Abstract 3574: Analysis of whole genome and transcriptome sequencing in single cell

Abstract

Abstract The rapid advancement in Next Generation Sequencing (NGS) technology has allowed extensive genetic profiling and analysis of tumor tissues. While accurate tumor characterization is crucial for cancer therapy, current sequencing methods are unable to resolve the genetic differences between the heterogeneous cell populations within tumor. By profiling specific subpopulations of cells from a larger heterogeneous population, single cell sequencing can improve the efficiency of cancer treatments through early detection of rare tumor cells, monitoring of circulating tumor cells, and accurate profiling of intra-tumor heterogeneity. In this study, we compared commercially available single cell amplification methods and sequencing platforms for whole genome and whole transcriptome sequencing. We isolated single cells from a human breast cancer cell line, MCF7, and human embryonic kidney cell line, HEK293, by micromanipulation, and their whole genome and transcriptome were sequenced. For whole genome sequencing, the performance of Repli-g (Qiagen), Ampli1 (Slicon Biosystems) and WGA4 (Sigma) were assessed for whole genome amplification. Next, we used TruSeq DNA Library Kit and Ion Plus Fragment Library Kit for library preparation, and whole genome sequencing was performed on HiSeq2000 and Proton. The sequencing data were analyzed by SAMtools/GATK and tmap-f3. For whole transcriptome sequencing, cDNA was synthesized from single cells using SMARTer ultra low input RNA kit (Clontech), and the cDNA library was prepared with Nextera XT library prep kit (Illumina) and Ion Total RNA-Seq Kit v2 (Life Technologies). Sequencing was performed on HiSeq2000 and Proton, and analyzed with Tophat and Cufflink. We show that the percentage of mappable reads and regions with more than 1X coverage in whole genome sequencing were similar to that of the genomic DNA data, but the coverage rate of regions with more than 10X was lower in single cell data on both NGS platforms. In the RNA-Seq, the gene detection rate in single cell and more than 100 cells were about 65% and 85% compare to total RNA, respectively. Citation Format: Nak-Jung Kwon, Woo Chung Lee, Jiwoong Kim, Hyeri Kim, Ahreum Seong, Bong Cho Kim, Doo Hyun Park, Kap-Seok Yang. Analysis of whole genome and transcriptome sequencing in single cell. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3574. doi:10.1158/1538-7445.AM2014-3574