Single Cell Mosaic Mutation Atlas of Human Organ

人体器官单细胞镶嵌突变图谱

• 批准号: 10687162
• 负责人: Ruli Gao
• 金额: $ 40万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10687162
• 关键词: Affect; Alleles; Atlases; Bayesian Method; Biological Phenomena; Brain; Case Study; Cell physiology; Cells; Computer software; Computing Methodologies; Copy Number Polymorphism; Data; Data Set; Detection; Development; Ecosystem; Eye; Genetic; Genomics; Genotype; Goals; Heart; Heart Diseases; Heart Transplantation; Human; Human body; Individual; Knowledge; Machine Learning; Malignant Neoplasms; Methods; Mitochondrial DNA; Modeling; Mosaicism; Mutate; Mutation; Mutation Detection; Normal tissue morphology; Organ; Phenotype; Point Mutation; Prevalence; Prevention strategy; Research; Somatic Mutation; Technology; Tissues; Validation; bioinformatics tool; cell type; clinical phenotype; computerized tools; cost effective; data resource; early onset; heart cell; human disease; human tissue; insight; mosaic variant; novel; novel strategies; prevent; single-cell RNA sequencing; synergism; tool; transcriptome

PROJECT SUMMARY/ABSTRACT Somatic mosaicism is a biological phenomenon that describes the presence of genetically distinct cells within a subject. Mosaic mutations dictate numerous human phenotypes and are causal factors for a range of human diseases such as autisim, cardiac disorders and cancers. Analysis of somatic mutations in normal tissues is important for the understanding of both normal phenotype manifestations and the early onset of human diseases. However, our current knowledge of the mosaic mutations is only the tip of the iceberg due to the technical and computational challenges in detecting mosaic mutations with bulk genomic methods. In the past 3-5 years, high throughout single cell RNA sequencing (scRNA-seq) technologies have emerged as powerful tools to dissect the cellular ecosystems of human tissues by profiling thousands of single cell transcriptomes. The human cell atlas (HCA) projects have generated huge number of scRNA-seq datasets for many human organs from eye to brain. Whereas these projects are focused on delineating cell types and cell states within each tissue, they provide tremendous data resources to investigate the full spectrum of rare mosaic mutations in human organs. The lack of robust computational tools presents as one major gap in knowledge to construct a global mosaic mutation atlas of human organs from these data. Previous studies used bulk mutation calling methods to perform single cell genotyping from scRNA-seq data, which however had low sensitivity that is equivalent to bulk approaches. The central hypothesis is that rare mosaic mutations and their diversified effects on cellular functions can be uncovered by genotyping single cells from scRNA-seq data. This project has three major research goals: 1) Develop robust computational methods to accurately detect rare mosaic mutations from scRNA-seq data. This includes a Bayesian method MosaiCopy for detection of copy number variations, a toolkit MosaiTect for discovery of allele-specific point mutations, and a model-based method MosaiMtTect to detect mutations in mtDNAs in individual cells. 2) Estimate the functional effects of mosaic mutations in rare cells by developing a machine-learning software scGPS (single cell Genotype-Phenotype Synergy). Additionally, this method will quantify the threshold of phenotype manifestation for each mosaic mutation. 3) Genotype HCA datasets to investigate the cell type and cell state specific mutations and their functions in affected cells of human organs. As a case study and validation of the results, the in-house heart cell atlas datasets will be generated from healthy hearts (collected during heart transplantation). The overall goal of this project is to develop novel computational methods to investigate the global pictures of mosaic mutations and functional effects on cells of human organs. Successful completion of this project will lead to new insights into the effects of genomic diversification on cell functions within human body. In long term, this study will have significant impact on the development of novel prevention strategies for human diseases by inhibiting the manifestations of clinical phenotypes at the very early stage of normal development.

项目概要/摘要 体细胞嵌合是一种生物现象，描述了遗传上不同的细胞在体内的存在。 主题。嵌合突变决定了许多人类表型，并且是一系列人类疾病的致病因素 自闭症、心脏病和癌症等疾病。正常组织中体细胞突变的分析是 对于理解正常表型表现和人类早期发病很重要 疾病。然而，我们目前对嵌合突变的了解只是冰山一角，因为 使用大量基因组方法检测嵌合突变的技术和计算挑战。在过去 3-5年内，高通量单细胞RNA测序（scRNA-seq）技术已成为强大的 通过分析数千个单细胞转录组来剖析人体组织的细胞生态系统的工具。 人类细胞图谱 (HCA) 项目已经为许多人类生成了大量的 scRNA-seq 数据集。 从眼睛到大脑的器官。而这些项目的重点是描绘细胞类型和细胞状态 每个组织，他们提供巨大的数据资源来研究罕见嵌合突变的全谱 在人体器官中。缺乏强大的计算工具是构建知识方面的一个主要差距 根据这些数据绘制人体器官的全球镶嵌突变图谱。先前的研究使用批量突变调用 从 scRNA-seq 数据进行单细胞基因分型的方法，但其灵敏度较低 相当于批量方法。中心假设是罕见的嵌合突变及其多样化 通过 scRNA-seq 数据对单细胞进行基因分型可以揭示对细胞功能的影响。这个项目 具有三个主要研究目标：1）开发强大的计算方法来准确检测罕见的马赛克 scRNA-seq 数据中的突变。这包括用于检测拷贝数的贝叶斯方法 MosaiCopy 变异、用于发现等位基因特异性点突变的工具包 MosaiTect 以及基于模型的方法 MosaiMtTect 用于检测单个细胞中 mtDNA 的突变。 2）预估马赛克的功能效果 通过开发机器学习软件 scGPS（单细胞基因型-表型）来研究稀有细胞的突变 协同作用）。此外，该方法将量化每个嵌合体表型表现的阈值 突变。 3) 基因型 HCA 数据集，用于研究细胞类型和细胞状态特异性突变及其 在人体器官受影响的细胞中发挥功能。作为案例研究和结果验证，内部心脏 细胞图谱数据集将从健康心脏（在心脏移植期间收集）生成。整体 该项目的目标是开发新颖的计算方法来研究马赛克的全局图片 突变和对人体器官细胞的功能影响。该项目的成功完成将导致 关于基因组多样化对人体内细胞功能的影响的新见解。从长远来看，这 研究将对人类疾病新型预防策略的制定产生重大影响 在正常发育的早期阶段抑制临床表型的表现。