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Dictionary of human intestinal organoid responses to secreted niche factors at single cell resolution

GSE313368 Homo sapiens (human) Single-cell RNA-seq 39 samples Submitted 2025/12/11 Platform GPL34281Platform GPL34284Platform GPL30173
At a glance

Single-cell RNA-seq · human · 39 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
39 samples: 36 distinct titles, 2 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
Dictionary of human intestinal organoid responses to secreted niche factors at single cell resolutionNature communications · 2026
Summary (written by the submitters)
The intestinal epithelium is often a site of pathology, such as in inflammatory bowel disease (IBD), and its maintenance is highly modulated by interactions with the microenvironment. However, a systematic understanding of how the myriad of niche cues impact distinct epithelial cell types in a diseased context is still lacking. To address this gap, we first benchmarked diverse human colonic organoid injury models against IBD tissue and established a disease-relevant model of epithelial inflammation using TNFɑ, IFNɣ, and IL1β.
Read the full summary ↓
Using this system, we built a dictionary of epithelial responses to 79 secreted niche factors at single cell resolution via donor-pooled, multiplexed single cell RNA-sequencing (scRNA-seq). The comprehensive nature of our atlas allowed us to map relationships between perturbations, infer the function of less well-characterized ligands, and identify cell type-specific perturbed pathways. Finally, we established the relevance of organoid-derived gene programs by mapping them to single cell and spatial atlases of human IBD tissue. Our resource offers a global view of epithelial responses to microenvironmental cues in a physiologically relevant disease context and generates new hypotheses for signaling factors that may be involved in epithelial homeostasis and repair.
Published in
Dictionary of human intestinal organoid responses to secreted niche factors at single cell resolution
Capeling MM, Chen B, Aliar K et al. · Nature communications 2026 · PMID 41513700 · doi:10.1038/s41467-025-68247-6
This dataset
Open GSE313368 in finder → Find more human 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 GSE313368_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 PRJNA1379334 and SRA study SRP654366. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41513700) ↗
Study design
36 distinct titles, 2 of them repeated
Cultrex Experiment ×3 Injury Atlas, Control ×2

How we read this: Read from 39 sample titles: 36 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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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.