Accession PRJCA003899
Title High-throughput Single-cell CNV Detection Reveals Clonal Evolution During Hepatocellular Carcinoma Recurrence
Relevance Medical
Data types Whole genome sequencing
Raw sequence reads
Organisms Homo sapiens
Description Single-cell genomics provides substantial resources for dissecting cellular heterogeneity and cancer evolution, but classical DNA amplification-based methods are low-throughput and introduce coverage bias during sample preamplification. We developed a single-cell DNA library preparation method without preamplification in nanolitre scale (scDPN). The method has a throughput of up to 1,800 cells per run for copy number variation (CNV) detection. Also, it has a lower level of amplification bias and noise than the multiple displacement amplification (MDA) method and showed high sensitivity and accuracy based on evaluation in cell lines and tumour tissues. We used this approach to profile the tumour clones in paired primary and relapsed tumour samples of hepatocellular carcinoma (HCC). This approach provides a comprehensive and scalable solution to understand genome heterogeneity and evolution.
Sample scope Monoisolate
Release date 2021-07-08
Publication
PubMed ID Article title Journal name DOI Year
34280548 scDPN for High-throughput Single-cell CNV Detection to Uncover Clonal Evolution During HCC Recurrence Genomics, Proteomics & Bioinformatics 10.1016/j.gpb.2021.03.008 2021
Grants
Agency program Grant ID Grant title
Technology and Innovation Commission of Shenzhen Municipality GJHZ20180419190827179
Science, Technology and Innovation Commission of Shenzhen Municipality JCYJ20170303151334808
Submitter Shiping    Liu  (liushiping@genomics.cn)
Organization BGI
Submission date 2020-11-20

Project Data

Resource name Description
BioSample (290)  show -