science Lab, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Nature Communications
|February 2, 2023
Summary
Spatial Cellular Estimator for Tumors (SpaCET) accurately identifies cell types in spatial transcriptomics tumor data. This method enhances understanding of tumor microenvironments and intercellular interactions, improving cancer research.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Spatial transcriptomics (ST) enables in situ gene expression profiling of tumor tissues.
- Tumor ST data presents challenges in cell type deconvolution due to mixed cell types and densities within capturing spots.
- Existing deconvolution methods are often inadequate for complex tumor ST data.
Purpose of the Study:
- To develop a novel computational method, Spatial Cellular Estimator for Tumors (SpaCET), for accurate cell type deconvolution in tumor ST data.
- To improve the inference of cancer cell abundance, local cell densities, and immune/stromal cell lineage fractions.
- To reveal intercellular interactions at the tumor-immune interface that drive cancer progression.
Main Methods:
- SpaCET integrates a gene pattern dictionary of copy number alterations and expression changes in malignancies to estimate cancer cell abundance.
- A constrained regression model is employed to calibrate local cell densities and determine immune and stromal cell fractions.
- The method was validated using simulated and real ST data with histopathology annotations.
Main Results:
- SpaCET demonstrates higher accuracy in cell type deconvolution compared to existing methods.
- The tool successfully infers cancer cell abundance and diverse cell lineage fractions.
- SpaCET facilitates the analysis of ligand-receptor coexpression to uncover intercellular communication.
Conclusions:
- SpaCET is an accurate and effective tool for cell type deconvolution in tumor spatial transcriptomics.
- The method provides valuable insights into the tumor microenvironment and cell composition.
- SpaCET enables the study of intercellular interactions, particularly at the tumor-immune interface, to understand cancer progression.
