CITE-seq protein-mRNA single cell data from high and low vaccine responders (to reproduce Figs 4-6 and associated Extended Data Figs)

CITE-seq single cell data of baseline PBMC samples from 20 healthy individuals (10 high and 10 low responders) vaccinated with influenza pandemic H1N1 and seasonal vaccines in 2009. <br>The data set contains five RDS files with <i>Seurat 2.3.4 </i>objects used as the input data at different steps of the workflow: <br>(1-2) Input data for the step 1 (pre-processed demultiplexed data):H1_day0_demultilexed_singlets.RDSneg_control_object.rds<br>(3) Output of step 1 (data normalization):H1_day0_scranNorm_adtbatchNorm.rds<br>(4) Output of step 2 (clustering):H1_day0_scranNorm_adtbatchNorm_dist_clustered.rds<br>(5) Output of step 5 (TSNE):H1_day0_scranNorm_adtbatchNorm_dist_clustered_TSNE.rds<br>(6) Output of step 6 (clusters re-labeling):H1_day0_scranNorm_adtbatchNorm_dist_clustered_TSNE_labels.rds<br>The last object contains single cell data set clustered at multiple resolutions with PCA and tSNE results. Clusters annotated as doublets were removed, and the remaining clusters were labeled to reflect the cluster resolution and relationship between clusters at different resolutions (e.g., total B cells and switched B cells are linked since cells in the latter are largely contained within the former) This object can be used to reproduce all CITE-seq figures (Figs. 4-6, Extended Figs. 8-10) and tables in the paper.<br><br>Two additional text files required for the workflow are provided: <br>clustree_node_labels_withCellTypeLabels.txt - annotation of the clustersB_CD40act_genes_JI2014&amp;Blood2004.txt - list of genes in the CD40act signature.<br>To test the workflow download the files into the citeseq/data directory. The R scripts are available on https://github.com/kotliary/baseline.<br><br>This item is a part of the collection: https://doi.org/10.35092/yhjc.c.4753772<br><b>If you use our data (including CITE-seq data) or code for your work please cite the following publication</b>:Kotliarov, Y., Sparks, R. et al. Broad immune activation underlies shared set point signatures for vaccine responsiveness in healthy individuals and disease activity in patients with lupus. Nat. Med. DOI: https://doi.org/10.1038/s41591-020-0769-8 (2020)<br><b>Abstract</b>Responses to vaccination and to diseases vary widely across individuals, which may be partly due to baseline immune variations. Identifying such baseline predictors and their biological basis are of broad interest given their potential importance for cancer immunotherapy, disease outcomes, vaccination and infection responses. Here we uncover baseline blood transcriptional signatures predictive of antibody responses to both influenza and yellow fever vaccinations in healthy subjects. These same signatures evaluated at clinical quiescence are correlated with disease activity in systemic lupus erythematosus patients with plasmablast-associated flares. CITE-seq profiling of 82 surface proteins and transcriptomes of 53,201 single cells from healthy high and low influenza-vaccination responders revealed that our signatures reflect the extent of activation in a plasmacytoid dendritic cell—Type I IFN—T/B lymphocyte network. Our findings raise the prospect that modulating such immune baseline states may improve vaccine responsiveness and mitigate undesirable autoimmune disease activities.<br><br><b>General contact</b>: John Tsang ([e-mail])<b>Questions about software/code</b>: Yuri Kotliarov ([e-mail])<br>

2009年接种甲型H1N1流感大流行疫苗与季节性流感疫苗的20名健康个体（10名高应答者、10名低应答者）的基线外周血单个核细胞（Peripheral Blood Mononuclear Cell, PBMC）样本的CITE-seq单细胞数据。
本数据集包含5个RDS格式文件，内含工作流不同步骤所使用的<i>Seurat 2.3.4</i>对象作为输入/输出数据：
(1-2) 步骤1的输入数据（已完成预处理与解多路复用的数据）：H1_day0_demultilexed_singlets.RDS与neg_control_object.rds；
(3) 步骤1的输出结果（数据标准化后的数据）：H1_day0_scranNorm_adtbatchNorm.rds；
(4) 步骤2的输出结果（聚类后的数据）：H1_day0_scranNorm_adtbatchNorm_dist_clustered.rds；
(5) 步骤5的输出结果（t分布随机邻域嵌入，t-SNE）：H1_day0_scranNorm_adtbatchNorm_dist_clustered_TSNE.rds；
(6) 步骤6的输出结果（聚类重标记后的数据）：H1_day0_scranNorm_adtbatchNorm_dist_clustered_TSNE_labels.rds。
最后一个对象包含经多分辨率聚类的单细胞数据集，附带主成分分析（Principal Component Analysis, PCA）与t-SNE分析结果。已移除标注为双细胞的聚类，剩余聚类被标记以反映聚类分辨率以及不同分辨率下聚类间的关联（例如，总B细胞与转换型B细胞存在关联，因为后者的细胞大多包含于前者之中）。该对象可用于复现论文中所有CITE-seq相关图表（图4-6、扩展图8-10）与表格。

本数据集还提供了工作流所需的2个额外文本文件：
clustree_node_labels_withCellTypeLabels.txt——聚类注释文件；
B_CD40act_genes_JI2014&Blood2004.txt——CD40活化特征的基因列表。
若要测试该工作流，请将文件下载至citeseq/data目录。相关R脚本可在https://github.com/kotliary/baseline获取。

本数据集隶属于如下数据集集合：https://doi.org/10.35092/yhjc.c.4753772
<b>若您在研究中使用本数据集（包括CITE-seq数据）或代码，请引用以下文献</b>：Kotliarov, Y., Sparks, R. 等. 广泛的免疫激活构成健康个体疫苗应答与狼疮患者疾病活动的共同设定点特征. 自然·医学. DOI: https://doi.org/10.1038/s41591-020-0769-8 (2020)
<b>摘要</b>：疫苗接种与疾病的个体应答差异显著，这可能部分源于基线免疫状态的差异。识别此类基线预测因子及其生物学基础，因其在癌症免疫治疗、疾病转归、疫苗接种与感染应答中的潜在重要性而受到广泛关注。本研究中，我们鉴定出可预测健康受试者对流感与黄热疫苗接种的抗体应答的基线血液转录特征。在临床静息状态下评估的这些相同特征，与伴有浆母细胞相关发作的系统性红斑狼疮患者的疾病活动度相关。我们对82种表面蛋白与53201个单细胞的转录组进行CITE-seq分析，样本来自对流感疫苗呈高、低应答的健康个体，结果显示，我们鉴定的特征反映了浆细胞样树突状细胞—I型干扰素—T/B淋巴细胞网络的激活程度。我们的研究结果提示，调控此类免疫基线状态或可改善疫苗应答，并减轻不良自身免疫性疾病活动。

<b>通用联系人</b>：John Tsang ([e-mail])
<b>软件/代码相关问题</b>：Yuri Kotliarov ([e-mail])