← BioTransfer GEO Dataset Finder
GEO series

Inflammasome-resistant IPSC-derived myeloid-derived suppressor cells (iMDSCs) ameliorate xenogeneic GVHD

GSE246047 Homo sapiens (human) RNA-seq (gene expression) 28 samples Submitted 2025/09/30 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 28 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
28 samples: 28 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)
Acute graft versus host disease (aGVHD) is considered a major complication of allogeneic hematopoietic stem cell transplant (aHSCT). Front-line pharmaceutical treatment is not uniformly effective and has toxic side effects. Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of immature myeloid cells with potent immunosuppressive functions.
Read the full summary ↓
Their potential to control GVHD has been proven in different rodent models. However, a high MDSC to T cell ratio and multiple doses of MDSCs are needed to achieve maximum effectiveness. Clinical translation of in vitro MDSC generation limits its clinical usage by a relatively low yield and need for personalized patient products, especially challenging for multiple treatment courses. Therefore, we developed an in vitro method to generate MDSCs on a large scale from a human induced pluripotent stem cell (iPSC)-derived CD34+ cells. iMDSCs shared a similar morphology, phenotype, and suppressive function with peripheral blood (PB)-MDSCs. In striking contrast to PB-MDSCs, iMDSCs retained 95% of suppressor function when exposed to the damage-associated molecular pattern stimuli, LPS + ATP, released during GVHD. RNAseq analysis and gene knockdown studies revealed that inflammasome-resistant iMDSCs maintained expression of phosphoglycerate dehydrogenase, an enzyme involved in purine metabolism. Consistent with the retention of suppressor function during lethal xenogeneic GVHD-induced tissue injury, recipients of human iMDSCs and peripheral blood mononuclear cells (PBMC) experienced therapeutic benefits. Together, these data provide a platform for translating in vitro generated, off-the-shelf iMDSCs into the clinic for suppressing GVHD and other adverse immune responses.
This dataset
Open GSE246047 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 GSE246047_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1031175 and SRA study SRP468030. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
28 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 28 sample titles: 28 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

Search all human RNA-seq datasets in GEO →

Share this dataset

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.