GEO series

Monocytes defined by platelet interactions and oxidative stress signaling underlie HIV-associated atherosclerosis

GSE319215 Homo sapiens (human) Single-cell RNA-seq 30 samples Submitted 2026/02/17 Platform GPL34284

At a glance

Single-cell RNA-seq · human · 30 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: 30 samples: 14 conditions, mostly in duplicateGroups 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: Monocytes Defined by Platelet Interactions and Oxidative Stress Signaling Underlie HIV-Associated AtherosclerosisJournal of the American Heart Association · 2026

Summary (written by the submitters)

Background: Monocytes contribute to atherosclerosis (AS) by migrating into inflamed endothelium and differentiating into foam cells. In people living with HIV (PLWH), chronic inflammation increases AS risk, yet the role of specific monocyte clusters remains unclear. We investigated how distinct monocyte populations contribute to vascular pathology in early HIV-associated AS.

Read the full summary ↓

Methods: We profiled 123,965 circulating monocytes using single-cell RNA-seq and integrated plasma microparticle proteomics in 32 individuals stratified by HIV and AS status. Supervised learning identified cluster- and disease-specific signatures, validated by platelet-monocyte co-cultures, RT-qPCR, bulk RNA-seq, flow cytometry and ELISA. Results: Seven monocyte clusters were identified, including a subset characterized by platelet–monocyte complexes (PMCs). Bulk RNA-seq of platelet–monocyte co-cultures revealed platelet-driven upregulation of genes involved in inflammation, lipid metabolism, oxidative stress, and endothelial adhesion. PMC-derived macrophages secreted higher levels of TGF-β and IL-10, displayed decreased CD14 and increased CD80/CD86 while retaining CD36, and promoted endothelial-to-mesenchymal transition (decreased CDH5, PECAM1; increased S100A4) and vascular inflammation (increased ICAM1, VCAM and IL-6, CCL2 secretion). Additionally, CD14+ monocytes from HIV+AS- and HIV+AS+ groups showed enhanced ROS-NRF2 pathway activities, supported by increased basal and H2O2-induced p90RSK phosphorylation, indicating oxidative stress priming. Conclusions: Two monocyte clusters contribute independently to vascular immune dysregulation in PLWH. PMC-derived macrophages promote endothelial dysfunction while adopting a profibrotic cytokine profile. CD14+ monocytes show heightened oxidative signaling and stress responses, consistent with vascular activation. Together, these mechanisms may accelerate AS development in HIV, even in the absence of traditional cardiovascular risk factors.

Published in

Monocytes Defined by Platelet Interactions and Oxidative Stress Signaling Underlie HIV-Associated Atherosclerosis

Wang RH, Boodoo ZD, Sutton KR et al. · Journal of the American Heart Association 2026 · PMID 41823228 · doi:10.1161/JAHA.125.046482

This dataset

Open GSE319215 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 GSE319215_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 PRJNA1422638 and SRA study SRP676467. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41823228) ↗

Study design

14 conditions, mostly in duplicate

nonPMC, Rep1, Day ×2 PMC, Rep1, Day ×2 nonPMC, Rep2, Day ×2 PMC, Rep2, Day ×2 nonPMC, Rep3, Day ×2 PMC, Rep3, Day ×2 nonPMC, Rep4, Day ×2 PMC, Rep4, Day ×2 +8 more

Supports a between-group comparison across 28 samples.

How we read this: 14 replicated groups read from 30 sample titles; they account for 28 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

GSM9513584 nonPMC, Rep1, Day2 nonPMC, Rep1, Day
GSM9513585 PMC, Rep1, Day2 PMC, Rep1, Day
GSM9513586 nonPMC, Rep1, Day7 nonPMC, Rep1, Day
GSM9513587 PMC, Rep1, Day7 PMC, Rep1, Day
GSM9513588 nonPMC, Rep2, Day2 nonPMC, Rep2, Day
GSM9513589 PMC, Rep2, Day2 PMC, Rep2, Day
GSM9513590 nonPMC, Rep2, Day7 nonPMC, Rep2, Day
GSM9513591 PMC, Rep2, Day7 PMC, Rep2, Day
GSM9513592 nonPMC, Rep3, Day2 nonPMC, Rep3, Day
GSM9513593 PMC, Rep3, Day2 PMC, Rep3, Day
GSM9513594 nonPMC, Rep3, Day7 nonPMC, Rep3, Day
GSM9513595 PMC, Rep3, Day7 PMC, Rep3, Day
GSM9513596 nonPMC, Rep4, Day2 nonPMC, Rep4, Day
GSM9513597 PMC, Rep4, Day2 PMC, Rep4, Day
GSM9513598 nonPMC, Rep4, Day7 nonPMC, Rep4, Day
GSM9513599 PMC, Rep4, Day7 PMC, Rep4, Day
GSM9513600 nonPMC, Rep5, Day2 nonPMC, Rep5, Day
GSM9513601 PMC, Rep5, Day2 PMC, Rep5, Day
GSM9513602 nonPMC, Rep5, Day7 nonPMC, Rep5, Day
GSM9513603 PMC, Rep5, Day7 PMC, Rep5, Day
GSM9513604 nonPMC, Rep6, Day7 nonPMC, Rep6, Day
GSM9513605 PMC, Rep6, Day7 PMC, Rep6, Day
GSM9513606 nonPMC, Rep6, Day2 nonPMC, Rep6, Day
GSM9513607 PMC, Rep6, Day2 PMC, Rep6, Day
GSM9513608 nonPMC, Rep7, Day7 nonPMC, Rep7, Day
GSM9513609 PMC, Rep7, Day7 PMC, Rep7, Day
GSM9513610 nonPMC, Rep7, Day2 nonPMC, Rep7, Day
GSM9513611 PMC, Rep7, Day2 PMC, Rep7, Day
GSM9513612 nonPMC, Rep8, Day7
GSM9513613 PMC, Rep8, Day7

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