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在高质量的长时间读取的单细胞RNA测序数据中对体变异的新检测

Arthur Dondi^1,2, Nico Borgsmüller^1,2, Pedro F Ferreira^1,2

¹Department of Biosystems Science and Engineering, ETH Zurich, 4056 Basel, Switzerland.

Genome research

|March 19, 2025

概括

一个新的计算工作流程LongSom使用长读单细胞RNA测序来识别瘤中的遗传和转录基因变异. 这种方法可以重建癌症的克隆异质性,并有助于在没有正常样本的情况下了解治疗耐药性.

科学领域:

基因组学就是基因组学.
癌症生物学癌症生物学
生物信息学是一种生物信息学.

背景情况:

癌症的特点是遗传和转录基因变异,导致克隆异质性和治疗耐药性.
长读单细胞RNA测序 (LR scRNA-seq) 提供了同时检测遗传和转录组变异的潜力.

研究的目的:

介绍LongSom,使用LR scRNA-seq数据进行新型变种检测的计算工作流.
重建瘤克隆异质性并识别具有不同治疗结果的亚克隆.

主要方法:

长Som利用高质量的LR scRNA-seq数据进行体质单核酸变体 (SNVs),线粒体SNVs (mtSNVs),拷贝数变化 (CNAs) 和基因融合的de novo调用.
工作流包括根据突变配置文件重新注释细胞类型,并应用过器/统计测试来区分体性SNV与噪声和生殖系多态.
变种检测在不需要匹配的正常样本的情况下进行.

主要成果:

朗索姆在人类卵巢癌样本中成功检测出临床相关的体性SNV,与匹配的DNA样本进行验证.
工作流通过利用检测到的体质SNV和融合,确定了具有明显预测治疗结果的子克隆.
细胞类型在变异调用之前使用突变配置文件重新注释.

结论:

长Som可以从LR scRNA-seq数据中进行全面的新变种检测.
工作流程有助于研究癌症演变,克隆异质性和治疗耐药性.
LongSom在没有正常样本的情况下工作的能力简化了复杂瘤基因组的分析.

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