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Sci Rep. 2015 Jun 19;5:11415. doi: 10.1038/srep11415.

Quantitative assessment of single-cell whole genome amplification methods for detecting copy number variation using hippocampal neurons.

Author information

1
Department of Biology, South University of Science and Technology of China, Shenzhen 518055, China.
2
Department of Biology, Boston University, Boston, MA 02215, USA.
3
1] Department of Chemistry, Boston University, Boston, MA 02215, USA [2] State Key Laboratory of Respiratory Disease, Guangzhou Institute of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.
4
State Key Laboratory of Respiratory Disease, Guangzhou Institute of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.
5
Department of Chemistry, Boston University, Boston, MA 02215, USA.

Abstract

Single-cell genomic analysis has grown rapidly in recent years and finds widespread applications in various fields of biology, including cancer biology, development, immunology, pre-implantation genetic diagnosis, and neurobiology. To date, the amplification bias, amplification uniformity and reproducibility of the three major single cell whole genome amplification methods (GenomePlex WGA4, MDA and MALBAC) have not been systematically investigated using mammalian cells. In this study, we amplified genomic DNA from individual hippocampal neurons using three single-cell DNA amplification methods, and sequenced them at shallow depth. We then systematically evaluated the GC-bias, reproducibility, and copy number variations among individual neurons. Our results showed that single-cell genome sequencing results obtained from the MALBAC and WGA4 methods are highly reproducible and have a high success rate. The MALBAC displays significant biases towards high GC content. We then attempted to correct the GC bias issue by developing a bioinformatics pipeline, which allows us to call CNVs in single cell sequencing data, and chromosome level and sub-chromosomal level CNVs among individual neurons can be detected. We also proposed a metric to determine the CNV detection limits. Overall, MALBAC and WGA4 have better performance than MDA in detecting CNVs.

PMID:
26091148
PMCID:
PMC4650676
DOI:
10.1038/srep11415
[Indexed for MEDLINE]
Free PMC Article

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