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Inflammatory transcriptomic signatures and cell type compositions in inflamed and non-inflamed colonic mucosa of ulcerative colitis

GSE243625 Homo sapiens (human) Single-cell RNA-seq 45 samples Submitted 2024/03/19 Platform GPL16791
At a glance

Single-cell RNA-seq · human · 45 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
45 samples: 15 × N vs 15 × I vs 15 × AGroups 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
Inflammatory transcriptomic signatures and cell type compositions in inflamed and non-inflamed colonic mucosa of ulcerative colitisGenes & diseases · 2025
Summary (written by the submitters)
Ulcerative colitis is a chronic inflammatory disorder in the lower part of the digestive system. Despite of many functional studies, the hidden mechanisms of this complex disease limit complete remission. Here, we investigated transcriptomic signature of ulcerative colitis using RNA sequencing obtained from 15 active (inflamed), 15 inactive (non-inflamed) samples of ulcerative colitis, and 15 healthy controls.
Read the full summary ↓
The transcriptomics profiling revealed that inflammatory transcriptomic signature was highly enriched in active samples compared to inactive or healthy control samples. However, this bulk RNAseq cannot provide the cell type information, which play a key role in such a chronic inflammation. To overcome this limitation, we borrowed the power of single cell RNAseq. Deconvolution of bulk RNAseq based on the single cell expression estimated active UC enriched cell types including inflammatory fibroblast and inflammatory monocytes. This integrative approach using bulk and single cell transcriptomics provide not only target genes but also target cell types of ulcerative colitis for therapeutic purpose.
Published in
Inflammatory transcriptomic signatures and cell type compositions in inflamed and non-inflamed colonic mucosa of ulcerative colitis
Song EM, Saqib J, Joo YH et al. · Genes & diseases 2025 · PMID 39926325 · doi:10.1016/j.gendis.2024.101447
This dataset
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.

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 GSE243625_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1019284 and SRA study SRP461950. Searching any of these in the dataset finder brings you back here.

Study design
15 × N vs 15 × I vs 15 × A

Supports a between-group comparison across 45 samples.

How we read this: 3 replicated groups read from 45 sample titles; they account for 45 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

+ 5 more — browse all 45 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.