GEO series

The transcriptional landscape of mouse blood stem/progenitor cell transitions at single cell resolution

GSE81682 Mus musculus (mouse) Single-cell RNA-seq 3840 samples Submitted 2016/07/04 Platform GPL17021

At a glance

Single-cell RNA-seq · mouse · 3840 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: 3840 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)

Current gene-expression databases for the haematopoietic system provide information on gene expression profiles present in bulk populations. Although informative, these studies lack the resolution that can be gained at a single-cell level. In particular, population-average data assumes homogeneity within the population and may as such obscure the ability to detect the heterogeneity of decision-making processes in individual cells.

Read the full summary ↓

Here we report 1656 single cell transcriptomes analysed by single-cell RNA sequencing. Cells were FACS sorted on broad gates encompassing haematopoietic stem and progenitor populations (HSPCs), with index sorting data collected to permit retrospective identification of populations by surface marker expression. Our dataset thus represents the gene expression landscape of HSPCs at single-cell resolution, capturing the heterogeneity in and between cell populations. Pseudotime analysis visualized haematopoietic stem (HSC) to progenitor transitions, identified HSC as well as lineage-specific transcriptional programs, and also highlighted putative lineage branching points. To provide access to the wider scientific community, a user-friendly website was developed with intuitive search and display functionality.

This dataset

Open GSE81682 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 GSE81682_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 PRJNA322317 and SRA study SRP075481. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 27365425) ↗

Samples in this study

The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.

+ 3840 more — browse all 3840 samples with per-sample file links →

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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.