Probing Dynamics of The Human Genome by Single Cell Sequencing

通过单细胞测序探测人类基因组的动态

• 批准号: 8906832
• 负责人: XIAOLIANG SUNNEY XIE
• 金额: $ 84.5万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-20 至 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8906832
• 关键词: Biology; Cells; Chimera organism; Copy Number Polymorphism; DNA Sequence; Gene Expression Profile; Genetic Variation; Genome; Genomic Instability; Hereditary Disease; Heterogeneity; Human Genome; Individual; Malignant Neoplasms; Measures; Methods; Morphologic artifacts; Mutation; Nucleotides; Reading; Single Nucleotide Polymorphism; Solid; Technology; Testing; Theoretical model; Time; Tissues; Variant; base; cancer cell; digital; genome sequencing; genome-wide; next generation; research study; single cell sequencing; tool; transcriptome sequencing

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

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会导致来自同一谱系的细胞，特别是癌细胞之间的遗传变异。然而，我们还没有能够研究基因组的这种动态，因为工具是不可用的，尽管在过去几年中，下一代基因组测序取得了巨大的进步。单细胞全基因组扩增和测序对于表征细胞之间的这种异质性是非常期望的。然而，现有的扩增方法，如PCR或多重置换扩增（MDA），严重限制了强偏见和人为因素，如嵌合体。我们已经开发了几种策略，可以显着降低偏倚，并允许单细胞定量的基因组和转录组。 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扩增循环（MALBAC），它大大地克服了上述困难。它使我们能够读出数字化的拷贝数变异，并以单细胞的总体~80%的效率鉴定独特的单核苷酸多态性。我们能够以极低的假阳性率调用SNV，并首次直接测量全基因组突变率。我们还开发了一种用于数字RNAseq的方法，该方法将允许以单拷贝灵敏度和无扩增偏倚确定单细胞转录组。 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进行验证。单细胞基因组测序是终极实验。我们建议表征来自癌症组织的100个个体细胞的拷贝数和单核苷酸变异，从中我们将能够提取关于遗传变异如何在固体中真实的时间发生的信息。