Abstract 3519: High-throughput unbiased microwell-based single-cell CNV detection reveals tumor clonal subtypes in hepatocellular carcinoma

Abstract

Abstract Single-cell genomics is critical for understanding cellular heterogeneity in cancer, but existing DNA amplification and library preparation methods are expensive and introduce coverage bias during sample pre-amplification. Based on a massively parallel 5184-microwell chip, we developed a high-throughput unbiased single-cell DNA library preparation approach up to 1800 cells for copy number variation (CNV) detection, which provides a comprehensive, scalable solution for revealing genome heterogeneity. Compared with existing methods, it is more efficient and cost-effective to construct a barcoded-single-cell library without pre-amplification in 2 days. Besides, it has significantly lower Median Absolute Pairwise Difference (MAPD:0.17±0.004) values which are used to evaluate the amplification biases and noises, than using MDA (multiple displacement amplification) method (MAPD: 0.79±0.23) or MALBAC (multiple annealing and looping-based amplification cycles) approach (MAPD : 0.24±0.002). We further successfully applied this approach to obtain single-cell CNV information of more than 500 cells from a tumor sample of hepatocellular carcinoma in one run. Using the unsupervised clustering, we identified at least two clonal subpopulations with a multitude of non-integer alterations across the genome. Overall, this is an unbiased, effective, high-throughput and low cost approach to figure out tumor heterogeneity and understand clonal evolution in tumor at the single-cell level. Citation Format: Liang Wu, Yuzhou Wang, Miaomiao Jiang, Shiping Liu. High-throughput unbiased microwell-based single-cell CNV detection reveals tumor clonal subtypes in hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3519.