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B cell convergence to distinct broadly reactive epitopes following vaccination with chimeric influenza virus hemagglutinins

GSE226633 Homo sapiens (human) Single-cell RNA-seq 19 samples Submitted 2025/03/04 Platform GPL18573
At a glance

Single-cell RNA-seq · human · 19 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
19 samples: 19 samples, each with a distinct title — no replicated groupsGroups 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
Long-lasting B cell convergence to distinct broadly reactive epitopes following vaccination with chimeric influenza virus hemagglutininsImmunity · 2025
Summary (written by the submitters)
In a phase I clinical trial, the chimeric hemagglutinin (cHA) immunogen induced antibody responses against the conserved HA stalk domain. However, the landscape of the B cell specificities and subsets induced by this vaccine remain undetermined. Here, we paired single cell RNA-sequencing and B cell receptor repertoire sequencing to analyze the relationship between transcriptome and B cell specificity following cHA immunization.
Read the full summary ↓
We show that the cHA inactivated vaccine with a squalene-based adjuvant induced a robust activated B cell and memory B cell phenotype against two broadly neutralizing epitopes of the stalk domain. The overall specificities of the acute plasmablast and memory B cell responses were distinct, with the plasmablast compartment largely targeting non-neutralizing epitopes of the HA stalk. Altogether, these data indicate the B cell landscape following cHA vaccination includes diverse B cell subsets that are differentially induced by distinct vaccine formulations, including memory and de novo B cell responses against diverse broadly conserved epitopes.
Published in
Long-lasting B cell convergence to distinct broadly reactive epitopes following vaccination with chimeric influenza virus hemagglutinins
Guthmiller JJ, Yu-Ling Lan L, Li L et al. · Immunity 2025 · PMID 40132593 · doi:10.1016/j.immuni.2025.02.025
This dataset
Open GSE226633 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 GSE226633_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 PRJNA940845 and SRA study SRP425567. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40132593) ↗
Study design
19 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 19 sample titles: 19 distinct titles with little repetition. 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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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.