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Computational Tools for Single Cell Analysis: Application to Retinal Degeneration

单细胞分析计算工具：在视网膜变性中的应用

基本信息

批准号：
9764371
负责人：
Jiang Qian
金额：
$ 40.94万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2022-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9764371
关键词：
ATAC-seq Address Adopted Algorithms Binding Biological Biological Models Blindness Cells ChIP-seq Chromatin Communities Computational algorithm Computing Methodologies Conflict (Psychology)Data Data Analyses Data Set Databases Disease Event Gene Expression Gene Expression Profile Gene Expression Profiling Gene Expression Regulation Genes Genomics Heterogeneity Lead Methodology Methods Modeling Molecular Pathway interactions Pattern Process Resources Retinal Degeneration Retinal Diseases Role Sampling Techniques Testing Tissues Transcriptional Regulation Variant Wild Type Mouse Work base biological systems cell behavior cell type computerized tools design genome-wide genomic data human disease improved insight new therapeutic target novel photoreceptor degeneration protein protein interaction single cell analysis single-cell RNA sequencing therapeutic target tool transcriptome sequencing

项目摘要

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.

确定视网膜变性过程中改变的调节网络将提供对机制的见解