Compensation Automation for HIV Vaccine Development

HIV 疫苗开发的补偿自动化

• 批准号: 8115478
• 负责人: Leonore A. Herzenberg
• 金额: $ 8.88万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-16 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8115478
• 关键词: Address; Anti-Retroviral Agents; Area; Attention; Automation; Biomedical Research; Cells; Clinical Research; Color; Communities; Computer software; Data; Data Analyses; Data Quality; Detection; Development; Dyes; Enzymes; Error Sources; Evaluation; Financial compensation; Flow Cytometry; Fluorescence; Fred Hutchinson Cancer Research Center; Frequencies; Goals; HIV; HIV vaccine; Human; Human Resources; Intervention; Laboratories; Measurement; Measures; Medicine; Methods; Modeling; Monitor; National Institute of Allergy and Infectious Disease; Peripheral Blood Lymphocyte; Phase; Phosphorylation; Play; Population; Positioning Attribute; Preparation; Procedures; Process; Production; Reading; Reagent; Relative (related person); Reliance; Research; Research Personnel; Research Project Grants; Role; Rosa; Running; Sampling; Services; Site; Source; Staining method; Stains; Standardization; T-Cell Activation; T-Lymphocyte Subsets; Technology; Testing; Time; Universities; Vaccine Research; Vaccines; Washington; Work; Workload; base; chemokine; computerized data processing; cost; cytokine; data integration; data modeling; experience; improved; insight; instrument; instrumentation; interest; mathematical theory; method development; operation; public health relevance; quality assurance; research study; response; skills; software development; success; technology development; tool; vaccine development; virtual

DESCRIPTION (provided by applicant): Many basic and clinical studies, particularly in the HIV arena, rely on flow cytometry for collecting and analysizing the study data needed. Recent advances in flow cytometry instrumentation and reagent availability have opened the way to processing larger numbers of samples and to using the technology to address broader questions at the single cell level, e.g., examination of protein phosphorylation, cytokine, chemokine and enzyme production, a measure of T cell activation at the "fine" subset level, in stimulated and non-stimulated peripheral blood lymphocyte samples from HIV-infected and non-infected subjects. However, having invented/developed much of the basic technology incorporated in today's flow cytometry instruments, and also having run a major basic and clinical research laboratory for many years, and also having led a major basic and clinical research laboratory and the main flow cytometry service center at Stanford, we are keenly aware keenly aware that the capabilities of the new FACS instruments (and the older ones) far outstrip the current data handling capabilites of most laboratories employing these instruments. This is a particular problem in HIV research, an area of special interest in our laboratory and one in which we have direct experience with the difficulties involved in processing flow data. Recognizing this problem, we propose here to develop software that will fully automate the standardization and fluorescence compensation of flow cytometry data. These initial computation steps, which prepare raw flow data for analysis, are very time consuming and require substantial skill However, our preliminary studies indicate that they can be executed reliably and consistently by software, and with greater attention to data quality, by software that completes these operations without user intervention. We propose to develop this software and, once developed, to make it rapidly available to the HIV research community and beyond as "freeware" and through cooperating commercial sources. This project will involve development and verification of a new method for evaluating fluorescence compensation based on a comprehensive data model. In addition to full automation this method will have the advantage over the current methods of providing error estimates and other quality assurance information to confirm that the results of the automated analysis are reliable. PUBLIC HEALTH RELEVANCE: The studies we propose focus on development of methods for pre-processing flow cytometry data. To be successful, these tools must perform three basic functions without investigator intervention: 1) they must "standardize" the data so that it can be compared when collected on different days or at different sites; 2) they must "compensate" the data to correct the values obtained in each fluorescence channel for spectral overlap from dyes read in other channels; and, 3) because data quality is central, they must evaluate the raw and processed data and warn the user if data quality has been compromised. Finally, when analyses involve distinguishing between dully stained and unstained cells, the tools should be able to compute virtual FMO distributions that allow data for a stained sample to be used to determine appropriate thresholds for making this distinction. Basically, at the close of the data preparation phase, the data should be ready for analysis and the investigator should either be confident that the processed data is reliable or know why it is not!

描述（由申请人提供）：许多基础和临床研究，特别是在 HIV 领域，依赖流式细胞术来收集和分析所需的研究数据。流式细胞术仪器和试剂可用性的最新进展为处理大量样品和使用该技术解决单细胞水平上更广泛的问题开辟了道路，例如检查蛋白质磷酸化、细胞因子、趋化因子和酶的产生（一种测量方法）在来自 HIV 感染者和未感染者的受刺激和未受刺激的外周血淋巴细胞样本中，T 细胞在“精细”子集水平上的激活。然而，发明/开发了当今流式细胞术仪器中的许多基本技术，并且运营了一个主要的基础和临床研究实验室多年，并且还领导了一个主要的基础和临床研究实验室和主要的流式细胞术服务在斯坦福大学中心，我们敏锐地意识到新的 FACS 仪器（和旧仪器）的功能远远超过了大多数使用这些仪器的实验室当前的数据处理能力。这是艾滋病毒研究中的一个特殊问题，也是我们实验室特别感兴趣的一个领域，我们对处理流数据所涉及的困难有直接的经验。认识到这个问题，我们在此建议开发能够完全自动化流式细胞术数据标准化和荧光补偿的软件。这些准备原始流量数据进行分析的初始计算步骤非常耗时，并且需要大量技能。但是，我们的初步研究表明，它们可以通过软件可靠且一致地执行，并且更加注重数据质量，通过软件完成这些操作无需用户干预。我们建议开发该软件，开发完成后，将其作为“免费软件”并通过合作商业来源快速提供给艾滋病毒研究界及其他领域。该项目将涉及开发和验证一种基于综合数据模型评估荧光补偿的新方法。除了完全自动化之外，该方法还具有优于当前方法的优点，即提供误差估计和其他质量保证信息，以确认自动分析的结果是可靠的。公共卫生相关性：我们提出的研究重点是开发流式细胞术数据预处理方法。为了取得成功，这些工具必须在没有研究者干预的情况下执行三个基本功能：1）它们必须“标准化”数据，以便可以在不同日期或不同地点收集数据时进行比较； 2) 他们必须“补偿”数据，以纠正每个荧光通道中获得的值，以防止其他通道中读取的染料的光谱重叠； 3) 由于数据质量至关重要，因此他们必须评估原始数据和处理后的数据，并在数据质量受到损害时向用户发出警告。最后，当分析涉及区分暗染色和未染色细胞时，这些工具应该能够计算虚拟 FMO 分布，允许使用染色样本的数据来确定进行这种区分的适当阈值。基本上，在数据准备阶段结束时，数据应该准备好进行分析，调查人员应该相信处理后的数据是可靠的，或者知道为什么不可靠！

项目成果

Automatic clustering of flow cytometry data with density-based merging.

• DOI: 10.1155/2009/686759
• 发表时间: 2009
• 期刊: Advances in bioinformatics
• 作者: Walther G;Zimmerman N;Moore W;Parks D;Meehan S;Belitskaya I;Pan J;Herzenberg L
• 通讯作者: Herzenberg L