GEO series

Single-cell chromatin accessibility data using scATAC-seq

GSE74310 Homo sapiens (human) Single-cell RNA-seq 576 samples Submitted 2016/06/01 Platform GPL18573

At a glance

Single-cell RNA-seq · human · 576 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Topic: Acute myeloid leukemiaThis series is part of our curated briefing on it.
Samples: 576 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)

A single hematopoietic stem cell can give rise to all blood cells with remarkable fidelity. Here, we define the chromatin accessibility and transcriptional landscape controlling this process in thirteen primary cell types that traverse the hematopoietic hierarchy. Exploiting the finding that enhancer landscapes better reflect cell identity than mRNA levels, we enable "enhancer cytometry" for accurate enumeration of pure cell types from complex populations.

Read the full summary ↓

We further reveal the lineage ontogeny of genetic elements linked to diverse human diseases. In acute myeloid leukemia, chromatin accessibility reveals distinctive regulatory evolution in pre-leukemic HSCs (pHSCs), leukemia stem cells, and leukemic blasts. These leukemic cells demonstrate unique lineage infidelity, confirmed by single cell regulomes. We further show that pHSCs have a competitive advantage that is conferred by reduced chromatin accessibility at HOXA9 targets and is associated with adverse patient outcomes. Thus, regulome dynamics can provide diverse insights into human hematopoietic development and disease.

This dataset

Open GSE74310 in finder → Find more human ChIP / ATAC / CUT&Tag datasets → Acute myeloid leukemia landscape →

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 GSE74310_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 PRJNA299657 and SRA study SRP065190. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 27526324) ↗

Samples in this study

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

+ 576 more — browse all 576 samples with per-sample file links →

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Search all human ChIP / ATAC / CUT&Tag datasets in GEO →

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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.