Single-cell aneuploidy and chromosomal arm imbalances define subclones with divergent transcriptomic phenotypes

Area of Science:

• Genomics
• Cancer Biology
• Bioinformatics

Background:

• Cancers exhibit genomic instability, leading to distinct tumor subclones.
• Single-cell DNA sequencing (scDNA-seq) detects genomic alterations but lacks phenotypic data.
• Single-cell RNA sequencing (scRNA-seq) provides biological insights but is less accurate for genomic events.

Purpose of the Study:

• To develop a computational framework for integrating scRNA-seq and scDNA-seq data.
• To define subclonal cellular phenotypes at single-cell resolution.
• To reveal intratumoral heterogeneity and subclonal biology in cancers.

Main Methods:

• Developed scAlign, a computational framework for integrating scRNA-seq and scDNA-seq from the same specimen.
• Defined subclones based on aneuploidy and chromosomal arm imbalance using scDNA-seq as ground truth.
• Assigned epithelial cells in G0/G1 phase to specific subclones using scAlign based on gene dosage from scRNA-seq data.

Main Results:

• Successfully integrated multi-omics single-cell data to define subclonal architecture.
• Identified differential gene expression and biological pathway activities within specific cancer subclones.
• Demonstrated that integrative analysis provides deeper insights than individual genomic modalities.

Conclusions:

• Integrative multi-omics analysis of single-cell datasets offers a comprehensive understanding of cancer biology.
• scAlign enables precise characterization of subclonal phenotypes and intratumoral heterogeneity.
• This approach reveals critical insights into subclonal biology, advancing cancer research.