Automated comparison of flow data from HIV and vaccine infected subjects.

自动比较 HIV 和疫苗感染受试者的流量数据。

• 批准号: 8456054
• 负责人: Leonore A. Herzenberg
• 金额: $ 22.58万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-04-06 至 2015-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8456054
• 关键词: Address; Algorithms; Analysis of Variance; Antigens; Area; Automation; CD4 Positive T Lymphocytes; Cell Communication; Cell Count; Cells; Clinical; Clinical Research; Complex; Computer software; Data; Data Analyses; Data Collection; Data Display; Development; Disease; Documentation; Evaluation; Financial compensation; Flow Cytometry; Fluorescence; Freezing; Frequencies; Future; HIV; HIV vaccine; Immune system; In Vitro; Individual; Laboratories; Light; Manuals; Measures; Methods; Mining; Modeling; Noise; Outcomes Research; Performance; Probability; Process; Publishing; Research; Research Personnel; Role; Sampling; Specific qualifier value; Staining method; Stains; Statistical Methods; Systems Development; T-Lymphocyte Subsets; Technology; Testing; Time; Vaccine Research; Vaccines; Work; base; clinical practice; computerized data processing; cytokine; data acquisition; data integration; data modeling; design; detector; drug development; improved; in vivo; indexing; innovation; instrument; instrumentation; interest; response; software development; statistics; tool; user-friendly; vaccine development

DESCRIPTION (provided by applicant): Our laboratory conceived and developed the initial flow cytometry instruments and has since introduced many innovations that increase the broad utility and accessibility of this technology. As part of this effort, we developed key software an analysis capabilities that are now ubiquitous among research and clinical users of flow technology, among whom HIV researchers and clinicians clearly predominate. In studies here, we propose to exploit our long-term flow expertise in the interests of introducing much needed flow capabilities to facilitate high normal and high-throughput work HIV trial samples and samples analyzed during vaccine development Basically, we propose to develop innovative methods to automate data handling and processing tasks for flow data. This will include the application of our recently published methods for locating and identifying cells in subsets of interest, so that we can do statistically robust comparisons of subset marker expression in/on these subsets. These methods will also allow us to develop innovative methods for identifying individual or groups of marker changes stimulated by antigen or other stimulation of frozen or freshly collected samples from trial and/or other subjects. Importantly, this software is intended to provide (for the first time) a robust single measure of non-coordinated changes in expression of several intracellular cytokines induced in T cell subsets stimulated in vivo or ex vivo in vaccine development models. Because of its design, we propose to create the software we build for these tasks in an integrated format that is readily amenable to automation to enable high-throughput analyses.

描述（由申请人提供）：我们的实验室构思并开发了最初的流式细胞仪，此后推出了许多创新，提高了该技术的广泛实用性和可及性。 作为这项工作的一部分，我们开发了关键软件和分析功能，这些软件现在在流程技术的研究和临床用户中无处不在，其中艾滋病毒研究人员和临床医生显然占主导地位。 在这里的研究中，我们建议利用我们的长期流动专业知识，以引入急需的流动能力，以促进高正常和高通量工作艾滋病毒试验样品和疫苗开发过程中分析的样品基本上，我们建议开发创新方法自动执行流数据的数据处理和处理任务。 这将包括应用我们最近发布的方法来定位和识别感兴趣的子集中的细胞，以便我们可以对这些子集中/上的子集标记表达进行统计稳健的比较。 这些方法还将使我们能够开发创新方法，用于识别由来自试验和/或其他受试者的冷冻或新鲜收集的样品的抗原或其他刺激所刺激的个体或标记物变化组。 重要的是，该软件旨在（首次）提供一种强大的单一测量方法，测量疫苗开发模型中体内或离体刺激的 T 细胞亚群中诱导的几种细胞内细胞因子表达的非协调变化。 由于其设计，我们建议以集成格式创建我们为这些任务构建的软件，该软件易于自动化以实现高通量分析。