Developing Computational Models on Single-Cell DNA-Methylation Data for Characterizing Functional Heterogeneity of Stem Cells in Mammalian Hematopoiesis

开发单细胞 DNA 甲基化数据的计算模型，用于表征哺乳动物造血干细胞的功能异质性

• 批准号: 493935791
• 负责人: Dr. Michael Scherer
• 金额: --
• 依托单位: Centre for Genomic Regulation (CRG)
• 依托单位国家: 德国
• 项目类别: WBP Fellowship
• 财政年份: 2022
• 资助国家: 德国
• 起止时间: 2021-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/493935791?language=en
• 关键词: Developing; Computational; Models; Single; Cell

DNA methylation, the reversible addition of a methyl group to CpG dinucleotides in the DNA, is an important layer of epigenetic regulation and indispensable for cellular differentiation in mammals. In the context of the differentiation of blood stem cells, differences in DNA methylation have been associated with long-term fate biases of hematopoietic stem cells (HSCs) that are not visible on other, more dynamic epigenetic layers including the transcriptome. Dynamics of DNA methylation are further associated with the age-related decline in HSC function as well as early steps of leukemia formation. However, the heterogeneity of DNA-methylation patterns in HSCs and their immediate progeny as well as associated functional consequences are unknown, mostly due to the lack of appropriate single-cell methods. Recently, profiling DNA methylation of single cells has become feasible, but data generated using genome-wide approaches is noisy and sparse. Furthermore, an integration with lineage tracing approaches that track the cellular fate of HSCs is required for associating such DNA-methylation dynamics with its functional consequences. In this project, I will contribute to the development of a targeted single-cell DNA-methylation assay (scTAM-seq). This targeted approach allows for generating high-resolution profiles of up to 700 genomic positions with low sequencing effort, and is characterized by small dropout rates and the ability to capture lineage-tracing barcodes. A challenge exclusive to scTAM-seq is the selection of potentially informative regions. Thus, I will develop scalable software solutions for the selection of regions of interest using tools that I co-developed in my PhD (e.g., RnBeads, MeDeCom). To further facilitate the analysis of a biological system using scTAM-seq, I will develop a machine-learning model for denoising the data and for extracting single-cell methylation states. Computational models such as deep autoencoders can be leveraged for understanding the data generated by scTAM-seq and for extracting a low-dimensional visualization of the data. Using this toolset, I will explore lineage biases in HSCs. Particularly, I will investigate DNA methylation dynamics in a mouse model system that allows for tracking cell fates and lineage potential of HSCs. In summary, I will develop tools for the analysis of targeted single-cell DNA-methylation data and apply these tools to characterize the functional heterogeneity of methylation states in hematopoiesis.

DNA甲基化是DNA中CpG二核苷酸上甲基的可逆加成，是表观遗传调控的重要一层，是哺乳动物细胞分化不可或缺的。在造血干细胞分化的背景下，DNA甲基化的差异与造血干细胞（HSC）的长期命运偏差有关，这些偏差在其他更动态的表观遗传层（包括转录组）上不可见。DNA甲基化的动力学进一步与HSC功能的年龄相关性下降以及白血病形成的早期步骤相关。然而，HSC及其直接后代中DNA甲基化模式的异质性以及相关的功能后果是未知的，主要是由于缺乏适当的单细胞方法。最近，分析单细胞的DNA甲基化已经变得可行，但使用全基因组方法生成的数据是嘈杂和稀疏的。此外，需要与追踪HSC的细胞命运的谱系追踪方法整合，以将这种DNA甲基化动态与其功能后果相关联。在这个项目中，我将致力于开发靶向单细胞DNA甲基化检测（scTAM-seq）。这种靶向方法允许以低测序工作量生成高达700个基因组位置的高分辨率图谱，并且其特征在于低脱落率和捕获谱系追踪条形码的能力。scTAM-seq独有的挑战是选择潜在的信息区域。因此，我将开发可扩展的软件解决方案，用于使用我在博士学位期间共同开发的工具（例如，RnBeads，MeDeCom）。为了进一步方便使用scTAM-seq分析生物系统，我将开发一个机器学习模型来对数据进行去噪和提取单细胞甲基化状态。可以利用诸如深度自动编码器之类的计算模型来理解由scTAM-seq生成的数据并提取数据的低维可视化。使用这个工具集，我将探索造血干细胞的谱系偏见。特别是，我将研究小鼠模型系统中的DNA甲基化动态，该系统可以跟踪HSC的细胞命运和谱系潜力。总之，我将开发用于分析靶向单细胞DNA甲基化数据的工具，并应用这些工具来表征造血中甲基化状态的功能异质性。