Probing Dynamics of The Human Genome by Single Cell Sequencing

描述（由申请人提供）：我们体内的每个细胞都有一个基因组，携带着我们生命的蓝图。我们的基因组是动态的，即， 随时间变化基因组的不稳定性引起源自相同细胞的细胞之间的遗传变异。 特别是癌细胞。然而，我们还没有能够研究基因组的这种动力学 因为工具是不可用的，尽管下一代基因组有了巨大的进步， 在过去的几年里，排序。单细胞全基因组扩增和测序是非常可取的 用于表征细胞之间的这种异质性。然而，现有的扩增方法，如PCR或PCR扩增， 多重置换扩增（MDA）受到强偏倚和人为因素如嵌合体的严重限制。 我们已经开发了几种策略，可以显着降低偏倚，并允许单细胞定量 基因组和转录组。 我们开发了一种新的全基因组扩增方法：基于多重退火和循环的方法 扩增循环（MALBAC），这大大规避了上述困难。它能让我们读出 数字化拷贝数变异，并确定独特的单核苷酸多态性，总体约80% 单个电池的效率。我们能够以极低的假阳性率调用SNV， 首次测量全基因组突变率。我们还开发了一种方法， RNAseq，其将允许确定具有单拷贝灵敏度的单细胞转录组，而不需要 放大偏差 癌症是一种遗传性疾病。关于癌症的起源，有许多理论模型， 很难通过实验来测试。单细胞基因组测序是终极实验。我们 建议表征来自100个个体细胞的拷贝数和单核苷酸变异， 癌症组织，从中我们将能够提取有关基因变异如何发生在真实的 时间在实体瘤我们还计划同时确定基因组和转录组的相同 细胞使用上述技术。 拟议的基因组动态研究的意义超出了癌症研究， 可能对生物学和医学有其他广泛的影响。

DESCRIPTION (provided by applicant): Every cell in our body has a genome that carries the blueprint of our lives. Our genome is dynamical, i.e., changing with time. Genomic instability gives rise to genetic variations among cells originating from the same lineage, particularly cancer cells. However, we have not yet been able to study such dynamics of genomes because tools are not available, despite the tremendous advances in the next generation of genome sequencing in the past few years. Single cell whole genome amplification and sequencing is highly desirable for characterizing such heterogeneity among cells. However, existing amplification methods, such as PCR or multiple displacement amplification (MDA), are severely limited by strong bias and artifacts such as chimeras. We have developed several strategies that can significantly reduce the bias and allow single cell quantification of genome and transcriptome. We have developed a new whole genome amplification method: Multiple Annealing and Looping Based Amplification Cycle (MALBAC), which greatly circumvents the above difficulties. It allows us to read out digitized copy number variations and identify unique single nucleotide polymorphisms with overall ~80% efficiency of a single cell. We were able to call SNVs with extremely low false positive rates and directly measure the genome-wide mutation rate for the first time. We have also developed a method for digital RNAseq, which will allow determination of a single cell transcriptome with single copy sensitivity and no amplification bias. Cancer is a genetic disease. There have been many theoretical models about the genesis of cancer that have been difficult to test experimentally. Single cell genome sequencing is the ultimate experiment. We propose to characterize the copy number and single nucleotide variations of one hundred individual cells from cancer tissues, from which we will be able to extract information regarding how genetic variations occur in real time in a solid tumor. We also plan to simultaneously determine the genome and the transcriptome of the same cell using the techniques described above. The implication of the proposed research on dynamics of the genome goes beyond cancer research and may have other broad implications to biology and medicine.