Data Analysis Core

数据分析核心

• 批准号: 10673215
• 负责人: Bing Ren
• 金额: $ 50.01万
• 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/10673215
• 关键词: 3-Dimensional; Age; Anti-Inflammatory Agents; Atlases; Back; Biological; Biological Assay; Bone Marrow; Brain; Brain Mapping; Breast; CISH gene; Candidate Disease Gene; Cell Aging; Cell model; Cells; Chromatin; Chromosome Mapping; Collaborations; Colon; Communities; Computer software; DNA Methylation; Data; Data Analyses; Data Coordinating Center; Data Files; Data Set; Enhancers; Ensure; Epigenetic Process; Exhibits; FAIR principles; Female; Gene Expression; Genes; Genetic; Genetic Transcription; Genomics; Goals; Hepatocyte; Hippocampus; Image; Link; Liver; Longevity; Machine Learning; Maps; Metadata; Modeling; Molecular Conformation; Mus; Pathway interactions; Phenotype; Population; Process; Publishing; Recording of previous events; Regulation; Regulatory Element; Reproducibility; Research Design; Resolution; Resources; Space Perception; Tissues; Work; age group; analytical method; analytical tool; cell type; chromatin modification; data format; data quality; data repository; 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; pharmacologic; programs; promoter; public repository; repository; senescence; sex; 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 数据分析核心将计算定义和表征转录组和表观基因组签名 在健康雄性小鼠的脑、骨髓、结肠、乳腺和肝细胞类型中的细胞衰老， 年轻的和年老的老鼠我们将使用已建立的可扩展管道来处理 生物分析核心并创建脑、骨髓、结肠、乳腺和肝细胞类型的综合图谱 使用从所有单细胞测序和成像测定一起得到的细胞谱。使用该集成 图，我们将根据基因识别每种组织驻留细胞类型内的衰老细胞群体 已知细胞衰老标记物的表达和表观基因组谱，并定义了异质性亚- 衰老细胞类型以及具有非典型特征的“衰老样”细胞。对于每一个衰老和 衰老样亚型，我们将定义其顺式调控程序，包括候选顺式调控元件 （cCREs）、染色质状态、转录调节因子和cCRE活性的靶基因（即，基因增强子- 启动子网络），以及它们的空间取向和微环境。我们将描述 衰老和衰老样细胞亚型以及细胞类型的丰度和顺式调节， 衰老生态位跨越整个生命周期，并与性别，细胞类型和组织区域有关。这种综合转录组学 衰老细胞的表观基因组图谱将比单独的转录组更好地定义和解析衰老细胞。 我们将评估衰老细胞的遗传和药理学清除作用以及抗炎作用。 感官形态学我们将建立一个管道来确定单个衰老细胞的表观遗传年龄， DNA甲基化谱，有益与有害衰老细胞的候选预测因子。在整个 我们将与UCSD表观基因组学中心和该项目的生物分析核心密切合作。 跟踪数据质量的项目，通过结合批次和其他技术，将研究设计与下游分析联系起来 协变量，为验证研究的目标选择提供信息，并组织所有数据的元数据和相关数据 实验最后，我们将创建一个基于我们的开源软件的元数据存储库。 在其他大型项目中组织所有原始数据和处理数据，提供综合结果文件， 处理项目使用的管道和分析工具，并确保所有项目数据公平，可互操作 并遵循社区标准格式。使用此存储库，我们将项目数据传输到联合体 组织和数据协调中心（CODCC），并与联盟中的其他小组合作， 共享数据和资源。