Derks Lucca L M, van Leeuwen Anaïs J C N, Steemers Alexander S, Trabut Laurianne, van Roosmalen Markus J, Poort Vera M, Hagelaar Rico, Verheul Mark, Middelkamp Sjors, van Boxtel Ruben
Princess Máxima Center for Pediatric Oncology, Utrecht 3584 CS, the Netherlands; Oncode Institute, Utrecht 3521 AL, the Netherlands.
Oncode Institute, Utrecht 3521 AL, the Netherlands; Center for Molecular Medicine, University Medical Center Utrecht, Utrecht 3584 CG, the Netherlands.
STAR Protoc. 2025 Mar 21;6(1):103499. doi: 10.1016/j.xpro.2024.103499. Epub 2024 Dec 21.
The study of somatic mutations in single cells provides insights into aging and carcinogenesis, which is complicated by the dependency on whole-genome amplification (WGA). Here, we describe a detailed workflow starting from single-cell isolation to WGA by primary template-directed amplification (PTA), sequencing, quality control, and downstream analyses. A machine learning approach, the PTA Analysis Toolkit (PTATO), is used to filter the hundreds to thousands of artificial variants induced by WGA from true mutations at high sensitivity and accuracy. For complete details on the use and execution of this protocol, please refer to Middelkamp et al..
对单细胞中的体细胞突变进行研究有助于深入了解衰老和致癌作用,而全基因组扩增(WGA)的依赖性使这一过程变得复杂。在此,我们描述了一个详细的工作流程,从单细胞分离开始,通过初级模板导向扩增(PTA)进行WGA、测序、质量控制及下游分析。一种机器学习方法,即PTA分析工具包(PTATO),用于以高灵敏度和准确性从真实突变中筛选出由WGA诱导产生的成百上千个人造变异。有关本方案使用和执行的完整详细信息,请参阅Middelkamp等人的文献。