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

项目摘要 多细胞生物的细胞内基因组是相同的，但不同的细胞类型显示不同的功能 因为它们的表观基因组不同因此，绘制不同基因组的表观遗传图谱， 组织内的细胞类型对于理解细胞类型特异性基因表达调控是至关重要的。技术 绘制表观遗传因子目前依赖于我们的能力，以高纯度分离所需的细胞类型， 生物化学测定，如定量蛋白质-DNA接触，也需要大量的起始细胞。 然而，细胞类型特异性标记物和抗体通常是未知的或不可用的，这对细胞类型特异性标记物和抗体的检测提供了主要的限制。 高纯度分离细胞类型的挑战。虽然表达细胞类型特异性的转基因动物模型 荧光报告基因在某些情况下可以克服这种限制，这些动物模型的产生是时间的问题。 消耗。此外，组织经常含有罕见的细胞类型，使得分离大量的细胞具有挑战性。 这些细胞是绘制转录因子或染色质的结合景观的测定所需的 修饰蛋白质。为了克服这些现有方法的局限性，本提案的总体目标是 建立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 通过开发单细胞测序方法来同时定量转录组和表观基因组 相同单元单细胞转录组将用于计算机模拟无偏鉴定细胞类型， 并且将属于相同细胞类型的细胞的相应表观基因组合并以产生高- 优质细胞类型特异性表观遗传景观。更具体地说，在目标1中，我们建议开发一种单细胞 多组学技术同时定量来自同一细胞的mRNA、5 mC和DNA可及性。不像 最近开发的一种方法，通过将mRNA从基因组中物理分离来进行这些测量， 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 这些组合测量来自同一个单元。在目标2中，我们提出了一种新的单细胞方法 同时定量来自同一细胞的mRNA和蛋白质-DNA接触。在初步实验中，我们 绘制基因组-核纤层相互作用或染色质修饰蛋白的结合模式 来自同一个细胞的mRNA。最后，作为概念验证，本提案中开发的方法可以 为了绘制体内组织样本的细胞类型特异性表观遗传图谱，我们将量化甲基化组， 大鼠视网膜中细胞类型的DNA可及性模式。视网膜是一种被广泛研究的神经组织， 我们的产品包括各种类型的产品，包括罕见的产品，因此可以作为验证我们技术的绝佳测试平台。因此，在本发明中， 通过这些多组学单细胞方法的发展，我们希望开发出一种技术， 在没有细胞类型特异性的先验知识的情况下， 标记，使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.