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B cell imprinting in children impairs antibodies to the hemagglutinin stalk

GSE297740 Homo sapiens (human) RNA-seq (gene expression) 68 samples Submitted 2026/01/29 Platform GPL30882Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 68 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
68 samples: 68 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
B cell imprinting in children impairs antibodies to the haemagglutinin stalkNature · 2026
Summary (written by the submitters)
Immune imprinting or Original Antigenic Sin (OAS) is a phenomenon where the immune system preferentially recalls its initial response to a related, often evolving pathogen upon subsequent exposure. Despite its important implications for vaccine development, the causes of imprinting remain unclear. To understand the basis and impact of imprinting by influenza A viruses, we characterized the B cell responses of young children following consecutive first infections with divergent H1N1 and H3N2 strains.
Read the full summary ↓
Children had a primary but otherwise similar B cell response to that of adults. Adult B cells commonly cross-reacted with past strains using more stereotyped and mutated immunoglobulin genes, indicating significant homosubtypic imprinting. In children, following consecutive heterosubtypic primary infections, up to 6% of memory B cells are H1/H3 cross-reactive and bind the highly conserved central stalk epitope, a lead target for broadly protective vaccine candidates. Over 90% of these B cells had higher affinity for the imprinting H3N2 strain, resulting in reduced breadth and neutralization potency against H1N1 strains. Mechanistically, the imprinting H3 and affected H1 strains shared a single residue change in the stalk epitope (D46N) that was central to the nearly universal shift in reactivity, despite differing by only a single atomic group. In conclusion, imprinting by influenza viruses can cause a deleterious shift of nearly the entire memory recall response against key, conserved epitopes.
Published in
B cell imprinting in children impairs antibodies to the haemagglutinin stalk
Sun J, Jo G, Troxell CA et al. · Nature 2026 · PMID 41813896 · doi:10.1038/s41586-026-10248-6
This dataset
Open GSE297740 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 GSE297740_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1266080 and SRA study SRP586821. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41813896) ↗
Study design
68 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 68 sample titles: 68 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

+ 28 more — browse all 68 samples with per-sample file links →

Datasets on a similar topic

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