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Memory B cells dominate the early antibody-secreting cell response to SARS-CoV-2 mRNA vaccination in naïve individuals independently of their antibody affinity

GSE244297 Homo sapiens (human) Single-cell RNA-seq 24 samples Submitted 2024/08/20 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 24 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
24 samples: 24 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
Affinity-independent memory B cell origin of the early antibody-secreting cell response in naive individuals upon SARS-CoV-2 vaccinationImmunity · 2024
Summary (written by the submitters)
Memory B cells (MBCs) formed over the individual’s lifetime constitute nearly half of the adult peripheral blood B cell repertoire in humans. To assess their response to novel antigens, we tracked the origin and followed the differentiation paths of MBCs in the early anti-S response to mRNA vaccination in SARS-CoV-2-naïve individuals on single-cell and monoclonal antibody level.
Read the full summary ↓
Newly generated and pre-existing MBCs differed in their differentiation paths despite similar levels of SARS-CoV-2 and common corona virus S-reactivity. Pre-existing highly mutated MBCs showed no signs of germinal center re-entry and rapidly developed into mature antibody secreting cells (ASCs). In contrast, newly generated MBCs derived from naïve precursors showed strong signs of antibody affinity maturation before differentiating into ASCs. Thus, although pre-existing human MBCs have an intrinsic propensity to differentiate into ASCs, the quality of the anti-S antibody and MBC response improved through the clonal selection and affinity maturation of naïve precursors.
Published in
Affinity-independent memory B cell origin of the early antibody-secreting cell response in naive individuals upon SARS-CoV-2 vaccination
Li Z, Obraztsova A, Shang F et al. · Immunity 2024 · PMID 39168129 · doi:10.1016/j.immuni.2024.07.023
This dataset
Open GSE244297 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 GSE244297_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 PRJNA1022135 and SRA study SRP463802. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39168129) ↗
Study design
24 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 24 sample titles: 24 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.