GEO series

Mice are not men: scRNA-sequencing of the human atherosclerotic plaque identifies species-specific immune cell phenotypes that associate with clinical disease

GSE245373 Mus musculus (mouse) Single-cell RNA-seq 11 samples Submitted 2024/08/06 Platform GPL24676Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 11 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: 11 samples: 8 × PlaqueGroups 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: Cross-species single-cell RNA sequencing reveals divergent phenotypes and activation states of adaptive immunity in human carotid and experimental murine atherosclerosisCardiovascular research · 2024

Summary (written by the submitters)

Local and systemic immunity shapes atherosclerosis, the most frequent cause of cardiovascular disease. The distinct function of immune cells has been mostly defined by preclinical mouse studies, while recent immune cell atlases of human atherosclerosis suggest divergent immune cell mechanisms. Until now, it is not clear whether genetic animal models replicate the immunological complexity of human disease.

Read the full summary ↓

Here we demonstrate significant variations in the immune cell composition in murine and human atherosclerotic plaques by integrative single-cell RNA-sequencing. In contrast to widely applied mouse models, human plaque leukocytes primarily contained various T-cell phenotypes displaying activation, memory formation, and pro-inflammatory signaling. In a clinical validation cohort, the abundance of activated T cell clusters in human plaques associated with the extend of clinical disease emphasizing the specific role of T-cell-driven immunity in plaque development and instability. Collectively, our study questions the translatability of mouse models for studying the immune system in atherosclerosis.

Published in

Cross-species single-cell RNA sequencing reveals divergent phenotypes and activation states of adaptive immunity in human carotid and experimental murine atherosclerosis

Horstmann H, Michel NA, Sheng X et al. · Cardiovascular research 2024 · PMID 39041203 · doi:10.1093/cvr/cvae154

This dataset

Open GSE245373 in finder → Find more 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 GSE245373_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 PRJNA1027902. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39041203) ↗

Study design

8 × Plaque

Supports a between-group comparison across 8 samples.

How we read this: 1 replicated groups read from 11 sample titles; they account for 8 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

GSM7841477 Plaque_1 Plaque
GSM7841478 Plaque_2 Plaque
GSM7841479 Plaque_3 Plaque
GSM7841480 Plaque_4 Plaque
GSM7841481 Plaque_5 Plaque
GSM7841482 Plaque_6 Plaque
GSM7841483 Plaque_7 Plaque
GSM7841484 Plaque_8 Plaque
GSM7841485 baseline_ApoE
GSM7841486 adv_ApoE
GSM7841487 adv_Wt

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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.