GEO series

Single-cell multi-omics analysis revealing immune features of inactivated SARS-CoV-2 vaccination in systemic lupus erythematosus patients

GSE224198 Homo sapiens (human) Single-cell RNA-seq 26 samples Submitted 2024/05/29 Platform GPL20795

At a glance

Single-cell RNA-seq · human · 26 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Samples: 26 samples: 10 conditions, mostly in duplicateGroups are read from the sample names, not from the paper. Check the list below.
Data you can get: A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper: Single-cell multi-omics analysis revealing immune features of inactivated COVID-19 vaccination in systemic lupus erythematosus patientsScience bulletin · 2025

Summary (written by the submitters)

The COVID-19 vaccine-induced immunity has been widely reported in the general population, but the protective immune response of COVID-19 vaccines in SLE patients (SLEs) remains largely unknown. Here, we performed a comprehensive and time-series investigation of immunological alterations induced by inactivated COVID-19 vaccines in SLEs and healthy controls using single-cell multi-omics methods.

Read the full summary ↓

Compared with healthy controls (HCs), the lower titres of neutralizing antibodies and global transcriptomic and epigenomic changes in peripheral immune cells were detected in SLEs. Increased expression levels of inflammatory responses related genes and AP-1 family members were commonly detected in SLEs and HCs, along with increased activation of T cells and diversity of T cell receptor repertoire. However, this vaccine enhanced chromatin accessibility at interferon response factors (IRF) loci in monocytes and conventional dendritic cells (cDCs) from SLEs at the late stage after vaccination, which was confirmed by transcriptome data and distinct from HCs. Vaccine induced less differentiations of B cells, expanded clonotypes and class switching clonotypes in SLEs, which was associated with impaired neutralization antibodies. Our study provides a system biology assessment of the transcriptomic and epigenomic landscape during an immune response induced by COVID-19 vaccine within SLEs, providing insights that will be useful in the optimization of vaccination strategies in immune compromised populations.

Published in

Single-cell multi-omics analysis revealing immune features of inactivated COVID-19 vaccination in systemic lupus erythematosus patients

Zheng Y, Wang S, Zhou X et al. · Science bulletin 2025 · PMID 38851910 · doi:10.1016/j.scib.2024.05.038

This dataset

Open GSE224198 in finder → Find more human RNA-seq datasets →

Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE224198_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA930046. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38851910) ↗

Study design

10 conditions, mostly in duplicate

RNA_H01D ×4 RNA_H02D ×4 RNA_E01D ×3 RNA_E04D ×3 RNA_E02D ×2 RNA_P01D ×2 ATAC_E02D ×2 ATAC_P01D ×2 +2 more

Supports a between-group comparison across 26 samples.

How we read this: 10 replicated groups read from 26 sample titles; they account for 26 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

GSM7017326 RNA_E01D0 RNA_E01D
GSM7017327 RNA_E01D14 RNA_E01D
GSM7017328 RNA_E01D60 RNA_E01D
GSM7017329 RNA_E02D0 RNA_E02D
GSM7017330 RNA_E02D60 RNA_E02D
GSM7017331 RNA_E04D0 RNA_E04D
GSM7017332 RNA_E04D14 RNA_E04D
GSM7017333 RNA_E04D60 RNA_E04D
GSM7017334 RNA_P01D0 RNA_P01D
GSM7017335 RNA_P01D60 RNA_P01D
GSM7017336 RNA_H01D0 RNA_H01D
GSM7017337 RNA_H01D14 RNA_H01D
GSM7017338 RNA_H01D60 RNA_H01D
GSM7017339 RNA_H01D7 RNA_H01D
GSM7017340 RNA_H02D0 RNA_H02D
GSM7017341 RNA_H02D14 RNA_H02D
GSM7017342 RNA_H02D60 RNA_H02D
GSM7017343 RNA_H02D7 RNA_H02D
GSM7017344 ATAC_E02D0 ATAC_E02D
GSM7017345 ATAC_E02D60 ATAC_E02D
GSM7017346 ATAC_P01D0 ATAC_P01D
GSM7017347 ATAC_P01D60 ATAC_P01D
GSM7017348 ATAC_H01D0 ATAC_H01D
GSM7017349 ATAC_H01D60 ATAC_H01D
GSM7017350 ATAC_H02D0 ATAC_H02D
GSM7017351 ATAC_H02D60 ATAC_H02D

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