GEO series

Methylation of SRSF2P95H by SETD2 suppresses myeloid-derived suppressor cell function by inhibiting inflammatory signaling in myelodysplastic syndrome

GSE265871 Mus musculus (mouse) DNA methylation 13 samples Submitted 2025/10/24 Platform GPL24247

At a glance

DNA methylation · mouse · 13 samples

What it is: DNA methylationChemical marks on DNA that help switch genes on or off.
Organism: Mus musculus (mouse)
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.

Summary (written by the submitters)

Myelodysplastic syndrome (MDS) is a malignant hematologic cancer accompanied with ineffective hematopoietic and myeloid bias. MDS patients with the SRSF2 mutation have a poor prognosis and require specific and effective therapeutic agents. Here, we find that the low expression of SETD2 in the SRSF2P95H background is associated with anomalously increased myeloid derived suppressor cells (MDSCs) differentiation and inflammatory response.

Read the full summary ↓

Our results reveal that the low expression of SETD2 in the hematopoietic stem and progenitor cells (HSPCs) of MDS patients with SRSF2P95H/+ is associated with patient outcomes. The deletion of Setd2 in Srsf2P95H/+ mice led to lethal MDS in mice, and mechanistic studies showed that Setd2 catalyzing the K17me1 and K65me2 methylation modification of Srsf2P95H/+ protein, to inhibit the aberrant expression of Ceacam1-4 isoforms and inhibit the increased differentiation potential of HSPCs into MDSCs and the formation of Il-1β-related inhibitory tumor microenvironment. Overall, our findings proposed the possibility of targeting the CEACAM1-4- IL-1β signaling axis in the clinical treatment of SRSF2P95H/+ MDS patients

This dataset

Open GSE265871 in finder → Find more mouse 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 GSE265871_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: Look for beta-value tables or raw IDAT files among the supplementary files.

Also filed as BioProject PRJNA1104591 and SRA study SRP504130. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

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

GSM8230438 HSPCs, SC_mdf, scRNAseq
GSM8230439 HSPCs, SC_mf, scRNAseq
GSM8230440 HSPCs, SC_WT, scRNAseq
GSM8230441 HSPCs, mx1, RNA-seq
GSM8230442 HSPCs, mx2, RNA-seq
GSM8230443 HSPCs, md1, RNA-seq
GSM8230444 HSPCs, md2, RNA-seq
GSM8230445 HSPCs, mf1, RNA-seq
GSM8230446 HSPCs, mf2, RNA-seq
GSM8230447 HSPCs, mf3, RNA-seq
GSM8230448 HSPCs, mdf1, RNA-seq
GSM8230449 HSPCs, mdf2, RNA-seq
GSM8230450 HSPCs, mdf3, RNA-seq

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GSE243566 Systematic perturbations of SETD2, NSD1, NSD2, NSD3 and ASH1L reveal their distinct contributions to H3K36 methylation 125 samples shares setd2, methylation
GSE292018 Histone 3 lysine 36 trimethylation by SETD2 shapes an epigenetic landscape in intestinal stem cells to orchestrate lipid metabolism and prevent cell senescence [CUT&Tag] 8 samples shares setd2, methylation
GSE292016 Histone 3 lysine 36 trimethylation by SETD2 shapes an epigenetic landscape in intestinal stem cells to orchestrate lipid metabolism and prevent cell senescence [ATAC-seq I] 2 samples shares setd2, methylation
GSE292017 Histone 3 lysine 36 trimethylation by SETD2 shapes an epigenetic landscape in intestinal stem cells to orchestrate lipid metabolism and prevent cell senescence [ATAC-seq II] 2 samples shares setd2, methylation
GSE311155 Age- and Sex- Driven Transcriptional and Metabolic Diversity in Myeloid-Derived Suppressor Cells After Mouse Sepsis 57 samples shares myeloid-derived, suppressor
GSE223029 Increase of blood CSF3R+ myeloid-derived suppressor cell predicts breast cancer recurrence 24 samples shares myeloid-derived, suppressor

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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.