Early Transcriptome Signatures from Immunized Mouse Dendritic Cells Predict Late Vaccine-Induced T-Cell Responses

Systems biology offers promising approaches for identifying response-specific signatures to vaccination and assessing their predictive value. Here, we designed a modelling strategy aiming to predict the quality of late T-cell responses after vaccination from early transcriptome analysis of dendritic cells. Using standardized staining with tetramer, we first quantified antigen-specific T-cell expansion 5 to 10 days after vaccination with one of a set of 41 different vaccine vectors all expressing the same antigen. Hierarchical clustering of the responses defined sets of high and low T cell response inducers. We then compared these responses with the transcriptome of splenic dendritic cells obtained 6 hours after vaccination with the same vectors and produced a random forest model capable of predicting the quality of the later antigen-specific T-cell expansion. The model also successfully predicted vector classification as low or strong T-cell response inducers of a novel set of vaccine vectors, based on the early transcriptome results obtained from spleen dendritic cells, whole spleen and even peripheral blood mononuclear cells. Finally, our model developed with mouse datasets also accurately predicted vaccine efficacy from literature-mined human datasets.

系统生物学为鉴定疫苗接种的应答特异性特征、评估其预测价值提供了颇具前景的研究路径。本研究设计了一套建模策略，旨在通过对树突状细胞（dendritic cells）的早期转录组分析，预测疫苗接种后晚期T细胞应答的质量。研究人员首先采用四聚体（tetramer）标准化染色方法，对接种41种表达同一抗原的不同疫苗载体后5至10天的抗原特异性T细胞扩增水平进行定量。通过对各类应答结果进行层级聚类，划分出高T细胞应答诱导组与低T细胞应答诱导组。随后，研究人员将上述应答特征与接种后6小时获取的脾脏树突状细胞转录组数据进行比对，构建出可精准预测后续抗原特异性T细胞扩增质量的随机森林模型。该模型还可基于从脾脏树突状细胞、全脾脏乃至外周血单个核细胞（peripheral blood mononuclear cells）获取的早期转录组结果，成功对新型疫苗载体的T细胞应答诱导能力进行强弱分类。最后，本研究基于小鼠数据集开发的模型，同样可通过文献挖掘获得的人类数据集准确预测疫苗的效力。