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Enhanced interferon-stimulated gene signatures in B cells following influenza B virus infection compared to influenza vaccination

GSE309140 Homo sapiens (human) Single-cell RNA-seq 16 samples Submitted 2026/03/09 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: 4 × JCC300 Gene Expression reaction vs 4 × JCC300 TCR reaction vs 4 × JCC300 BCR reaction vs 4 × JCC300 Surface Protein reactionGroups 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
IFN-gene signatures in B cells following influenza A and B virus infection and influenza vaccinationEMBO molecular medicine · 2026
Summary (written by the submitters)
Despite influenza vaccination, some individuals succumb to life-threatening influenza or even death. Yet our understanding of differential immune features generated by vaccination versus infection by influenza A and influenza B viruses (IAVs, IBVs) is limited. We sought to define molecular signatures of influenza-specific B cells elicited following influenza virus infection or vaccination towards both IAV and IBV subtypes.
Read the full summary ↓
Using barcoded fluorescently-labelled haemagglutinin (HA)-specific probes, we performed single-cell RNA sequencing of influenza-specific B cells on day 0, 7 and 28 following influenza inactivated vaccination, in comparison to hospitalized patients during acute IAVor IBV as well as at 1-month convalescence. Comparing B cell responses elicited by infection and vaccination, the most striking findings stemmed from increased interferon-stimulated gene signatures, especially IF44L, IFITM1 and XAF1, in total B cells during acute IBV infection, which decreased at 1 month following patients’ recovery. These interferon-stimulated genes were not observed in B cells following influenza vaccination encompassing both IAV and IBV components or IAV infection in our patients. For HA-specific B cells, transcriptomic analysis revealed phenotypic differentiation and isotype class-switching following vaccination, with evidence of clonal sharing between memory and atypical B cell phenotypes. In in vitro influenza virus infection experiments, IBVs showed higher infectivity of human peripheral blood mononuclear cells, including B cells, in comparison to IAVs. Additional co-culture of infected PBMCs with plasma obtained from patients hospitalized with IAV, IBV, COVID-19 and RSV increased the infectivity of immune cells by both IBV and IAV, suggesting that B cell infectivity we observed might also be driven by plasma mediators. IBV infection also resulted in reduced B cell proliferation compared to IAV, potentially associated with the antiproliferative effect of IFITM1. Our findings comparing and contrasting influenza-specific B cell responses elicited following acute influenza virus infection versus vaccination provide key insights into understanding B cell immunity towards IBV versus IAV infections, in contrast to vaccination responses, to inform rational vaccine design and therapeutic targets.
Published in
IFN-gene signatures in B cells following influenza A and B virus infection and influenza vaccination
Zhang W, Allen EK, Li S et al. · EMBO molecular medicine 2026 · PMID 41803327 · doi:10.1038/s44321-026-00395-8
This dataset
Open GSE309140 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 GSE309140_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 PRJNA1334201 and SRA study SRP627080. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41803327) ↗
Study design
4 × JCC300 Gene Expression reaction vs 4 × JCC300 TCR reaction vs 4 × JCC300 BCR reaction vs 4 × JCC300 Surface Protein reaction

Supports a between-group comparison across 16 samples.

How we read this: 4 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
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