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Immune Signaling Mediates Stromal Changes to Support Epithelial Reprogramming in Celiac Duodenum

GSE277276 Homo sapiens (human) Single-cell RNA-seq 34 samples Submitted 2025/07/30 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 34 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
34 samples: 34 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
Immune signaling mediates stromal changes to support epithelial reprogramming in celiac duodenumCell reports · 2025
Summary (written by the submitters)
Coeliac Disease (CeD) is a chronic autoimmune disorder affecting 0.5-1% of the general population with a wide geographical distribution. Despite recent efforts to deeply phenotype gluten-specific immune activation at the single cell level, recent clinical studies targeting gluten degradation and other immune tolerance mechanisms have been unsuccessful.
Read the full summary ↓
To this end, a deeper understanding of immune and non-immune cellular dynamics and interactions are required to characterize tissue-specific mechanisms responsible for CeD pathogenesis, repair, and resolution. Here, we assembled the most comprehensive scRNAseq dataset in Coeliac Disease to date, including 203,555 cells across 21 active CeD and 11 control duodenal samples. Compared to control duodenum, CeD was characterized by single cell differential changes in abundance, gene expression and cell-cell interactions across cellular compartments. In the immune compartments, CeD samples showed expected increases in plasma cell abundance and shifts toward type 1 effector biology (e.g., increase in cycling CD8pos, γδ T cells and IFNG transcriptional shifts) and Tfh-related biology (e.g., increases in IL21 signaling to effector T cells). In addition, activated myeloid subsets, including DC2 and monocytes, were increased in disease and were characterized by increased pro-inflammatory pathway expression, including IL-1β. Non-immune compartments showed increased stem/crypt and secretory enterocytes in CeD samples with a decrease in absorptive enterocytes, reflecting the villus atrophy and crypt hyperplasia hallmarks of CeD epithelial dysfunction. Accompanying the epithelial changes, distinct changes in stromal populations were identified, particularly with increases in abundance and transcriptional activity of NRG1 and SMOC2 fibroblasts. Cell-cell interaction analysis across multiple cellular compartments proposed a distinct increased role of fibroblasts to support the epithelial reprogramming of the increased stem/crypt epithelial fraction in CeD, mediated by myeloid derived IL-1β signal and lymphoid-derived IFN-γ. This dataset reveals a previously unknown role for T-myeloid-stromal-epithelial cell communication in CeD, highlighting key mechanisms of the tissue-level cellular dynamics in response to gluten ingestion.
Published in
Immune signaling mediates stromal changes to support epithelial reprogramming in celiac duodenum
Richards D, Sohn K, Basavarajappa SC et al. · Cell reports 2025 · PMID 40694474 · doi:10.1016/j.celrep.2025.116039
This dataset
Open GSE277276 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 GSE277276_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 PRJNA1161628 and SRA study SRP532925. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40694474) ↗
Study design
34 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 34 sample titles: 34 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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