Compensation Automation for HIV Vaccine Development

描述（由申请人提供）：许多基础和临床研究，特别是在HIV竞技场中，依赖流式细胞术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更大数量的样品和使用该技术在单细胞水平上解决更广泛的问题开辟了道路，在来自HIV感染和非感染受试者的刺激和非刺激外周血淋巴细胞样品中，检查蛋白磷酸化、细胞因子、趋化因子和酶的产生，这是在“精细”亚群水平上T细胞活化的量度。然而，由于发明/开发了当今流式细胞仪中所包含的大部分基本技术，并且多年来一直在运行一个主要的基础和临床研究实验室，并且还领导了一个主要的基础和临床研究实验室以及斯坦福大学的主要流式细胞仪服务中心，我们敏锐地意识到，新的流式细胞仪（和旧的）的能力远远超过了使用这些仪器的大多数实验室的当前数据处理能力。这是HIV研究中的一个特殊问题，我们实验室特别感兴趣的一个领域，我们在处理流数据时遇到了困难。认识到这个问题，我们在这里提出开发软件，将完全自动化的标准化和荧光补偿的流式细胞术数据。这些初始计算步骤，准备原始流量数据进行分析，是非常耗时的，需要大量的技能，然而，我们的初步研究表明，他们可以可靠地执行，并通过软件一致，并更加注重数据质量，软件完成这些操作，而无需用户干预。我们提议开发这一软件，一旦开发出来，将作为“免费软件”并通过合作的商业来源迅速提供给艾滋病毒研究界和其他机构。该项目将涉及开发和验证一种基于综合数据模型的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，以确认自动化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，以便在不同日期或在不同地点收集时可以进行比较; 2）它们必须“补偿”数据，以校正每个荧光通道中获得的值，以用于来自其他通道中读取的染料的光谱重叠;以及3）由于数据质量是核心，所以他们必须评估原始数据和处理后的数据，并且如果数据质量受到损害，则警告用户。最后，当分析涉及区分染色暗淡和未染色的细胞时，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，研究者应该确信处理后的数据是可靠的，或者知道为什么不可靠！

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!