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Accurate determination of γδ T cells in multichannel mass and flow cytometry
Cytometry Part B: Clinical Cytometry ( IF 2.3 ) Pub Date : 2020-06-16 , DOI: 10.1002/cyto.b.21884
Charles Bruce Bagwell 1
Affiliation  

In order to better discuss the excellent and well thought-out points made by Nicola Beucke in his “Accurate determination of γδ T cells in multi-channel mass and flow cytometry” letter to the editor, I have taken the liberty of arbitrarily rendering a Cen-se' map from one of the whole blood-derived files presented in the mass cytometry reproducibility study (Bagwell, Hunsberger et al., 2019, Site 2–1). Cen-se' maps are produced by a stochastic embedding methodology that minimizes the loss of neighborhood information in the mapping process. The Cen-se' map's loss is typically less than 5 to 10% which has been shown to be lower than equivalent t-Distributed Stochastic Neighbor Embedding maps (Bagwell, Bray, et al., 2019). Figure 1a shows a frequency-based heatmap for all the cell types except for neutrophils in the sample. The map leverages all 30 markers in positioning the events and because of its high resolution, it typically separates the major cell types into distinct and nonoverlapping clusters. Since the events have already been classified independently by the probability state modeling system (Bagwell et al., 2015) in the Pathsetter software, labels and percentages are conveniently shown at respective median x and y locations within the map.

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FIGURE 1
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[Color figure can be viewed at wileyonlinelibrary.com]

Panel B shows the same data with a TCRγδ B1 intensity heatmap and panels C and D show the CD4 and CD8 intensity heatmaps. The top 10 marker intensities within this file's TCRγδ-labeled cell type are as follows:

CD3 1,239.18
CD45 878.26
CD127 119.83
TCRγδ 115.52
CXCR3 91.99
CD27 84.03
CD45RO 43.07
CD161 42.48
CD28 31.95
CD16 26.52



中文翻译:

在多通道质谱和流式细胞术中准确测定 γδ T 细胞

为了更好地讨论Nicola Beucke在他给编辑的“Accurate of γδ T cells in multi-channel mass and flow cytometry”信中提出的优秀和深思熟虑的观点,我冒昧地随意渲染了一个岑-se' 图来自质量流式细胞仪重现性研究中提供的全血衍生文件之一(Bagwell,Hunsberger 等人,  2019 年,站点 2-1)。Cen-se' 地图是通过随机嵌入方法生成的,该方法最大限度地减少了映射过程中邻域信息的丢失。Cen-se' map 的损失通常小于 5% 到 10%,这已被证明低于等效的 t-Distributed Stochastic Neighbor Embedding maps (Bagwell, Bray, et al.,  2019)。图 1a 显示了除样品中的中性粒细胞外的所有细胞类型的基于频率的热图。该地图利用所有 30 个标记来定位事件,并且由于其高分辨率,它通常将主要细胞类型分成不同且不重叠的簇。由于事件已经由 Pathsetter 软件中的概率状态建模系统 (Bagwell et al., 2015 ) 独立分类,因此标签和百分比方便地显示在地图中相应的中位数xy位置。

图片
图1
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[彩色图可在 wileyonlinelibrary.com 查看]

面板 B 显示具有 TCRγδ B1 强度热图的相同数据,面板 C 和 D 显示 CD4 和 CD8 强度热图。该文件的 TCRγδ 标记细胞类型中的前 10 个标记强度如下:

CD3 1,239.18
CD45 878.26
CD127 119.83
TCRγδ 115.52
CXCR3 91.99
CD27 84.03
CD45RO 43.07
CD161 42.48
CD28 31.95
CD16 26.52

更新日期:2020-06-16
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