GEO series

Single-cell transcriptome analysis reveals atypical monocytes circulating ahead of acute graft-versus-host disease clinical onset

GSE269338 Homo sapiens (human) Single-cell RNA-seq 14 samples Submitted 2025/07/07 Platform GPL20301

At a glance

Single-cell RNA-seq · human · 14 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: 14 samples: 14 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 transcriptome analysis reveals atypical monocytes circulating ahead of acute graft-versus-host disease clinical onsetJournal of leukocyte biology · 2025

Summary (written by the submitters)

Acute graft-versus-host disease (aGVHD) represents the rejection of the skin, gut and liver tissues of the recipient of an allogenic hematopoietic stem cell transplantation (HSCT) by the donor T-cells. The onset of aGVHD is often rapid and its evolution unpredictable. We undertook the single-cell RNA sequencing of peripheral blood mononuclear cells (PBMCs) collected before aGVHD clinical onset in three patients and at aGVHD diagnosis in one, using four HSCT recipients who remained free of aGVHD as controls.

Read the full summary ↓

This analysis unveiled the presence in the circulation of particular subpopulations of monocytes and cytotoxic T cells (CTLs) in pre-aGVHD samples up to 18 days before clinical disease. These pre-aGVHD monocytes were characterized by an upregulation of the M2 polarity marker CD163, and the transmembrane protein SIGLEC1/CD169, while their CTL counterparts stood out for the upregulation of the CXCL10 receptor CXCR3 and the antigenic stimulation marker CD70. The occurrence of CD163/CD169-co-expressing monocytes upstream of aGVHD onset was validated using RNAseq data from an independent cohort and by flow cytometry of blood samples. These findings point to potential early diagnostic tools and preventive therapeutic strategies for aGVHD.

Published in

Single-cell transcriptome analysis reveals atypical monocytes circulating ahead of acute graft-versus-host disease clinical onset

Martins F, Planet E, Marino D et al. · Journal of leukocyte biology 2025 · PMID 39432735 · doi:10.1093/jleuko/qiae229

This dataset

Open GSE269338 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 GSE269338_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 PRJNA1121253 and SRA study SRP512434. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39432735) ↗

Study design

14 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 14 sample titles: 14 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

GSM8313393 Sample_#1_1086_control_collected_day_60_post_allo_HSCT
GSM8313394 Sample_#1_1086_control_collected_day_100_post_allo_HSCT
GSM8313395 Sample_#2_1175_control_collected_day_30_post_allo_HSCT
GSM8313396 Sample_#2_1175_control_collected_day_60_post_allo_HSCT
GSM8313397 Sample_#2_1175_control_collected_day_100_post_allo_HSCT
GSM8313398 Sample_#3_1200_control_collected_day_30_post_allo_HSCT
GSM8313399 Sample_#3_1200_control_collected_day_60_post_allo_HSCT
GSM8313400 Sample_#3_1200_control_collected_day_100_post_allo_HSCT
GSM8313401 Sample_#4_963_control_collected_day_60_post_allo_HSCT
GSM8313402 Sample_#4_963_control_collected_day_100_post_allo_HSCT
GSM8313403 Sample_#5_1052_pre_aGVHD_collected_day_30_post_allo_HSCT
GSM8313404 Sample_#6_1121_pre_aGVHD_collected_day_60_post_allo_HSCT
GSM8313405 Sample_#7_1073_pre_aGVHD_collected_day_30_post_allo_HSCT
GSM8313406 Sample_#8_1063_aGVHD_collected_day_30_post_allo_HSCT

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