Method Development for Single-Cell Multi-omics

单细胞多组学方法开发

• 批准号: 10796111
• 负责人: Yaping Liu
• 金额: $ 25万
• 依托单位: CINCINNATI CHILDRENS HOSP MED CTR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10796111
• 关键词: 3-Dimensional; Academic Training; Address; Aliquot; Binding; Biological Assay; Cells; Complex; Computing Methodologies; Cultured Cells; DNA Methylation; Development; Disease; Drug Targeting; Early Intervention; Epigenetic Process; Gene Activation; Gene Expression; Gene Expression Regulation; Genetic; Genetic Variation; Genome; Genomics; Goals; Intervention; Knowledge; Maps; Measurement; Measures; Modification; Pathologic; Phenotype; Protocols documentation; Public Health; Role; Sampling; Techniques; Technology; Tissues; Variant; cell type; design; epigenomics; experimental study; gene repression; genetic variant; histone modification; individual variation; infancy; method development; multidisciplinary; multiple omics; predictive modeling; single cell sequencing; single-cell RNA sequencing; transcription factor; transcriptomics

Abstract. Epigenetic modifications, including DNA methylation, histone modifications, and three-dimensional (3D) genome topology, combine with genetic content to determine mammalian transcriptional factor (TF) binding and thus, gene regulation. Gene activation or repression potential, however, cannot be entirely predicted by looking at a single measurement. Accurate predictive models require multiple measurements to be measured simultaneously. At present, we are limited by the number of simultaneous measurements that we can perform in a single cell. In addition, the interactions between different epigenetic marks and their effects on gene expression are revealed either in homogenous cultured cells or bulk tissues that average the readout. The study of interactions between different cell-type-specific epigenetic marks and gene expression in heterogeneous tissues at the single cell level is still in its infancy. Progress made during the last decade addressed the heterogeneity of individual cells in terms of gene expression and epigenetic marks at different primary mammalian tissues using single-cell sequencing techniques, such as single-cell RNA-seq. Recently, we and others developed several technologies to simultaneously capture multiple measurements in the same assay (multi-omics techniques) and extended them to the single-cell level. However, current single-cell multi-omics technology can only capture a couple of measurements, which limits our ability to fully understand the integration of epigenetic marks, genomics, and their effects on gene expression. Importantly, adding an additional “omics” assay in the existing experimental protocol is not a simple combination of two existing assays. On the contrary, the add-on assay will often require the re-design of the whole experimental protocol and/or the development of a new computational method. Adding additional “omics” assay to the same experiment or single cell will significantly expand our current knowledge on gene regulation. This is not achievable by joining separate mono-omics experiments in aliquots of the same sample. Given these challenges, significance, and my unique multidisciplinary academic training, my long-term goal is to develop high-throughput experimental assays and computational methods to understand gene regulation by integrating the multi-omics information from the same assay or single-cells. In this proposal, we will develop a combined experimental assay and computational approach to characterize multiple high-quality cell-type-specific epigenomic and transcriptomic maps in the same assay and single cells. We will further develop an integrated assay to characterize the regulatory role of genetic variants on gene expression through multiple intermediate epigenomic activities in the same single cells. These approaches will eventually allow us to address the fundamental questions for the interpretations of genetic variants and therefore bridge the gaps between genetic and phenotypic variations across different healthy and pathological conditions.

摘要。