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Inference of emergent spatio-temporal processes from single-cell sequencing reveals feedback between de novo DNA methylation and chromatin condensates [RNA-seq]

GSE166225 Mus musculus (mouse) Single-cell RNA-seq 288 samples Submitted 2025/05/31 Platform GPL17021
At a glance

Single-cell RNA-seq · mouse · 288 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
288 samples: 288 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.
Paper
Scaling and self-similarity in the formation of the embryonic epigenomeNature physics · 2026
Summary (written by the submitters)
Recent breakthroughs in single-cell genomics allow probing molecular states of cells with unprecedented detail along the sequence of the DNA. Biological function relies, however, on emergent processes in the three-dimensional space of the nucleus, such as droplet formation through phase separation. Here, we use single-cell multi-omics sequencing to develop a theoretical framework to rigorously map epigenome profiling along the DNA sequence onto a description of the emergent spatial dynamics in the nucleus.
Read the full summary ↓
Drawing on scNMT-seq multi-omics sequencing in vitro and in vivo we exemplify our approach in the context of exit from pluripotency and global de novo methylation of the genome. We show how DNA methylation patterns of the embryonic genome are established through the interplay between spatially correlated DNA methylation and topological changes to the DNA. This feedback leads to the predicted formation of 30-40nm sized condensates of methylated DNA and determines genome-scale DNA methylation rates. We verify these findings with orthogonal single cell multi-omics data that combine the methylome with HiC measurements. Notably, this scale of chromatin organization has recently been described by super-resolution microscopy. Using this framework, we identify local methylation correlations in gene bodies that precede transcriptional changes at the exit from pluripotency. Our work provides a general framework of how mechanistic insights into emergent processes underlying cell fate decisions can be gained by the combination of single-cell multi-omics and methods from theoretical physics that have not been applied in the context of genomics before.
Published in
Scaling and self-similarity in the formation of the embryonic epigenome
Olmeda F, Lohoff T, Kafetzopoulos I et al. · Nature physics 2026 · PMID 42318073 · doi:10.1038/s41567-026-03263-x
This dataset
Open GSE166225 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 GSE166225_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 PRJNA699616 and SRA study SRP304791. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42318073) ↗
Study design
288 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 288 sample titles: 288 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

+ 248 more — browse all 288 samples with per-sample file links →

Datasets on a similar topic

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.