AstroPath Integration Resource Core

AstroPath 集成资源核心

• 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
• 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

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.

总结 AstroPath-Genomics Core将成为传播强大分子病理学、信息学和大数据的中心 科学和基因组学方法，以实现前列腺TBEL计划的目标。它将提供 为三个项目提供重要支持。关键战略是利用现有的基础设施、设备和 在SKCCC的服务，同时还提供专门的方法和能力，高度定制， 支持TBL计划中的活动。这一模式将允许有效和具有成本效益地利用 这些强大的技术，通过目前的AstroPath-Genomics的专门努力和专业知识， 核心人员通过与所有项目和核心人员的密切互动，无需建立 设备、行政基础设施、生物标本SOP和实验室信息化管理系统 （LIMS），从零开始。分子病理学、信息学和大数据科学的集中化，以及 数学/计算综合方法将产生成本效益高的战略，避免重复工作 TBL计划的每个组成部分。核心将在三个主要目标下开展工作。目标1将 部署先进的数字病理学工具，用于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 从炎症到瘤形成的转变。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将利用散装， 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和癌病变的上皮和基质区室。具体技术包括，基因组 广泛和超深度靶向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 可以通过实施创新的贝叶斯非负矩阵分解和转移 通过CoGAPS和ProjectR框架的学习方法。目标3将实施和扩展 用于高维单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。完成这些 为了实现目标，核心领导人组建了一个令人印象深刻的专家团队来支持TBEL中心， 在大数据科学/信息学、分子病理学、基因组学技术、 免疫病理学、计算/系统生物学和应用数学。