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Early-life RNA sequencing of colon stromal cells and iNKT cells

GSE297816 Mus musculus (mouse) Single-cell RNA-seq 40 samples Submitted 2026/04/07 Platform GPL21273Platform GPL24247Platform GPL19057
At a glance

Single-cell RNA-seq · mouse · 40 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. 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
40 samples: 32 distinct titles, 8 of them repeatedGroups 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
Early-life Wnt4 expressing colon stromal cells orchestrate lifelong mucosal homeostasis via BMP-driven iNKT cell imprintingNature communications · 2026
Summary (written by the submitters)
The early-life intestinal microenvironment plays a pivotal role in shaping immune cell development, yet the mechanisms regulating colonic invariant natural killer T (iNKT) cells remain incompletely understood. To investigate how embryonic macrophages regulate iNKT cells during early life, we collected Day 9 whole-colon lamina propria cells after embryonic macrophage depletion and performed both bulk RNA sequencing and single-cell RNA sequencing.
Read the full summary ↓
We also performed bulk RNA sequencing on distinct colonic stromal cell populations, conventional αβT cells, and iNKT cells under homeostatic early-life conditions to identify factors that directly imprint iNKT cells during this developmental period.
Published in
Early-life Wnt4 expressing colon stromal cells orchestrate lifelong mucosal homeostasis via BMP-driven iNKT cell imprinting
Lin X, Lee CH, Zhang T et al. · Nature communications 2026 · PMID 42091861 · doi:10.1038/s41467-026-72734-9
This dataset
Open GSE297816 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 GSE297816_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 PRJNA1266225 and SRA study SRP586908. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42091861) ↗
Study design
32 distinct titles, 8 of them repeated
WT, P1 stromal cell_1, Day ×2 WT, P1 stromal cell_2, Day ×2 WT, P1 stromal cell_3 Day ×2 WT, P1 stromal cell_4, Day ×2 WT, P2 stromal cell_1, Day ×2 WT, P2 stromal cell_2, Day ×2 WT, P2 stromal cell_3 Day ×2 WT, P2 stromal cell_4, Day ×2

How we read this: Read from 40 sample titles: 32 distinct titles with little repetition. 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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