1.scDPN for High-throughput Single-cell CNV Detection to Uncover Clonal Evolution During HCC Recurrence
Wu LIANG ; Jiang MIAOMIAO ; Wang YUZHOU ; Zhou BIAOFENG ; Sun YUNFAN ; Zhou KAIQIAN ; Xie JIARUI ; Zhong YU ; Zhao ZHIKUN ; Dean MICHAEL ; Hou YONG ; Liu SHIPING
Genomics, Proteomics & Bioinformatics 2021;19(3):346-357
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 hepato-cellular 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 chro-mosomes 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 hetero-geneity and evolution.
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