Single-cell Sequencing Analysis-driven Nanomedicine Design for Mimicking Endogenous Immune Repair in Myocardial Infarction
Single-cell RNA-seq · mouse · 18 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
- 18 samples: 18 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.
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 GSE288676_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 PRJNA1219050 and SRA study SRP561432. 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 18 sample titles: 18 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.
- GSM8772907 PBS1,Day3
- GSM8772908 PBS2,Day3
- GSM8772909 PBS3,Day3
- GSM8772910 aNMP-2a_1,Day3
- GSM8772911 aNMP-2a_2,Day3
- GSM8772912 aNMP-2a_3,Day3
- GSM8772913 aNMP-2a0_1,Day7
- GSM8772914 aNMP-2a0_2,Day7
- GSM8772915 aNMP-2a0_3,Day7
- GSM8772916 aNMP-2ab_1,Day7
- GSM8772917 aNMP-2ab_2,Day7
- GSM8772918 aNMP-2ab_3,Day7
- GSM8772919 aNMP-2ab0_1,Day14
- GSM8772920 aNMP-2ab0_2,Day14
- GSM8772921 aNMP-2ab0_3,Day14
- GSM8772922 aNMP-2abc_1,Day14
- GSM8772923 aNMP-2abc_2,Day14
- GSM8772924 aNMP-2abc_3,Day14
- GSE308859 Integrated Spatial Transcriptomics and Single-Cell RNA Sequencing Reveal Lars2-Mediated Spatiotemporal Dynamics of Myocardial Remodeling in a Mouse Model of Transverse Aortic Constriction (TAC) 8 samples shares single-cell, myocardial
- GSE192687 Single-cell RNAseq analysis of renal endothelial cells in murine diabetic nephropathy 5464 samples shares single-cell
- GSE277789 Spatiotemporal and genetic cell lineage tracing of endodermal organogenesis at single-cell resolution [Smart-seq3-RNA-seq-add] 830 samples shares single-cell
- GSE290574 Gene Regulatory Networks Orchestrating Oocyte Fate Bifurcation in Primordial Follicles Revealed by Single-Cell Transcriptomics 386 samples shares single-cell
- GSE166225 Inference of emergent spatio-temporal processes from single-cell sequencing reveals feedback between de novo DNA methylation and chromatin condensates [RNA-seq] 288 samples shares single-cell
- GSE239602 Single-cell and spatial transcriptomics uncovers the role of senescent vascular cells in pathological arterial remodeling during murine atherosclerosis 82 samples shares single-cell
- GSE278990 Single-cell screens identify ADAM12 as a fibroblast checkpoint impeding anti-tumor immunity 73 samples shares single-cell
- GSE254396 Single-cell and bulk RNA sequencing of mouse atherosclerosis disease stage course [TRAP-seq] 71 samples shares single-cell
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.