Single cell RNA-seq of mouse brain microvascular fragments with Smart-seq2 method
Single-cell RNA-seq · mouse · 9 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
- 9 samples: 9 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 GSE313643_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 PRJNA1380455 and SRA study SRP654874. 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 9 sample titles: 9 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.
- GSM9371635 Brain aEC
- GSM9371636 Brain capilEC
- GSM9371637 Brain vEC
- GSM9371638 Brain CP-EC
- GSM9371639 Brain PC
- GSM9371640 Brain aSMC
- GSM9371641 Brain vSMC
- GSM9371642 Brain FB
- GSM9371643 Brain MG
- GSE311449 Single cell RNA-seq of mouse brain vascular cells with Smart-seq2 method 391 samples shares smart-seq2, rna-seq, method, brain
- GSE326452 Bulk RNA-seq of mouse brain following closed head injury: role of IL-1R1 signaling 200 samples shares rna-seq, brain
- GSE277487 Single-nucleus RNA-seq of mouse cortex following CCI-induced traumatic brain injury across multiple time points 12 samples shares rna-seq, brain
- GSE319252 Eicosapentaenoic Acid Reprograms Cerebrovascular Metabolism and Impairs Repair after Brain Injury, with Relevance to Chronic Traumatic Encephalopathy [mouse_brain_OnurE_RNA-seq] 12 samples shares rna-seq, brain
- GSE241165 Characterizing the gene expression profiles of 4T1 and 4T1.2 cells at different stages of bone metastsis using SMART-Seq2 technology 1248 samples shares smart-seq2
- GSE303081 A spatial and projection-based transcriptomic atlas of paraventricular hypothalamic cell types (Smart-Seq2) 81 samples shares smart-seq2
- GSE277789 Spatiotemporal and genetic cell lineage tracing of endodermal organogenesis at single-cell resolution [Smart-seq3-RNA-seq-add] 830 samples shares rna-seq
- GSE326100 scRNA-seq of mouse cochlea organoid cells 768 samples shares rna-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.