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Reveal the key mediators of inflammation in Crohn's disease undergoing biological treatments

GSE266616 Homo sapiens (human) Single-cell RNA-seq 85 samples Submitted 2024/12/16 Platform GPL18573Platform GPL24676
At a glance

Single-cell RNA-seq · human · 85 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
Homo sapiens (human)
Samples
85 samples: 85 samples, each with a distinct title — no replicated groupsGroups 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
Multiomic analysis reveals cellular, transcriptomic and epigenetic changes in intestinal pouches of ulcerative colitis patientsNature communications · 2025
Summary (written by the submitters)
Crohn’s disease (CD) is a major subtype of life-long Inflammatory Bowel Disease (IBD) affecting a growing population in United States. To advance the knowledge in pathology, our study investigates into the common inflammatory feature in CD across different inflammation statuses by single-cell transcriptomics. First, we provided a detailed mucosal cellular landscape of terminal ileum (TI) and ascending colon (AC), with endoscopic diagnosis (CD inflamed, adjacent-to-inflamed, non-inflamed and non-IBD control), including a cohort of patients undergoing biological treatments.
Read the full summary ↓
Differences between regions (TI and AC), and features of mucosal inflammation status in cellular composition are captured within the cellular landscapes. Second, we identified an inflammation cascade that include follicle-associated enterocytes and macrophages via molecules like CCL20 and S100As, as key mediators of CD inflammation. These key mediators serve as hubs in imputed cell-cell interactions under CD inflammation, expanding the interplay network with increasing disease severity. Finally, we demonstrate a scoring method by surveying the mucosa, using GWAS loci, and transcriptome features including differentially expressed genes (DEGs), and genes from a novel singular value decomposition (SVD)-based co-expression network analysis framework. Applying independently to the epithelial and immune lineage, we identify inflammation signatures that reflect disease severity. Furthermore, we report a short, robust list of epithelial and immune transcript makers whose expressions harmonize with endoscopic diagnosis and histological assessment. These markers are merely within known IBD GWAS loci. Collectively, this study narrates a shared inflammation pattern involving cells and cytokines for active CD and present a short list of markers for metrical inflammation assessment.
Published in
Multiomic analysis reveals cellular, transcriptomic and epigenetic changes in intestinal pouches of ulcerative colitis patients
Zhao Y, Zhou R, Xie B et al. · Nature communications 2025 · PMID 39837850 · doi:10.1038/s41467-025-56212-2
This dataset
Open GSE266616 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 GSE266616_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 PRJNA1107958 and SRA study SRP505777. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39837850) ↗
Study design
85 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 85 sample titles: 85 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

+ 45 more — browse all 85 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.