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Single-cell transcriptomic analysis identified resistant MDSCs and a stress-tolerant gene co-expression network as common MDSC features across multiple disease settings

GSE248954 Homo sapiens (human) Single-cell RNA-seq 34 samples Submitted 2025/03/26 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.
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
Single-cell transcriptomic analysis identified resistant MDSCs and a stress-tolerant gene co-expression network as common MDSC features across multiple disease settingsFrontiers in immunology · 2025
Summary (written by the submitters)
Background: Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of immunosuppressive myeloid cells. The identification of a molecular signature common to MDSC regardless of tissue source would aid in the classification of cells as MDSCs. Methods: Single-cell RNA sequencing (scRNA-seq) was performed on GM-CSF+ IL-6 induced human MDSCs to characterize the extent of heterogeneity within monocytic MDSCs (M-MDSCs).
Read the full summary ↓
Cytokine-treated PBMCs were also cultured in the absence of serum to include an additional element of cell stress. Independent published bulk and single-cell transcriptomic datasets were used for validation. Findings: A cluster of cells with preserved MDSC features was induced by the combination of inflammatory signals and cell stress in the form of serum starvation (resistant MDSCs, rMDSCs). A gene co-expression module (the yellow module) was identified specific to rMDSCs. The genes upregulated in MDSCs can be further classified into stress-tolerant vs. -sensitive features. This yellow module mostly contained stress-tolerant genes and showed excellant separation for distinguishing M-MDSCs from control cells across a range of in vitro and in vivo conditions (ROC AUC=0.954), a feature not found in the stress-sensitive genes. Importantly, rMDSCs were identified in scRNA-seq datasets of immune cells from multiple human cancer types. Tumor C1Q macrophages, which have been associated with immunosuppression, highly expressed the yellow module gene signature. Interpretation: These results demonstrate the importance of the combined roles of inflammation and cellular stress in shaping the features of M-MDSCs, and highlights cellular resilience represented by rMDSCs and the role of stress-tolerant features in defining common MDSC features.
Published in
Single-cell transcriptomic analysis identified resistant MDSCs and a stress-tolerant gene co-expression network as common MDSC features across multiple disease settings
Chen T, Hughes J, Gregory A et al. · Frontiers in immunology 2025 · PMID 40264770 · doi:10.3389/fimmu.2025.1565211
This dataset
Open GSE248954 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 GSE248954_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 PRJNA1046502 and SRA study SRP474932. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40264770) ↗
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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