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Single-cell dynamics of genome-nucleolus interactions captured by nucleolar laser microdissection (NoLMseq)

GSE269201 Mus musculus (mouse) Single-cell RNA-seq 178 samples Submitted 2025/10/05 Platform GPL24247Platform GPL34290Platform GPL34328
At a glance

Single-cell RNA-seq · mouse · 178 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
178 samples: 7 conditions, mostly in duplicateGroups 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
Single-cell dynamics of genome-nucleolus interactions captured by nucleolar laser microdissection (NoLMseq)Nature communications · 2025
SuperSeries — this record groups several sub-series.
Summary (written by the submitters)
This SuperSeries is composed of the SubSeries listed below. Gene positioning in nuclear space is central to regulation, with repressive chromatin domains often contacting the nuclear lamina or nucleoli. The nucleolus undergoes structural changes in different cellular states, potentially altering genome organization. Yet, how nucleolar states influence 3D-genome architecture remains underexplored, largely due to the lack of methods able to map nucleolar-associated domains (NADs) in single cells and stressed nucleoli.
Read the full summary ↓
We developed NoLMseq, a technique combining laser-capture microdissection and DNA sequencing to map NADs in single cells. NoLMseq uncovered unexplored features of chromosome organization around nucleoli, including NAD heterogeneity in mouse embryonic stem cells, yielding two major populations with distinct chromatin and developmental states. NADs predominantly contact nucleoli monoallelically, with contact frequency correlating with gene expression and chromatin states. Under nucleolar stress, NoLMseq revealed extensive chromosome reorganization, highlighting the importance of nucleolus integrity in genome organization. Thus, NoLMseq provides a critical tool to study 3D-genome responses to nucleolar states in health and disease.
Published in
Single-cell dynamics of genome-nucleolus interactions captured by nucleolar laser microdissection (NoLMseq)
Walavalkar K, Gupta S, Kresoja-Rakic J et al. · Nature communications 2025 · PMID 41408051 · doi:10.1038/s41467-025-66294-7
This dataset
Open GSE269201 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 GSE269201_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 PRJNA1120492 and SRA study SRP512059. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41408051) ↗
Study design
7 conditions, mostly in duplicate
ESC E14 nucleolus ×53 F123 nucleolus ×50 ActD treated ESC nucleolus ×34 NPC nucleolus ×23 F123 RNAseq ×4 F123 ChIPseq H3K4me ×2 F123 ChIPseq H3K9me ×2 F123 ChIPseq Input ×1 +9 more

Supports a between-group comparison across 168 samples.

How we read this: 7 replicated groups read from 178 sample titles; they account for 168 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

+ 138 more — browse all 178 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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