Transformative Computational Infrastructures for Cell-Based Biomarker Diagnostics

项目摘要 患者样品中异常细胞群的存在可诊断多种人类疾病， 尤其是白血病和淋巴瘤。用于基于细胞的诊断评估的主要技术之一 流式细胞术，它使用荧光试剂来测量细胞群的分子特征 复杂的混合物。虽然细胞仪评价常规用于诊断血液传播性 恶性肿瘤，它可以更广泛地应用于其他疾病的诊断（如哮喘，过敏和 自身免疫性），如果它可以可重复地用于解释更高复杂性的染色板并识别 更微妙的细胞群体差异。流式细胞术分析也广泛用于单细胞表型分析 在转化研究中探索正常和异常生物过程的机制。 最近，质谱细胞术的发展有望进一步增加单细胞分析的应用。 细胞计数评估，以了解广泛的生理，病理和治疗过程。 细胞术数据分析的当前实践依赖于二维数据图的“手动门控”，以 识别复杂混合物中的细胞亚群。然而，这个过程是主观的、劳动密集型的，而且 不可再现，使得难以在多中心转化研究或临床试验中部署， 协议标准化和协调是必不可少的。该项目的目标是开发、验证和 传播一个用户友好的基础设施，用于细胞计数数据的计算分析， 和发现应用程序，可以帮助克服当前手动分析的局限性，并提供 更有效、客观和准确的分析，通过以下目标：具体目标1 -实现一个新的 计算基础设施- FlowGate -用于细胞计数数据分析，包括可视化分析和 机器学习;具体目标2 -评估FlowGate在细胞群体表征中的效用， 机械转化研究（T1）;具体目标3 -评估 用于临床诊断的FlowGate与当前诊断标准治疗分析的比较 具体目标4 -开发培训和教育资源， 推广活动，以鼓励采用和使用由此产生的FlowGate网络基础设施。 该项目将通过克服采用的关键障碍，对推进转化科学产生重大影响 这些计算方法，通过促进分析管道优化，提供直观的用户 接口，并提供有针对性的培训活动。开发的计算 用于改进AML和CLL诊断的基础设施将有助于对精确度的新强调 通过更精确地量化肿瘤和正常反应性肿瘤的患者特异性特征， 细胞群虽然FlowGate将由加州大学圣地亚哥分校、加州大学欧文分校和斯坦福大学CTSA开发， 由此产生的计算基础设施将免费提供给整个研究界。

Project Summary The presence of abnormal cell populations in patient samples is diagnostic for a variety of human diseases, especially leukemias and lymphomas. One of the main technologies used for cell-based diagnostic evaluation is flow cytometry, which employs fluorescent reagents to measure molecular characteristics of cell populations in complex mixtures. While cytometry evaluation is routinely used for the diagnosis of blood-borne malignancies, it could be more widely applied to the diagnosis of other diseases (e.g. asthma, allergy and autoimmunity) if it could be reproducibly used to interpret higher complexity staining panels and recognize more subtle cell population differences. Flow cytometry analysis is also widely used for single cell phenotyping in translational research studies to explore the mechanisms of normal and abnormal biological processes. More recently, the development of mass cytometry promises to further increase the application of single cell cytometry evaluation to understand a wide range of physiological, pathological and therapeutic processes. The current practice for cytometry data analysis relies on “manual gating” of two-dimensional data plots to identify cell subsets in complex mixtures. However, this process is subjective, labor intensive, and irreproducible making it difficult to deploy in multicenter translational research studies or clinical trials where protocol standardization and harmonization are essential. The goal of this project is to develop, validate and disseminate a user-friendly infrastructure for the computational analysis of cytometry data for both diagnostic and discovery applications that could help overcome the current limitations of manual analysis and provide for more efficient, objective and accurate analysis, through the following aims: Specific Aim 1 – Implement a novel computational infrastructure – FlowGate – for cytometry data analysis that includes visual analytics and machine learning; Specific Aim 2 – Assess the utility of FlowGate for cell population characterization in mechanistic translational research studies (T1); Specific Aim 3 – Assess the robustness and accuracy of FlowGate for clinical diagnostics in comparison with the current standard-of-care analysis of diagnostic cytometry data (T2); Specific Aim 4 – Develop training and educational resources and conduct directed outreach activities to stimulate adoption and use of the resulting FlowGate cyberinfrastructure. The project will have a major impact in advancing translational science by overcoming key hurdles for adoption of these computational methods by facilitating analysis pipeline optimization, providing intuitive user interfacing, and delivering directed training activities. The application of the developed computational infrastructure for improved diagnostics of AML and CLL will contribute to the new emphasis on precision medicine by more precisely quantifying the patient-specific characteristics of neoplastic and normal reactive cell populations. Although FlowGate will be developed by the UC San Diego, UC Irvine, and Stanford CTSAs, the resulting computational infrastructure will be made freely available to the entire research community.