The Hallmarks of Aging: Assessing accumulation of DNA lesions with age using single cell DNA sequencing in GESTALT

单细胞DNA测序是一种新技术，可以在单细胞水平上分析基因突变，并有可能以最高分辨率剖析遗传异质性。在拟议的项目中，我们从收集的单个细胞中扩增DNA，对扩增后的DNA进行质量控制，并对每个格式塔参与者的3-4个细胞的高质量扩增DNA进行DNA测序。到目前为止，我们已经成功地从30名参与者的119个单核细胞中收集了单细胞测序数据。然后，我们实施了SCCaller的管道(董等人，自然方法，2017)，以确定每个单细胞中核DNA的体细胞突变，同时根据变异型调用者的测序覆盖率和敏感性进行调整。我们的初步分析表明，同一个体的3-4个单个细胞的核体细胞突变数量不同，这种巨大的差异可能是由于单细胞多重置换扩增(SCMDA)过程中DNA扩增的不均匀所致。目前，我们正在将更新和更准确的单细胞变体识别软件SCAN-SNV应用到我们的数据集，以获得一致的体细胞突变集。我们还在研究已识别的体细胞突变的突变特征(即不同突变过程产生的突变类型的独特组合)以及年龄对顶级突变特征的影响。除了研究核基因组的体细胞突变外，我们还在测试这些数据是否可以用于研究线粒体基因组的变异。我们应用了专门为分析线粒体基因组而设计的程序，对每个单个细胞和同一个体合并的单个细胞的数据进行了测序。我们首次展示了单细胞DNA测序可以用来识别线粒体DNA(MtDNA)变种，既有同质(影响细胞内所有mtDNA拷贝的变种)，也有异质(在一个DNA位点有多个等位基因的变种)。我们观察到，单细胞测序鉴定了在大量DNA(从棕黄色外套中提取的DNA)中观察到的绝大多数同质性，表明几乎所有的同质性都在棕黄色外套和单核细胞之间共享。此外，单细胞测序捕获了在大宗DNA中没有观察到的额外异质性，表明许多异质性是以细胞特有的方式作为体细胞突变获得的。此外，单细胞DNA特有的异质性更有可能是有害的，进一步支持了体细胞突变通常是由细胞单独获得的。

Single-cell DNA sequencing is a new technology that can profile genetic mutations at the single-cell level and has the potential to dissect genetic heterogeneity at the highest resolution. For the proposed project, we amplified DNA from the collected single cells, performed a quality control of the DNA after amplification and performed DNA sequencing on high quality amplified DNA from 3-4 cells for each GESTALT participant. So far, we have successfully collected single-cell sequencing data from 119 monocytes extracted from the 30 participants. We then implemented the pipeline of SCcaller (Dong et al., Nature Methods, 2017) to identify somatic mutations in the nuclear DNA in each single cell while adjusting for sequencing coverage and sensitivity of the variant caller. Our preliminary analysis shows that the numbers of nuclear somatic mutations vary among the 3-4 single cells from the same individual and this large variation could be due to the unevenness of the DNA amplification from the single-cell multiple displacement amplification (SCMDA) process. Currently, we are applying a newer and more accurate single-cell variant identification software Scan-SNV to our data set in order to obtain a consensus somatic mutation set. We are also studying the mutational signatures of the identified somatic mutations (i.e., the unique combinations of mutation types generated by different mutational processes) and the effect of age on the top mutational signatures. Besides studying the somatic mutations in the nuclear genome, we are also testing whether the data can be used to study variation in the mitochondrial genome. We applied programs, designed specifically for analyzing mitochondrial genome, to sequencing data from each single cell and from merged single cells of the same individual. We were able to show for the first time that single-cell DNA sequencing can be used to identify mitochondrial DNA (mtDNA) variants both homoplasmies (variants affecting all of the mtDNA copies within a cell) and heteroplasmies (variants with more than one allele at a DNA site). We observed that single-cell sequencing identifies vast majority of homoplasmies observed in bulk DNA (DNA extracted from buffy coat), indicating the sharing of almost all homoplasmies between the buffy coat and monocytes. Furthermore, single-cell sequencing captures additional heteroplasmies that were not observed in bulk DNA, indicating many heteroplasmies are acquired as somatic mutations in a cell-specific manner. In addition, heteroplasmies unique to single-cell DNA are more likely to be deleterious, further supporting that somatic mutations are often acquired by cells individually.