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Modulation of predicted key driver genes of an atherosclerosis-associated gene regulatory network in human endothelial cells

GSE287081 Homo sapiens (human) Single-cell RNA-seq 24 samples Submitted 2025/01/16 Platform GPL30173

At a glance

Single-cell RNA-seq · human · 24 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: 24 samples: 6 conditions, mostly with about 4 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.
Paper: Single-cell RNA sequencing reveals sex differences in the subcellular composition and associated gene-regulatory network activity of human carotid plaquesNature cardiovascular research · 2025

Summary (written by the submitters)

Carotid stenosis is a major cause of ischemic strokes both in males and females, but the clinical manifestation and carotid plaque histology differ. Single-cell RNA-Seq of endarterectomy samples from women and men revealed marked differences at the cell subtype level depending on sex. To provide clinical and pathophysiological context, cell subtypes displaying sex differences were integrated with 135 tissue-specific gene-regulatory networks (GRNs) of the STARNET study, leading to the identification of several atherosclerotic arterial wall sex-differential and cell-type-specific GRNs.

Read the full summary ↓

PLVAP and FAM110D, two predicted key drivers of the male-dominant, endothelial cell-associated network GRN195 were overexpressed in human arterial endothelial cells (HAECs) to modulate the GRN and study its effect on cellular functions, including cell adhesion and proliferation.

Published in

Single-cell RNA sequencing reveals sex differences in the subcellular composition and associated gene-regulatory network activity of human carotid plaques

Sukhavasi K, Mocci G, Ma L et al. · Nature cardiovascular research 2025 · PMID 40211055 · doi:10.1038/s44161-025-00628-y

This dataset

Open GSE287081 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 GSE287081_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 PRJNA1211101 and SRA study SRP557594. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40211055) ↗

Study design

6 conditions, mostly with about 4 replicates each

HAEC oeCTL ×4 HAEC oeFAM110D ×4 HAEC oePLVAP ×4 TeloHAEC oeCTL ×4 TeloHAEC oeFAM110D ×4 TeloHAEC oePLVAP ×4

Supports a between-group comparison across 24 samples.

How we read this: 6 replicated groups read from 24 sample titles; they account for 24 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

GSM8738355 HAEC oeCTL rep1 HAEC oeCTL
GSM8738356 HAEC oeCTL rep2 HAEC oeCTL
GSM8738357 HAEC oeCTL rep3 HAEC oeCTL
GSM8738358 HAEC oeCTL rep4 HAEC oeCTL
GSM8738359 HAEC oeFAM110D rep1 HAEC oeFAM110D
GSM8738360 HAEC oeFAM110D rep2 HAEC oeFAM110D
GSM8738361 HAEC oeFAM110D rep3 HAEC oeFAM110D
GSM8738362 HAEC oeFAM110D rep4 HAEC oeFAM110D
GSM8738363 HAEC oePLVAP rep1 HAEC oePLVAP
GSM8738364 HAEC oePLVAP rep2 HAEC oePLVAP
GSM8738365 HAEC oePLVAP rep3 HAEC oePLVAP
GSM8738366 HAEC oePLVAP rep4 HAEC oePLVAP
GSM8738367 TeloHAEC oeCTL rep1 TeloHAEC oeCTL
GSM8738368 TeloHAEC oeCTL rep2 TeloHAEC oeCTL
GSM8738369 TeloHAEC oeCTL rep3 TeloHAEC oeCTL
GSM8738370 TeloHAEC oeCTL rep4 TeloHAEC oeCTL
GSM8738371 TeloHAEC oeFAM110D rep1 TeloHAEC oeFAM110D
GSM8738372 TeloHAEC oeFAM110D rep2 TeloHAEC oeFAM110D
GSM8738373 TeloHAEC oeFAM110D rep3 TeloHAEC oeFAM110D
GSM8738374 TeloHAEC oeFAM110D rep4 TeloHAEC oeFAM110D
GSM8738375 TeloHAEC oePLVAP rep1 TeloHAEC oePLVAP
GSM8738376 TeloHAEC oePLVAP rep2 TeloHAEC oePLVAP
GSM8738377 TeloHAEC oePLVAP rep3 TeloHAEC oePLVAP
GSM8738378 TeloHAEC oePLVAP rep4 TeloHAEC oePLVAP

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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.