Simultaneous mapping of somatic mosaicism and kb-resolution 3D genome in single cells.

单细胞中体细胞嵌合体和 kb 分辨率 3D 基因组的同时作图。

基本信息

批准号：
10660575
负责人：
Fulai Jin
金额：
$ 40.25万
依托单位：
CASE WESTERN RESERVE UNIVERSITY
依托单位国家：
美国
项目类别：
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/10660575
关键词：
3-Dimensional ATAC-seq Address Affect Benchmarking Biotin Catalogs Cell Line Cell division Cell model Cell physiology Cells ChIP-seq Clonal Expansion Complex Copy Number Polymorphism DNA DNA Methylation DNA Sequence Alteration DNA sequencing Data Detection Development Disease Funding Genetic Genome Genome Mappings Genomics Genotype Hi-C Human Islet Cell Islets of Langerhans Joints Lead Libraries Ligation Malignant Neoplasms Maps Methods Molecular Mosaicism Mutation Names Nature Neurons Normal Cell Paper Phase Play Population Positioning Attribute Protocols documentation Publishing Research Resolution Role Scanning Shotguns Single Nucleotide Polymorphism Somatic Cell Somatic Mutation Technology Testing Time Tissue Sample Tissues Variant autism spectrum disorder bisulfite sequencing cell type deep learning epigenome epigenomics experimental study feasibility testing genome sequencing human tissue improved multiple omics novel scale up tool transcriptomics whole genome

项目摘要

Project Abstract In early development and over the lifetime of a human, the genome of every somatic cell will eventually accumulate hundreds of mutations during multiple cell divisions. Although most somatic mutations are predicted to be non-functional, it is known for a long time that some of the somatic mutations, including single nucleotide variants (SNVs), copy number variants (CNVs), translocations, etc., may cause serious diseases like cancer. In the past decade, more and more studies suggested that somatic mutations may also play important roles in milder complex diseases, such as autism. However, although single-cell or ultra-deep whole genome sequencing (WGS) technologies can now identify many rare somatic mutations, these technologies tell little about the consequences or mechanisms of somatic mutations. In fact, unless a somatic mutation causes significant clonal expansion, characterizing the molecular functions of a somatic mutation in its native tissue context is extremely challenging. In general, WGS protocol precludes most of the commonly pursued epigenomic technologies such as ATAC-seq and ChIP-seq. We recent demonstrated that using a novel deep-learning-based pipeline named DeepLoop, we can upgrade the super sparse single cell Hi-C maps to kilobase resolution, which may serve as a robust readout of genome activity. This motivates us to optimize a technology named Dip-C to simultaneously map somatic mutations and 3D genome from single cells. If successful, the project will deliver a long needed multi- OMIC tool for SMaHT network. We will test Dip-C in both model cell line and human tissues and verify its unique capability to resolve how somatic mutations may affect a small number of cells in large population or complex tissue.

项目摘要在人类的早期发育和一生中，每个体细胞的基因组最终都会在多次细胞分裂过程中积累数百个突变。尽管大多数体细胞突变是预计是无功能的，长期以来人们都知道一些体细胞突变，包括单核苷酸变异（SNV）、拷贝数变异（CNV）、易位等，可能会导致严重的后果。癌症等疾病。在过去的十年中，越来越多的研究表明体细胞突变可能在自闭症等较轻微的复杂疾病中也发挥着重要作用。然而，尽管单细胞或超深度全基因组测序（WGS）技术现在可以识别许多罕见的体细胞突变，这些技术几乎无法说明体细胞突变的后果或机制。事实上，除非体细胞突变导致显着的克隆扩张，表征了其天然组织环境中的体细胞突变极具挑战性。一般来说，WGS 协议排除大多数常用的表观基因组技术，例如 ATAC-seq 和 ChIP-seq。我们最近证明使用一种名为 DeepLoop 的新颖的基于深度学习的管道，我们可以升级超稀疏单细胞 Hi-C 映射到千碱基分辨率，这可以作为稳健的读数基因组活性。这促使我们优化一种名为 Dip-C 的技术，以同时绘制体细胞图谱来自单细胞的突变和 3D 基因组。如果成功，该项目将提供长期需要的多 SMaHT 网络的 OMIC 工具。我们将在模型细胞系和人体组织中测试 Dip-C 并验证其独特的能力可以解决体细胞突变如何影响大量细胞的情况群体或复杂组织。