γδ T cells are often neglected in T cell immunomonitoring studies, but their unique properties, bridging innate and adaptive immunity, are attracting growing interest in different research fields like cancer, auto-immunity, and inflammation. Recently, Bagwell et al. evaluated the reproducibility of deep immunophenotyping by mass cytometry in combination with an automated data analysis system in a multi-site study (Bagwell et al., 2019). The 30-marker panel used in that study allowed the identification of 37 immune populations including several subsets of T cells, B cells, NK cells, monocytes, and dendritic cells. Because there remain a number of unoccupied (heavy metal) channels, even more markers can be added to study additional subpopulations. This last stage in the process of validation for the development of a commercially available kit shows that this system consisting of a dry antibody cocktail and automated data analysis achieves a high reproducibility across research centers, an important step toward the urgently needed standardization of mass and flow cytometry. Careful selection of antibodies and an accurate validation of the proposed subsequent automated data analysis tool is essential—in particular when the aim is to commercialize such an approach. In the context of the proposal by Bagwell et al., we believe that optimization of the proposed antibody panel by substituting the pan-γδ T cell receptor (TCR) antibody deriving from the clone “B1” with a different clone, for example, “11F2,” is essential. “B1-antibodies” may not be able to identify the entirety of the γδ T cell population when used in combination with a CD3 antibody, probably due to close proximity of the recognized epitopes causing steric hindrance (BD datasheet “clone B1”, Wistuba-Hamprecht, Pawelec, & Derhovanessian, 2014; Beucke et al., 2020). Another advantage of “11F2-antibodies” is that they are known to work well in combination with additional TCR γδ antibodies such as those specific for Vδ1 (clone REA173) orVδ2 (clone 123R3). Furthermore, the data analysis approach might be improved by examining the known surface marker expression patterns of γδ T cells: usually, the majority of γδ T cells does not express CD4 or CD8, but between 5 and 30% of γδ T cells do express CD8 and a small percentage expresses CD4 (Andreu-Ballester et al., 2012; Garcillan et al., 2015; Hayday, 2000; Wistuba-Hamprecht, Haehnel, Janssen, Demuth, & Pawelec, 2015; Ziegler et al., 2014). The model of Bagwell and colleagues classifies γδ T cells as CD8-negativeor -dim and CD4-negative, so depending on the individual a considerable proportion of γδ T cells might be missed. We feel that the readers and potential users of the proposed antibody panel and analysis approach of Bagwell et al. should be aware of these potential limitations.

γδ T 细胞在 T 细胞免疫监测研究中经常被忽视，但其独特的特性，桥接先天免疫和适应性免疫，在癌症、自身免疫和炎症等不同研究领域引起了越来越多的兴趣。最近，Bagwell 等人。在一项多站点研究中，结合自动数据分析系统评估了通过质谱流式细胞仪进行深度免疫表型分析的可重复性（Bagwell 等人，2019）。该研究中使用的 30 个标记组允许识别 37 个免疫群体，包括 T 细胞、B 细胞、NK 细胞、单核细胞和树突细胞的几个亚群。由于仍有许多未占用的（重金属）通道，因此可以添加更多标记来研究其他亚群。商业化试剂盒开发验证过程的最后阶段表明，该系统由干抗体混合物和自动数据分析组成，在研究中心实现了高重现性，这是朝着迫切需要的质量和流量标准化迈出的重要一步细胞术。仔细选择抗体和对提议的后续自动数据分析工具进行准确验证是必不可少的——尤其是当目标是将这种方法商业化时。在 Bagwell 等人的提议的背景下，我们认为通过用不同的克隆替换源自克隆“B1”的泛γδ T 细胞受体 (TCR) 抗体来优化提议的抗体组，例如，“ 11F2，”是必不可少的。当与 CD3 抗体结合使用时，“B1 抗体”可能无法识别整个 γδ T 细胞群，这可能是由于已识别的表位非常接近导致空间位阻（BD 数据表“克隆 B1”，Wistuba- Hamprecht、Pawelec 和 Derhovanessian，2014 年；Beucke 等人，2020 年）。“11F2 抗体”的另一个优点是已知它们与其他 TCR γδ 抗体结合使用效果很好，例如对 Vδ1（克隆 REA173）或 Vδ2（克隆 123R3）特异的抗体。此外，可以通过检查已知的 γδ T 细胞表面标记表达模式来改进数据分析方法：通常，大多数 γδ T 细胞不表达 CD4 或 CD8，但 5% 到 30% 的 γδ T 细胞确实表达 CD8和一小部分表达 CD4（Andreu-Ballester 等人，2012；Garcillan 等人，2015；Hayday，2000；Wistuba-Hamprecht，Haehnel，Janssen，Demuth，& Pawelec，2015；Ziegler 等人，2014）。Bagwell 及其同事的模型将 γδ T 细胞分类为 CD8 阴性或 -dim 和 CD4 阴性，因此可能会遗漏相当大比例的 γδ T 细胞，具体取决于个体。我们认为 Bagwell 等人提出的抗体面板和分析方法的读者和潜在用户。应该意识到这些潜在的限制。