GEO series

Human and mouse trigeminal ganglia cell atlas implicates multiple cell types in migraine

GSE197289 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 36 samples Submitted 2022/02/26 Platform GPL21697Platform GPL21626

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 36 samples

What it is: ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism: Mus musculus (mouse)
Samples: 36 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 sensitization of trigeminal ganglion neurons contributes to primary headache disorders such as migraine, but the specific neuronal and non-neuronal trigeminal subtypes involved remain unclear. We thus developed a cell atlas in which human and mouse trigeminal ganglia are transcriptionally and epigenomically profiled at single-cell resolution.

Read the full summary ↓

These data describe evolutionarily conserved and human-specific gene expression patterns within each trigeminal ganglion cell type, as well as the transcription factors and gene regulatory elements that contribute to cell-type-specific gene expression. We then leverage these data to identify trigeminal ganglion cell types that are implicated both by human genetic variation associated with migraine and two mouse models of headache. This trigeminal ganglion cell atlas improves our understanding of the cell types, genes, and epigenomic features involved in headache pathophysiology and establishes a rich resource of cell-type-specific molecular features to guide the development of more selective treatments for headache and facial pain.

This dataset

Open GSE197289 in finder → Find more 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 GSE197289_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA809683 and SRA study SRP361144. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 35349784) ↗

Samples in this study

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

+ 36 more — browse all 36 samples with per-sample file links →

Datasets on a similar topic

GSE244512 KDM6A Regulates Immunogenicity of Multiple Myeloma 70 samples shares multiple
GSE303731 scRNA-seq of vascular endothelial cells from multiple mouse tissues and human limb muscle 8 samples shares multiple
GSE244510 KDM6A Regulates Immunogenicity of Multiple Myeloma (ChIP-seq) 24 samples shares multiple
GSE309679 Addressing the multiple facets of ligand-receptor network inference 13 samples shares multiple
GSE68103 Multiplex Single Cell Profiling of Chromatin Accessibility by Combinatorial Cellular Indexing 12 samples shares multiple

Search all RNA-seq datasets in GEO →

Share this dataset

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.