← BioTransfer GEO Dataset Finder
GEO series

B cell characteristics define HCV reinfection outcome

GSE269792 Homo sapiens (human) Single-cell RNA-seq 870 samples Submitted 2024/06/13 Platform GPL18573
At a glance

Single-cell RNA-seq · human · 870 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
870 samples: 870 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 characteristics define HCV reinfection outcomeJournal of hepatology · 2024
Summary (written by the submitters)
In individuals highly exposed to hepatitis C virus (HCV), reinfection is common, suggesting that natural development of sterilising immunity is difficult. In those that are reinfected, some will develop a persistent infection, while a small proportion repeatedly clear the virus, suggesting natural protection is possible. The aim of this study was to characterise immune responses associated with rapid natural clearance of HCV reinfection.
Read the full summary ↓
Broad neutralising antibodies (BnAbs) and Envelope 2 (E2)-specific memory B cell (MBCs) responses were examined longitudinally in 15 subjects with varied reinfection outcomes. BnAb responses were associated with MBC recall, but not with reinfection clearance. Strong evidence of antigen imprinting was found, and the B cell receptor repertoire showed a high level of clonality with ongoing somatic hypermutation of many clones over subsequent reinfection events. Single cell transcriptomic analyses showed that cleared reinfections featured an activated transcriptomic profile in HCV-specific B cells that rapidly expanded upon reinfection. MBC quality, but not necessarily breadth of nAb responses, is important for protection against antigenically diverse variants, which is encouraging for HCV vaccine development.
Published in
B-cell characteristics define HCV reinfection outcome
Underwood AP, Gupta M, Wu BR et al. · Journal of hepatology 2024 · PMID 38604387 · doi:10.1016/j.jhep.2024.04.004
This dataset
Open GSE269792 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 GSE269792_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 PRJNA1123702 and SRA study SRP513808. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38604387) ↗
Study design
870 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 the first 400 of 870 sample titles: 400 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

+ 830 more — browse all 870 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.