GEO series

Single-cell chromatin profiles reveal a spectrum of stem cell epigenetic states

GSE70253 Mus musculus (mouse) Single-cell RNA-seq 123 samples Submitted 2015/10/12 Platform GPL17021

At a glance

Single-cell RNA-seq · mouse · 123 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: 123 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)

Chromatin profiling provides a versatile means to chart gene regulatory elements and investigate their mechanisms of regulation. However, current methods yield ensemble profiles that are insensitive to cell-to-cell variation. This is a major limitation as transcriptional states and phenotypes vary markedly across individual cells. Here we combined microfluidics, DNA barcoding and sequencing to profile chromatin at single-cell resolution.

Read the full summary ↓

We demonstrate the technology by profiling a mixture of ES cells, fibroblasts and hematopoietic progenitors, and deconvoluting in silico high-quality maps for each cell type. We find that regulatory elements differ in their variability, with bivalent promoters showing particularly heterogeneous patterns across single-cells that in contrast with to the relative stability of active promoters and enhancers. Finally, we document a spectrum of ES cell states whose chromatin landscapes exhibit varying degrees of pluripotency and priming signatures. Our study presents an innovative single-cell analysis tool for single-cell analysis, and reveals aspects of epigenetic heterogeneity not be captured by transcriptional analysis alone.

This dataset

Open GSE70253 in finder → Find more mouse ChIP / ATAC / CUT&Tag 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 GSE70253_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 PRJNA288086 and SRA study SRP059893. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 26458175) ↗

Samples in this study

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+ 123 more — browse all 123 samples with per-sample file links →

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