GEO series

Single-cell landscape of peripheral immune cells in MAFLD/MASH [scRNA-seq]

GSE267033 Homo sapiens (human) Single-cell RNA-seq 15 samples Submitted 2025/04/30 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 15 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: 15 samples: 9 × White_blood_cells_patient vs 6 × White_blood_cells_controlGroups 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 landscape of peripheral immune cells in MASLD/MASHHepatology communications · 2025

Summary (written by the submitters)

Background & Aims: Metabolic dysfunction associated steatotic liver disease (MAFLD) progresses to metabolic dysfunction-associated steatohepatitis (MASH) and is a major cause of liver cirrhosis. Although liver inflammation is the hallmark feature of MASH vs MAFLD, the involvement of the peripheral immune cell compartments in disease progression is poorly understood and single cell profiles of peripheral immune cells in MAFLD/MASH are not known.

Read the full summary ↓

Methods: MAFLD/MASH patients and healthy volunteers have been prospectively enrolled into a cross-sectional study. Patients have been histologically stratified and further characterized by liver bulk RNA-Seq. Peripheral immune cells from patients and control blood samples have been comprehensively profiled using bulk and single RNA-Seq. Results: 22 patients with fibrosis stage less then F3 have been histologically stratified into patients with low, medium and high disease activity score (NAS). In contrast to fibrosis, NAS group correlated with non-invasive imaging readouts and blood biomarkers of liver damage and inflammation (ALT, AST). Prevalence of type 2 diabetes and obesity was increased with NAS stage. Bulk RNA-seq profiling of patient liver biopsies revealed gene signatures that were positively and negatively associated with NAS. Known marker genes for liver fibrosis where up-regulated on RNA level. Blood bulk RNA-sequencing showed only moderate differences in patients versus healthy controls. In contrast, single cell analysis of white blood cells revealed multiple alterations of immune (sub-) populations including increased abundance of immature B-cells and myeloid suppressor cells in MAFLD/MASH patients as well as disease-association of neutrophil sub-populations.

Published in

Single-cell landscape of peripheral immune cells in MASLD/MASH

Steixner-Kumar AA, Santacruz D, Geiger T et al. · Hepatology communications 2025 · PMID 40257301 · doi:10.1097/HC9.0000000000000643

This dataset

Open GSE267033 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 GSE267033_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 PRJNA1109376 and SRA study SRP506563. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40257301) ↗

Study design

9 × White_blood_cells_patient vs 6 × White_blood_cells_control

Supports a case/control comparison: 9 samples read as cases, 6 as controls.

How we read this: 2 replicated groups read from 15 sample titles; they account for 15 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

GSM8258781 White_blood_cells_patient_15 White_blood_cells_patie…
GSM8258782 White_blood_cells_patient_2 White_blood_cells_patie…
GSM8258783 White_blood_cells_patient_5 White_blood_cells_patie…
GSM8258784 White_blood_cells_patient_6 White_blood_cells_patie…
GSM8258785 White_blood_cells_patient_8 White_blood_cells_patie…
GSM8258786 White_blood_cells_patient_10 White_blood_cells_patie…
GSM8258787 White_blood_cells_control_1 White_blood_cells_contr…
GSM8258788 White_blood_cells_control_2 White_blood_cells_contr…
GSM8258789 White_blood_cells_control_3 White_blood_cells_contr…
GSM8258790 White_blood_cells_control_4 White_blood_cells_contr…
GSM8258791 White_blood_cells_control_5 White_blood_cells_contr…
GSM8258792 White_blood_cells_patient_13 White_blood_cells_patie…
GSM8258793 White_blood_cells_patient_14 White_blood_cells_patie…
GSM8258794 White_blood_cells_control_9 White_blood_cells_contr…
GSM8258795 White_blood_cells_patient_36 White_blood_cells_patie…

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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.