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

• DOI: 10.1038/srep11415
• Publication Date: 2015-06-19T13:41:31Z
• Authors: Luwen Ning; Zhoufang Li; Guan Wang; Wen Hu; Qingming Hou; Yin Tong; Meng Zhang; Yao Chen; Li Qin; Xiaoping Chen; Heng-Ye Man; Pinghua Liu; Jiankui He
• Keywords: 0301 basic medicine; Base Composition; Genome; DNA Copy Number Variations; Pyramidal Cells; Gene Dosage; High-Throughput Nucleotide Sequencing; Reproducibility of Results; Genomics; Polymerase Chain Reaction; Sensitivity and Specificity; Article; Rats; 03 medical and health sciences; Animals; Humans; Single-Cell Analysis

AbstractSingle-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.