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

摘要 表观基因组分析可以帮助解剖控制时空的转录调控序列 在动物发育和疾病发病过程中的基因表达模式，但一个主要的挑战， 表观基因组分析是原生组织的异质性。传统的表观基因组检测需要大量的 作为输入的组织仅产生总体平均信号。为解决这个主要瓶颈，我们建议 开发超高通量单细胞多组学方法Paired-Tag，用于组蛋白联合分析 修饰和转录组。在初步的实验中，我们已经证明了的可行性和实用性， 该方法通过分析单个细胞的核转录组和多个组蛋白修饰， 在成年小鼠额叶皮层和海马体中的分辨率。在拟议的研究中，我们将进一步优化 目前的配对标签协议，并证明其在癌症表观基因组分析的效用。如果成功， 研究将增加一个主要的工具包，用于生产细胞类型分辨的染色质状态图， 在复杂组织中的转录组，并使下一代肿瘤样品的表观基因组分析。

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.