Project description:Comparative genomics studies in primates are extremely restricted due to our limited access to samples from non-human apes. In order to gain better insight into the genetic processes that underlie variation in complex phenotypes in primates, we must have access to faithful model systems for a wide range of cell types. To facilitate this, we have generated a panel of 7 fully characterized chimpanzee induced pluripotent stem cell (iPSC) lines derived from healthy donors. To begin demonstrating the utility of comparative iPSC panels, we collected RNA-sequencing and DNA methylation data from the chimpanzee iPSCs and the corresponding fibroblast lines, as well as from 7 human iPSCs and their source lines, which encompass multiple populations and cell types. We observe much less within-species variation in iPSCs than in somatic cells, indicating that the reprogramming process erases many inter-individual differences. The low within-species regulatory variation in iPSCs allowed us to identify many novel inter-species regulatory differences of small magnitude.
Project description:Comparative genomics studies in primates are extremely restricted due to our limited access to samples from non-human apes. In order to gain better insight into the genetic processes that underlie variation in complex phenotypes in primates, we must have access to faithful model systems for a wide range of cell types. To facilitate this, we have generated a panel of 7 fully characterized chimpanzee induced pluripotent stem cell (iPSC) lines derived from healthy donors. To begin demonstrating the utility of comparative iPSC panels, we collected RNA-sequencing and DNA methylation data from the chimpanzee iPSCs and the corresponding fibroblast lines, as well as from 7 human iPSCs and their source lines, which encompass multiple populations and cell types. We observe much less within-species variation in iPSCs than in somatic cells, indicating that the reprogramming process erases many inter-individual differences. The low within-species regulatory variation in iPSCs allowed us to identify many novel inter-species regulatory differences of small magnitude. We used ChIP-seq to characterize the genome-wide distribution of two types of histone modifications (H3K27me3 and H3K27ac) in three of our chimpanzee iPSCs and compared them to histone modification data from three human iPSC lines from the Roadmap Epigenomics project:
Project description:Analysis of contribution of cell type of origin and individual to gene expression differences in iPSCs. The hypothesis tested in the present study was that cell type of origin affects iPSC gene expression. Results show that individual has a much stronger effect than cell type of origin on differences between iPSCs derived from multiple individuals. DNA obtained from feeder-free iPSCs derived from multiple individuals and tissues and their corresponding cell type of origin
Project description:Analysis of contribution of cell type of origin and individual to gene expression differences in iPSCs. The hypothesis tested in the present study was that cell type of origin affects iPSC gene expression. Results show that individual has a much stronger effect than cell type of origin on differences between iPSCs derived from multiple individuals. Total RNA obtained from feeder-free iPSCs derived from multiple individuals and tissues and their corresponding cell type of origin
Project description:Analysis of contribution of cell type of origin and individual to gene expression differences in iPSCs. The hypothesis tested in the present study was that cell type of origin affects iPSC gene expression. Results show that individual has a much stronger effect than cell type of origin on differences between iPSCs derived from multiple individuals.
Project description:Analysis of contribution of cell type of origin and individual to gene expression differences in iPSCs. The hypothesis tested in the present study was that cell type of origin affects iPSC gene expression. Results show that individual has a much stronger effect than cell type of origin on differences between iPSCs derived from multiple individuals.
Project description:Induced pluripotent stem cells (iPSCs) are an essential tool for studying cellular differentiation and cell types that are otherwise difficult to access. We investigated the use of iPSCs and iPSC-derived cells to study the impact of genetic variation on gene regulation across different cell types and as models for studies of complex disease. To do so, we established a panel of iPSCs from 58 well-studied Yoruba lymphoblastoid cell lines (LCLs); 14 of these lines were further differentiated into cardiomyocytes. We characterized regulatory variation across individuals and cell types by measuring gene expression levels, chromatin accessibility and DNA methylation. Our analysis focused on a comparison of inter-individual regulatory variation across cell types. While most cell type-specific regulatory quantitative trait loci (QTLs) lie in chromatin that is open only in the affected cell types, we found that 20% of cell type-specific regulatory QTLs are in shared open chromatin. This observation motivated us to develop a deep neural network to predict open chromatin regions from DNA sequence alone. Using this approach, we were able to use the sequences of segregating haplotypes to predict the effects of common SNPs on cell type-specific chromatin accessibility.
Project description:Induced pluripotent stem cells (iPSCs) are an essential tool for studying cellular differentiation and cell types that are otherwise difficult to access. We investigated the use of iPSCs and iPSC-derived cells to study the impact of genetic variation on gene regulation across different cell types and as models for studies of complex disease. To do so, we established a panel of iPSCs from 58 well-studied Yoruba lymphoblastoid cell lines (LCLs); 14 of these lines were further differentiated into cardiomyocytes. We characterized regulatory variation across individuals and cell types by measuring gene expression levels, chromatin accessibility and DNA methylation. Our analysis focused on a comparison of inter-individual regulatory variation across cell types. While most cell type-specific regulatory quantitative trait loci (QTLs) lie in chromatin that is open only in the affected cell types, we found that 20% of cell type-specific regulatory QTLs are in shared open chromatin. This observation motivated us to develop a deep neural network to predict open chromatin regions from DNA sequence alone. Using this approach, we were able to use the sequences of segregating haplotypes to predict the effects of common SNPs on cell type-specific chromatin accessibility.