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

衰老的标志：使用 GESTALT 中的单细胞 DNA 测序评估 DNA 损伤随年龄的积累

• 批准号: 10691058
• 负责人: Luigi Ferrucci
• 金额: $ 1.15万
• 依托单位: NATIONAL INSTITUTE ON AGING
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10691058
• 关键词: Address; Affect; Age; Aging; Alleles; Ancillary Study; Apoptosis; Base Excision Repairs; Biological; Cancer Etiology; Cell Aging; Cell Cycle Arrest; Cells; Comparative Biology; Comparative Study; Computer software; Consensus; Consumption; DNA; DNA Damage; DNA Repair; DNA Sequence Alteration; DNA amplification; DNA lesion; DNA sequencing; Data; Data Set; Development; Environmental Pollutants; Exposure to; Flow Cytometry; Free Radicals; Frequencies; Genetic Heterogeneity; Genome; Genomic Instability; Goals; Growth Factor; Human; Individual; Inflammation; Inflammatory; Innate Immune System; Invertebrates; Ionizing radiation; Lesion; Longevity; Maintenance; Mammals; Metabolism; Methods; Mitochondria; Mitochondrial DNA; Mutation; Nature; Nonhomologous DNA End Joining; Nuclear; Nucleotide Excision Repair; Organism; Participant; Pathway interactions; Peripheral Blood Mononuclear Cell; Phenotype; Process; Quality Control; Resolution; Scanning; Site; Somatic Mutation; Stress; Testing; Time; Tissues; Ultraviolet Rays; Variant; age effect; design; genome sequencing; heteroplasmy; hormonal signals; mitochondrial dysfunction; mitochondrial genome; monocyte; new technology; programs; repaired; response; senescence; single cell sequencing; whole genome

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.

单细胞DNA测序是一项新技术，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。对于拟议的项目，我们从收集的单细胞中扩增DNA，扩增后对DNA进行质量控制，并对来自每个GESTALT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名参与者中提取的119个单核细胞中收集了单细胞测序数据。然后，我们实现了SCcaller的流水线（Dong等人，Nature Methods，2017）来鉴定每个单细胞中的核DNA中的体细胞突变，同时调整变体识别器的测序覆盖率和灵敏度。我们的初步分析表明，核体细胞突变的数量在来自同一个体的3-4个单细胞之间变化，这种大的变化可能是由于单细胞多重置换扩增（SCMDA）过程中DNA扩增的不均匀性。目前，我们正在将更新和更准确的单细胞变体鉴定软件Scan-SNV应用于我们的数据集，以获得一致的体细胞突变集。我们还在研究所鉴定的体细胞突变的突变特征（即，由不同突变过程产生的突变类型的独特组合）和年龄对顶部突变标记的影响。除了研究核基因组中的体细胞突变外，我们还在测试这些数据是否可以用于研究线粒体基因组中的变异。我们应用专门为分析线粒体基因组而设计的程序，对来自同一个体的每个单细胞和合并的单细胞的数据进行测序。我们首次能够证明单细胞DNA测序可用于鉴定线粒体DNA（mtDNA）变体，包括同质性（影响细胞内所有mtDNA拷贝的变体）和异质性（在DNA位点具有一个以上等位基因的变体）。我们观察到，单细胞测序鉴定了在大量DNA（从血沉棕黄层提取的DNA）中观察到的绝大多数同质性，表明血沉棕黄层和单核细胞之间共享几乎所有同质性。此外，单细胞测序捕获了在大量DNA中未观察到的额外异质性，表明许多异质性以细胞特异性方式作为体细胞突变获得。此外，单细胞DNA特有的异质性更可能是有害的，进一步支持体细胞突变通常由细胞单独获得。