Data Analysis Core

数据分析核心

• 批准号: 10530866
• 负责人: Jeffrey M Spraggins
• 金额: $ 53.9万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10530866
• 关键词: 3-Dimensional; Age; Anatomy; Atlases; Biological; Biological Assay; Cell Cycle; Cells; Chemicals; Classification; Clinical; Communities; Computer software; Cues; Custom; Data; Data Analyses; Data Set; Data Sources; Development; Dimensions; Dropout; Eye; Gender; Gene Expression; Goals; Human; Human BioMolecular Atlas Program; Image; Imaging technology; Individual; Kidney; Label; Learning; Machine Learning; Magnetic Resonance Imaging; Maps; Masks; Measurement; Medical Imaging; Memory; Methods; Microscopy; Mining; Mission; Modality; Modeling; Molecular; Molecular Analysis; Multimodal Imaging; Nature; Nephrons; Organ; Pancreas; Population; Procedures; Process; Production; Proteomics; Published Comment; Quality Control; Reporting; Research; Resolution; Resources; Sampling; Scheme; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Speed; Technology; Tissue Donors; Tissue Sample; Tissues; Universities; Variant; Visualization; Work; X-Ray Computed Tomography; automated segmentation; base; cell type; cohort; computerized data processing; computing resources; data mining; demographics; human tissue; imaging facilities; insight; interest; member; multimodal data; multimodality; multiple omics; open source; spatial integration; spatiotemporal; tissue mapping; transcriptomics

PROJECT SUMMARY – Data Analysis Core. The characterization pipelines built by the VU Biomolecular Multimodal Imaging Center (BIOMIC) Data Analysis Core (DAC) will produce a multimodal molecular atlas of kidney across multiple scales in 2-D and 3-D, comprising rich and varied molecular data from MALDI IMS, MxIF, CODEX, spatial and non-spatial proteomics/ transcriptomics, united by common spatial coordinate space. The DAC will build on our previous developments, including spatial segmentation for both acquisition and analysis, comprehensive molecular identification across modalities, automated data mining across modalities by spatial masks using clinical and temporal cues, and situating data within an “average” human kidney developed from 1000s of medical images. In Aim 1 we will further develop and scale our modality-specific processing methods to prepare the different measurement types for subsequent spatial integration, multimodal analysis, and content/cross-omic relationship mining. Aim 2 will focus on expanding and scaling our characterization pipeline, bringing together the broad array of distinct imaging and -omics measurement types individually processed by the methods in Aim 1. By integrating these varied datasets spatially, temporally, as well as content-wise, we will empirically mine them for cross-modal and molecular-functional relationships. These technologies will establish both 2-D and 3-D multimodal tissue maps that concurrently report hundreds to thousands of biomolecules at cellular resolutions, together with corresponding functional and cell type annotations. In Aim 3 we will compose all spatial and molecular information into a reference atlas by expanding the spatio-temporal mapping of molecular variation, organization, and function beyond single tissue block analyses (as in Aim 2) to multi-block and multi-donor tissue cohorts. Finally, Aim 4 will enable continued coordination with the HuBMAP consortium and HIVE team members by integrating our analyses into the HIVE’s ASCT+B tables for interactive exploration, visualization, and searchable analyses.

项目摘要-数据分析核心。 VU生物分子多模态成像中心（BIOMIC）数据分析建立的表征管道 核心（DAC）将产生跨越2-D和3-D的多个尺度的肾脏的多模态分子图谱，包括 来自MALDI IMS、MxIF、CODEX、空间和非空间蛋白质组学的丰富多样的分子数据 转录组学，由共同的空间坐标空间联合。发展援助委员会将在我们以前的发展基础上， 包括用于采集和分析的空间分割， 模态，通过使用临床和时间线索的空间掩模跨模态进行自动数据挖掘，以及 将数据定位在从1000个医学图像开发的“平均”人类肾脏内。在目标1中， 进一步开发和扩展我们特定于模式的处理方法，以准备不同的测量类型 用于随后的空间整合、多模态分析和内容/交叉组学关系挖掘。目标2将 专注于扩大和扩展我们的表征管道，汇集广泛的不同的 通过目标1中的方法单独处理的成像和组学测量类型。通过整合这些 不同的数据集空间，时间，以及内容明智的，我们将凭经验挖掘他们的跨模态和 分子功能关系这些技术将建立2-D和3-D多模式组织图 同时以细胞分辨率报告数百到数千个生物分子， 相应的功能和细胞类型注释。在目标3中，我们将组成所有空间和分子 通过扩展分子变异，组织， 和功能超出单一组织块分析（如目标2），以多块和多供体组织队列。 最后，目标4将通过以下方式与HuBMAP联盟和HIVE团队成员继续协调： 将我们的分析整合到HIVE的ASCT+B表中，以进行交互式探索、可视化和可搜索 分析。