scRNA-seq of biophysically separated immune cells in atherosclerosis
Single-cell RNA-seq · human · 8 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
- 8 samples: 4 × Healthy vs 4 × AtherosclerosisGroups 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.
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 GSE335789_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 PRJNA1479448 and SRA study SRP710465. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 8 samples.
How we read this: 2 replicated groups read from 8 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.
- GSM9821013 Healthy1 Healthy
- GSM9821014 Healthy2 Healthy
- GSM9821015 Healthy3 Healthy
- GSM9821016 Healthy4 Healthy
- GSM9821017 Atherosclerosis1 Atherosclerosis
- GSM9821018 Atherosclerosis2 Atherosclerosis
- GSM9821019 Atherosclerosis3 Atherosclerosis
- GSM9821020 Atherosclerosis4 Atherosclerosis
- GSE272294 Decoding the immune checkpoint signatures in human atherosclerosis: implications of type 2 diabetes and lipid-lowering intervention [human PBMC scRNA-Seq] 24 samples shares atherosclerosis, scrna-seq
- GSE253903 High-Dimensional Single-Cell Multimodal Landscape of Human Carotid Atherosclerosis [scRNA-seq] 12 samples shares atherosclerosis, scrna-seq
- GSE309595 Longitudinal Analysis of Multiple Myeloma: Therapeutic Response and Immune Microenvironment Dynamics in the Context of Tumor Heterogeneity [scRNA-seq] 270 samples shares scrna-seq
- GSE278450 Glioblastoma scRNA-seq shows treatment-induced, immune-dependent increase in mesenchymal cancer cells and structural variants in distal neural stem cells 22 samples shares scrna-seq
- GSE297041 Chemoradiation Reprograms Tumor Cells and Immune Microenvironment In Cervical Cancer [scRNA-seq] 18 samples shares scrna-seq
- GSE267033 Single-cell landscape of peripheral immune cells in MAFLD/MASH [scRNA-seq] 15 samples shares scrna-seq
- GSE190564 Divergent spatial microdomains drive inflammation and repair in Ulcerative and Immune Checkpoint Therapy Colitis - whole tissue scRNA-Seq 62 samples shares scrna-seq
- GSE246215 Cross-tissue human fibroblast atlas reveals myofibroblast subtypes with distinct roles in immune modulation [scRNA-seq] 38 samples shares scrna-seq
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.