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

计算生物学 生物 基因剂量 转录组 拷贝数变化 基因组 基因 基因表达谱 遗传学 基因表达
作者
Ali Mahdipour–Shirayeh,Natalie Erdmann,Chungyee Leung-Hagesteijn,Rodger E. Tiedemann
出处
期刊:Briefings in Bioinformatics [Oxford University Press]
卷期号:23 (1) 被引量:8
标识
DOI:10.1093/bib/bbab413
摘要

Abstract Chromosome copy number variations (CNVs) are a near-universal feature of cancer; however, their individual effects on cellular function are often incompletely understood. Single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) might be leveraged to reveal the function of intra-clonal CNVs; however, it cannot directly link cellular gene expression to CNVs. Here, we report a high-throughput scRNA-seq analysis pipeline that provides paired CNV profiles and transcriptomes for single cells, enabling exploration of the effects of CNVs on cellular programs. RTAM1 and -2 normalization methods are described, and are shown to improve transcriptome alignment between cells, increasing the sensitivity of scRNA-seq for CNV detection. We also report single-cell inferred chromosomal copy number variation (sciCNV), a tool for inferring single-cell CNVs from scRNA-seq at 19–46 Mb resolution. Comparison of sciCNV with existing RNA-based CNV methods reveals useful advances in sensitivity and specificity. Using sciCNV, we demonstrate that scRNA-seq can be used to examine the cellular effects of cancer CNVs. As an example, sciCNV is used to identify subclonal multiple myeloma (MM) cells with +8q22–24. Studies of the gene expression of intra-clonal MM cells with and without the CNV demonstrate that +8q22–24 upregulates MYC and MYC-target genes, messenger RNA processing and protein synthesis, which is consistent with established models. In conclusion, we provide new tools for scRNA-seq that enable paired profiling of the CNVs and transcriptomes of single cells, facilitating rapid and accurate deconstruction of the effects of cancer CNVs on cellular programming.
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