Estimation of cell lineages in tumors from spatial transcriptomics data.
Beibei RuJinlin HuangYu ZhangKenneth D AldapePeng JiangPublished in: Nature communications (2023)
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.
Keyphrases
- single cell
- copy number
- cell therapy
- genome wide
- stem cells
- clinical trial
- electronic health record
- randomized controlled trial
- mitochondrial dna
- gene expression
- dna methylation
- poor prognosis
- long non coding rna
- bone marrow
- microbial community
- oxidative stress
- data analysis
- cell cycle arrest
- deep learning
- endoplasmic reticulum stress
- transcription factor
- binding protein
- genome wide identification
- ionic liquid
- network analysis