Stanford Tissue Mapping Center

斯坦福大学组织绘图中心

• 批准号: 10213802
• 负责人: SYLVIA KATINA PLEVRITIS
• 金额: $ 37.23万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-19 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10213802
• 关键词: 3-Dimensional; ATAC-seq; Adopted; Antibodies; Architecture; Biological Assay; Cells; Computer software; Data; Data Analyses; Epitopes; Genetic Transcription; Goals; Human BioMolecular Atlas Program; Image; Intestines; Investigation; Link; Maps; Measures; Methods; Organ; Pattern; Phase; Population; Process; Quality Control; Resolution; Standardization; Tissues; Work; antibody detection; base; cell type; data analysis pipeline; data standards; data submission; differential expression; epigenome; experience; imaging modality; novel; programs; protein expression; public repository; scale up; single-cell RNA sequencing; tool; transcriptome; transcriptome sequencing

ABSTRACT: Data Analysis Core In our investigation of the cellular architecture of bowel tissue, we will generate single cell omics data using RNA-seq and ATAC-seq to profile the gene transcription and regulatory programs of cells. These complementary profiles will be used to define previously-described cell types and potentially discover novel types. Our spatial data will be mapped through the highly multiplexed tagged-antibody CODEX imaging method. The differential expression of mapped epitopes in the cell populations will provide a link to the omics data. We have existing standardized pipelines to process each of these data types; these tools are already in use in the ENCODE consortium. We also have tools to integrate these disparate data types. We will continue to refine these integrative methods as we investigate our proposed organ of focus along with other organs in coordination with HuBMAP. All of data will be submitted to the HIVE and public repositories.

摘要：数据分析核心 在我们对肠组织细胞结构的研究中，我们将生成单细胞组学 使用 RNA-seq 和 ATAC-seq 来分析基因转录和调控程序的数据 细胞。这些互补的概况将用于定义先前描述的细胞类型和 潜在地发现新类型。我们的空间数据将通过高度复用的映射 标记抗体 CODEX 成像方法。映射表位的差异表达 细胞群将提供组学数据的链接。我们有现有的标准化管道 处理每种数据类型；这些工具已在 ENCODE 联盟中使用。 我们还有工具来集成这些不同的数据类型。我们将继续完善这些 当我们研究我们提出的重点器官以及其他器官时，采用综合方法 与 HuBMAP 协调。所有数据将提交到 HIVE 和公共存储库。