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A cell-intrinsic glucocorticoid biosynthesis and sensing circuit maintains homeostatic Th17 cell state [RNA-seq]

GSE296482 Mus musculus (mouse) RNA-seq (gene expression) 31 samples Submitted 2026/06/12 Platform GPL21626Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 31 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
31 samples: 8 conditions, mostly in triplicateGroups 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.
Summary (written by the submitters)
T helper 17 (Th17) cells are heterogeneous; homeostatic Th17 (Th17Hom) cells maintain tissue homeostasis and pro-inflammatory Th17 (Th17Inf) cells drive autoimmune inflammation. IL-23 drives Th17Inf, but the signals that maintain Th17Hom remain unclear. Here, we found that differential glucocorticoid (GC) production downstream of CYP11A1, a critical enzyme for steroidogenesis, distinguished Th17Hom cells from Th17Inf cells.
Read the full summary ↓
Although TCR signaling opened the Cyp11a1 locus, TGFβ1 and IL-6, key cytokines for Th17Hom cell differentiation, maintained and amplified Cyp11a1. Th17Hom cell-derived GC signaled through the glucocorticoid receptor (GR), which was higher in Th17Hom cells compared to Th17Inf cells, thereby forming a circuit that maintained the homeostatic state. Integration of multi-omics data from CYP11A1- and GR-deficient Th17Hom cells revealed a gene network underlying this circuit. TGFβ1, a key node in the network, restored GC sensing to Th17Inf. We thus identify a GC signaling circuit that maintains Th17 homeostasis with implications for treating Th17-mediated autoimmunity.
This dataset
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.

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 GSE296482_RAW.tar (it can run to tens of gigabytes).

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.

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1259465 and SRA study SRP583369. Searching any of these in the dataset finder brings you back here.

Study design
8 conditions, mostly in triplicate
Cyp11a1-KO-CNS ×6 GR-CNS-WT ×5 Cyp11a1-WT-CNS ×4 GR-CNS-KO ×4 Ctrl-2d ×3 Dex-2d ×3 TGF-2d ×3 TG+De-2d ×3

Supports a between-group comparison across 31 samples.

How we read this: 8 replicated groups read from 31 sample titles; they account for 31 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.

Samples in this study
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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.