Decoding Single-cell DNA Methylomes for Epigenetic Cell Identity

项目总结/摘要 关于小区状态的详细信息，例如，其谱系、有丝分裂史、增殖潜力和功能 能力，巩固通过其DNA和染色质的表观遗传修饰。其中 DNA甲基化修饰已经被广泛研究和分析，以剖析组织异质性、疾病 细胞的起源，并实现液体活检为基础的疾病诊断，由于其化学稳定性和基因组- 广泛分布。与大量组织甲基化组检测相比， 从数千到数百万个细胞中，单细胞DNA甲基化组分析在细胞身份相关性分析中是有利的。 应用.尽管近年来单细胞DNA甲基化组数据的数量和种类迅速增加， 多年来，用于其分析的强大且易于使用的计算工具的可用性仍然是未满足的需求。 基于单细胞甲基化组数据解释细胞状态的最佳策略尚未达成共识。 达到了我的实验室的长期目标是在人类单细胞水平上阐明表观遗传细胞的身份， 小鼠为了实现这个目标，我建议开发一套计算工具，用于分析单细胞甲基化 数据，包括数据预处理、质量控制、插补、甲基化组签名等功能 提取、细胞状态注释和探索性可视化。这些软件工具将被设计成 高效，模块化，将被设计为在高性能计算环境和 基本的笔记本电脑这些工具将能够提醒研究者潜在的数据质量问题， 加速甲基化检测的发展，并发现DNA甲基化组与DNA甲基化之间的生物学联系。 细胞的遗传组成、有丝分裂史、细胞周期阶段、分化能力和功能状态。他们可以 也可用于研究大量组织样品中的细胞群体特征。与这些计算工具一起，我们 还旨在分发一个细胞类型分辨率参考甲基化组目录，以造福研究界。我 拟议的工作将提供计算工具和甲基化参考，以加深我们对甲基化的理解 DNA甲基化在确定细胞谱系中的作用，并为表观遗传细胞分型提供实用工具。的 待开发的方法可以很容易地插入到更广泛的探索和转化应用中， 生物医学背景。

PROJECT SUMMARY / ABSTRACT Detailed information about the state of a cell, e.g., its lineage, mitotic history, proliferation potential, and functional competence, consolidates through epigenetic modifications of its DNA and chromatin. Among these modifications, DNA methylation has been widely studied and profiled to dissect tissue heterogeneity, disease cell of origin, and implement liquid biopsy-based disease diagnosis, thanks to its chemical stability and genome- wide distribution. Compared to bulk tissue methylome assays, which yield convoluted, hard-to-decipher signals from thousands to millions of cells, single-cell DNA methylome profiling is advantageous in cell identity-related applications. Despite the rapid increase in the volume and variety of single-cell DNA methylome data in recent years, availability of powerful and easy-to-use computational tools for their analyses is still an unmet demand. Consensus on the optimal strategy of interpreting cell states based on single-cell methylome data has not been reached. My lab’s long-term goal is to elucidate epigenetic cell identities at the single-cell level in humans and mice. Towards that goal, I propose to develop a suite of computational tools, for analyzing single-cell methylation data, that will encompass functions for data preprocessing, quality control, imputation, methylome signature extraction, cell state annotation, and exploratory visualization. These software tools will be engineered to be efficient, modular, and will be designed to operate both in high-performance computing environments and on basic laptops. These tools would be able to alert the investigator of potential data quality issues, feedback to accelerate methylation assay development, and discover biological links between the DNA methylome and the cell’s genetic makeup, mitotic history, cell-cycle stage, differentiation capacity, and functional state. They can also be used to study cell population traits in bulk tissue samples. Together with these computational tools, we also aim to distribute a cell-type-resolution reference methylome catalog to benefit the research community. My proposed work will deliver computational tools and methylation references to deepen our understanding of the role of DNA methylation in determining cell lineages and provide practical tools for epigenetic cell typing. The methods to be developed could be readily plugged into exploratory and translational applications in broader biomedical contexts.