Molecular and Computational Tools for Identifying Somatic Mosaicism in Human Tissues

识别人体组织中体细胞镶嵌的分子和计算工具

• 批准号: 10661147
• 负责人: Alan P Boyle
• 金额: $ 40.13万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10661147
• 关键词: Address; Bar Codes; Benchmarking; Biological Assay; Brain; Cataloging; Cell Fraction; Cell Nucleus; Cells; Centrifugation; Cloud Computing; Complement; Computer software; Computing Methodologies; Copy Number Polymorphism; DNA Insertion Elements; Data; Development; Disease susceptibility; Event; Exhibits; Frequencies; Gastric Glands; Gene Frequency; Genetic Polymorphism; Genome; Genomics; Goals; Human; Human Genome; Individual; Inherited; Investigation; Large Intestine; Link; Malignant Neoplasms; Molecular; Mosaicism; Mutation Detection; Output; Percoll; Phase; Phenotype; Population; Preparation; Prevalence; Prostate; Reporting; Resolution; Retrotransposon; Running; Short Tandem Repeat; Single Nucleotide Polymorphism; Small Intestines; Somatic Mutation; Standardization; Structure; Surveys; Technology; Testing; Time; Tissues; Variant; Work; analysis pipeline; computerized tools; density; disease phenotype; exome sequencing; experience; genetic variant; genome sequencing; high throughput screening; human tissue; improved; innovation; iodixanol; mosaic; nanopore; open source; personalized approach; single cell sequencing; software repository; targeted sequencing; tool; trait; whole genome; zygote

Abstract Human genomes harbor significant variation both between and within individuals. Numerous studies have explored inherited variation across human populations and linked various germline polymorphisms to human traits and disease susceptibility. Genomic sequences also vary within an individual, occurring after zygote formation and leading to variation present in a frequency spectrum ranging from individual cells to entire tissues. This somatic mosaicism of genome variation has been well established in cells of phenotypically normal individuals and has been shown to also be associated with some disease phenotypes, particularly cancers. However, these investigations have been mostly limited to higher frequency mosaicism (e.g. >5-10% variant allele frequency) due to technical limitations in both molecular assays and computational methodology. Compounding these technological challenges is that each human tissue exhibits apparently different rates of somatic mosaicism. For example, it is currently estimated that each cell within the human brain contains hundreds to a few thousand somatic single-nucleotide variants (SNVs) and that a smaller fraction of cells harbor somatic copy number variations (CNVs), mobile element insertions (MEIs), and short tandem repeat expansions (STRs). In contrast, somatic mutation rates have been reported to be significantly higher in the large and small intestines and lower in gastric and prostatic glands. These rates have been ascertained through a variety of approaches, including SNP microarrays, bulk and single cell whole genome sequencing, and direct amplification and sequencing of candidate events, each with its own advantages and limitations. However, there has yet to be a systematic investigation of human somatic mosaicism across the entire frequency spectrum within human tissues. Our team has extensive collective experience developing tools for identifying somatic mosaicism in the human brain, including recent surveys of SNV prevalence from whole genome and exome sequencing, CNVs from single cell short-read and nanopore genome sequencing, and retrotransposons through targeted capture. Here, we propose to improve, optimize, and extend our approaches to other human tissues as part of the SMaHT initiative, which will provide an excellent platform for systematically identifying, cataloging, and exploring human somatic mosaicism across tissues. We will achieve this through two phases: in the UG3 phase of this project, we will (1) improve molecular assays for nanopore targeted bulk capture and single cell sequencing and (2) improve computational approaches for detecting somatic mosaicism from single cell and bulk tissue data, while in the UH3 phase we will (3) optimize, benchmark, and validate molecular assays for high- throughput application across human tissues and (4) improve efficiency, runtime, and structured reporting of somatic variants. Collectively, these efforts will enhance our ability to detect at scale previously overlooked classes of somatic variation and extend the size range and frequency spectrum for which they may be ascertained.

抽象的 人类基因组在个体之间和个体内部都存在显着差异。许多研究已经 探索了人类群体的遗传变异，并将各种种系多态性与人类联系起来 性状和疾病易感性。基因组序列在个体内也会发生变化，发生在受精卵之后 形成并导致从单个细胞到整个组织的频谱中存在的变化。 这种基因组变异的体细胞嵌合现象已在表型正常的细胞中得到很好的证实。 个体，并且已被证明还与某些疾病表型相关，特别是癌症。 然而，这些研究大多仅限于较高频率的嵌合体（例如> 5-10％的变异 等位基因频率）由于分子测定和计算方法的技术限制。 使这些技术挑战更加复杂的是，每个人体组织表现出明显不同的速率 体细胞嵌合体。例如，目前估计人脑内的每个细胞都含有 数百到数千个体细胞单核苷酸变异（SNV），并且一小部分细胞含有 体细胞拷贝数变异 (CNV)、移动元件插入 (MEI) 和短串联重复扩增 （STR）。相比之下，据报道，大型和小型体细胞的体细胞突变率显着更高。 肠以及胃和前列腺的下部。这些比率是通过各种方式确定的 方法，包括 SNP 微阵列、批量和单细胞全基因组测序以及直接扩增 以及候选事件的排序，每个都有自己的优点和局限性。然而，目前还没有 对人类整个频谱范围内的人体体细胞嵌合现象进行系统研究 组织。我们的团队拥有丰富的集体经验，开发用于识别体细胞嵌合体的工具 人脑，包括最近对全基因组和外显子组测序、CNV 的 SNV 流行率的调查 来自单细胞短读长和纳米孔基因组测序，以及通过靶向捕获的逆转录转座子。 在这里，我们建议改进、优化并将我们的方法扩展到其他人体组织，作为 SMaHT 计划将为系统地识别、编目、 并探索人类跨组织体细胞嵌合现象。我们将通过两个阶段来实现这一目标： 在该项目的 UG3 阶段，我们将 (1) 改进纳米孔靶向批量捕获和单 细胞测序和（2）改进用于检测单细胞体细胞嵌合的计算方法和 大量组织数据，而在 UH3 阶段，我们将 (3) 优化、基准测试和验证高通量的分子测定。 跨人体组织的吞吐量应用程序，以及（4）提高效率、运行时间和结构化报告 体细胞变异。总的来说，这些努力将增强我们大规模检测以前被忽视的能力 体细胞变异的类别并扩展它们可能的尺寸范围和频谱 确定。