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hiv-seq reveals global host gene expression differences between hiv-transcribing cells from viremic and suppressed people with hiv

GSE266455 Homo sapiens (human) Single-cell RNA-seq 48 samples Submitted 2025/12/10 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 48 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
48 samples: 48 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
HIV-seq reveals gene expression differences between HIV-transcribing cells from viremic and suppressed people with HIVNature communications · 2026
Summary (written by the submitters)
In our study, we aimed to improve the identification of active reservoir cells producing HIV in individuals undergoing antiretroviral therapy (ART). We developed a technique called HIV-Seq to enhance the efficiency of capturing HIV transcripts during single-cell RNA sequencing (scRNAseq) analysis. By applying this technique and surface phenotyping using CITE-seq, we compared the transcriptional features of HIV RNA+ cells before and after ART initiation.
Read the full summary ↓
Our findings highlight distinct transcriptional signatures of active reservoir cells during viremia and suppression, demonstrating the utility of HIV-Seq in elucidating the mechanisms underlying reservoir persistence during ART.
Published in
HIV-seq reveals gene expression differences between HIV-transcribing cells from viremic and suppressed people with HIV
Frouard J, Telwatte S, Luo X et al. · Nature communications 2026 · PMID 41776157 · doi:10.1038/s41467-026-68797-3
This dataset
Open GSE266455 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 GSE266455_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 PRJNA1107214 and SRA study SRP505425. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41776157) ↗
Study design
48 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 48 sample titles: 48 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

+ 8 more — browse all 48 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.