← BioTransfer GEO Dataset Finder
GEO series

Microbiome-Derived Metabolites Shape CD4⁺ T-Cell Differentiation and Immune Aging in Chronic HIV-1 Infection [RNA-Seq]

GSE329693 Homo sapiens (human) Single-cell RNA-seq 36 samples Submitted 2026/06/15 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 36 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
36 samples: 8 conditions, mostly with about 5 replicates eachGroups 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 role of aromatic gut-derived bacterial metabolites (GDBMs) in shaping immune cell metabolism and function remains poorly explored. Using ex vivo metabolomic profiling of paired plasma and CD4⁺ T-cells from people living with HIV-1 (PLWH), we identified a network of aromatic GDBMs whose cell-associated abundance, rather than systemic levels, was linked to broad alterations in CD4⁺ T-cell metabolic and functional states.
Read the full summary ↓
Among these metabolites, p- cresol sulfate (PCS) emerged as a mechanistic prototype investigated in depth. Ex vivo flow cytometry and single-cell RNA sequencing of CD4⁺ T-cells stratified by cell-associated PCS levels revealed dose-dependent enrichment of transcriptional programs associated with impaired differentiation capacity, regulatory-like identity, and cellular senescence. Consistently, in vitro transcriptomic and proteomic analyses of PCS-exposed CD4⁺ T cells demonstrated induction of cell-cycle arrest, mitochondrial dysfunction, and senescence-associated programs, including upregulation of p16 and p21. Integration of these immunometabolic features with measurements of HIV-1 reservoir size in PLWH revealed that CD4⁺ T-cell states defined by cell-associated GDBMs track with intact proviral DNA levels in vivo. Together, these findings define a microbiome-derived axis that reshapes CD4⁺ T-cell metabolism and fate and promotes immune aging–associated states in PLWH. Our data suggest that cell-associated GDBMs may foster immunometabolic CD4⁺ T-cell states previously linked to long-term HIV-1 reservoir persistence in vivo.
This dataset
Open GSE329693 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 GSE329693_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 PRJNA1460727 and SRA study SRP696557. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 conditions, mostly with about 5 replicates each
Proliferating CD4⁺ T-cells, 10uM PCS,HC ×5 Non-proliferating CD4⁺ T-cells, 10uM PCS,HC ×5 Proliferating CD4⁺ T-cells, 50uM PCS,HC ×5 Non-proliferating CD4⁺ T-cells, 50uM PCS,HC ×5 Proliferating CD4⁺ T-cells, No PCS,HC ×4 Non-proliferating CD4⁺ T-cells, No PCS,HC ×4 Proliferating CD4⁺ T-cells, 100uM PCS,HC ×4 Non-proliferating CD4⁺ T-cells, 100uM PCS,HC ×4

Supports a between-group comparison across 36 samples.

How we read this: 8 replicated groups read from 36 sample titles; they account for 36 of them. 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

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.