Discovery and investigation of cell type abundance biomarkers using single-cell and bulk genomic data

点击翻译按钮获取中文摘要

The proposed research program will develop the first software pipeline to discover biomarkers based on cell type abundance (using single-cell genomics technology) and to validate their relevance to clinical outcomes (or other phenotypes) in large-scale genomic studies. Background: The tissue microenvironment consists of a dynamic mixture of various cell types. The changes in cell type abundance can modify an organ's conditions and functions. Hence, the biomarkers based on cell type abundance can be used as drug targets for various diseases. Our novel procedure of discovering and investigating the cell type abundance biomarkers involves three steps. In step 1, we use single-cell RNA-sequencing (scRNA-seq) to identify all cell types in a specific type of tissue and infer their abundance. The discovered biomarkers are the abundance of specific cell types that vary significantly between healthy and diseased persons. Although the new scRNA-seq technology enables us to study tissues at the single-cell level, statistical methods and software are urgently needed to extract information from the data and make inferences precise. Validating the abundance-biomarkers' clinical relevance requires many samples. Conducting such large-scale studies using scRNA-seq is not practical due to the limitations of single-cell technology. So, the step 2 is developing digital cytometry methods to bridge information between scRNA-seq and bulk RNA-seq, which enables the inference of cell type abundance by using more affordable bulk RNA-seq. At last, the step 3 is to conduct a large-scale genomic study and analyze the data to evaluate the biomarkers' clinical relevance. Objectives: My research objectives are motivated by supporting the three steps discussed above respectively. First, I will develop novel statistical methods and software to annotate cell types and infer their abundance using scRNA-seq data. Second, I will develop novel digital cytometry methods based on the scRNA-seq data. Third, I will develop novel statistical methods to support the design and analysis of large-scale genomic studies, including three research areas: the design of clinical trials, causal inference, and multitask predictions. I will conduct the proposal by training highly qualified personnel (HQP) under equity, diversity, and inclusion guidelines. Research deliverables and impacts: My primary deliverables are the statistical methods and the software pipeline, which other researchers can use in their genomic studies. Those methods will be applied to discover biomarkers based on the abundance of alveolar macrophages subtypes, which will be used as immunotherapy drug targets for chronic obstructive pulmonary disease (COPD). In Canada, COPD is the top 1 reason for hospitalization, which costs over $1.5 billion/yr in direct expenditures to treat two million COPD patients. No current therapy can reverse disease's progression of COPD. Our research may lead to the first therapy that cures COPD.