A marker-free technology for mapping the epigenome of cell types in mammalian tissues

项目总结 多细胞生物体的细胞内基因组是相同的，但不同的细胞类型表现出不同的功能 由于它们表观基因组的不同。因此，绘制不同物种的全基因组表观遗传图谱 组织中的细胞类型对于了解细胞类型特定的基因表达调控至关重要。技术 绘制表观遗传因子图目前依赖于我们以高纯度分离所需细胞类型的能力 生化分析，如量化蛋白质-DNA接触，也需要大量的起始细胞。 然而，细胞类型特异的标志物和抗体通常是未知的或不可用的，呈现出主要的 在高纯度分离细胞类型方面的挑战。而表达特定细胞类型的转基因动物模型 荧光记者在某些情况下可以克服这一限制，这些动物模型的产生就是时间 在消费。此外，组织中经常含有罕见的细胞类型，这使得分离大量的 这些细胞是绘制转录因子或染色质结合图谱的分析所需的 修饰蛋白质。为了克服目前这些方法的局限性，本提案的总体目标是 建立一种无标记的高通量技术来绘制组织内不同细胞类型的表观基因组图 通过发展单细胞测序方法同时量化转录组和表观基因组 同一间牢房。单细胞转录本将用于无偏见地识别硅胶中的细胞类型， 属于同一细胞类型的细胞的相应表观基因组将被汇集在一起，以产生高- 高质量的细胞类型特定的表观遗传景观。更具体地说，在目标1中，我们建议开发一种单细胞 多组学技术，可同时定量同一细胞的mRNA、5mC和DNA的可及性。不像 最近开发的一种方法，通过从基因组中物理分离mRNA来进行这些测量 DNA，我们的技术不涉及核酸的物理分离，从而实现了高通量 每天处理数千个单细胞。初步实验表明，我们可以有效地使 这些测量结果来自同一个细胞。在目标2中，我们建议开发一种新的单细胞方法 同时量化来自同一细胞的信使核糖核酸和蛋白质-DNA接触。在初步实验中，我们 绘制的基因组-核层相互作用或染色质修饰蛋白的结合模式 与来自同一细胞的基因同源。最后，作为概念证明，本提案中开发的方法可以 用于绘制体内组织样本中特定细胞类型的表观遗传学图谱，我们将量化甲基组和 大鼠视网膜细胞类型的DNA可及性模式。视网膜是经过充分研究的神经组织，有超过50个细胞。 类型，包括稀有类型，因此是验证我们技术的极佳试验台。因此， 通过这些多组学单细胞方法的发展，我们希望开发出一种能够 应用于绘制组织中不同细胞类型的表观基因组图，而不需要特定细胞类型的先验知识 标记，使人们能够更深入地了解异质组织中的基因调控机制。

PROJECT SUMMARY The genome within cells of a multicellular organism is identical, yet distinct cell types display varied functions due to differences in their epigenome. Therefore, mapping the genome-wide epigenetic landscape of different cell types within a tissue is critical for understanding cell type-specific gene expression regulation. Techniques to map epigenetic factors currently rely on our ability to isolate the desired cell types at high purity with certain biochemical assays, such as quantifying protein-DNA contacts, also requiring a large number of starting cells. However, cell type-specific markers and antibodies are frequently unknown or unavailable, presenting a major challenge in isolating cell types at high purity. While transgenic animal models that express cell type-specific fluorescent reporters can overcome this limitation in some cases, generation of these animal models is time consuming. Further, tissues frequently contain rare cell types, making it challenging to isolate large numbers of such cells that are required for assays mapping the binding landscape of transcription factors or chromatin modifying proteins. To overcome limitations of these current approaches, the overall goal of this proposal is to establish a marker-free high-throughput technology to map the epigenome of different cell types within a tissue by developing single-cell sequencing methods to simultaneously quantify the transcriptome and epigenome from the same cell. The single-cell transcriptomes will be used for the unbiased identification of cell types in silico, and the corresponding epigenomes of cells belonging to the same cell type will be pooled to generate high- quality cell type-specific epigenetic landscapes. More specifically, in Aim 1 we propose to develop a single-cell multiomics technology to simultaneously quantify mRNA, 5mC and DNA accessibility from the same cell. Unlike a recently developed method that makes these measurements by physically separating mRNA from genomic DNA, our technology does not involve the physical separation of nucleic acids, thereby enabling high-throughput processing of thousands of single cells per day. Preliminary experiments suggest that we can efficiently make these combined measurements from the same cell. In Aim 2, we propose to develop a new single-cell method to simultaneously quantify mRNA and protein-DNA contacts from the same cell. In preliminary experiments, we mapped genome-nuclear lamina interactions or the binding pattern of a chromatin modifying protein together with mRNA from the same cell. Finally, as proof-of-concept that the methods developed in this proposal can be used to map cell type-specific epigenetic profiles from in vivo tissue samples, we will quantify methylome and DNA accessibility patterns for cell types in the rat retina. The retina is well-studied neural tissue with over 50 cell types, including rare ones, and therefore serves as an excellent testbed to validate our technologies. Thus, through the development of these multiomics single-cell methods, we expect to develop a technology that can be applied to map the epigenome of different cell types in a tissue without a priori knowledge of cell type-specific markers, enabling deeper understanding of the mechanisms of gene regulation in heterogeneous tissues.