Data Analysis Core

数据分析核心

• 批准号: 10553047
• 负责人: Bing Ren
• 金额: $ 57.73万
• 依托单位: SANFORD BURNHAM PREBYS MEDICAL DISCOVERY INSTITUTE
• 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 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10553047
• 关键词: 3-Dimensional; Age; Anti-Inflammatory Agents; Atlases; Back; Biological; Biological Assay; Bone Marrow; Brain; Breast; CISH gene; Candidate Disease Gene; Cell Aging; Cell model; Cells; Chromatin; Collaborations; Colon; Communities; Computer software; DNA Methylation; Data; Data Analyses; Data Analytics; Data Coordinating Center; Data Files; Data Set; Enhancers; Ensure; Epigenetic Process; Exhibits; FAIR principles; Female; Gene Expression; Genes; Genetic; Genetic Transcription; Genomics; Germ Cells; Goals; Hepatocyte; Hippocampus (Brain); Image; Link; Liver; Longevity; Machine Learning; Maps; Metadata; Modeling; Molecular Conformation; Mus; Pathway interactions; Pharmacology; Phenotype; Population; Process; Publishing; Recording of previous events; Regulation; Regulatory Element; Reproducibility; Research Design; Resolution; Space Perception; Tissues; Work; age group; analytical method; analytical tool; base; cell type; chromatin modification; data format; data quality; data repository; data resource; data sharing; data standards; epigenome; epigenomics; experience; experimental study; file format; genomic data; genomic signature; histone modification; interoperability; machine learning classifier; machine learning model; male; molecular phenotype; mouse model; multiple omics; open source; programs; promoter; public repository; repository; senescence; single cell analysis; single cell sequencing; transcriptome; transcriptomics; validation studies

PROJECT SUMMARY The Data Analysis Core will computationally define and characterize transcriptomic and epigenomic signatures of cellular senescence in murine brain, bone marrow, colon, breast and liver cell types from healthy male and female young and old mice. We will use established, scalable pipelines to process data generated by the Biological Analysis Core and create an integrated map of brain, bone marrow, colon, breast and liver cell types using cellular profiles derived from all single cell sequencing and imaging assays together. Using this integrated map, we will identify populations of senescent cells within each tissue-resident cell type based on gene expression and epigenomic profiles of known cellular senescence markers, and define both heterogeneous sub- types of senescent cells as well as ‘senescent-like’ cells with non-canonical profiles. For each senescent and senescent-like sub-type, we will define its cis-regulatory programs including candidate cis-regulatory elements (cCREs), chromatin states, transcriptional regulators and target genes of cCRE activity (i.e., gene enhancer – promoter networks), as well as their spatial orientation and micro-environment. We will characterize changes in the abundance and cis-regulation of senescent and senescent-like cell sub-types as well as cell types in the senescent niche across life span and linked to sex, cell type and tissue region. This integrated transcriptomic and epigenomic map of senescent cells will define and resolve senescent cells better than transcriptome alone. We will evaluate the effects of genetic and pharmacologic clearance of senescent cells and anti-inflammatory senomorphics. We will establish a pipeline to determine epigenetic age of single senescent cells based on their DNA methylation profile, a candidate predictor of beneficial versus detrimental senescent cells. Throughout the project we will work closely with the UCSD Center for Epigenomics and the Biological Analysis Core of this project to track data quality, link study design to downstream analyses by incorporating batch and other technical covariates, inform selection of targets for validation studies, and organize meta-data and associated data for all experiments. Finally, we will create a meta-data repository based on open-source software employed by our group in other large-scale projects to organize all raw and processed data, provide integrated results files, processing pipelines and analytical tools used by the project, and ensure all project data is FAIR, interoperable and adheres to community standard formats. Using this repository, we will transfer project data to the Consortium Organization and Data Coordination Center (CODCC) and collaborate with other groups in the consortium to share data and resources.

项目摘要 数据分析核心将在计算上定义和表征转录组和表观基因组特征 来自健康男性和 雌性和老鼠。我们将使用已建立的可扩展管道来处理由 生物分析核心并创建大脑，骨髓，结肠，乳房和肝细胞类型的集成图 使用从所有单细胞测序和成像测定中得出的细胞谱。使用此集成 地图，我们将根据基因确定每种组织居民细胞类型中的感觉细胞的种群 已知细胞感应标记的表达和表观基因组谱，并定义两个异质亚 感觉细胞的类型以及具有非规范曲线的“感觉样”细胞。对于每个感觉和 类似于感觉的子类型，我们将定义其顺式调节程序，包括候选顺序调节元件 （CCRES），染色质状态，转录调节剂和CCRE活性的靶基因（即基因增强子 - 启动子网络）及其空间取向和微环境。我们将表征更改 感觉和感觉样细胞亚型的抽象和顺式调节以及细胞类型 跨寿命的感觉小众，与性别，细胞类型和组织区域相关。这个集成的转录组 与单独的转录组相比，感觉细胞的表观基因组图将更好地定义和解决感觉细胞。 我们将评估感觉细胞和抗炎的遗传和药物清除的影响 鼻象。我们将建立一条管道，以确定单个感觉细胞的表观遗传年龄 DNA甲基化曲线，有益与有害感觉细胞的候选预测指标。整个 项目我们将与UCSD表观基因组学中心紧密合作，并将其生物分析核心 通过编码批次和其他技术来跟踪数据质量，链接研究设计与下游分析的项目 协变量，为验证研究的目标选择，并组织所有人的元数据和相关数据 实验。最后，我们将根据我们的开源软件创建一个元数据存储库 其他大规模项目中的组以组织所有原始数据和处理的数据，提供集成的结果文件， 处理项目使用的管道和分析工具，并确保所有项目数据都是公平的，可互操作的 并遵守社区标准格式。使用此存储库，我们将将项目数据传输到财团 组织和数据协调中心（CODCC），并与财团中的其他团体合作 共享数据和资源。