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Tracing immunological interaction in trimethylamine N-oxide hydrogel-derived zwitterionic microenvironment during promoted diabetic wound regeneration

GSE267890 Mus musculus (mouse) Single-cell RNA-seq 12 samples Submitted 2025/05/01 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 12 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Mus musculus (mouse)
Samples
12 samples: 12 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)
The diabetic wound healing is challenging due to the sabotaged delicate balance of immune regulation via an undetermined pathophysiological mechanism, so it is crucial to decipher multicellular signatures underlying diabetic wound healing and seek therapeutic strategies. Here, we develop a strategy using novel trimethylamine N-oxide (TMAO)-derived zwitterionic hydrogel to promote diabetic wound healing, and explore the multi-cellular ecosystem around zwitterionic hydrogel, mapping out an overview of different cells in the zwitterionic microenvironment using single-cell RNA sequencing.
Read the full summary ↓
The diverse cellular heterogeneity has been revealed, highlighting the critical role of macrophage and neutrophils in managing diabetic wound healing. It is found that zwitterionic hydrogel can upregulate chemokine-secreted macrophages and downregulate extracellular traps (NETs)-neutrophils and facilitate their interactions compared with polyanionic and polycationic hydrogels, validating the underlying effect of zwitterionic microenvironment on the activation of adaptive immune system. These findings expand our horizons of the sophisticated orchestration of immune systems in zwitterion-directed diabetic wound repair and uncover new strategies of novel immunoregulatory biomaterials.
This dataset
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.

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 GSE267890_RAW.tar (it can run to tens of gigabytes).

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.

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1112591 and SRA study SRP508266. Searching any of these in the dataset finder brings you back here.

Study design
12 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 12 sample titles: 12 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
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