The datasets presented here comprise the sequencing data featured in the research paper titled: "Multimodal single-cell datasets characterize antigen-specific CD8+ T cells across SARS-CoV-2 vaccination and infection": https://www.nature.com/articles/s41590-023-01608-9 Peripheral Blood Mononuclear Cell (PBMC) samples utilized for both CITE-seq and ASAP-seq were systematically collected at four distinct time intervals: Pre-vaccination (Day 0) Post-primary vaccination (Day 2 and Day 10. Seven days post-boost vaccination (Day 28). The count matrix folder contains count matrices for each experimental type, specifically CITE-seq, ASAP-seq, and ECCITE-seq. In addition, we have included the fully integrated, processed Seurat objects for downstream analysis. Details of the content within the count matrix folder are as follows: The RNA, ATAC, and TCR modality outputs were generated using the 10x Cellranger pipeline. HTO and ADT modalities were mapped with Alevin. Outlined below are the three processed single-cell datasets: PBMC_vaccine_CITE.rds: 3' RNA and surface proteins (173 TotalSeq-A antibodies) PBMC_vaccine_ASAP.rds: Chromatin accessibility and surface proteins (173 TotalSeq-A antibodies) PBMC_vaccine_ECCITE_TCR.rds: 5' RNA, surface proteins (137 TotalSeq-C antibodies), TCR and dextramer loaded with peptides of SARS-CoV-2 spike protein. antigen_module_genes.rds: This file contains the vaccine-induced gene sets. antigen_module_peaks.rds: This file contains the DE peaks specific for vaccine-induced cells. To map the scRNA-seq query dataset onto our CITE-seq reference: library(Seurat) PBMC_CITE <- readRDS("/zenedo/PBMC_vaccine_CITE.rds") query_scRNA <- readRDS("/home/xx/your_own_data.rds") anchors <- FindTransferAnchors( reference = PBMC_CITE, query = query_scRNA, normalization.method = "SCT", k.anchor = 5, reference.reduction = "spca", dims = 1:50) query_scRNA <- MapQuery( anchorset = anchors, query = query_scRNA, reference = PBMC_CITE, refdata = list( l1 = "celltypel1", l2 = "celltypel2", l3 = "celltypel3"), reference.reduction = "spca", reduction.model = "wnn.umap") To use the scATAC-seq data, please run the commands below to update the path of the fragment file for the object. Vaccine_ASAP <- readRDS("PBMC_vaccine_ASAP.rds") # remove fragment file information Fragments(Vaccine_ASAP) <- NULL # Update the path of the fragment file Fragments(Vaccine_ASAP) <- CreateFragmentObject(path = "download/PBMC_vaccine_ASAP_fragments.tsv.gz", cells = Cells(Vaccine_ASAP))