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Ubiquitin-like proteins NEDD8 and SUMO2 control epithelial homeostasis, regeneration, and inflammation [scRNA-seq]

GSE326641 Mus musculus (mouse) Single-cell RNA-seq 8 samples Submitted 2026/06/25 Platform GPL34290
At a glance

Single-cell RNA-seq · mouse · 8 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Mus musculus (mouse)
Samples
8 samples: 2 × NEDD8_KO vs 2 × NEDD8_CtrlGroups 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)
Stratified epithelial differentiation involves incompletely understood transcriptional and proteomic remodeling. Multi-omic profiling implicated ubiquitin and related networks in differentiation dynamics. Systematic perturbation of ubiquitin-like machinery uncovered opposite functions of NEDD8 and SUMO2. Knockout mice established essential roles for NEDD8 in progenitor maintenance, skin regeneration, and inflammation, whereas SUMO2 was required for differentiation.
Read the full summary ↓
Beyond ubiquitin-proteasome-concordant changes, NEDD8 directed proteomic regulation correlated with RNA abundance. Integration of IP-MS with genome-wide suppressor screening revealed context-specific NEDDylated dependencies. Among effectors, HNRNPU emerged as a post-transcriptional regulator of epithelial cell state, whose binding repertoire was modulated by NEDDylation, linking NEDD8 induced proteomic remodeling to RNA regulation. Together, these findings define opposite roles for NEDD8 and SUMO2 in orchestrating epithelial homeostasis, regeneration, and inflammation, underscoring how ubiquitin-like networks govern tissue fate.
This dataset
Open GSE326641 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 GSE326641_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: 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 PRJNA1446691 and SRA study SRP688435. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
2 × NEDD8_KO vs 2 × NEDD8_Ctrl

Supports a between-group comparison across 4 samples.

How we read this: 2 replicated groups read from 8 sample titles; they account for 4 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.