Genome-Wide Detection of Single Nucleotide and Copy Number Variations of a Single Human Cell

By Chenghang Zong, Sijia Lu, Alec R. Chapman, Xiaohui S. Xie. Science, 2012

1,027 citations recorded by the source corpus.

Single-Cell Sequencing With the rapid progress in sequencing technologies, single-cell sequencing is now possible, promising insight into how cell-to-cell heterogeneity affects biological behavior. Achieving adequate genome coverage remains a challenge because single-cell sequencing relies on genome amplification that is prone to sequence bias. Zong et al. (p. 1622) report a new amplification method: multiple annealing and looping-based amplification cycles that allowed 93% genome coverage for a human cell. This coverage facilitated accurate detection of point mutations and copy number variations. Lu et al. (p. 1627) used the method to sequence 99 sperm cells from a single individual. Mapping the meiotic crossovers revealed a nonrandom distribution with a reduced recombination rate near transcription start sites. A whole-genome amplification method with reduced bias compares a single cell with its descendants. Kindred cells can have different genomes because of dynamic changes in DNA. Single-cell sequencing is needed to characterize these genomic differences but has been hindered by whole-genome amplification bias, resulting in low genome coverage. Here, we report on a new amplification method—multiple annealing and looping-based amplification cycles (MALBAC)—that offers high uniformity across the genome. Sequencing MALBAC-amplified DNA achieves 93% genome coverage ≥1x for a single human cell at 25x mean sequencing depth. We detected digitized copy-number variations (CNVs) of a single cancer cell. By sequencing three kindred cells, we were able to identify individual single-nucleotide variations (SNVs), with no false positives detected. We directly measured the genome-wide mutation rate of a cancer cell line and found that purine-pyrimidine exchanges occurred unusually frequently among the newly acquired SNVs.

Subfields: Cancer Research, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f3d379bbcc277d9fbcfd06fd466d2f42054acd31

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Citations in the corpus

Conducting a Microbiome Study (2014, methodology): “Moreover, both of these methods are suitable for amplification from single cells (Lasken, 2007; Zong et al., 2012).”
Lessons from the cancer genome. (2013, neutral): “Moreover, new protocols that yield more uniform and accurate whole-genome amplification have been developed (Zong et al., 2012).”
The technology and biology of single-cell RNA sequencing. (2015, background): “scRNA-seq combined with DNA sequencing (Dey et al., 2015; Macaulay et al., 2015; Wang et al., 2012a; Zong et al., 2012) or other lineaging methods promises to reveal the transcriptomic phenotype of cells together with their developmental history through inference of shared somatic mutations in the D”
Design and Analysis of Single-Cell Sequencing Experiments. (2015, methodology): “Another study employed a hiddenMarkov model for CNV detection, with the hidden states corresponding to the local copy number (Zong et al., 2012).”