Transcriptomic alterations across various cell types in the PFC
Single-nucleus RNA-seq · mouse · 12 samples
- What it is
- Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
- Organism
- Mus musculus (mouse)
- Samples
- 12 samples: 6 × Dep vs 6 × CtrlGroups are read from the sample names, not from the paper. Check the list below.
- 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.
- Paper
- Single-cell transcriptomics reveals Sox6 positive interneurons enriched in the prefrontal cortex of female mice vulnerable to chronic social stressMolecular psychiatry · 2025
Read the full summary ↓
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE291951_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA1236238 and SRA study SRP570440. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 12 samples.
How we read this: 2 replicated groups read from 12 sample titles; they account for 12 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
- GSM8844907 Dep1 Dep
- GSM8844908 Dep2 Dep
- GSM8844909 Dep3 Dep
- GSM8844910 Dep4 Dep
- GSM8844911 Dep5 Dep
- GSM8844912 Dep6 Dep
- GSM8844913 Ctrl1 Ctrl
- GSM8844914 Ctrl2 Ctrl
- GSM8844915 Ctrl3 Ctrl
- GSM8844916 Ctrl4 Ctrl
- GSM8844917 Ctrl5 Ctrl
- GSM8844918 Ctrl6 Ctrl
- GSE185862 A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation 95 samples shares across, types
- GSE223049 Bulk RNA-seq in young (2 months) and aged (22-24 months) mice across 23 cell-types 170 samples shares across, types
- GSE271670 Identifying specific functional roles for senescence across cell types 32 samples shares across, types
- GSE332646 Bulk RNA-seq of juvenile mouse liver following NDMA or saline treatment in WT and Mgmt-/- genotypes across multiple timepoints 214 samples shares across, types
- GSE237750 Transcriptomic profiling across 20 CC-RIX genetic backgrounds with and without high-fat high-sugar (HFHS) diet, and with and without metformin 2113 samples shares across
- GSE276656 Gene expression + ATAC multiome profiling of mPFC engram cells after OSK partial reprogramming 20 samples shares pfc
- GSE237737 Transcriptomic profiling across 20 CC-RIX genetic backgrounds with and without high-fat high-sugar (HFHS) diet, and with and without metformin [adipose] 705 samples shares across
- GSE237743 Transcriptomic profiling across 20 CC-RIX genetic backgrounds with and without high-fat high-sugar (HFHS) diet, and with and without metformin [liver] 705 samples shares across
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.