High-throughput Single Cell Co-assay of Histone Modifications and Transcriptome

摘要 表观基因组分析可以帮助剖析控制时空的转录调控序列 在动物发育和疾病发病机制中的基因表达模式，但对 表观基因组分析是指初级组织的异质性。传统的表观基因组分析 作为输入的组织只产生总体平均信号。为解决这一主要瓶颈问题，我们建议 建立一种超高通量的单细胞多组学方法--配对标签，用于组蛋白的联合分析 修饰和转录组。在初步实验中，我们已经证明了该方法的可行性和实用性。 该方法通过分析单细胞的核转录组和多种组蛋白修饰 成年小鼠额叶皮质和海马区的分辨率。在拟议的研究中，我们将进一步优化 目前的配对标签协议，并展示了其在癌症表观基因组分析中的应用。如果成功，则 研究将增加一个主要的工具包，用于制作染色质状态的细胞类型分辨图和 在复杂组织中进行转录组分析，并使下一代肿瘤样本的表观基因组分析成为可能。

Abstract Epigenome analysis can help dissect the transcriptional regulatory sequences that control spatiotemporal patterns of gene expression during anima development and disease pathogenesis, but a major challenge to epigenome analysis is the heterogeneity of primary tissues. Conventional epigenome assays that take bulk tissues as input only produce population average signals. To address this major bottleneck, we propose to develop an ultra-high throughput single-cell multi-omics method, Paired-Tag, for joint profiling of histone modifications and transcriptome. In preliminary experiments, we have demonstrated the feasibility and utility of this method through analysis of the nuclear transcriptome and multiple histone modifications at single cell resolution in the adult mouse frontal cortex and hippocampus. In the proposed study, we will further optimize the current Paired-Tag protocol and demonstrate its utility in cancer epigenome analysis. If successful, the research would add a major toolkit for the production of cell-type-resolved maps of chromatin state and transcriptome in complex tissues and enable next generation epigenome analysis of tumor samples.