Automating FACS data analysis for HIV studies

HIV 研究的自动化 FACS 数据分析

• 批准号: 7268033
• 负责人: Leonore A. Herzenberg
• 金额: $ 22.14万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2008-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7268033
• 关键词: Address; Algorithms; Area; Automation; Biological Assay; Cell surface; Cells; Clinical Research; Cluster Analysis; Color; Computer software; Data; Data Analyses; Data Collection; Data Set; Development; Development, Other; Disease; End Point; Financial compensation; Flow Cytometry; Fluorescence; Frequencies; HIV; HIV vaccine; Imagery; Individual; Intervention; Laboratories; Learning; Link; Manuals; Measurement; Measures; Memory; Methodology; Methods; Monitor; Numbers; Pathway interactions; Performance; Phosphoproteins; Preparation; Proteins; Protocols documentation; Research; Research Personnel; Sampling; Speed; Staining method; Stains; Standardization; Statistically Significant; System; T-Lymphocyte; T-Lymphocyte Subsets; Technology; Testing; Time; Vaccine Clinical Trial; Vaccines; Work; assay development; cytokine; design; improved; instrument; knowledge base; method development; particle; programs; prototype; response; software development; tool; transcription factor; vaccine development; vaccine effectiveness

DESCRIPTION (provided by applicant): Recent advances in flow cytometry (FACS) technology offer new and exciting approaches to understanding, monitoring and combating HIV disease. Multiparameter FACS (4 color, 2 scatter) instruments that measure four to six fluorescence colors are already routinely used by forefront laboratories doing HIV vaccine development and key HIV research. High-dimensional FACS instruments now coming into this arena extend this capability by providing high-quality measurements for up to a dozen or more fluorescence colors on or in individual cells. In addition, staining methods that detect memory and other T cell surface markers in combination with intracellular cytokines, transcription factors, phosphoproteins and virally-derived proteins now enable measurement of functional responses in ever more finely divided T cell subsets; and, new data analysis methods now enable better visualization of Hi-D FACS data and extraction of findings from that data. Much of this technology is already being applied in laboratories developing new assays to monitor HIV vaccine effectiveness. However, broad utilization of the new (and the older) FACS technology is sharply limited both by the time and effort required to learn how to effectively use the technology and, importantly, by the as yet unsolved problem of how to routinely apply the technology to assaying the large numbers of samples encountered in testing candidate HIV vaccines and other kinds of HIV clinical research. Studies proposed here address these issues, which are particularly crucial to progress in the development of HIV vaccines. We have already developed knowledge-based protocol design tools that speed up and the development and implementation of FACS staining protocols, facilitate FACS data collection, and permanently store protocol information necessary for FACS data analysis. In addition, we have already designed methods/software that can operate without intervention to standardize Hi-D FACS data and correct (compensate) for fluorescence overlaps. Finally, we have already implemented prototypes of new clustering algorithms that are statistically appropriate for analyzing FACS data, i.e., unlike microarray and other clustering algorithms, our algorithms work for data sets that contain relatively few measurements (fluorescence colors, etc.) taken for a very large number of items (cells or other particles). Here, we propose to complete and link these software components to create a working prototype that will support HIV vaccine development by developing software that will 1) automate preparation of FACS data for analysis (data standardization and fluorescence compensation); 2) facilitate FACS data analysis ("clustering" to provide subset identification and gating); 3) automate of statistical comparisons of data from test and reference samples in clinical vaccine trials; and thereby, 4) facilitate and improve the ability to evaluate HIV vaccine responses in research and clinical studies.

描述（由申请人提供）：流式细胞术（FACS）技术的最新进展为理解、监测和防治艾滋病毒疾病提供了令人兴奋的新方法。多参数流式细胞仪（4色，2散射）测量四到六个荧光颜色的仪器已经常规使用的前沿实验室进行艾滋病毒疫苗的开发和关键的艾滋病毒研究。现在进入这一竞技场的高维FACS仪器通过提供单个细胞上或细胞中多达十几种或更多荧光颜色的高质量测量来扩展这种能力。此外，检测记忆和其他T细胞表面标志物与细胞内细胞因子，转录因子，磷蛋白和病毒衍生蛋白质的组合的染色方法现在能够测量更细分的T细胞亚群中的功能反应;并且，新的数据分析方法现在能够更好地可视化Hi-D FACS数据并从该数据中提取发现。这项技术的大部分已经应用于实验室开发新的检测方法，以监测艾滋病毒疫苗的有效性。然而，新的（和旧的）流式细胞仪技术的广泛使用受到学习如何有效地使用该技术所需的时间和精力的限制，重要的是，还没有解决的问题是如何常规地应用该技术来分析在测试候选HIV疫苗和其他类型的HIV临床研究中遇到的大量样品。本文提出的研究旨在解决这些问题，这些问题对艾滋病毒疫苗的开发进展尤为重要。我们已经开发了基于知识的方案设计工具，加快了FACS染色方案的开发和实施，促进了FACS数据收集，并永久存储了FACS数据分析所需的方案信息。此外，我们已经设计了可以在没有干预的情况下操作的方法/软件，以标准化Hi-D FACS数据并校正（补偿）荧光重叠。最后，我们已经实现了新的聚类算法的原型，这些算法在统计上适用于分析FACS数据，即，与微阵列和其他聚类算法不同，我们的算法适用于包含相对较少测量（荧光颜色等）的数据集。用于非常大量的项目（细胞或其他粒子）。在这里，我们建议完成并连接这些软件组件，以创建一个工作原型，该原型将通过开发软件来支持HIV疫苗的开发，该软件将1）自动化制备用于分析的FACS数据（数据标准化和荧光补偿）; 2）促进FACS数据分析3）在临床疫苗试验中自动化来自测试样品和参考样品的数据的统计比较;从而，4）促进和提高在研究和临床研究中评估HIV疫苗应答的能力。