Experimental and bioinformatics considerations in cancer application of single cell genomics,Computational and Structural Biotechnology Journal

当前位置： X-MOL 学术 › Comput. Struct. Biotechnol. J. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Experimental and bioinformatics considerations in cancer application of single cell genomics
Computational and Structural Biotechnology Journal ( IF 4.4 ) Pub Date : 2020-12-23 , DOI: 10.1016/j.csbj.2020.12.021
Joanna Hui Juan Tan _{1,

2} , Say Li Kong ₁ , Joyce A Tai ₁ , Huay Mei Poh ₁ , Fei Yao ₃ , Yee Yen Sia ₁ , Edwin Kok Hao Lim ₁ , Angela Maria Takano ₄ , Daniel Shao-Weng Tan ₄ , Asif Javed _{1,

5} , Axel M Hillmer _{1,

6,

7}

Affiliation

Single cell genomics offers an unprecedented resolution to interrogate genetic heterogeneity in a patient’s tumour at the intercellular level. However, the DNA yield per cell is insufficient for today’s sequencing library preparation protocols. This necessitates DNA amplification which is a key source of experimental noise. We provide an evaluation of two protocols using micro-fluidics based amplification for whole exome sequencing, which is an experimental scenario commonly used in single cell genomics. The results highlight their respective biases and relative strengths in identification of single nucleotide variations. Towards this end, we introduce a workflow SoVaTSiC, which allows for quality evaluation and somatic variant identification of single cell data. As proof of concept, the framework was applied to study a lung adenocarcinoma tumour. The analysis provides insights into tumour phylogeny by identifying key mutational events in lung adenocarcinoma evolution. The consequence of this inference is supported by the histology of the tumour and demonstrates usefulness of the approach.

中文翻译：

单细胞基因组学在癌症应用中的实验和生物信息学考虑

单细胞基因组学为在细胞间水平探究患者肿瘤的遗传异质性提供了前所未有的分辨率。然而，每个细胞的 DNA 产量不足以满足当今的测序文库制备方案的要求。这需要 DNA 扩增，而 DNA 扩增是实验噪音的主要来源。我们对使用基于微流体的扩增进行全外显子组测序的两种方案进行了评估，这是单细胞基因组学中常用的实验场景。结果突出了他们在识别单核苷酸变异方面各自的偏见和相对优势。为此，我们引入了工作流程 SoVaTSiC，它可以对单细胞数据进行质量评估和体细胞变异识别。作为概念证明，该框架被应用于研究肺腺癌肿瘤。该分析通过识别肺腺癌进化中的关键突变事件，为肿瘤系统发育提供了见解。这一推论的结果得到了肿瘤组织学的支持，并证明了该方法的有效性。

更新日期：2020-12-23

点击分享查看原文

点击收藏

公开下载

阅读更多本刊最新论文本刊介绍/投稿指南11