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 ...