Tung James W, Parks David R, Moore Wayne A, Herzenberg Leonard A, Herzenberg Leonore A
Department of Genetics, Stanford University Medical School, Stanford, CA 94305-5120, USA.
Clin Immunol. 2004 Mar;110(3):277-83. doi: 10.1016/j.clim.2003.11.016.
The Fluorescence Activated Cell Sorter (FACS) is an invaluable tool for clinicians and researchers alike in phenotyping and sorting individual cells. With the advances in FACS methodology, notably intracellular staining for cytokines, transcription factors and phosphoproteins, and with increases in the number of fluorescence detection channels, researchers now have the opportunity to study individual cells in far greater detail than previously possible. In this chapter, we discuss High-Definition (Hi-D) FACS methods that can improve analysis of lymphocyte subsets in mouse and man. We focus on the reasons why fluorescence compensation, which is necessary to correct for spectral overlap between two or more fluorochromes used in the same staining combination, is best done as a computed transformation rather than using the analog circuitry available on many flow cytometers. In addition, we introduce a new data visualization method that scales the axes on histograms and two-dimensional contour (or dot) plots to enable visualization of signals from all cells, including those that have minimal fluorescence values and are not properly represented with traditional logarithmic axes. This "Logicle" visualization method, we show, provides superior representations of compensated data and makes correctly compensated data look correct. Finally, we discuss controls that facilitate recognition of boundaries between positive and negative subsets.
荧光激活细胞分选仪(FACS)对于临床医生和研究人员在对单个细胞进行表型分析和分选方面都是一项非常有价值的工具。随着FACS方法的进步,特别是细胞因子、转录因子和磷酸化蛋白的细胞内染色技术的发展,以及荧光检测通道数量的增加,研究人员现在有机会比以往更详细地研究单个细胞。在本章中,我们将讨论能够改进对小鼠和人类淋巴细胞亚群分析的高清(Hi-D)FACS方法。我们重点探讨了荧光补偿的原因,荧光补偿是校正同一染色组合中使用的两种或更多种荧光染料之间光谱重叠所必需的,最好将其作为一种计算变换来完成,而不是使用许多流式细胞仪上可用的模拟电路。此外,我们介绍了一种新的数据可视化方法,该方法可对直方图和二维等高线(或散点)图的坐标轴进行缩放,以便能够可视化来自所有细胞的信号,包括那些荧光值极低且用传统对数坐标轴无法正确表示的细胞。我们展示了这种“Logicle”可视化方法能够提供更好的补偿数据表示,并使正确补偿的数据看起来正确。最后,我们讨论了有助于识别阳性和阴性亚群之间界限的对照。