GEO series

Single Cell Methylome Signatures Reveal Distinct Neuronal Populations and Regulatory Elements in the Mammalian Cortex

GSE97179 Homo sapiens (human) Single-cell RNA-seq 6172 samples Submitted 2017/08/10 Platform GPL20301Platform GPL21103

At a glance

Single-cell RNA-seq · human · 6172 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)
Samples: 6172 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 mammalian brain contains diverse neuron cell types with distinct physiological, morphological and molecular characteristics. However, a comprehensive assessment of the epigenetically distinct neuronal classes is currently missing. Cytosine DNA methylation is a stable epigenetic mark that distinguishes neuron types and marks gene regulatory elements.

Read the full summary ↓

We developed an efficient single-nucleus methylome sequencing approach that allows robust high-throughput neuron-type classification. We generated >6,000 single-nucleus methylomes and identified 16 mouse and 21 human neuronal subpopulations in the frontal cortex. Both CG and non-CG methylation exhibited cell type-specific distributions that recapitulate and extend findings from single neuron transcriptome profiling. Moreover, we found approximately 500,000 neuron-type-specific regulatory elements showing strong differential methylation in mouse and human cortex. Distinct methylation signatures identified an unique human Parvalbumin-expressing inhibitory sub-type and a layer 6 specific excitatory sub-type in mouse. Comparative epigenomic analysis showed stronger conservation of gene regulatory elements in inhibitory compared with excitatory neurons. These findings demonstrate the utility of single nucleus methylome profiling for both expanding the atlas of brain cell types and identifying regulatory elements that potentially drive these differences.

This dataset

Open GSE97179 in finder → Find more methylation 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 GSE97179_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 PRJNA380955 and SRA study SRP102717. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 28798132) ↗

Samples in this study

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

+ 6172 more — browse all 6172 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.