sciCNV: high-throughput paired profiling of transcriptomes and DNA copy number variations at single-cell resolution.

Area of Science:

• Genomics
• Cancer Biology
• Bioinformatics

Background:

• Chromosome copy number variations (CNVs) are common in cancer but their functional impact on single cells is unclear.
• Single-cell RNA sequencing (scRNA-seq) can study cellular function but cannot directly link gene expression to CNVs.
• Understanding CNV effects is crucial for cancer research and therapeutic development.

Purpose of the Study:

• To develop a high-throughput scRNA-seq pipeline for paired CNV and transcriptome profiling of single cells.
• To introduce single-cell inferred chromosomal copy number variation (sciCNV) for accurate CNV detection from scRNA-seq data.
• To demonstrate the utility of the pipeline and sciCNV in exploring cancer CNV effects on cellular programs.

Main Methods:

• Developed a high-throughput scRNA-seq analysis pipeline for paired CNV and transcriptome data.
• Introduced RTAM1 and -2 normalization methods to improve scRNA-seq data alignment and CNV detection sensitivity.
• Developed the sciCNV tool to infer single-cell CNVs from scRNA-seq data with 19-46 Mb resolution.

Main Results:

• The pipeline successfully provides paired CNV profiles and transcriptomes for single cells.
• RTAM1 and -2 normalization enhanced transcriptome alignment and scRNA-seq sensitivity for CNV detection.
• sciCNV demonstrated improved sensitivity and specificity compared to existing RNA-based CNV methods.
• Applied sciCNV to identify subclonal multiple myeloma cells with +8q22-24 CNV, showing upregulation of MYC and related pathways.

Conclusions:

• The developed tools enable paired profiling of CNVs and transcriptomes in single cells using scRNA-seq.
• This facilitates the rapid and accurate deconstruction of cancer CNV effects on cellular programming.
• The findings provide a powerful approach for investigating the functional consequences of CNVs in cancer.