Data Analysis Core

数据分析核心

基本信息

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
项目类别：
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

项目摘要

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.

项目概要数据分析核心将通过计算定义和表征转录组和表观基因组特征健康男性和小鼠脑、骨髓、结肠、乳腺和肝细胞类型中细胞衰老的影响我们将使用已建立的、可扩展的管道来处理由雌性年轻和年老小鼠生成的数据。生物分析核心并创建大脑、骨髓、结肠、乳腺和肝细胞类型的综合图谱使用来自所有单细胞测序和成像测定的细胞图谱一起使用这种集成。地图上，我们将根据基因识别每种组织驻留细胞类型中的衰老细胞群体已知细胞衰老标记的表达和表观基因组图谱，并定义了异质子衰老细胞的类型以及具有非典型特征的“类衰老”细胞。类衰老亚型，我们将定义其顺式调节程序，包括候选顺式调节元件 (cCRE)、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），以及它们的空间方向和微观环境的变化。衰老和类衰老细胞亚型以及细胞类型的丰度和顺式调节整个生命周期的衰老生态位与性别、细胞类型和组织区域相关。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清除以及抗炎的效果我们将建立一个管道，根据单个衰老细胞的表观遗传年龄来确定它们的表观遗传年龄。 DNA 甲基化谱，是整个衰老细胞有益与有害的候选预测因子。项目我们将与加州大学圣地亚哥分校表观基因组学中心和该项目的生物分析核心密切合作项目跟踪数据质量，通过合并批次和其他技术将研究设计与下游分析联系起来协变量，告知验证研究目标的选择，并组织所有数据的元数据和相关数据最后，我们将基于我们使用的开源软件创建一个元数据存储库。在其他大型项目中组织所有原始和处理后的数据，提供综合结果文件，项目使用的处理流程和分析工具，并确保所有项目数据公平、可互操作并遵守社区标准格式使用此存储库，我们将项目数据传输到联盟。组织和数据协调中心（CODCC）并与联盟中的其他团体合作共享数据和资源。