GEO series

Vascular smooth muscle cell state trajectories mediate molecular mechanisms of coronary disease risk [scRNA-seq and scATAC-seq]

GSE321762 Mus musculus (mouse) Single-cell RNA-seq 54 samples Submitted 2026/02/27 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 54 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Mus musculus (mouse)
Samples: 54 samples: 54 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.
Paper: Vascular smooth muscle cell state trajectories mediate molecular mechanisms of coronary disease riskNature communications · 2026

Summary (written by the submitters)

Vascular smooth muscle cells contribute to heritable coronary artery disease risk and undergo complex transitions to multiple disease-related phenotypes. To investigate the genetic basis of these trajectories, we develop a dense timecourse single cell transcriptomic and epigenetic map of atherosclerosis in a murine disease model accompanied by high-plex in situ spatial data.

Read the full summary ↓

Using temporal data and probabilistic fate modeling, we identify key transcription factors that drive cell state changes through a combination of network-based prioritization and in silico transcription factor perturbation. Parallel knockout studies of validated coronary artery disease gene Tcf21 uncover its molecular mechanisms in smooth muscle cell transition, due in part to a role regulating the transition of smooth muscle cells in the secondary heart field. Integrating the murine atlas with human coronary artery disease genetics pinpoint smooth muscle cell phenotypes that mediate disease risk, highlighting causal disease mechanisms. Together, these studies resolve atherosclerosis trajectories at single cell resolution and identify genetic causal transcriptomic and epigenomic mechanisms of coronary artery disease risk.

Published in

Vascular smooth muscle cell state trajectories mediate molecular mechanisms of coronary disease risk

Li DY, Kundu S, Cheng P et al. · Nature communications 2026 · PMID 41844614 · doi:10.1038/s41467-026-70530-z

This dataset

Open GSE321762 in finder → Find more mouse ChIP / ATAC / CUT&Tag 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 GSE321762_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 PRJNA1429340 and SRA study SRP680058. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41844614) ↗

Study design

54 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 54 sample titles: 54 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

GSM9550071 Control 0wk 1 mixed RNA
GSM9550072 Control 0wk 2 mixed RNA
GSM9550073 Control 3wk 1 mixed RNA
GSM9550074 Control 3wk 2 mixed RNA
GSM9550075 Control 5wk 1 mixed RNA
GSM9550076 Control 5wk 2 mixed RNA
GSM9550077 Control 5wk 3 tdt RNA
GSM9550078 Control 5wk 3 nontdt RNA
GSM9550079 Control 7wk 1 mixed RNA
GSM9550080 Control 7wk 2 mixed RNA
GSM9550081 Control 9wk 1 mixed RNA
GSM9550082 Control 9wk 2 mixed RNA
GSM9550083 Control 12wk 1 tdt RNA
GSM9550084 Control 12wk 1 non-tdt RNA
GSM9550085 Control 16wk 1 tdt RNA
GSM9550086 Control 16wk 2 tdt RNA
GSM9550087 Control 16wk 3 tdt RNA
GSM9550088 Control 16wk 4 tdt RNA
GSM9550089 Control 16wk 5 tdt RNA
GSM9550090 Control 16wk 1 nontdt RNA
GSM9550091 Control 16wk 2 nontdt RNA
GSM9550092 Control 16wk 3 nontdt RNA
GSM9550094 Control 16wk 4 nontdt RNA
GSM9550095 Control 16wk 5 nontdt RNA
GSM9550096 Tcf21KO 5wk 1 tdt RNA
GSM9550097 Tcf21KO 5wk 1 nontdt RNA
GSM9550098 Tcf21KO 12wk 1 tdt RNA
GSM9550099 Tcf21KO 12wk 1 nontdt RNA
GSM9550100 Tcf21KO 16wk 1 tdt RNA
GSM9550101 Tcf21KO 16wk 2 tdt RNA
GSM9550102 Tcf21KO 16wk 3 tdt RNA
GSM9550103 Tcf21KO 16wk 1 nontdt RNA
GSM9550104 Control 0wk tdt ATAC
GSM9550105 Control 0wk nontdt ATAC
GSM9550106 Control 5wk 1 tdt ATAC
GSM9550107 Control 5wk 2 tdt ATAC
GSM9550108 Control 5wk 1 nontdt ATAC
GSM9550110 Control 7wk 1 mixed ATAC
GSM9550111 Control 9wk 1 mixed ATAC
GSM9550113 Control 9wk 2 mixed ATAC

+ 14 more — browse all 54 samples with per-sample file links →

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Search all mouse ChIP / ATAC / CUT&Tag datasets in GEO →

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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.