Published January 25, 2023 | Version v2
Dataset Open

COVID-19 vaccination single cell datasets

  • 1. New York Genome Center
  • 2. New York University Langone Health
  • 3. New York University Langone Vaccine Center
  • 4. New York University Grossman School of Medicine

Description

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:

  1. PBMC_vaccine_CITE.rds: 3' RNA and surface proteins (173 TotalSeq-A antibodies)
  2. PBMC_vaccine_ASAP.rds: Chromatin accessibility and surface proteins (173 TotalSeq-A antibodies)
  3. 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))

 

Files

Count Matrix for CITE-seq ASAP-seq and ECCITE-seq.zip

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