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CD16 and CD57 Expressing Gamma Delta T Cells in Acute HIV-1 Infection are Associated with the Development of Neutralization Breadth

GSE271442 Homo sapiens (human) RNA-seq (gene expression) 82 samples Submitted 2024/07/03 Platform GPL15520
At a glance

RNA-seq (gene expression) · human · 82 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
82 samples: 82 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
CD16 and CD57 expressing gamma delta T cells in acute HIV-1 infection are associated with the development of neutralization breadthPLoS pathogens · 2025
Summary (written by the submitters)
New HIV vaccine approaches are focused on eliciting broadly neutralizing antibodies. We characterized early gamma-delta (γδ) T cell responses starting from pre-acquisition and during acute HIV infection (AHI) in participants previously characterized for neutralization breadth development. We found significant differences in γδ T cell surface marker expression in participants that developed neutralization breadth compared to those that did not.
Read the full summary ↓
Activation of γδ T cells occurred within the first weeks of HIV acquisition and associated with viral load. Expression of CD16 on Vδ1 T cells and CD57 on Vδ2 T cells were found to be significantly higher in broad neutralizers during AHI, and associated with the development of neutralization breadth years later. In addition, the levels of CD16 on Vδ1 T cells was associated with early production of founder virus Env-specific IgM. Thus, γδ T cells may promote development of neutralization breadth, which has implications for HIV vaccine strategies.
Published in
CD16 and CD57 expressing gamma delta T cells in acute HIV-1 infection are associated with the development of neutralization breadth
Griffith GL, Machmach K, Jian N et al. · PLoS pathogens 2025 · PMID 39888945 · doi:10.1371/journal.ppat.1012916
This dataset
Open GSE271442 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 GSE271442_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 PRJNA1131432 and SRA study SRP517880. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39888945) ↗
Study design
82 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 82 sample titles: 82 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

+ 42 more — browse all 82 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.