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The immune profile of circulating autoreactive CD4 T cells is imprinted through tissue activation during autoimmune liver diseases (Exp 1)

GSE270732 Homo sapiens (human) Single-cell RNA-seq 13 samples Submitted 2024/12/19 Platform GPL30173
At a glance

Single-cell RNA-seq · human · 13 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
13 samples: 13 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
Single cell profiling of circulating autoreactive CD4 T cells from patients with autoimmune liver diseases suggests tissue imprintingNature communications · 2025
Summary (written by the submitters)
Autoimmune liver diseases (AILD) are immune-mediated disorders in which CD4 T cells play a central role. However, the link between circulating self-antigen-specific CD4 T cells and the targeted tissue has not been extensively studied in AILD. We hypothesized that circulating autoreactive CD4 T cells were clonally and functionally related to dominant intra-hepatic pathogenic CD4 T cell clones.
Read the full summary ↓
Single cell transcriptomic analysis of circulating self-antigen-specific CD4 T cells revealed a specific B-helper and immuno-exhausted transcriptional profile, which was conserved for different autoantigens, but distinct from several other types of foreign antigen specificities. In the blood, the dominant hepatic CD4 T cell clones had a similar transcriptomic signature and were enriched in the PD-1+ TIGIT+ HLA-DR+ CD4 T cell subset. In a mouse model, antigen-specific CD4 T cells acquired the immuno-exhausted transcriptional profile when they accumulated in the liver after local antigen reactivity. Locally, immune checkpoint molecules controlled the response of antigen-specific CD4 T cells responsible for liver damage. Our study reveals the origin and biology of liver-derived autoreactive CD4 T cells in the blood of AILD patients that are imprinted by the liver environment, and suggest a dysregulation of the immune checkpoint molecules pathways. Our study enables tracking and isolating circulating autoreactive CD4 T cells for future diagnostic and therapeutic purposes.
Published in
Single cell profiling of circulating autoreactive CD4 T cells from patients with autoimmune liver diseases suggests tissue imprinting
Cardon A, Guinebretière T, Dong C et al. · Nature communications 2025 · PMID 39880819 · doi:10.1038/s41467-025-56363-2
This dataset
Open GSE270732 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 GSE270732_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 PRJNA1128102 and SRA study SRP516028. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39880819) ↗
Study design
13 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 13 sample titles: 13 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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