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: 6519269
• 负责人: JAMES F. LEARY
• 金额: $ 25.96万
• 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/6519269
• 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种技术已经存在了30多年，但直到最近它在基础研究、临床和商业应用中的重要性才得到充分认识。对于基础研究，单细胞PCR利用多参数流式细胞术(FCM)的卓越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纪元。对于临床研究，干细胞分离(可能同时进行肿瘤清除和基因治疗)等应用即将到来。对于商业应用，对插入人类基因序列的细菌克隆进行高速分选可能会为制药行业的药物和疫苗设计提供新的能力。这些只是一些由于其他技术进步和重要的新应用而重生的所谓“成熟”技术的例子。然而，尽管细胞分选的重要性增加了，但这项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定至关重要的细胞统计分类领域。在这项提案中，我们解决了四个需要认真工作的问题领域，以使技术充分发挥其潜力。首先，需要更好地确定细胞亚群的数量和位置。只有当复杂的多维数据可以使用数据降维以更有用的方式查看时，人类模式识别才是有用的。我们一直在开发的新的数据挖掘技术，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，并帮助发现两个或更多个细胞样本之间的重要差异，为后续的统计分类技术提供训练集。(具体目标1)。其次，只有在通过使用Logistic回归和逐步判别函数分析方法确定它们对感兴趣细胞类型的分类和分类决策的区分能力(如果有的话)之后，才应增加额外的FCM参数，而不应那么武断，更多地基于统计决策(具体目标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(具体目标3)。流式细胞术在很大程度上避免了细胞分类错误的后果，因为它只被用作几个信息源中的一个。细胞分类作为一种结合了数据分析和决策的实时细胞分类，可能很快就会在临床上使用，例如为癌症患者重新注入他们自己的自体移植。细胞分类的错误可能会导致患者接受错误分类的细胞(例如肿瘤)的严重后果。分类决策需要在产量和纯度之间做出明智的权衡，并且它们应该包括错误分类的成本(具体目标4)。