GEO series

Oxygen-Tolerant Hydrogenase-Ag Nanoclusters-Peptide Hydrogel for Light-Driven H₂ Therapy in Diabetic Wound Healing

GSE297828 Mus musculus (mouse) RNA-seq (gene expression) 24 samples Submitted 2026/05/21 Platform GPL28330

At a glance

RNA-seq (gene expression) · mouse · 24 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: 24 samples: 8 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)

Diabetic wound healing is hindered by excessive reactive oxygen species (ROS), while conventional hydrogen (H₂) delivery methods lack localized efficacy. Escherichia coli [NiFe]-hydrogenase 2 (Hyd-2), though promising for H₂ production, suffers from oxygen sensitivity. Here, we engineer a peptide hydrogel (Fmoc-KYF-AgNCs) that shields Hyd-2 from oxygen inactivation, enabling sustained light-driven H₂ generation under aerobic conditions (retaining 80% activity after 12 hours).

Read the full summary ↓

In vitro, the hydrogel scavenges ROS in LPS-stimulated macrophages, suppresses pro-inflammatory cytokines, and enhances cell survival. In diabetic mice, the hydrogel accelerates wound closure (87% by day 11), reduces oxidative stress, promotes angiogenesis, and reprograms macrophages toward an anti-inflammatory M2 phenotype. Transcriptomic profiling reveals downregulation of inflammatory pathways and upregulation of tissue repair genes. This oxygen-tolerant system bridges enzymatic catalysis with precision medicine, offering a translatable platform for diabetic wound therapy and expanding hydrogenase applications in oxidative stress-related disorders.

This dataset

Open GSE297828 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 GSE297828_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 PRJNA1266236 and SRA study SRP586889. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

8 conditions, mostly in triplicate

Dia_day1 ×3 Dia_day7 ×3 Dia_day14 ×3 TEOA_day1 ×3 TEOA_day7 ×3 TEOA_day14 ×3 H2 ×3 Without_H2 ×3

Supports a between-group comparison across 24 samples.

How we read this: 8 replicated groups read from 24 sample titles; they account for 24 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

GSM9000449 Dia_day1_1 Dia_day1
GSM9000450 Dia_day1_2 Dia_day1
GSM9000451 Dia_day1_3 Dia_day1
GSM9000452 Dia_day7_1 Dia_day7
GSM9000453 Dia_day7_2 Dia_day7
GSM9000454 Dia_day7_3 Dia_day7
GSM9000455 Dia_day14_1 Dia_day14
GSM9000456 Dia_day14_2 Dia_day14
GSM9000457 Dia_day14_3 Dia_day14
GSM9000458 TEOA_day1_1 TEOA_day1
GSM9000459 TEOA_day1_2 TEOA_day1
GSM9000460 TEOA_day1_3 TEOA_day1
GSM9000461 TEOA_day7_1 TEOA_day7
GSM9000462 TEOA_day7_2 TEOA_day7
GSM9000463 TEOA_day7_3 TEOA_day7
GSM9000464 TEOA_day14_1 TEOA_day14
GSM9000465 TEOA_day14_2 TEOA_day14
GSM9000466 TEOA_day14_3 TEOA_day14
GSM9000467 H2-1 H2
GSM9000468 H2-2 H2
GSM9000469 H2-3 H2
GSM9000470 Without_H2_1 Without_H2
GSM9000471 Without_H2_2 Without_H2
GSM9000472 Without_H2_3 Without_H2

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GSE260694 Spatially tuneable multi-omics sequencing using light-driven combinatorial barcoding of molecules in tissues / Multiomic 6 samples shares light-driven
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Search all mouse RNA-seq datasets in GEO →

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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.