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高维单细胞癌症生物学

High-dimensional single-cell cancer biology.

作者信息

Irish Jonathan M, Doxie Deon B

机构信息

Vanderbilt University, Nashville, TN, USA,

出版信息

Curr Top Microbiol Immunol. 2014;377:1-21. doi: 10.1007/82_2014_367.

Abstract

Cancer cells are distinguished from each other and from healthy cells by features that drive clonal evolution and therapy resistance. New advances in high-dimensional flow cytometry make it possible to systematically measure mechanisms of tumor initiation, progression, and therapy resistance on millions of cells from human tumors. Here we describe flow cytometry techniques that enable a "single-cell " view of cancer. High-dimensional techniques like mass cytometry enable multiplexed single-cell analysis of cell identity, clinical biomarkers, signaling network phospho-proteins, transcription factors, and functional readouts of proliferation, cell cycle status, and apoptosis. This capability pairs well with a signaling profiles approach that dissects mechanism by systematically perturbing and measuring many nodes in a signaling network. Single-cell approaches enable study of cellular heterogeneity of primary tissues and turn cell subsets into experimental controls or opportunities for new discovery. Rare populations of stem cells or therapy-resistant cancer cells can be identified and compared to other types of cells within the same sample. In the long term, these techniques will enable tracking of minimal residual disease (MRD) and disease progression. By better understanding biological systems that control development and cell-cell interactions in healthy and diseased contexts, we can learn to program cells to become therapeutic agents or target malignant signaling events to specifically kill cancer cells. Single-cell approaches that provide deep insight into cell signaling and fate decisions will be critical to optimizing the next generation of cancer treatments combining targeted approaches and immunotherapy.

摘要

癌细胞彼此之间以及与健康细胞的区别在于驱动克隆进化和治疗抗性的特征。高维流式细胞术的新进展使得能够系统地测量来自人类肿瘤的数百万个细胞的肿瘤起始、进展和治疗抗性机制。在这里,我们描述了能够实现癌症“单细胞”视图的流式细胞术技术。像质谱流式细胞术这样的高维技术能够对细胞身份、临床生物标志物、信号网络磷酸化蛋白、转录因子以及增殖、细胞周期状态和凋亡的功能读数进行多重单细胞分析。这种能力与一种信号谱方法非常匹配,该方法通过系统地扰动和测量信号网络中的许多节点来剖析机制。单细胞方法能够研究原发性组织的细胞异质性,并将细胞亚群转化为实验对照或新发现的机会。可以识别干细胞或治疗抗性癌细胞的稀有群体,并将其与同一样本中的其他类型细胞进行比较。从长远来看,这些技术将能够追踪微小残留病(MRD)和疾病进展。通过更好地理解在健康和患病情况下控制发育和细胞间相互作用的生物系统,我们可以学会将细胞编程为治疗剂或靶向恶性信号事件以特异性杀死癌细胞。能够深入洞察细胞信号传导和命运决定的单细胞方法对于优化结合靶向方法和免疫疗法的下一代癌症治疗至关重要。

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