Identifying the regulatory networks altered during retinal degeneration will provide insights into the mechanisms underlying retinal disease. Analysis of such network perturbation at the single cell level will help us to pinpoint the key molecular events that could be missed in traditional analysis using the bulk samples. However, to identify the altered networks in retinal disease is still challenging, partly due to the lack of powerful computational tools. First, many clustering methods yield different and sometimes conflicting results. Second, single cell expression can be used to detect previously unrecognized cell types, while the current clustering algorithms are often not sensitive enough to detect novel, sometimes rare, cell types. Third, genomic interactions obtained from bulk samples and single cells are likely to be complementary to each other and reflect different aspects in terms of gene regulation, co-expression and protein-protein interactions. Novel integrative methods are desired to maximize the information we gain from these genomic datasets. To address these challenges, we will develop computational approaches for single cell data analysis. Specifically, we will develop an iterative clustering method for single cell gene expression analysis (Aim 1). Our approach is designed to be robust and sensitive. We will then develop a method to determine active regulatory networks by integrating single cell RNA-Seq dataset and ATAC-Seq from bulk samples (Aim 2). This method will enable us to identify the regulatory circuits at the single cell level. We will then perform single cell RNA-Seq in retinal degenerative models (Aim 3) and apply our computational approaches to the dataset. We expect to identify the drivers and pathways involved in photoreceptor degeneration. Finally, we will develop a database for cell marker genes by analyzing publically available single cell datasets (Aim 4). We believe that the computational algorithms and database we propose to develop will be valuable resource for the research community. The in- depth study on retinal degenerative models will reveal key molecular events that lead to the disease and provide novel therapeutic targets for the retinal degenerative diseases.
In this proposal, we will develop novel computational approaches for single cell data analysis and apply the approaches to retinal degeneration models. The results obtained from this project will not only provide insights into the mechanisms underlying human disease, but also help to identify potential therapeutic targets.
