Middelkamp Sjors, Manders Freek, Peci Flavia, van Roosmalen Markus J, González Diego Montiel, Bertrums Eline J M, van der Werf Inge, Derks Lucca L M, Groenen Niels M, Verheul Mark, Trabut Laurianne, Pleguezuelos-Manzano Cayetano, Brandsma Arianne M, Antoniou Evangelia, Reinhardt Dirk, Bierings Marc, Belderbos Mirjam E, van Boxtel Ruben
Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands.
Oncode Institute, Utrecht, the Netherlands.
Cell Genom. 2023 Aug 23;3(9):100389. doi: 10.1016/j.xgen.2023.100389. eCollection 2023 Sep 13.
Detection of somatic mutations in single cells has been severely hampered by technical limitations of whole-genome amplification. Novel technologies including primary template-directed amplification (PTA) significantly improved the accuracy of single-cell whole-genome sequencing (WGS) but still generate hundreds of artifacts per amplification reaction. We developed a comprehensive bioinformatic workflow, called the PTA Analysis Toolbox (PTATO), to accurately detect single base substitutions, insertions-deletions (indels), and structural variants in PTA-based WGS data. PTATO includes a machine learning approach and filtering based on recurrence to distinguish PTA artifacts from true mutations with high sensitivity (up to 90%), outperforming existing bioinformatic approaches. Using PTATO, we demonstrate that hematopoietic stem cells of patients with Fanconi anemia, which cannot be analyzed using regular WGS, have normal somatic single base substitution burdens but increased numbers of deletions. Our results show that PTATO enables studying somatic mutagenesis in the genomes of single cells with unprecedented sensitivity and accuracy.
全基因组扩增的技术局限性严重阻碍了单细胞体细胞突变的检测。包括初级模板导向扩增（PTA）在内的新技术显著提高了单细胞全基因组测序（WGS）的准确性，但每个扩增反应仍会产生数百个伪影。我们开发了一种全面的生物信息学工作流程，称为PTA分析工具箱（PTATO），以准确检测基于PTA的WGS数据中的单碱基替换、插入缺失（indels）和结构变异。PTATO包括一种机器学习方法和基于重现性的过滤，以高灵敏度（高达90%）区分PTA伪影和真实突变，优于现有的生物信息学方法。使用PTATO，我们证明了范可尼贫血患者的造血干细胞，无法使用常规WGS进行分析，其体细胞单碱基替换负担正常，但缺失数量增加。我们的结果表明，PTATO能够以前所未有的灵敏度和准确性研究单细胞基因组中的体细胞诱变。
