NUC-Seq: Single-Cell Exome Sequencing Using G2/M Nuclei. M. L. Leung1,3, Y. Wang1, J. Waters1, N. E. Navin1,2,3 1) Department of Genetics, University of Texas MD Anderson Cancer Center, Houston, TX; 2) Department of Bioinformatics and Computational biology, University of Texas MD Anderson Cancer Center, Houston, TX; 3) Graduate Program in Genes and Development, Graduate School of biomedical Sciences, University of Texas Health Science Center at Houston, Houston, TX.
Single-cell sequencing (SCS) methods have the potential to provide great insight into the genomics of rare cells and diverse populations, but are currently challenged by extensive technical errors. These errors include poor physical coverage and high FP and FN error rates, making it difficult to distinguish real biological variants. To address this problem, we have developed an SCS method called NUC-Seq that combines flow-sorting of single nuclei, limited multiple-displacement-amplification, low-input library preparation and exome capture to generate high coverage (>90%) data on single mammalian cells. To mitigate error rates, we collected nuclei that are in the G2/M stage of the cell cycle, providing 4 copies of the genome as input material for whole genome amplification (instead of 2 copies). We developed our method using a normal fibroblast cell line (SKN2) in which we can assume that variants in single cells will be highly similar to the population sample. We sequenced the reference population sample and the exomes of 9 single cells in G1/0 stage and 10 single cells in G2/M stage of the cell cycle. Our data suggest that G2/M cells provide several major technical improvements over using G1/0 cells, including decreased allelic dropout rates (21.52%), improved coverage breadth (95.94%), improved coverage uniformity and better detection efficiencies for SNVs (92.53%). On average, we detect 24 false positive errors per mega-bases in each single cell genome. We show that these errors occur randomly in each cell, allowing accurate variant detection using two or more single cells. Our data suggest that, regardless of whether G1/0 or G2/M cells are used as input material, major technical improvement are achieved compared to existing single-cell sequencing methods. In summary, we expect that NUC-Seq will have broad applications in fields as diverse as cancer research, microbiology, neurobiology, development and in vitro prenatal genetic diagnosis, and will greatly improve our fundamental understanding of human diseases.
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