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 |