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Effect of hemin on gene expression in erythroid progenitors derived from human sickle cell disease peripheral blood mononuclear cells (PBMC)

GSE302368 Homo sapiens (human) RNA-seq (gene expression) 30 samples Submitted 2025/07/26 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 30 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
30 samples: 10 conditions, mostly in triplicateGroups 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)
Sickle cell disease endured chronic hemolysis and systemic hypxic conditions. SCD patients have aberrant metabolic regulation of fumarate, glutamate, glycine, malate, and other metabolites involved in glycolysis, nucleotide catabolism, and glutathione metabolism. To investigate the effect of hemin on the erythroid progenitors (EPs) derived form SCD PBMC, the transcriptome of hemin treated SCD EPs were explored under hypxoic condition (1% O2).
Read the full summary ↓
Meanwhile, to explore chemical compounds that can alleviate hemin effect,gene expression profiles of dimethyl alpha ketoglutarate (DMKG), dimethyl fumarate (DMF) were studied. We found that DMKG and DMF showed protection effect against hemin through inducing the expression of NRF2-mediated antioxidantive signaling. NRF2 activation induced cellular anti-ferroptosis response to increase the SCD EPs viability and proliferation. Our findings suggest DMF and DMKG could be explored as novel SCD treatment options.
This dataset
Open GSE302368 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 GSE302368_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 PRJNA1290260 and SRA study SRP600290. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
10 conditions, mostly in triplicate
Patient1, PBS# ×3 Patient1, Hemin# ×3 Patient2, DMF# ×3 Patient2, DMKG# ×3 Patient2, PBS# ×3 Patient2, Hemin# ×3 Patient2, Hemin+DMF# ×3 Patient2, Hemin+DMKG# ×3 +2 more

Supports a between-group comparison across 30 samples.

How we read this: 10 replicated groups read from 30 sample titles; they account for 30 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
Datasets on a similar topic

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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.