Abstract
There is no effective way to detect structure variations (SVs) and extra-chromosomal circular DNAs (ecDNAs) at single-cell whole-genome level. Here, we develop a novel third-generation sequencing platform-based single-cell whole-genome sequencing (scWGS) method named SMOOTH-seq (single-molecule real-time sequencing of long fragments amplified through transposon insertion). We evaluate the method for detecting CNVs, SVs, and SNVs in human cancer cell lines and a colorectal cancer sample and show that SMOOTH-seq reliably and effectively detects SVs and ecDNAs in individual cells, but shows relatively limited accuracy in detection of CNVs and SNVs. SMOOTH-seq opens a new chapter in scWGS as it generates high fidelity reads of kilobases long.
Highlights
Single-cell whole-genome sequencing is a powerful tool to reveal cell to cell heterogeneity in biological samples and identify genomic changes such as copy number variations (CNVs) and point mutations [1,2,3]
Methods are all based on next-generation sequencing platforms generating highly accurate but relatively short reads, which are well-suited for calling copy number variations (CNVs), small indels, and single-nucleotide variations (SNVs), but not optimal for structural variations (SVs)
To resolve the challenges in detecting SVs and extra-chromosomal circular DNAs (ecDNAs) in individual cells, we developed a single-cell genome sequencing method based on third-generation sequencing (TGS) platform and we named it as SMOOTH-seq
Summary
Single-cell whole-genome sequencing (scWGS) is a powerful tool to reveal cell to cell heterogeneity in biological samples and identify genomic changes such as copy number variations (CNVs) and point mutations [1,2,3]. The long ecDNAs which are of hundreds to thousands of kilobase sizes can be highly amplified in cells and drive massive oncogene over-expression [16, 17]. These abnormal changes in the genomic structure may play critical roles for tumorigenesis and metastasis, yet there is no effective scWGS methods to systematically identify them
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