AstroPath Integration Resource Core

AstroPath 集成资源核心

基本信息

批准号：
10698143
负责人：
Alexander S Szalay
金额：
$ 28.24万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-15 至 2027-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10698143
关键词：
Architecture Astronomy Berry Cell Nucleus Cells Characteristics Chromatin Computer Analysis Coupled DNA Methylation Data Databases Dedications Development Epithelial Cells Epithelium Equipment Functional Imaging Gene Expression Genetically Engineered Mouse Genomic approach Genomics Goals Growth Human Human Resources Image Image Analysis Immune Evasion Immune response Immunofluorescence Immunologic Immunohistochemistry In Situ In Situ Hybridization Indolent Inflammation Inflammatory Informatics Infrastructure Laboratories Lasers Lesion Magnetic Resonance Imaging Malignant Neoplasms Malignant neoplasm of prostate Mathematics Measurement Measures Mediating Metadata Methodology Methods Microscope Modality Modeling Molecular Mus Natural regeneration Neoplasms Pathologic Pathology Pattern Phenotype Prostate Reporting Research Design Research Personnel Research Support Resolution Resources Sampling Scanning Science Services Slide Statistical Algorithm Statistical Data Interpretation System Systems Biology Technology Testing Tissues Translations Work XCL1 gene big-data science bisulfite sequencing computational platform computer framework cost effective data integration data streams digital pathology diverse data epigenomics genome-wide genomic data high dimensionality image processing imaging approach immunopathology innovation mRNA Expression molecular array molecular imaging molecular pathology multidimensional data multimodal data multimodality neoplastic cell novel novel strategies pathology imaging programs relational database single nucleus RNA-sequencing spectrograph statistics tool transcriptome sequencing transcriptomics transfer learning translational potential tumor microenvironment

项目摘要

SUMMARY The AstroPath-Genomics Core will be a hub to disseminate powerful molecular pathology, informatics, big data science, and genomics methods tailored to accomplish the goals of the Prostate TBEL Program. It will provide critical support for ALL three projects. The key strategy is to leverage existing infrastructure, equipment and services at the SKCCC, while also providing specialized approaches and capabilities that are highly tailored to support the activities in the TBEL Program. This model will allow efficient and cost-effective harnessing of these powerful technologies through the dedicated effort and expertise of the current AstroPath-Genomics Core personnel through the close interaction with all Projects and Cores here, without having to establish the equipment, administrative infrastructure, biospecimen SOPs, and laboratory informatics management systems (LIMS), from scratch. This centralization of the molecular pathology, informatics and big data science, and mathematical/computational integration methods will yield cost-effective strategies that avoid duplicating effort in each component of the TBEL Program. The Core will carry out its work in three major Aims. Aim 1 will deploy advanced digital pathology tools for multi-analyte immunohistochemistry, immunofluorescence, and in situ hybridization approaches and image analysis for deep characterization of the microenvironmental transitions from inflammation to neoplasia. Specific technologies include multi-spectral multi-analyte imaging, Chromogenic iterative multiplex-IHC(ChIM-IHC), and multiplex ACD in situ hybridization. Aim 2 will utilize bulk, single cell, and spatial genomics approaches to measure the genomic and epigenomic alterations to the epithelial and stromal compartments of PIA, PIN, and cancer lesions. Specific technologies include, genome wide and ultra-deep targeted DNA methylation analysis, multi-modal single nucleus ATAC- and coupled RNA- seq (snATAC-/snRNA-seq), and spatial transcriptomics. Integration across the multi-modal data streams will be possible through implementation of the innovative Bayesian non-negative matrix factorization and transfer learning approaches through the CoGAPS and ProjectR framework. Aim 3 will implement and extend the AstroPath framework for computational analysis of high dimensional single cell and bulk genomics/epigenomics, multi-analyte digital pathology, and advanced imaging data. To accomplish these goals, the core leaders have assembled an impressive team of experts to support the TBEL Center, with complementary expertise in big data science/informatics, molecular pathology, genomics technologies, immunopathology, computational/systems biology and applied mathematics.

概括 AstroPath-Genomics Core 将成为传播强大的分子病理学、信息学、大数据的中心为实现前列腺 TBEL 计划的目标而量身定制的科学和基因组学方法。它将提供对所有三个项目的关键支持。关键策略是利用现有的基础设施、设备和 SKCCC 的服务，同时还提供高度定制的专业方法和能力支持 TBEL 计划的活动。该模型将允许高效且经济高效地利用这些强大的技术是通过当前 AstroPath-Genomics 的专注努力和专业知识实现的核心人员通过与所有项目和核心的密切互动，无需建立设备、管理基础设施、生物样本 SOP 和实验室信息学管理系统（LIMS），从头开始。分子病理学、信息学和大数据科学的集中化，数学/计算集成方法将产生具有成本效益的策略，避免重复工作 TBEL 计划的每个组成部分。核心将在三个主要目标上开展工作。目标1将部署先进的数字病理学工具进行多分析物免疫组织化学、免疫荧光和原位杂交方法和图像分析用于微环境的深入表征从炎症到肿瘤的转变。具体技术包括多光谱多分析物成像、显色迭代多重 IHC (ChIM-IHC) 和多重 ACD 原位杂交。目标 2 将利用批量，单细胞和空间基因组学方法来测量基因组和表观基因组的改变 PIA、PIN 和癌症病变的上皮和基质区室。具体技术包括，基因组广泛和超深的靶向 DNA 甲基化分析、多模式单核 ATAC- 和偶联 RNA- seq (snATAC-/snRNA-seq) 和空间转录组学。跨多模式数据流的集成将通过实施创新的贝叶斯非负矩阵分解和传递成为可能通过 CoGAPS 和 ProjectR 框架的学习方法。目标 3 将实施并扩展用于高维单细胞和块体计算分析的 AstroPath 框架基因组学/表观基因组学、多分析物数字病理学和高级成像数据。为了完成这些为了实现这一目标，核心领导者组建了一支令人印象深刻的专家团队来支持 TBEL 中心，大数据科学/信息学、分子病理学、基因组技术的互补专业知识，免疫病理学、计算/系统生物学和应用数学。