Data Analysis Core

项目摘要-数据分析核心。 VU生物分子多模式成像中心(BIOMIC)数据分析构建的表征管道 CORE(DAC)将在2-D和3-D多个尺度上制作多模式肾脏分子图谱，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/ 转录学，由共同的空间坐标空间联合。发改委将在我们之前的发展基础上再接再厉， 包括用于采集和分析的空间分割，全面的分子识别 形态，通过使用临床和时间线索的空间掩码跨形态进行自动数据挖掘，以及 将数据放置在从数千张医学图像发展而来的“普通”人类肾脏中。在目标1中，我们将 进一步开发和扩展我们特定于医疗设备的处理方法，以准备不同的测量类型 用于后续的空间集成、多模式分析和内容/跨经济关系挖掘。目标2将 专注于扩展和扩展我们的表征渠道，将广泛的不同 由AIM 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 我们将在空间、时间以及内容方面对不同的数据集进行经验挖掘，以获得跨模式和 分子-功能关系。这些技术将建立2-D和3-D多模式组织图 同时报告了成百上千个细胞分辨率的生物分子，以及 相应的功能和单元类型注释。在目标3中，我们将组成所有的空间和分子 通过扩展分子变异、组织、 其功能超越了单一组织块分析(如目标2)，适用于多块和多供体组织队列。 最后，AIM 4将通过以下方式继续与HuBMAP联盟和HIVE团队成员进行协调 将我们的分析集成到HIVE的ASCT B表中，以进行交互式探索、可视化和可搜索 分析。

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.