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SARS-CoV-2 infection of human pluripotent stem cell-derived vascular cells reveals smooth muscle cells as key mediators of vascular pathology during infection

GSE240766 Homo sapiens (human) RNA-seq (gene expression) 101 samples Submitted 2025/01/08 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 101 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
101 samples: 37 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
SARS-CoV-2 infection of human pluripotent stem cell-derived vascular cells reveals smooth muscle cells as key mediators of vascular pathology during infectionNature communications · 2024
Summary (written by the submitters)
Nearly 20% of hospitalized patients diagnosed with severe SARS-CoV-2 infection are at risk for thromboembolic events. We developed a model of SARS-CoV-2 infection using human-induced pluripotent stem cell-derived endothelial cells, pericytes, and smooth muscle cells to recapitulate the vascular pathology associated with SARS-CoV-2 exposure.
Read the full summary ↓
Our results demonstrate that perivascular cells, particularly smooth muscle cells (SMCs), are a specifically susceptible vascular target for SARS-CoV-2 infection. Utilizing RNA sequencing, we characterized the transcriptomic changes accompanying SARS-CoV-2 infection of SMCs, and endothelial cells (ECs). We observed that infected human SMCs shift to a pro-inflammatory state and increase the expression of key mediators of the coagulation cascade. Further, we showed human ECs exposed to the secretome of infected SMCs produce hemostatic factors that can contribute to vascular dysfunction, despite not being susceptible to direct infection. The findings here recapitulate observations from patient sera in human COVID-19 patients and provide mechanistic insight into the unique vascular implications of SARS-CoV-2 infection at a cellular level.
Published in
SARS-CoV-2 infection of human pluripotent stem cell-derived vascular cells reveals smooth muscle cells as key mediators of vascular pathology during infection
Richards A, Khalil AS, Friesen M et al. · Nature communications 2024 · PMID 39737992 · doi:10.1038/s41467-024-54917-4
This dataset
Open GSE240766 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 GSE240766_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 PRJNA1005213 and SRA study SRP454968. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39737992) ↗
Study design
37 conditions, mostly in duplicate
HUVEC ×3 H1 STEM ×3 EC A, L ×2 EC A HI, L ×2 EC B, L ×2 EC B HI, L ×2 SMC A, L ×2 SMC A HI, L ×2 +54 more

Supports a between-group comparison across 76 samples.

How we read this: 37 replicated groups read from 101 sample titles; they account for 76 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

+ 61 more — browse all 101 samples with per-sample file links →

Datasets on a similar topic

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