CLASSIFIERS FOR HIGH SPEED, HIGH-RESOLUTION CELL SORTING

用于高速、高分辨率细胞分选的分类器

• 批准号: 6385688
• 负责人: JAMES F. LEARY
• 金额: $ 24.11万
• 依托单位: UNIVERSITY OF TEXAS MED BR GALVESTON
• 依托单位国家: 美国
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-04-01 至 2004-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6385688
• 关键词: bacteria; breast neoplasms; cell line; cell population study; cell sorting; cell type; classification; clinical research; computer program /software; computer system design /evaluation; data collection methodology /evaluation; digital imaging; hematopoietic stem cells; human subject; metastasis; neoplastic cell; polymerase chain reaction; statistics /biometry; stem cells

While cell sorting as a technology has been in existence for over 30 years, only recently has its importance for basic research, clinical and commercial applications been fully appreciated. For basic research, single-cell PCR exploits the superb analytical power of multiparameter flow cytometry (FCM) to select small cell subpopulations from complex mixtures, ushering in a new age of single-cell molecular biology. For clinical research, applications such as stem cell isolation (with perhaps simultaneous tumor purging and gene therapy) are on the horizon. For commercial applications, high-speed sorting of bacterial clones with inserted human gene sequences may provide new capabilities for drug and vaccine design by the pharmaceutical industry. These are but a few examples of a so-called "mature" technology undergoing a re-birth due to other technological advances and important new applications. Yet despite the increased importance of cell sorting, the technology fails to take advantage of technological and basic research advances particularly in the area of statistical classification of cells essential to an intelligent sort decision. In this proposal we address four problem areas that need serious work to make the technology reach its full potential. First, there is a need to better identify the number and location of cell subpopulations. Human pattern recognition can only be useful if complex multidimensional data can be viewed in a more useful fashion using data dimensionality reduction. New data mining techniques we have been developing such as "subtractive clustering" can assist human pattern recognition in complex multidimensional data spaces and help discover the important differences between two or more cell samples for training sets for subsequent statistical classification techniques. (Specific Aim 1). Second, additional FCM parameters should be added only after determining through the use of logistic regression and stepwise discriminant function analysis methods what, if any, discriminating power they add to the classification of cell types of interest and sort decision boundaries should be less arbitrary and more based on statistical decision making (Specific Aim 2). Third, very high-speed cell sorting needs to be done as a multi-step rather than single-step classification decision (Specific Aim 3). Flow cytometry has largely avoided the consequences of such mistakes in classifying cells because it is used as only one, of several sources of information. Cell sorting, as real-time cell classification combining data analysis and decision-making, may soon be used in the clinical arena, e.g. for re-infusing cancer patients with their own autologous transplants. Mistakes in cell classification could lead to serious consequences for patients receiving misclassified (e.g. tumor) cells. Sort decisions need to make intelligent tradeoffs between yield and purity, and they should include costs of misclassification (Specific Aim 4).

虽然细胞分选作为一项技术已经存在了30多年，但直到最近，人们才充分认识到它在基础研究、临床和商业应用方面的重要性。在基础研究方面，单细胞PCR利用多参数流式细胞术（FCM）高超的分析能力，从复杂的混合物中选择小细胞亚群，开创了单细胞分子生物学的新时代。在临床研究方面，干细胞分离（可能同时进行肿瘤清除和基因治疗）等应用即将出现。在商业应用方面，插入人类基因序列的细菌克隆的高速分选可能为制药工业设计药物和疫苗提供新的能力。这些只是所谓的“成熟”技术由于其他技术进步和重要的新应用而经历重生的几个例子。然而，尽管细胞分选的重要性日益增加，但该技术未能利用技术和基础研究的进步，特别是在对智能分选决策至关重要的细胞统计分类领域。在本提案中，我们解决了四个需要认真工作以使该技术充分发挥潜力的问题领域。首先，有必要更好地确定细胞亚群的数量和位置。只有使用数据降维以更有用的方式查看复杂的多维数据时，人类模式识别才能发挥作用。我们一直在开发的新数据挖掘技术，如“减法聚类”，可以帮助人类在复杂的多维数据空间中进行模式识别，并帮助发现两个或多个细胞样本之间的重要差异，用于后续统计分类技术的训练集。（具体目标1）。其次，只有在通过使用逻辑回归和逐步判别函数分析方法确定了它们对感兴趣的细胞类型的分类所增加的判别能力之后，才能添加额外的FCM参数，排序决策边界应该减少任意性，更多地基于统计决策（Specific Aim 2）。第三，非常高速的细胞分选需要作为一个多步骤而不是单步分类决策来完成（Specific Aim 3）。流式细胞术在很大程度上避免了细胞分类错误的后果，因为它只被用作几种信息来源中的一种。细胞分选作为结合数据分析和决策的实时细胞分类，可能很快就会应用于临床领域，例如癌症患者自身的自体移植再输注。细胞分类错误可能导致患者接受错误分类（如肿瘤）细胞的严重后果。排序决策需要在产量和纯度之间做出明智的权衡，它们应该包括错误分类的成本（具体目标4）。