Single-cell genomics provides substantial resources for dissecting cellular heterogeneity and cancer evolution. Unfortunately, classical DNA amplification-based methods have low throughput and introduce coverage bias during sample preamplification. We developed a single-cell DNA library preparation method without preamplification in nanolitre scale (scDPN) to address these issues. The method achieved a throughput of up to 1800 cells per run for copy number variation (CNV) detection. Also, our approach demonstrated a lower level of amplification bias and noise than the multiple displacement amplification (MDA) method and showed high sensitivity and accuracy for cell line and tumor tissue evaluation. We used this approach to profile the tumor clones in paired primary and relapsed tumor samples of hepatocellular carcinoma (HCC). We identified three clonal subpopulations with a multitude of aneuploid alterations across the genome. Furthermore, we observed that a minor clone of the primary tumor containing additional alterations in chromosomes 1q, 10q, and 14q developed into the dominant clone in the recurrent tumor, indicating clonal selection during recurrence in HCC. Overall, this approach provides a comprehensive and scalable solution to understand genome heterogeneity and evolution

单细胞基因组学为剖析细胞异质性和癌症进化提供了大量资源。不幸的是，经典的基于 DNA 扩增的方法通量低，并且在样品预扩增过程中引入了覆盖偏差。我们开发了一种单细胞 DNA 文库制备方法，无需纳升规模 (scDPN) 的预扩增来解决这些问题。该方法在拷贝数变异 (CNV) 检测中实现了每次运行高达 1800 个细胞的吞吐量。此外，与多重置换扩增 (MDA) 方法相比，我们的方法显示出较低水平的扩增偏差和噪声，并显示出对细胞系和肿瘤组织评估的高灵敏度和准确性。我们使用这种方法来分析成对的原发性和复发性肿瘤样本中的肿瘤克隆肝细胞癌（HCC）。我们确定了三个克隆亚群，在整个基因组中具有大量的非整倍体改变。此外，我们观察到包含染色体 1q、10q 和 14q 额外改变的原发肿瘤的次要克隆发展为复发肿瘤中的优势克隆，表明 HCC 复发期间的克隆选择。总体而言，这种方法提供了一个全面且可扩展的解决方案来了解基因组异质性和进化