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Phenotypic and transcriptomic characterisation of the human B cell response to a novel Ebola vaccine regimen (Ad26.ZEBOV, MVA-BN-Filo)

GSE273114 Homo sapiens (human) Single-cell RNA-seq 126 samples Submitted 2025/06/13 Platform GPL20301
At a glance

Single-cell RNA-seq · human · 126 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
126 samples: 126 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
Prediction and characterisation of the human B cell response to a heterologous two-dose Ebola vaccineNature communications · 2025
Summary (written by the submitters)
Ebola virus disease (EVD) outbreaks are increasing in frequency — threatening health and wellbeing of affected communities. Early and effective public health measures to manage outbreaks rely on health care and frontline workers; consequently, protecting these at high-risk groups is a key pillar of EVD vaccination strategies. IgG specific to the viral glycoprotein is used as the correlate of protection in recent vaccine licensure.
Read the full summary ↓
Here, we use contemporary cellular and transcriptomic analyses to dissect B cell responses to the heterologous Ad26.ZEBOV, MVA-BN-Filo Ebola vaccine. We reveal robust plasma cell and persistent B cell memory responses following vaccination. Using machine-learning, trained on blood gene expression post-vaccination, we were able predict the magnitude of antibody responses. Moreover, we described a B cell receptor CDRH3 sequence which was exclusively seen post-vaccination that is similar to known Ebola GP-binding antibody sequences. Single cell analysis was used to further characterise changes to plasma cell frequency, subclass usage and CDRH3 properties, following vaccination. These results demonstrate that early immune responses —captured in blood using systems immunology approaches — are delineative and predictive of B cell responses to vaccination.
Published in
Prediction and characterisation of the human B cell response to a heterologous two-dose Ebola vaccine
O'Connor D, Clutterbuck EA, Gibani MM et al. · Nature communications 2025 · PMID 40634393 · doi:10.1038/s41467-025-61571-x
This dataset
Open GSE273114 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 GSE273114_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 PRJNA1140197 and SRA study SRP522211. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40634393) ↗
Study design
126 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 126 sample titles: 126 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

+ 86 more — browse all 126 samples with per-sample file links →

Datasets on a similar topic

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