Single-cell multimodal profiling of atherosclerosis identifies CD200 as a cell surface lineage marker of vascular smooth muscle cells and their derived cells
Single-cell RNA-seq · mouse · 24 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
- 24 samples: 24 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
- Single-Cell Multimodal Profiling of Atherosclerosis Identifies CD200 as a Cell Surface Lineage Marker of Vascular Smooth Muscle Cells and Their Derived CellsCirculation · 2024
Read the full summary ↓
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE246779_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA1034596 and SRA study SRP469709. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 24 sample titles: 24 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.
- GSM7876627 ZsGreen1+, Ldlr KO, 8 weeks WD [RBM022]
- GSM7876628 ZsGreen1+, Ldlr KO, 8 weeks WD [RBM022F]
- GSM7876629 ZsGreen1+, Ldlr KO, 8 weeks WD [RBM022M]
- GSM7876630 ZsGreen1-, Ldlr KO, 8 weeks WD [RBM023]
- GSM7876631 ZsGreen1-, Ldlr KO, 8 weeks WD [RBM023F]
- GSM7876632 ZsGreen1-, Ldlr KO, 8 weeks WD [RBM023M]
- GSM7876633 ZsGreen1+, Ldlr KO, 16 weeks WD [RBM024]
- GSM7876634 ZsGreen1+, Ldlr KO, 16 weeks WD [RBM024F]
- GSM7876635 ZsGreen1+, Ldlr KO, 16 weeks WD [RBM024M]
- GSM7876636 ZsGreen1-, Ldlr KO, 16 weeks WD [RBM025]
- GSM7876637 ZsGreen1-, Ldlr KO, 16 weeks WD [RBM025F]
- GSM7876638 ZsGreen1-, Ldlr KO, 16 weeks WD [RBM025M]
- GSM7876639 ZsGreen1+, Ldlr KO, 26 weeks WD [RBM032]
- GSM7876640 ZsGreen1+, Ldlr KO, 26 weeks WD [RBM032F]
- GSM7876641 ZsGreen1+, Ldlr KO, 26 weeks WD [RBM032M]
- GSM7876642 ZsGreen1-, Ldlr KO, 26 weeks WD [RBM033]
- GSM7876643 ZsGreen1-, Ldlr KO, 26 weeks WD [RBM033F]
- GSM7876644 ZsGreen1-, Ldlr KO, 26 weeks WD [RBM033M]
- GSM7876645 ZsGreen1+, Ldlr KO, 0 week WD [RBM042]
- GSM7876646 ZsGreen1+, Ldlr KO, 0 week WD [RBM042F]
- GSM7876647 ZsGreen1+, Ldlr KO, 0 week WD [RBM042M]
- GSM7876648 ZsGreen1-, Ldlr KO, 0 week WD [RBM043]
- GSM7876649 ZsGreen1-, Ldlr KO, 0 week WD [RBM043F]
- GSM7876650 ZsGreen1-, Ldlr KO, 0 week WD [RBM043M]
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- GSE239591 Single-cell and spatial transcriptomics uncovers the role of senescent vascular cells in pathological arterial remodeling during murine atherosclerosis 6 samples shares atherosclerosis, single-cell, vascular
- GSE215963 Subclonal evolution revealed by single-cell profiles of expressed mutations identifies adaptation mechanisms to immunotherapy in melanoma. 1497 samples shares single-cell, identifies
- GSE277789 Spatiotemporal and genetic cell lineage tracing of endodermal organogenesis at single-cell resolution [Smart-seq3-RNA-seq-add] 830 samples shares single-cell, lineage
- GSE254396 Single-cell and bulk RNA sequencing of mouse atherosclerosis disease stage course [TRAP-seq] 71 samples shares atherosclerosis, single-cell
- GSE225615 Single-cell multiomic analysis identifies macrophage subpopulations in promoting cardiac repair 48 samples shares single-cell, identifies
- GSE269748 A single-cell atlas deconstructs heterogeneity across multiple models in murine traumatic brain injury and identifies novel cell-specific targets 36 samples shares single-cell, identifies
- GSE297298 Single-cell RNA-sequencing identifies anti-cancer immune phenotypes in the early lung metastatic niche during breast cancer 18 samples shares single-cell, identifies
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.