Data Analysis, Integration, and Sharing Core

数据分析、整合、共享核心

• 批准号: 10531006
• 负责人: Robert michael Angelo
• 金额: $ 46.44万
• 依托单位: STANFORD UNIVERSITY
• 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/10531006
• 关键词: Age; Algorithms; Anatomy; Atlases; Biological Assay; Blood Vessels; Bone Marrow; Cell Communication; Cell Differentiation process; Cells; Censuses; Classification; Collaborations; Complex; Data; Data Analyses; Erythroid; FAIR principles; Gender; Gene set enrichment analysis; Genes; Genetic Transcription; Hematopoiesis; Hematopoietic; Histologic; Human; Human BioMolecular Atlas Program; Image; Length; Link; Location; Lymphatic; Lymphoid; Manuals; Metadata; Mitotic; Modality; Morphology; Myelogenous; Neighborhoods; Organ; Patients; Peer Review; Phagocytes; Phenotype; Polysaccharides; Population; Principal Component Analysis; Quality Control; Race; Resolution; Sampling; Shapes; Spatial Distribution; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Structure; Tissue atlas; Tissue imaging; Tissues; Work; base; bone; cell type; cohort; computerized data processing; computerized tools; data pipeline; data quality; data sharing; deep learning algorithm; demographics; differential expression; feature extraction; human tissue; interoperability; large datasets; multimodality; nano-string; progenitor; programs; protein expression; stem; tissue mapping; tool; transcriptome; transcriptomics

Project Summary - Data Analysis Core In close collaboration with the bone marrow Tissue Mapping Core Organ Specific Project (OSP), the data analysis core (DAC) will oversee sample metadata curation, low level data processing and quality control, spatial feature extraction, cross-assay integration, tissue atlas construction, and HIVE data delivery. We will leverage vetted, peer-reviewed data pipelines for scalable processing and analysis of multiplexed tissue imaging data previously that adhere to FAIR data sharing standards and have been constructed to prioritize interoperability and transparency. The DAC will use these tools to construct a multiscale, multimodal atlas of human bone marrow that is contextualized by age, gender, race, and anatomic site. Highly multiplexed subcellular imaging using MIBI-TOF will provide a comprehensive census of bone marrow structure, cell types, and functional states that will be coregistered with de novo spatial glycan imaging and tissue transcriptomics performed on adjacent serial sections using MALDI and Nanostring DSP, respectively. Whole cell segmentation together with single channel protein expression data will be leveraged for cell clustering and annotation in high resolution MIBI-TOF images. Meanwhile, pixel-based approaches will be employed to identify complex phenotypic features in a cell- agnostic manner that can be readily deployed on both MIBI-TOF and MALDI data. These data will be interrogated with respect to cell or pixel-based features to identify composite tissue neighborhoods consisting of recurring combinations of either feature type. Lastly, image co-registration across each modality will permit the spatial distributions of cell subsets characterized by MIBI to be correlated with local glycan composition and transcriptional programs.

项目摘要 - 数据分析核心 与骨髓组织图谱核心器官特定项目 (OSP) 密切合作，数据 分析核心（DAC）将监督样本元数据管理、低级数据处理和质量控制、空间 特征提取、跨分析集成、组织图谱构建和 HIVE 数据传输。我们将利用 经过审查、同行评审的数据管道，用于可扩展地处理和分析多重组织成像数据 以前遵守 FAIR 数据共享标准，并已构建优先考虑互操作性 和透明度。 DAC 将使用这些工具构建多尺度、多模式的人体骨骼图谱 与年龄、性别、种族和解剖部位相关的骨髓。高度多重亚细胞成像 使用 MIBI-TOF 将提供骨髓结构、细胞类型和功能状态的全面普查 这将与在相邻区域上进行的从头空间聚糖成像和组织转录组学共同注册 串行部分分别使用 MALDI 和 Nanostring DSP。全细胞分割与单细胞分割 通道蛋白表达数据将用于高分辨率 MIBI-TOF 中的细胞聚类和注释 图像。同时，基于像素的方法将用于识别细胞中的复杂表型特征。 可以轻松部署在 MIBI-TOF 和 MALDI 数据上的不可知方式。这些数据将 询问基于细胞或像素的特征，以识别由以下组成的复合组织邻域 任一特征类型的重复组合。最后，跨每种模式的图像共同配准将允许 以 MIBI 为特征的细胞亚群的空间分布与局部聚糖组成相关 转录程序。