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Identification of Crohn’s disease subtypes in single cell RNA sequencing signatures of treatment naïve samples across the gastrointestinal tract

GSE290695 Homo sapiens (human) Single-cell RNA-seq 95 samples Submitted 2025/04/01 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 95 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
95 samples: 6 conditions, mostly in duplicateGroups 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)
Crohn’s disease (CD) is characterized by chronic inflammation throughout the intestines. Previous single cell transcriptomic studies investigated the effect of CD post therapeutic intervention, however, the extent to which therapy modifies the intestinal inflammatory environment remains to be elucidated. To this end, a treatment naïve pediatric CD cohort was recruited to investigate initial disease onset across the intestines, highlighting shared and tissue-specific consequences of disease.
Read the full summary ↓
Ileum, colon, and rectum biopsies were obtained, and single cell RNA-sequencing was performed. A clustering stability assessment workflow was developed to ensure clustering and downstream results were robust. Tensor decomposition was utilized to identify clinically meaningful groups within the intestines. Other bioinformatic tools were utilized to investigate differences across group status within each tissue. Expected cell types were identified after the clustering stability assessment was performed. Inflammation did not strongly influence cellular proportion profiles due to heterogeneity across donor and tissue. Tensor decomposition revealed distinct mesenchymal and cytotoxic T cell-mediated sources of disease pathology, corresponding to previously identified fibrotic and pro-inflammatory disease progression. Integrating transcriptomics and genome wide association summary statistics for CD suggested myeloid cells and T cells drive disease, highlighting potential cellular therapeutic targets. Tensor decomposition stratified donors into clinically meaningful groups based on their transcriptomic profile, suggesting these signatures can be utilized for personalized medicine.
This dataset
Open GSE290695 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 GSE290695_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 PRJNA1227436 and SRA study SRP565898. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly in duplicate
pediatric rectum, dono ×32 pediatric colon, dono ×30 pediatric ileum, dono ×27 pediatric colon, donor 15 sample ×2 pediatric colon, donor 16 sample ×2 pediatric colon, donor 28 sample ×2

Supports a between-group comparison across 95 samples.

How we read this: 6 replicated groups read from 95 sample titles; they account for 95 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

+ 55 more — browse all 95 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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