GEO series

Three immunoregulatory signatures define non-productive HIV infection in stem cell memory CD4+ T cells

GSE309709 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2026/05/15 Platform GPL34281

At a glance

RNA-seq (gene expression) · human · 16 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: 16 samples: 16 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.

Summary (written by the submitters)

The persistent HIV reservoir constitutes the main obstacle to curing HIV/AIDS disease. Our understanding of how non-productive HIV infections are established in primary human CD4+ T cells during the first round of infection is, however, still incomplete. In this study, we leverage the HIV reporter virus pMorpheus-V5 to delineate cellular expression patterns that are upregulated in non-productively infected primary stem cell memory (TSCM) CD4+ T cells.

Read the full summary ↓

We find that CD4+ TSCM harboring non-productive proviruses display a distinct transcriptomic signature comprising 118 upregulated genes. This non-productive expression profile is distinct from that of productively infected cells as well as from negative-exposed and mock-infected cells. Among the cellular genes most upregulated in CD4+ T cells harboring non-productive proviruses are CCR4-binding migratory chemokines (CCL22, CCL17), tryptophan catabolic enzymes (IDO1, KYNU), and genes encoding cytoskeletal rearrangement proteins (BASP1, TNFAIP2). Intracellular flow cytometry-based analyses confirm that non-productively infected CD4+ TSCM cells are enriched for CCL22 and IDO1 co-expression compared to the other CD4+ T memory subsets, underscoring a clear CD4+ T cell subset specificity for the upregulation of these two immune gene sets associated with non-productive infections. These findings suggest that primary human CD4+ TSCM harboring non-productive proviruses display a distinct immunoregulatory phenotype, which may facilitate immune evasion and contribute to the persistence of the HIV reservoir.

This dataset

Open GSE309709 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 GSE309709_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 PRJNA1336782 and SRA study SRP630162. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

16 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 16 sample titles: 16 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

GSM9277811 Donor 2, Negative but exposed to pMorpheus reporter
GSM9277812 Donor 4, Productive infection
GSM9277813 Donor 4, Nonproductive infection
GSM9277814 Donor 4, Mock infection control and No exposure to pMorpheus
GSM9277815 Donor 4 Negative but exposed to pMorpheus reporter
GSM9277816 Donor 2, Productive infection
GSM9277817 Donor 2, Nonproductive infection
GSM9277818 Donor 2, Mock infection control and No exposure to pMorpheus
GSM9277819 Donor 1, Negative but exposed to pMorpheus reporter
GSM9277820 Donor 1, Productive infection
GSM9277821 Donor 1, Nonproductive infection
GSM9277822 Donor 1, Mock infection control and No exposure to pMorpheus
GSM9277823 Donor 3, Negative but exposed to pMorpheus reporter
GSM9277824 Donor 3, Productive infection
GSM9277825 Donor 3, Nonproductive infection
GSM9277826 Donor 3, Mock infection control and No exposure to pMorpheus

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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.