CITE-seq protein-mRNA single cell data from high and low vaccine responders (to reproduce Figs 4-6 and associated Extended Data Figs)
datasetposted on 23.02.2020 by Yuri Kotliarov, Matthew P. Mulé, Andrew Martins, Rachel Sparks, John Tsang
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
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.
The data set contains five RDS files with Seurat 2.3.4 objects used as the input data at different steps of the workflow:
(1-2) Input data for the step 1 (pre-processed demultiplexed data):
(3) Output of step 1 (data normalization):
(4) Output of step 2 (clustering):
(5) Output of step 5 (TSNE):
(6) Output of step 6 (clusters re-labeling):
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.
Two additional text files required for the workflow are provided:
clustree_node_labels_withCellTypeLabels.txt - annotation of the clusters
B_CD40act_genes_JI2014&Blood2004.txt - list of genes in the CD40act signature.
To test the workflow download the files into the citeseq/data directory. The R scripts are available on https://github.com/kotliary/baseline.
This item is a part of the collection: https://doi.org/10.35092/yhjc.c.4753772
If you use our data (including CITE-seq data) or code for your work please cite the following publication:
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)
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.
General contact: John Tsang (firstname.lastname@example.org)
Questions about software/code: Yuri Kotliarov (email@example.com)
Funded by the Intramural Research Program of the National Institute of Allergy and Infectious Diseases (NIAID) and NIH Institutes supporting the Center for Human Immunology (CHI), National Institutes of Health, Bethesda, MD, USA
1ZICAI001226 (NIH CHI)
Select an IC:
- AI - National Institute of Allergy and Infectious Diseases (NIAID)