GEO series

High-Dimensional Single-Cell Multimodal Landscape of Human Carotid Atherosclerosis [scRNA-seq]

GSE253903 Homo sapiens (human) Single-cell RNA-seq 12 samples Submitted 2024/04/12 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 12 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Samples: 12 samples: 12 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: High-Dimensional Single-Cell Multimodal Landscape of Human Carotid AtherosclerosisArteriosclerosis, thrombosis, and vascular biology · 2024

Summary (written by the submitters)

Atherosclerotic plaques are complex tissues composed of a heterogeneous mixture of cells. However, our understanding of the comprehensive transcriptional and phenotypical landscape of the cells within these lesions is limited.

Read the full summary ↓

To characterize the landscape of human carotid atherosclerosis in greater detail, we combined cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and single-cell RNA sequencing (scRNA-seq) to classify all cell types within lesions (n=21; 13 symptomatic) to achieve a comprehensive multimodal understanding of the cellular identities of atherosclerosis and their association with clinical pathophysiology. We identified 25 cell populations, each with a unique multi-omic signature, including macrophages, T cells, NK cells, mast cells, B cells, plasma cells, neutrophils, dendritic cells, endothelial cells, fibroblasts, and smooth muscle cells (SMCs). Among the macrophages, we identified 2 proinflammatory subsets enriched in IL1B or C1Q expression, 2 TREM2 positive foam cells (one expressing inflammatory genes), and subpopulations with a proliferative gene signature and SMC-specific gene signature with fibrotic pathways upregulated. Further characterization revealed various subsets of SMCs and fibroblasts, including SMC-derived foam cells. These foamy SMCs were localized in the deep intima of coronary atherosclerotic lesions. Utilizing CITE-seq data, we developed a flow cytometry panel, using cell surface proteins CD29, CD142, and CD90, to isolate SMC-derived cells from lesions. Lastly, we observed reduced proportions of efferocytotic macrophages, classically activated endothelial cells, and contractile and modulated SMC-derived cells, while inflammatory SMCs were enriched in plaques of clinically symptomatic vs asymptomatic patients. Our multimodal atlas of cell populations within atherosclerosis provides novel insights into the diversity, phenotype, location, isolation, and clinical relevance of the unique cellular composition of human carotid atherosclerosis. These findings facilitate both the mapping of cardiovascular disease susceptibility loci to specific cell types as well as the identification of novel molecular and cellular therapeutic targets for the treatment of the disease.

Published in

High-Dimensional Single-Cell Multimodal Landscape of Human Carotid Atherosclerosis

Bashore AC, Yan H, Xue C et al. · Arteriosclerosis, thrombosis, and vascular biology 2024 · PMID 38385291 · doi:10.1161/ATVBAHA.123.320524

This dataset

Open GSE253903 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 GSE253903_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 PRJNA1067646 and SRA study SRP485269. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38385291) ↗

Study design

12 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 12 sample titles: 12 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

GSM8029962 carotid_4, scRNA-seq
GSM8029963 carotid_6, scRNA-seq
GSM8029964 carotid_9, scRNA-seq
GSM8029965 carotid_13, scRNA-seq
GSM8029966 carotid_14, scRNA-seq
GSM8029967 carotid_15, scRNA-seq
GSM8029968 carotid_16, scRNA-seq
GSM8029969 carotid_17, scRNA-seq
GSM8029970 carotid_18, scRNA-seq
GSM8029971 carotid_19, scRNA-seq
GSM8029972 carotid_20, scRNA-seq
GSM8029973 carotid_21, scRNA-seq

Datasets on a similar topic

GSE253904 High-Dimensional Single-Cell Multimodal Landscape of Human Carotid Atherosclerosis 24 samples shares high-dimensional, atherosclerosis, single-cell, multimodal, carotid
GSE294300 Single-cell gene expression profiles of cells from primary colorectal cancer and adjacent normal tissue [scRNA-Seq] 36 samples shares single-cell, scrna-seq
GSE337246 Integrated single-cell profiling of RNA and DNA interactomes reveals targetable chromatin architectures in cancer [scRNA-Seq organoids] 35 samples shares single-cell, scrna-seq
GSE278688 Integrated Single-Cell and Spatial Transcriptomics Uncover Distinct Cellular Subtypes Involved in Neural Invasion in PDAC [scRNA-Seq] 29 samples shares single-cell, scrna-seq
GSE327101 Single-Cell Multimodal Atlas Reveals Regulatory Programs in the Human Brain 220 samples shares single-cell, multimodal
GSE279945 OP3: single-cell multimodal dataset in PBMCs for perturbation prediction benchmarking 102 samples shares single-cell, multimodal
GSE332630 Multimodal single-cell analyses reveal distinct fusion-regulated transcriptional programs in Ewing sarcoma 22 samples shares single-cell, multimodal
GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 samples shares single-cell

Search all human RNA-seq datasets in GEO →

Share this dataset

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.