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.

识别视网膜变性期间改变的调节网络将有助于深入了解其机制潜在的视网膜疾病。在单细胞水平上分析此类网络扰动将有助于我们查明使用大量样品进行传统分析时可能会遗漏的关键分子事件。然而，为了识别视网膜疾病中改变的网络仍然具有挑战性，部分原因是缺乏强大的计算工具。首先，许多聚类方法会产生不同的结果，有时甚至是相互矛盾的结果。第二，单细胞表达可用于检测以前未识别的细胞类型，而当前的聚类算法通常不够灵敏，无法检测新的、有时是罕见的细胞类型。三、基因组学从大量样品和单细胞中获得的相互作用可能是相互补充的，并且反映了基因调控、共表达和蛋白质-蛋白质相互作用的不同方面。小说我们需要综合方法来最大化我们从这些基因组数据集中获得的信息。致地址这些挑战，我们将开发用于单细胞数据分析的计算方法。具体来说，我们将开发用于单细胞基因表达分析的迭代聚类方法（目标 1）。我们的方法是设计坚固且灵敏。然后我们将开发一种方法来确定活跃的监管网络整合来自大量样本的单细胞 RNA-Seq 数据集和 ATAC-Seq（目标 2）。这个方法将使我们识别单细胞水平的调节电路。然后我们将在视网膜中进行单细胞 RNA 测序退化模型（目标 3）并将我们的计算方法应用于数据集。我们期望确定参与光感受器变性的驱动因素和途径。最后，我们将开发一个细胞数据库通过分析公开的单细胞数据集来标记基因（目标 4）。我们认为，计算我们建议开发的算法和数据库将成为研究界的宝贵资源。在- 对视网膜变性模型的深入研究将揭示导致该疾病的关键分子事件和为视网膜退行性疾病提供新的治疗靶点。