Data Analysis, Integration, and Sharing Core

项目摘要-数据分析核心 在与骨髓组织映射核心器官特异性项目（OSP）的密切合作下， 分析核心（DAC）将监督样本元数据管理、低级别数据处理和质量控制、空间 特征提取、交叉分析整合、组织图谱构建和HIVE数据交付。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路复用组织成像数据 以前，它遵循FAIR数据共享标准，并已被构建为优先考虑互操作性 和透明度。DAC将使用这些工具构建多尺度、多模态的人体骨骼图谱 根据年龄、性别、种族和解剖部位进行情境化。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 这将与在邻近的组织中进行的从头空间聚糖成像和组织转录组学共同配准。 分别使用MALDI和Nanostring DSP进行连续切片。全细胞分割与单个 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复杂的表型特征， 可以容易地在MIBI-TOF和MALDI数据上部署的不可知方式。这些数据将 关于基于细胞或像素的特征进行询问，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，每个模态的图像配准将允许 通过MIBI表征的细胞亚群的空间分布与局部聚糖组成相关， 转录程序。

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.