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Nutrigenomic profiling identifies ZIP10 (SLC39A10) as a regulator of erythroid zinc homeostasis with genetic associations to anemia risk

GSE310594 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2025/11/19 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 12 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
Mus musculus (mouse)
Samples
12 samples: 3 × G1E-ER4 cells, differentiating, siControl, 24 hours, biol vs 3 × G1E-ER4 cells, differentiating, siControl, DTPA, 24 hours, biol vs 3 × G1E-ER4 cells, differentiating, siZIP10, 24 hours, biol vs 3 × G1E-ER4 cells, differentiating, siZIP10, DTPA, 24 hours, biolGroups 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
Nutrigenomic profiling identifies ZIP10 (SLC39A10) as a regulator of erythroid zinc homeostasis with genetic associations to anemia riskProceedings of the National Academy of Sciences of the United States of America · 2026
Summary (written by the submitters)
To determine the physiological roles of ZIP10 and zinc in erythroid progenitor cells, Zip10 was depleted via nucleofector-mediated siRNA delivery into cells and cellular zinc was restricted by DTPA treatment. The transcriptome profiles acquired from RNA-seq were compared with those of control cells.
Published in
Nutrigenomic profiling identifies ZIP10 (SLC39A10) as a regulator of erythroid zinc homeostasis with genetic associations to anemia risk
Kim J, Sun Y, Ye K et al. · Proceedings of the National Academy of Sciences of the United States of America 2026 · PMID 41701827 · doi:10.1073/pnas.2533600123
This dataset
Open GSE310594 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 GSE310594_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 PRJNA1366545 and SRA study SRP647050. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41701827) ↗
Study design
3 × G1E-ER4 cells, differentiating, siControl, 24 hours, biol vs 3 × G1E-ER4 cells, differentiating, siControl, DTPA, 24 hours, biol vs 3 × G1E-ER4 cells, differentiating, siZIP10, 24 hours, biol vs 3 × G1E-ER4 cells, differentiating, siZIP10, DTPA, 24 hours, biol

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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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