Developing a whole-genome sequencing method for single human cells

描述(申请人提供)：目前的基因组方法仅限于报告来自复杂细胞群体的平均信号，因为它们需要大量的输入材料。在像肿瘤这样的复杂组织中，遗传异质性很常见，重要的信息可能会丢失。为了解决这个问题，我们建议开发一种全基因组测序方法，它可以从单个人类细胞的基因组中获得高覆盖率(80%)的测序数据。从这些数据中，我们将识别全谱的体细胞突变，包括存在于单个细胞基因组中的点突变、插入突变、结构变异和拷贝数异常。通过比较多个细胞的基因组，我们可以描绘克隆多样性，并推断肿瘤基因组如何进化复杂的体细胞突变。为此，我们建议开发一种名为Cell-Seq的创新方法，它将最低限度的等温扩增与Phi29聚合酶和TN5转座酶相结合，可以同时切割DNA片段并为下一代测序添加接头，仅从单个细胞开始。在目标1中，我们将开发和优化Cell-Seq方法。在目标2中，我们将在两个克隆细胞培养中验证该方法，方法是将单个细胞的基因组与数百万个细胞样本进行比较，以确定错误率并确定与该方法相关的潜在偏差。在目标3中，我们将细胞序列应用于人类乳腺肿瘤样本，并对10个单细胞的基因组进行测序，以研究克隆多样性和基因组进化。我们假设乳腺肿瘤被组织成一个或多个主要的克隆亚群，这些亚群稳定地扩展形成肿瘤块，而不是像流行的肿瘤进展模型假设的那样，形成数百万个不同的克隆。我们预计，聚类分析将显示几个主要群体的证据，并且每个群体内的基因组突变将高度相似。所提出的单细胞测序方法是创新的，因为它可以完全解决复杂细胞群体中的异质性，而标准的批量基因组方法仅限于报告平均信号。这项研究具有重要意义，因为实现这些目标将提高我们对人类乳腺癌克隆多样性的基本理解，以及我们对肿瘤基因组如何进化复杂体细胞突变的知识。我们的长期目标是使用单细胞测序来研究人类肿瘤的单个细胞是如何导致转移和对化疗产生耐药性的。我们的工作与美国国立卫生研究院的使命直接一致，即通过开发早期发现和诊断肿瘤异质性的新方法来降低乳腺癌的死亡率。我们的工作也与SCAP的目标一致，即通过开发新的单细胞基因组技术来治疗人类疾病。除了有益于癌症的研究，我们预计我们的工具将对许多其他人类疾病产生广泛的积极影响，包括神经疾病、免疫疾病、发育缺陷和传染病。

DESCRIPTION (provided by applicant): Current genomic methods are limited to reporting an average signal from a complex population of cells because they require a large amount of input material. In complex tissues such as tumors, where genetic heterogeneity is common, important information may be lost. To address this problem, we propose to develop a whole-genome sequencing method that can obtain high-coverage (>80%) sequencing data from the genome of a single human cell. From this data we will identify the full spectrum of somatic mutations, including point mutations, indels, structural variants and copy number aberration that are present in the genome of a single cell. By comparing the genomes of multiple cells we can delineate clonal diversity and infer how tumor genomes evolve complex somatic mutations. To do this, we propose to develop an innovative method called Cell-Seq which combines minimal isothermal amplification with a Phi29 polymerase and a Tn5 transposase that can simultaneously cleave DNA fragments and add adapters for next-generation sequencing, starting with only a single cell. In aim 1 we will develop and optimize the Cell-Seq method. In aim 2 we will validate the method in two clonal cell cultures by comparing the genomes of single cells to million cell samples, to determine error rates and identify potential biases associated with the method. In aim 3 we will apply Cell-Seq to a human breast tumor sample and sequence the genomes of 10 single cells to investigate clonal diversity and genome evolution. We hypothesize that breast tumors are organized into one or more major clonal subpopulations that stably expand to form the tumor mass, not millions of diverse clones, as the prevailing model for tumor progression assumes. We expect that clustering analysis will show evidence for a few major groups, and that within each group genomic mutations will be highly similar. The proposed single-cell sequencing approach is innovative because it can fully resolve heterogeneity in complex populations of cells, whereas standard bulk genomic methods are limited to reporting an average signal. This research is significant because achieving these aims will improve our fundamental understanding of clonal diversity in human breast cancers, and our knowledge of how tumor genomes evolve complex somatic mutations. Our long-term goal is to use single-cell sequencing to study how single cells from human tumors seed metastases and evolve resistance to chemotherapy. Our work is directly aligned with the mission of the NIH to reduce mortality rates in breast cancer through the development of new modalities for early detection and diagnosing heterogeneity in tumors. Our work is also aligned with the goal of the SCAP to cure human diseases through the development of new single-cell genomic technologies. In addition to benefiting the study of cancer, we expect that our tools will have a broad positive impact on many other human diseases, including neurological disorders, immunological diseases, developmental defects and infectious disease.