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.
The genome within all cells of a mammalian system is identical, yet distinct cell types display varied phenotypes due to differences in their epigenome that play a key role in regulating gene expression, and dysregulation of these regulatory factors is associated with developmental disorders, cancer and aging. Current techniques to map epigenetic landscapes in different cell types are dependent on the ability to isolate these cell types at high purity using cell surface markers and high-quality antibodies that are frequently unavailable, or through the expression of cell type-specific fluorescent reporters in transgenic animal models that are extremely time intensive to generate. Therefore, in this proposal we plan to develop single-cell multiomics technologies to simultaneously quantify the transcriptome and different epigenetic marks/features from the same cell, thereby enabling unbiased cell type identification in silico using the transcriptome and quantification of gene regulatory landscapes by pooling together the epigenome of cells that correspond to the same cell type, resulting in an marker-free approach to map the epigenome of different cell types, including rare ones, within complex tissues.