The generation of the B cell response upon vaccination is characterized by the induction of different functional and phenotypic subpopulations and is strongly dependent on the vaccine formulation, including the adjuvant used. Here, we have profiled the different B cell subsets elicited upon vaccination, using machine learning methods for interpreting high-dimensional flow cytometry data sets. The B cell response elicited by an adjuvanted vaccine formulation, compared to the antigen alone, was characterized using two automated methods based on clustering (FlowSOM) and dimensional reduction (t-SNE) approaches. The clustering method identified, based on multiple marker expression, different B cell populations, including plasmablasts, plasma cells, germinal center B cells and their subsets, while this profiling was more difficult with t-SNE analysis. When undefined phenotypes were detected, their characterization could be improved by integrating the t-SNE spatial visualization of cells with the FlowSOM clusters. The frequency of some cellular subsets, in particular plasma cells, was significantly higher in lymph nodes of mice primed with the adjuvanted formulation compared to antigen alone. Thanks to this automatic data analysis it was possible to identify, in an unbiased way, different B cell populations and also intermediate stages of cell differentiation elicited by immunization, thus providing a signature of B cell recall response that can be hardly obtained with the classical bidimensional gating analysis. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

中文翻译：

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多参数流式细胞术数据的计算分析以剖析疫苗研究中的 B 细胞亚群。

接种疫苗后 B 细胞反应的产生的特点是诱导不同的功能和表型亚群，并且强烈依赖于疫苗配方，包括所用的佐剂。在这里，我们使用机器学习方法来解释高维流式细胞术数据集，分析了接种疫苗时引起的不同 B 细胞亚群。与单独的抗原相比，由佐剂疫苗制剂引起的 B 细胞反应使用基于聚类 (FlowSOM) 和降维 (t-SNE) 方法的两种自动化方法进行表征。聚类方法基于多个标记物表达确定了不同的 B 细胞群，包括浆母细胞、浆细胞、生发中心 B 细胞及其亚群，而这种分析在 t-SNE 分析中更加困难。当检测到未定义的表型时，可以通过将细胞的 t-SNE 空间可视化与 FlowSOM 簇集成来改进它们的表征。与单独的抗原相比，用佐剂制剂引发的小鼠淋巴结中一些细胞亚群，特别是浆细胞的频率显着更高。由于这种自动数据分析，有可能以无偏见的方式识别不同的 B 细胞群以及免疫引起的细胞分化的中间阶段，从而提供经典二维难以获得的 B 细胞回忆反应的特征。门控分析。© 2019 作者。Cytometry Part A 由 Wiley Periodicals, Inc. 代表 International Society for Advancement of Cytometry 出版。通过将细胞的 t-SNE 空间可视化与 FlowSOM 集群相结合，可以改进它们的表征。与单独的抗原相比，用佐剂制剂引发的小鼠淋巴结中一些细胞亚群，特别是浆细胞的频率显着更高。由于这种自动数据分析，有可能以无偏见的方式识别不同的 B 细胞群以及免疫引起的细胞分化的中间阶段，从而提供经典二维难以获得的 B 细胞回忆反应的特征。门控分析。© 2019 作者。Cytometry Part A 由 Wiley Periodicals, Inc. 代表 International Society for Advancement of Cytometry 出版。通过将细胞的 t-SNE 空间可视化与 FlowSOM 集群相结合，可以改进它们的表征。与单独的抗原相比，用佐剂制剂引发的小鼠淋巴结中一些细胞亚群，特别是浆细胞的频率显着更高。由于这种自动数据分析，有可能以无偏见的方式识别不同的 B 细胞群以及免疫引起的细胞分化的中间阶段，从而提供经典二维难以获得的 B 细胞回忆反应的特征。门控分析。© 2019 作者。Cytometry Part A 由 Wiley Periodicals, Inc. 代表 International Society for Advancement of Cytometry 出版。