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Paired transcriptomics reveals similarities between cytokine-stimulated organoids and ulcerative colitis epithelial responses

GSE288517 Homo sapiens (human) Single-cell RNA-seq 23 samples Submitted 2026/05/26 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 23 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
23 samples: 23 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.
Summary (written by the submitters)
Background and aims: Anti-TNF therapy is central for treating moderate-to-severe ulcerative colitis (UC), yet ~40% of patients are primary non-responders. The molecular basis of non-response in colonic mucosa remains unclear, particularly in a spatial context. We aimed to identify compartment-specific molecular signatures in colonic epithelium and lamina propria distinguishing non-responders from responders with similar baseline disease activity.
Read the full summary ↓
Methods: UC patients with clear endoscopic response (n=6) or non-response (n=6) after 6 months of anti-TNF therapy were included. Baseline serum cytokines were profiled by multiplex. Pre-treatment inflamed and uninflamed colonic biopsies were laser microdissected into epithelial and lamina propria compartments for RNA sequencing. We explored differentially expressed genes, enrichment and weighted gene co-expression networks. Cellular deconvolution and pseudobulk validation were performed using an independent single-cell atlas, with confirmation by immunohistochemistry. Results: Baseline cytokines showed no significant difference, though responders had higher levels and non-responders greater variability. Inflammation induced more epithelial changes, dominated by interferon signalling and antigen presentation, versus cell cycle and adaptive immune responses in lamina propria. The main differences between responders and non-responders were observed in uninflamed lamina propria. Non-responders more resembled inflamed tissue, showing elevated cell cycle genes and a humoral activity–enriched co-expression network with systematically higher expression of immunoglobulin and B-cell–related genes. The differences disappeared with inflammation. Cellular deconvolution identified expression predominantly in IgA plasma cells, as confirmed through tissue staining and gene expression. Independent pseudobulk analyses validated the anti-TNF non-response-associated pattern. Conclusions: UC uninflamed lamina propria can harbor signatures predictive of anti-TNF non-response, involving cell cycle and humoral immune activation suggesting a TNF-independent immune architecture.
This dataset
Open GSE288517 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 GSE288517_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 PRJNA1217910. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
23 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 23 sample titles: 23 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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