Immune profiling of multi-parameter flow cytometry using computational statistics

使用计算统计进行多参数流式细胞术的免疫分析

• 批准号: 7812893
• 负责人: Cliburn C Chan
• 金额: $ 49.92万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-24 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7812893
• 关键词: Acquired Immunodeficiency Syndrome; Acute; Address; Algorithms; Antigens; Area; Arts; Bayesian Method; Biological Assay; Biological Markers; Blood specimen; Cancer Vaccines; Cell surface; Cells; Classification; Clinical; Clinical Research; Clinical Trials; Collaborations; Color; Complex; Computer Simulation; Computer software; Data; Data Set; Development; Diagnostic; Doctor of Philosophy; Escape Mutant; Evaluation; Flow Cytometry; Frequencies; Generations; HIV; HIV Infections; HIV vaccine; Human; Immune; Immune response; Immunization; Immunologic Monitoring; Immunologist; Immunology; Infection; Infection Control; Institution; Laboratories; Libraries; Link; Mathematics; Measurement; Measures; Melanoma Vaccine; Memorial Sloan-Kettering Cancer Center; Methodology; Methods; Metric; Modeling; Monitor; Operative Surgical Procedures; Outcome; Outcome Study; Phenotype; Population; Research; Research Personnel; Sampling; Screening procedure; Software Design; Specificity; Staging; Staining method; Stains; Statistical Methods; Statistical Models; Systems Biology; T-Lymphocyte; Techniques; Technology; Therapeutic; Training; Vaccination; Vaccines; Validation; Viral; Work; base; biological systems; cohort; cytokine; design; melanoma; novel; peripheral blood; post-doctoral training; prognostic; programs; simulation; software development; statistics; success; technological innovation; theories; tool; vaccine development; vaccine efficacy; virology; working group

DESCRIPTION (provided by applicant): This application addresses broad Challenge Area (04) Clinical Research and specific Challenge Topic 04-AI- 102: The human immune response to infection and immunization - Profiling via modern immunological methods and systems biology. The ability to monitor complex immune responses quantitatively is essential for the development of effective vaccines and the discovery of diagnostic or prognostic biomarkers for clinical trials. Multi-parameter flow cytometry (FCM) can measure multiple immune parameters (cell phenotype, activation or maturation status, intracellular cytokine or other effector molecule concentrations) with a single peripheral blood (PB) sample, and provides a detailed snapshot of the immune response that is ideal for profiling. The purpose of this project is to develop and validate objective statistical methods to profile FCM data, and apply these methods to discover FCM-based immune correlates of efficacy in well characterized HIV and advanced melanoma cohorts. Recent advances in polychromatic FCM technology allow the simultaneous measurement of up to 20 fluorescent markers at the single cell level, and these state-of-the-art assays show tremendous promise for profiling the immune responses to infection and vaccination. However, software for analysis of FCM data has not kept pace and still relies on serial 2D gating methods that are sub-optimal for analysis of multi-dimensional data sets. As a result, FCM results can be highly variable across different institutions. Systems biological approaches that handle multi-dimensional data directly are needed to design software that can keep up with the rapid pace of FCM technological innovations. We propose to develop computational statistical models to characterize immune response profiles using multi- parameter FCM, and to implement efficient software for automated FCM analysis and discovery of predictive immune signatures. Our specific aims are to 1) develop multivariate computational statistical methods to characterize FCM data consistently across multiple samples; 2) validate automated cell subset identification on a broad set of FCM samples; and 3) identify immune signatures based on statistical models of FCM data that predict infection or vaccination outcome. The research will substantially extend the utility of FCM analysis with effective, automated statistical methods and tools for identifying heterogeneous cell subsets and immune signatures from FCM data. This will benefit anyone using multi-parameter FCM, with particular impact on vaccine development, clinical diagnostics and immune therapeutics, given their common need for objective FCM software for immune profiling. Hence, our proposal directly addresses the objectives of Challenge Topic 04-AI-102, and represents methodological advances with major potential impact on the rational design and development of safe and effective vaccines. The proposed application seeks to develop automated cell subset identification and predictive immune profiling of infection and vaccination outcomes from multi-parameter flow cytometry data. Success in this project will result in statistical methodology and software that will produce more accurate, reproducible flow cytometry analysis, as well as identify immune correlates of infection control and vaccine efficacy.

描述（由申请人提供）：此申请涉及广泛的挑战领域（04）临床研究和特定挑战主题04-AI- 102：人类对感染和免疫的免疫反应 - 通过现代免疫方法和系统生物学进行分析。定量监测复杂免疫反应的能力对于开发有效疫苗以及发现临床试验的诊断或预后生物标志物。多参数流式细胞仪（FCM）可以用单个外周血（PB）样​​品测量多个免疫参数（细胞表型，激活或成熟状态，细胞内细胞因子或其他效应分子浓度），并提供了免疫反应的详细快照。是分析的理想选择。该项目的目的是开发和验证客观的统计方法来介绍FCM数据，并将这些方法应用于良好表征的HIV和晚期黑色素瘤同龄人的疗效的基于FCM的免疫相关性。多色FCM技术的最新进展允许在单细胞水平上同时测量多达20个荧光标记物，这些最新的测定法显示了对感染和疫苗接种的免疫反应的巨大希望。但是，用于分析FCM数据的软件尚未保持步伐，并且仍然依赖于串行2D门控方法，这些方法是为了分析多维数据集的次级优势。结果，FCM结果在不同机构之间可能是高度可变的。直接处理多维数据的系统生物学方法是设计可以跟上FCM技术创新速度快速发展的软件。我们建议开发计算统计模型，以使用多参数FCM表征免疫反应概况，并实施有效的软件进行自动FCM分析和发现预测性免疫特征。我们的具体目的是1）开发多元计算统计方法，以跨多个样本始终如一地表征FCM数据； 2）在一组FCM样品集中验证自动细胞子集识别； 3）基于预测感染或疫苗接种结果的FCM数据的统计模型确定免疫特征。这项研究将通过有效，自动化的统计方法和工具实质上扩展FCM分析的实用性，以识别FCM数据中的异质细胞子集和免疫特征。这将使使用多参数FCM的任何人都受益，这对疫苗开发，临床诊断和免疫治疗剂特别影响，鉴于其对免疫分析的客观FCM软件的普遍需求。因此，我们的建议直接解决了挑战主题2012年4月4日的目标，并代表了方法论进步，对安全有效疫苗的理性设计和开发产生了重大潜在影响。提出的应用程序旨在开发自动细胞子集鉴定和对多参数流式细胞仪数据的感染和疫苗接种结果的预测性免疫分析。该项目的成功将导致统计方法和软件，这些方法和软件将产生更准确，可再现的流式细胞仪分析，并确定感染控制和疫苗功效的免疫相关性。