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Cellular Taxonomy of the Mouse Striatum as Revealed by Single-Cell RNAseq

GSE82187 Mus musculus (mouse) Single-cell RNA-seq 1208 samples Submitted 2016/07/15 Platform GPL13112Platform GPL19057
At a glance

Single-cell RNA-seq · mouse · 1208 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
1208 samples
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.
Summary (written by the submitters)
The striatum contributes to many cognitive processes and disorders, but its cell types are incompletely characterized. We show that microfluidic and FACS-based single-cell RNA sequencing of mouse striatum provides a well-resolved classification of striatal cell type diversity. Transcriptome analysis revealed 10 differentiated distinct cell types, including neurons, astrocytes, oligodendrocytes, ependymal, immune, and vascular cells, and enabled the discovery of numerous novel marker genes.
Read the full summary ↓
Furthermore, we identified two discrete subtypes of medium spiny neurons (MSN) which have specific markers and which overexpress genes linked to cognitive disorders and addiction. We also describe continuous cellular identities, which increase heterogeneity within discrete cell types. Finally, we identified cell type specific transcription and splicing factors that shape cellular identities by regulating splicing and expression patterns. Our findings suggest that functional diversity within a complex tissue arises from a small number of discrete cell types, which can exist in a continuous spectrum of functional states.
This dataset
Open GSE82187 in finder → Find more mouse 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 GSE82187_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 PRJNA324641 and SRA study SRP076212. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 27425622) ↗
Samples in this study

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