Single Cell Mosaic Mutation Atlas of Human Organ

项目摘要/摘要 体细胞嵌合体是一种生物学现象，它描述了在细胞内存在遗传上不同的细胞 主题。镶嵌突变决定了许多人类的表型，并是一系列人类的致病因素 自闭症、心脏疾病和癌症等疾病。分析正常组织中的体细胞突变 对于理解正常的表型表现和人类早期发病很重要 疾病。然而，我们目前对马赛克突变的了解只是冰山一角，因为 用大量基因组方法检测马赛克突变的技术和计算挑战。在过去 3-5年来，高通量单细胞rna测序(scrna-seq)技术已经成为一项强大的技术。 通过描绘数千个单细胞转录本来剖析人类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产生了大量scRNA-seq数据集 从眼睛到大脑的器官。鉴于这些项目的重点是在 每一个组织，他们都提供了大量的数据资源来研究罕见的镶嵌突变的全谱 在人体器官中。缺乏强大的计算工具是构建知识的一个主要缺口 来自这些数据的人类器官的全球马赛克突变图谱。以前的研究使用批量突变调用 方法从scRNA-seq数据中进行单细胞基因分型，但灵敏度较低，即 相当于批量方法。中心假设是罕见的马赛克突变及其多样性 通过对scRNA-seq数据中的单个细胞进行基因分型，可以揭示对细胞功能的影响。这个项目 有三个主要的研究目标：1)开发健壮的计算方法来准确检测罕见的花叶 来自scRNA-seq数据的突变。这包括用于检测拷贝数的贝叶斯方法MosaiCopy Variance，用于发现等位基因特定点突变的工具包MosaiTect，以及基于模型的方法 MosaiMtTect检测单个细胞中线粒体DNA的突变。2)评估马赛克的功能效应 开发机器学习软件scGPS(单细胞基因表型)研究稀有细胞的突变 协同效应)。此外，该方法将量化每个马赛克的表型表现阈值 突变。3)研究细胞类型和细胞状态特定突变及其 人体器官中受影响细胞的功能。作为一个案例研究和结果的验证，内部心脏 细胞图谱数据集将从健康的心脏中生成(在心脏移植期间收集)。整体而言 该项目的目标是开发新的计算方法来研究马赛克的全球图片 突变及其对人体器官细胞的功能影响。该项目的成功完成将导致 对基因组多样化对人体内细胞功能影响的新见解。从长远来看，这 这项研究将对开发新的人类疾病预防战略产生重大影响 在正常发育的非常早期阶段抑制临床表型的表现。

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.