High-throughput analysis of the effects of gene promoters and chromosomal environments on single-cell gene expression

项目总结/摘要 在相同环境中遗传相同的细胞在基因上可能有很大的个体差异。 表情基因表达的这种“单细胞变异性”在发育过程中使细胞类型多样化， 有助于癌细胞的化疗抗性，并阻碍纯细胞类型的产生 重新编程协议。虽然单细胞变异性是基因表达的一个重要特性， 发育和疾病，启动子的序列特征和表观遗传特征， 预测基因单细胞变异性的染色体是未知的。为了弥补这一差距，我们建议 开发一种高通量技术，测量不同类别的单细胞变异性， 不同染色体环境中的启动子。我们建议将转基因整合到数千个 基因组中的位置，并测量由此产生的转基因表达的单细胞变异性， 所有地点都是平行的这些数据将使我们能够利用现有的表观基因组图谱来识别 放大或抑制基因表达的单细胞变异性的染色体环境的特征。 我们建议将这项技术应用于多种细胞类型和携带不同启动子的转基因。 由此产生的数据将与一个框架分开的独立贡献的发起人进行分析 和染色体环境到单细胞变异性，也量化了 影响单细胞变异性的特异性启动子和染色体环境。我们的目标 开发这项技术是为了了解控制单细胞的基因组的特性， mRNA表达的变异性。

Project Summary/Abstract Genetically identical cells in the same environment can have large individual differences in gene expression. This “single-cell variability” in gene expression diversifies cell types during development, contributes to chemotherapeutic resistance in cancer cells, and hinders the production of pure cell types in reprogramming protocols. Although single-cell variability is an important property of gene expression in development and disease, the sequence features of promoters and the epigenetic features of chromosomes that predict a gene’s single-cell variability are unknown. To address this gap, we propose to develop a high-throughput technology that measures the single-cell variability of different classes of promoters in diverse chromosomal environments. We propose to integrate transgenes into thousands of locations across the genome and measure the resulting single-cell variability of transgene expression from all locations in parallel. These data will allow us to leverage existing epigenomic maps to identify the features of chromosomal environments that amplify or dampen single-cell variability in gene expression. We propose to apply this technology to multiple cell types and with transgenes carrying diverse promoters. The resulting data will be analyzed with a framework separates the independent contributions of promoters and chromosomal environments to single-cell variability, and also quantifies any interactions between specific promoters and chromosomal environments that impact single-cell variability. Our goal in developing this technology is to understand the properties of the genome that control the single-cell variability of mRNA expression.