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Dietary cysteine enhances intestinal stemness via CD8+ T cell-derived IL-22

GSE279403 Mus musculus (mouse) RNA-seq (gene expression) 28 samples Submitted 2025/10/22 Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 28 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
28 samples: 7 × Small intestine, Lgr5-low, CD, vs 7 × Small intestine, Lgr5-low, Cystine, vs 7 × Small intestine, Lgr5-high, CD, vs 7 × Small intestine, Lgr5-high, Cystine,Groups 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)
A critical question in physiology is to understand how tissuesadaptand alter their composition in responsetodietary cues.The mammalian small intestine, avitaldigestive organ thatabsorbs nutrients, is maintainedby rapidly renewing Lgr5+ intestinalstem cells (ISCs) located attheintestinal crypt base.
Read the full summary ↓
While Lgr5+ ISCs drive intestinal adaptation by altering self-renewing and differentiating divisions inresponse todiverse diets such as high-fat diets and fasting regimens, little is known about howmicronutrients, like amino acids, instruct Lgr5+ ISCfate decisions to control intestinal homeostasis and repair after injury. Here, we demonstrate that cysteine, an essential amino acid, enhances theability of Lgr5+ ISCs to repair intestinal injury.
This dataset
Open GSE279403 in finder → Find more mouse 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 GSE279403_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: 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 PRJNA1172650 and SRA study SRP538425. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
7 × Small intestine, Lgr5-low, CD, vs 7 × Small intestine, Lgr5-low, Cystine, vs 7 × Small intestine, Lgr5-high, CD, vs 7 × Small intestine, Lgr5-high, Cystine,

Supports a between-group comparison across 28 samples.

How we read this: 4 replicated groups read from 28 sample titles; they account for 28 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
Datasets on a similar topic

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