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Prior vaccination prevents overactivation of innate immune responses during COVID-19 breakthrough infection

GSE261862 Homo sapiens (human) Single-cell RNA-seq 16 samples Submitted 2025/01/30 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 16 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
16 samples: 16 × PBMCs, scRNA-seq, sublibraryGroups 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
Prior vaccination prevents overactivation of innate immune responses during COVID-19 breakthrough infectionScience translational medicine · 2025
Summary (written by the submitters)
At this stage in the COVID-19 pandemic, most infections are 'breakthrough' infections that occur in individuals with prior immunity to SARS-CoV-2 through infection or vaccination. Understanding both innate and adaptive immune induction in the setting of breakthrough infection is critical to refining vaccine strategies to ensure long-term efficacy against emerging variants, yet existing studies have primarily focused on adaptive immune responses.
Read the full summary ↓
Here, we performed single-cell transcriptomic, proteomic, and functional profiling of innate and adaptive immunity during primary and breakthrough COVID-19 infections by comparing immune responses between unvaccinated and vaccinated individuals during the SARS-CoV-2 Delta wave. Breakthrough infections were characterized by a significantly less activated transcriptomic profile in CD56dim NK cells and monocytes, with induction of pathways limiting NK cell proliferation and monocyte migratory potential. Furthermore, we observed a female-specific trend of increased transcriptomic activation of CD16+ monocytes and type-2 conventional dendritic cells (cDC2s) during breakthrough infections. Despite these differences, antibody-dependent cellular cytotoxicity responses were similar between breakthrough and primary infection groups. These insights suggest that prior vaccination prevents overactivation of innate immune responses during breakthrough infections with discernible sex-specific patterns and underscore the potential of harnessing vaccines in mitigating pathologic immune responses resulting from overactivation.
Published in
Prior vaccination prevents overactivation of innate immune responses during COVID-19 breakthrough infection
Chan L, Pinedo K, Stabile MA et al. · Science translational medicine 2025 · PMID 39879318 · doi:10.1126/scitranslmed.adq1086
This dataset
Open GSE261862 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 GSE261862_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 PRJNA1089238 and SRA study SRP496248. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39879318) ↗
Study design
16 × PBMCs, scRNA-seq, sublibrary

Supports a between-group comparison across 16 samples.

How we read this: 1 replicated groups read from 16 sample titles; they account for 16 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
Datasets on a similar topic

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